dataesr/scientific-paragraphs-categorization huggingface dataset:
Pangenome analysis Blast Ring genome plots were generated using BRIG version 0. 95  (Alikhan et al. 2011)  running Blast+ version 2. 2. 28  (Camacho et al. 2009)  with the following set up. The nucleotide sequence of one of the 15 Thermosipho genomes was used to create the blast-database with default settings. Nucleotide sequences of all coding genes were extracted from each genome (NCBI Genbank annotation) using CLC Main workbench Version 6. 8. 3 and were used for BLASTn analysis in BRIG with the following settings: max_target_seqs: 1; max e-value cut-off: 1. 0 -4. Alignments with a minimum of 70% similarity were visualized with BRIG. The same procedure was used for all genomes. Complete chromosome sequences or contigs were uploaded for all 15 genomes to the Panseq 2. 0 server (https://lfz.
0b10  (Swofford 2002 ) under a GTR+Γ+Ι model as the reference tree. Pairwise tetranucleotide frequency correlation coefficients (TETRA) and Average Nucleotide identity (ANI) were calculated using the JspeciesWS webtool (http://jspecies. ribohost. com/jspeciesws/) (Richter & Rosselló-Mora 2009; Goris et al. 2007 ). The pair-wise TETRA values were visualized with R (version 3. 2. 1) using the heatmap. 2 function (Package gplots) and the Thermosipho genomes were clustered using Jaccard distances based on pairwise TETRA values (Package Vegan. version 2. 3-0). The IMG/ER Pairwise ANI calculation was used to determine the number of shared genes between each genome separately  (Markowitz et al. 2009 ). The method uses pairwise bidirectional best nSimScan hits where similar genes share a minimum of 70% sequence identity with 70% coverage of the smaller gene.
Functional comparison of genome The 15 genomes were compared using the Clusters of Orthologous Genes (COGs) annotations. A COG reference database (version 10) was downloaded from the STRING database (http://string-db. org/). For each genome all protein sequences were aligned using BlastP, with the settings: 1 target sequence; maximum e-value: 1. 0e -20 ; database size 1. 0 7 ; tabular output. Only hits to COG database sequences were retained when the alignment was >= 70% of the length of the longest protein sequence and were used to build a protein COG classification table. The COG IDs were summarized by classifying them to any of the available COG categories (ftp://ftp. ncbi. nih. gov/pub/wolf/COGs/COG0303/cogs. csv). For the COG analysis of the species-specific genes, we used the results from the IMG phylogenetic profiler tool to identify cluster specific genes.
The COG classification table was screened for cluster specific genes and summarized into COG categories. In order to statistically compare species differences of COG categories we normalized counts by total COG gene annotations, giving relative abundances per genome. R (version 3. 3. 0) was used to identify COG categories that were significantly different between the three species using the non-parametric Kruskal-Wallis test (p-value <= 0. 01). The R-package ggplot2 (version 2. 1. 0) was used to generate the comparisons graphically.
Horizontal gene transfer detection Genes putatively acquired by HGT were identified using HGTector  (Zhu et al. 2014)  with BLASTp (blast+ v 2. 2. 26). The databaser. py script was used on 6 December 2015 to download per species one representative proteome of all microorganisms from the NCBI refseq database. We compared the predicted Thermosipho spp. protein sequences to the reference database using the following BLAST cut-offs: E-value: 1. 0 -5 , Percentage identity: 70%, percentage coverage: 50%, and a maximum of 100 hits were returned. To determine which genes were putatively acquired by any of the strains we set the HGTector self group to the genus Thermosipho (NCBI taxonomy ID: 2420) and the close group to either the family Fervidobacteriaceae (NCBI taxonomy ID: 1643950), or the order Thermotogales (NCBI taxonomy ID: 2419).
Defense genes Using the defense genes list created by  Makarova et al. , ( 2011) , we screened the IMG genome Clusters of Orthologous Genes (COG) annotations for the presence of any COGs involved in any of the mobile DNA defences  (Makarova et al. 2011). We summarized the identified COGs and distinguished between CRISPR-cas associated or restrictionmodification (RM) system genes. Crispr-spacer analysis. All genomes were uploaded to CRISPR finder (http://crispr. i2bc. paris-saclay. fr/) to detect CRISPR-arrays  (Grissa et al. 2007). CRISPR spacer sequences from each genome were compared using BLASTn against all Thermosipho sp. spacer sequences using the following settings: e-value cut-off: 1. 0 -5 , database size: 1. 0 7 , dust: no. The tabular blast results were visualized with R-statistics using the Markov Cluster Algorithm (MCL) (v1.
