dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Drs. Simon Andrews ,  Anne Segonds-Pichon , and  Felix Krueger ,  Babraham Bioinformatics , for help with data processing, statistical and bioinformatic analysis,  Dr. Kristina Tabbada  and  Clare Murnane ,  Babraham Institute Next Generation Sequencing facility , for next-generation sequencing,  Nicolas Le Novère  for advice with the kinetic analysis, all the staff of  Babraham Institute BSU  for animal care, and  Matthew Sale  for HCT116 cells and help with the cell cycle experiments. P. V. -W. thanks  Drs. Kazuyuki Ohbo ,  Yokohama City University , and  Masataka Nakamura ,  Tokyo Medical and Dental University , for hosting him for 2 months in 2012 in Japan, during which time the idea for this work was conceived. This work was funded by the  UK Biotechnology and Biological Sciences Research Council (BBSRC) , the  UK Medical Research Council  through project grant  MR/N009398/1  to P.
Systemic DENV infection was determined at each time point based on virus presence in head tissues. Systemic infection prevalence among midgut-infected mosquitoes ranged from 4. 5% to 100% across isolates and time points and was significantly influenced by the time point (p = 2. 0×10 -16 ) but not by uncontrolled variation in blood meal titers (p = 0. 08) or the interaction between time point and blood meal titer (p = 0. 61). To quantify the kinetics of systemic DENV infection, we assumed that the cumulative change in the proportion of mosquitoes with a systemic DENV infection over time post exposure followed a sigmoid function. A logistic model (see Materials and Methods) was fitted to the empirical data to estimate three parameters (K, B, M) describing the kinetics of each isolate (Table  2 ; Fig  2A ).
The majority of samples were tested using a qualitative version of the FFA  [27]. The qualitative version of the FFA is identical to the regular FFA except that it only tests the undiluted sample and therefore does not allow end-point titration. For a small subset of samples (S2 File) the FFA result was not reliable because of fungal contamination of the well. These undetermined samples were re-tested by two-step reverse transcription polymerase chain reaction (RT-PCR) detection of DENV RNA as previously described  [27]. Virus sequencing. The full-length genomes of two of the DENV isolates (Thai2010a and Thai2010b) were already sequenced from a previous study  [41]  and are available in GenBank under accession numbers HG316481 and HG316482, respectively. The full-length genome sequences of the six other DENV isolates of the study were obtained by high-throughput sequencing.
1. 1  [47]  and used to correct the chimeric construct. Only nucleotides with >10X coverage were conserved for generating the consensus sequence. The full-length genomes of the six newly sequenced DENV isolates were deposited in GenBank under accessions numbers MG877552 (Laos2012), MG877553 (Haiti2012), MG877