0) and Igraph (v1. 0. 1) packages. Igraph was used for matrix construction. MCL was run using the matrix with the inflation set to 1. 4 and max iterations set to 100  (Enright et al. 2002). In order to search for matching sequences within the genome but outside the CRISPR arrays, e. g target genes, we masked all CRISPR arrays using maskfeat  (EMBOSS v. 6. 5. 7 (Rice et al. 2000) ). Next we ran BLASTn (v2. 2. 26+) using the spacers of each genome against the own genome using the following settings: e-value cut-off: 1. 0 -5 , database size: 1. 0 7 , dust: no. CRISPR array spacers were also compared against the NCBI nucleotide database to find other species with similar sequences. BLASTn was run with the settings: evalue cut-off: 1. 0 -5 , dust: no. Each genome was screened for the presence of prophages (Supplementary information for details).
Global isolation of the genus Thermosipho from hydrothermal vents and oilfields. We screened the NCBI-non-redundant (nr) database using BLASTn with Thermosipho 16S rRNA genes as probes to assess how our genomic analyses span the available environmental diversity of Thermosipho lineages. This analysis revealed that most lineages or species, e. g. T. melanesiensis, T. affectus and T. africanus, are well covered by our genomic analysis (Figure  1 , Supplementary materials Figure  S1 ). Nonetheless, there are several lineages, including T. geolei, T. ferriphilus and T. activus, for which we do not have genomic data yet. Moreover, the 16S rRNA gene phylogeny suggests ecological differences between the different lineages (Figure  1 ). T. africanus isolates appear to be habitat 'generalists' and have been isolated and detected in oil reservoirs, marine hydrothermal vents and terrestrial hot springs.
Genome overview The current study added 13 new genomes, with different levels of completion, to the two existing ones (Table  1 ). All genomes share a low GC content (29. 9 % -32. 7 %). Interestingly, the T. affectus genomes are the smallest in this dataset (≈ 1. 77Mbp), while the T. melanesiensis (≈ 1. 9Mbp) and T. africanus (≈ 2. 0Mbp) genomes are larger.
The remaining clusters encode genes encoding CRISPR-cas proteins (Table  3 ) and genes involved in various types of cellular activities. Only a few clusters are dominated by hypothetical proteins (Supplementary materials Table  S1 ). The co-localization of unique genes suggests acquisition by HGT for these clusters. HGTector  (Zhu et al. 2014)  analysis suggested that between 4. 0 % (T. affectus BI1063) and 5. 8 % (T. africanus H17ap60344) of the genes in each genome have been acquired from species not belonging to the Thermotogales by HGT (Table  2 , Supplementary materials Table  S2 ). The majority of the putative HGT genes (average ≈ 85%) showed similarity to genes found in other orders within the Thermotogae, such as the Kosmotogales and the Petrotogales. The remaining genes (average ≈ 15%) were mainly shared between the Thermosipho genomes and genomes of Firmicutes and Euryarchaeota (Supplementary materials Table  S2 ).
Within each genome about 50 % of the putative HGT genes belonged to the COG categories: "Carbohydrate transport and metabolism", "Energy production and conversion", or were not assigned to a COG category (e. g. Hypothetical genes) (data not shown). The ratio between these three categories was different for the three species, with T. africanus having more carbohydrate genes, while T. affectus species had more unassigned genes. Interestingly, many of the putative HGT genes could be identified in several Thermosipho genomes, which indicates that the common ancestor of the Thermosipho isolates acquired most of the putative HGT genes. Only a few putative HGT genes fall into the genome or species-specific gene sets. This is due to the fact that most of these proteins have no high quality blastP match in the genomes in the HGTector database and will therefore not classify as putative HGT.
Large differences among the genomes were also seen for COG category G (Carbohydrate transport and metabolism). In agreement with this, phenotypic differences in carbohydrate metabolism is one of the main other features, to distinguish between the three species  (Podosokorskaya et al. 2011). Also for this category, we find that the T. africanus genomes have relatively more genes than the other two species (Figure  4A ). This difference is even more pronounced for species-specific genes (Figure  4B ), where T. africanus genomes have more genes present in this category (Supplementary Materials Table  S3 ). A screening of the genomes using PFAM annotations and the carbohydrate database dbCAN  (Yin et al. 2012) , showed a similar pattern as with the COG annotations (Supplementary information; Supplementary materials Table  S4 ).
In agreement with the distribution of RM-systems, Pacbio sequencing of T. melanesiensis BI431 and T. affectus BI1063 and BI1070 revealed that strain BI431 was methylated, while the two T. affectus genomes lacked methylation. The T. melanesiensis BI431 methylation sites detected matched those predicted in the Rebase restriction database  (Roberts et al. 2015) ; both methyl-6-adenosine (m6A) and methyl-4-cytosine (m4C) methylation were detected along the entire BI431 genome. The dominant methylation sites indicate that methylation was mainly due to the type II and III systems (Table  4 ). We did not perform Pacbio sequencing of the T. africanus genomes, but the presence of a type-III methylase enzyme suitable for a functional RM-system suggests the possibility of a methylated genome.
Closer examination revealed that also these genes are part of a predicted prophage region  (Haverkamp et al. , in prep. ; Supplementary information). Finally, we searched NCBI's non-redundant nucleotide database for matches to the Thermosipho spacer sequences. This identified two identical 44 bp spacers, one in T. africanus H17ap60334 (array 2, spacer 7) and one in T. africanus TCF52B (array 5, spacer 20) that matched a sequence in the genome of Pseudothermotoga elfii DSM 9442 (genbank refseq ID: NC_022792). The identified region in that genome  (bp 200095 -200125 ) is a CRISPR spacer sequence of 38bp, which is identical to the Thermosipho spacer for 31 bases. Interestingly, the first six bases of the Pseudothermotoga spacer has no match to the Thermosipho H17ap60334 and TCF52B spacer, while the last 12 bases of the Thermosipho spacers have no match to the Pseudothermotoga spacer.
Acknowledgements The work of THAH and CLN, was supported was funded by the  Norwegian Research Council  (award  180444/V40 ). JL and CG were supported by  Agence Nationale de la Recherche (ANR)  ( ANR-12-BSV3-OO23-01 ). OAP and IVK were supported by the  Russian Science Foundation (RSF)  grant #  16-14-00121. We thank the  Norwegian Sequencing Centre  for sequencing and support with the bioinformatics analysis. We thank  Ifremer  and chief scientists of the  French oceanographic cruises "Biolau" (1989), "Marvel" (1997) and "Atos " (2001). Strains are available under request at the  'Souchothèque de Bretagne' (catalogue LMBE) culture collection  (http://www. univ. brest. fr/souchoteque/collection+LM2E).
This corresponded to a total of 1200 night-traps per trapping session. Locally-made live cage-traps were used, and the lines were placed in the same positions during each trapping session using GPS coordinates. Villages and isolated houses, which corresponded to a fourth habitat category, human settlement, were also sampled opportunistically using cage-traps distributed to residents. Rodent and shrew species were identified in the field using morphological criteria, but were confirmed using molecular methods if needed using a mitochondrial gene for barcoding of some rodent species  [36]. Complete data on the animals used as reference specimens for the barcoding assignment are available on the 'Barcoding Tool/RodentSEA' section of the CERoPath project web site (www. ceropath. org).
Rodent Screening for O. tsutsugamushi Rodent spleens were placed in RNAlater ® storage solution (Sigma-Aldrich, Saint Louis, MO, USA) then stored at -20 • C until further analysis. Genomic DNA was then extracted from the spleen using the DNeasy®96 Tissue Kit (Qiagen, Germany). Spleen DNA samples were screened for the presence of bacteria using universal primers (16S-V4F [GTGCCAGCMGCCGCGGTAA] and 16S-V4R [GGACTACHVGGGTWTCTAATCC]) targeting the hypervariable region V4 of the 16S rRNA gene (251 bp) via Illumina MiSeq (Illumina) sequencing. The V4 region has been proven to have excellent taxonomic resolution at the genus level  [37]. A multiplexing strategy enabled the identification of bacterial genera in each individual sample. We followed the method described in Kozich et al. [38]  to perform PCR amplification, indexing, pooling, de-multiplexing and finally taxonomic identification using the SILVA SSU Ref NR 119 database as a reference  [39]  (http://www.
Statistical Analyses To analyze the infection of rodents, we first modeled the probability of presence/absence of O. tsutsugamushi as a function of several environmental indices with logistic regression, GLMM with logit function and random effects (locality), using package 'lme4'  [40, 41]  implemented in R freeware  [41, 42]. The binomial variable, infected or non-infected individual rodent, was used in the logistic regression. The initial model included the environmental indices related to the habitat structure, and calculated for a buffer of 100 m for each individual rodent trapped: cover of agriculture on steep land, cover of agriculture on flat land, cover of irrigated agricultural land, cover of forest, cover of human settlement, cover of irrigated land, a proxy of habitat diversity (patch density), and a proxy of habitat fragmentation (edge density).
No interactions were added among the independent variables. We evaluated support for competing models, investigating the relationship between the prevalence of O. tsutsugamushi and all explanatory variables of interest. We used AIC adjusted for sample size (AICc) to assess the relative information content of the models. We quantified the uncertainty that the 'best' model would emerge as superior if different data were used with Akaike weights (w r ), the probability that a particular model is the best one from the available data. Selection of the best competing models was made using the package 'glmulti' (version 1. 0. 7 2)  [42, 43]  implemented in R, which allows the exploration of all models using automated model selection and model-averaging procedure using a genetic algorithm. We also modeled the probability of presence/absence of O.
Acknowledgments: This study was funded by the  French ANR CERoPath  (www. ceropath. org) CP&ES , grant  ANR 11 CPEL 002   BiodivHealthSEA  project (www. biodivhealthsea. org). SM is supported by the  Thailand International Cooperation Agency (TICA). Special thanks to  Annelise Tran  for the GIS development. We thank the CERoPath team and the drivers for their invaluable help during fieldwork.
CDKN2A/p16 gene expression in metastatic lymph-nodes of women with brain metastases Transcriptomic analyses were performed on lasermicrodissected tumor cells from metastatic lymph-nodes of the 28 women with HER2 or triple-negative breast cancer. Multivariate analysis was carried out to compare data from women with brain metastases and those without. Table  2  shows the genes with the greatest fold changes between the two groups, and corresponding p-values adjusted for other metastatic localizations. CDKN2A, also called p16 ink4a , evidenced a fold change of 2, the highest value (at the top of Table  2 ). Using RT-qPCR on all 28 metastatic lymph-nodes, we confirmed that CDKN2A mRNAs was significantly more expressed in women who developed brain metastases than in women who did not (RQ = 4. 3 for CDKN2A, p < 0.
Patient data Fifty-two patients from two different hospitals with available tumor samples and follow-up data were included in this study (28 from Saint-Louis Hospital, Paris corresponding to the experimental set, and 24 from Centre-Oscar-Lambret, Lille corresponding to the validation series). In compliance with French Bioethics law (2004-800; June 8, 2004), all patients had been informed of the research use of the part of their samples remaining after diagnosis had been established, and none opposed it. Informed consent was obtained from each patient. The Clinical Research Board Ethics Committee approved this study (CPP-Ile-de-France#13218). All patients, diagnosed between 2003 and 2013, had metastatic HER2 or triple-negative breast carcinoma either at initial diagnosis or during follow-up of the disease and classified M1 according to TNM classification  [14].
Laser-microdissection of tumor cells from metastatic lymph-nodes and transcriptomic data processing After consultation of the Pathology Department register (Saint-Louis Hospital), we identified 359 patients with frozen metastatic samples collected between 2003 and 2013, 143 being lymph node metastases from breast cancers. Among women with HER2-or triple negative sub-type and distant metastases during follow-up, we identified 32 women with available frozen tumor samples (Supplementary Figure  1 ). Cryo-cut sections of these 32 frozen metastatic lymph-nodes from 32 patients in Saint-Louis-Hospital were laser-microdissected to select tumor cells in the samples. Using a PALMMicrobeam/Zeiss-system, a minimum of 1000 tumor cells were laser-microdissected on 7 μm-thick tissue sections for a minimum surface area of 35.
Hybridization was performed according to Agilent 60-mer-oligomicroarray protocol: 1. 65 μg Cy3-labeled cRNA were hybridized (overnight/65°C) on Agilent-Whole-Human-Genome-Oligo-Microarrays 8 × 60K V2, and fluorescence signals detected using Agilent-Microarray-Scanner. Agilent-FE-Software determined feature intensities. Quantile normalization was performed using the limma package on R-software version 3. 2. 1 (Foundation for Statistical Computing, Vienna, Austria), based on log2 single-intensity expression data.
We performed the same analyses on transcriptomic data downloaded from public databanks in which annotations for brain metastases were available. All these transcriptomic data were obtained for primary breast cancers, and downloaded from Gene-Expression-Omnibus website (GSE2603  [20] , GSE2034  [6] , GSE7390  [21] , GSE12276  [8] ). 583 transcriptomes obtained with HG-U133A GeneChip arrays (Affymetrix) and 204 transcriptomes produced with HG-U133A plus 2. 0 GeneChip arrays (Affymetrix) were separately analyzed after quantile normalization. For graphic representations and correspondence analyses, transcript levels of genes such as ESR1, ERBB2 or CDKN2A were discretized into binary variables (positive or negative) using the Fisher-Jenks algorithm (classInt R-package). Correspondence analyses between qualitative variables were performed with the FactoMine Rpackage.
To specifically focus on associations between two variables, we used the chi-square test (for qualitative variables) or the t-test (for quantitative variables). For the correlation study between p16 immunostaining scores and Ki67 expression levels in metastatic lymph-nodes, the Pearson correlation coefficient was calculated. Association between p16 immunostaining scores and overall survival was performed using a Cox model with the "survival" R-package. Two data sets were studied separately with univariate and multivariate tests, one series corresponding to the transcriptomic study including 28 lymph-nodes and the other was the validation series including 24 metastatic lymph-nodes. Overall survival was calculated from the date of metastatic disease.
9 AE 2. 01; BIO, mean AE SD = 23. 1 AE 4. 03, P = 0. 443), suggesting that Wnt-dependent melanocyte fate specification is complete in the head region by 24 hpf. Next, we assessed melanocyte differentiation, looking for any changes associated with BIO treatment during the differentiation phases. In these BIO treatments, we saw distinctive morphological changes to melanocytes, but interestingly, the phenotype differed depending upon the period of BIO treatment. Embryos treated during the early differentiation time window and examined at 48 hpf displayed a dramatic increase in cell dendricity (Figure  3C, E ) compared with mock-treated controls (Figure  3B, D ). To quantitate this striking phenotype, we used IMAGEJ software  (Schneider et al. , 2012)  to estimate the roundness parameter, an established formula relating cell perimeter and cell area (Figure  3F ).
Treatment during the late differentiation phase alone (i. e. , from 48 to 72 hpf) resulted in a similar phenotype (data not shown). A multidistance spatial cluster analysis using Ripley's K Function  (Ripley, 1977)  was performed using SPATSTAT package in R  (Baddeley and Turner, 2005)  to quantify the decrease in patterned organization of melanocytes in the dorsal head using comparable pictures of DMSO-and BIO-treated fish (Figure  S1 ). We found that the Ripley's K function confirms the nonrandom organization of melanocytes in dorsal heads of DMSO-treated embryos (scale of analysis of r = 35 pixels). Indeed, at this scale of analysis, for eight of the ten embryos, the observed K value was smaller than the expected K value if cells were randomly spread (Figure  E, G, H, J ). The result of the Ripley's K analysis performed on BIO-treated embryos showed that, at r = 35, the observed K values were found within the lower confidence envelop (in the gray area) of the graph meaning that cells were randomly spread in 90% of the cases in nine of ten embryos (Figure  S1K-T ).
Real-time quantitative PCR Real-time quantitative PCR was performed in triplicate using SYBR Green I PCR Master Mix (Roche, West Sussex, UK) for WT embryos, and a Lightcycler II machine (Roche) was used according to the manufacturer's instructions. For mitfa w2 mutant embryos, the Fast SYBR â Green Master Mix (Thermo Fisher, Paisley, UK) was used and samples were run in triplicate using the StepOne TM System (Thermo Fisher) according to manufacturer's instructions. Standard curves for both gapdh and mitfa primers demonstrated nearly 100% efficiency (98. 7% and 98. 5%, correspondently). Primers were designed spanning an intron using PRIMER3 PLUS software (http://www. bioinformatic s. nl/cgibin/primer3plus/primer3plus. cgi. ). The following primers were used: gapdh: forward 5 0 ACCAACTGCCTGGCTCCT3 0 , reverse 5 0 TACTTTGCCTACAGCCTTGG3 0 ; mitfa: forward 5 0 CTGGACCATGTGGCAAGTTT3 0 , reverse 5 0 GAGGTTGTGGTTGTCCTTCT3 0.
Ripley's K function analysis To quantify and compare melanocyte organization in DMSO-treated embryos and BIO-treated embryos, the multidistance spatial cluster analysis, based on Ripley's K Function, was performed using R statistical software (using packages SPATSTAT and DPLYR  (R Core Team (2012) ). Melanocyte positions were determined on the dorsal head of ten treated 72 hpf embryos for each conditions (DMSO and BIO treated) using comparable pictures. x and y coordinates for each melanocyte's position were determined using ImageJ. This data were then used to perform the Ripley's K function analysis which allows testing for random partition of the cells. A z-test allowed comparison of the two populations for cellular spatial organization using the results of this analysis; the result was considered significant if P < 0.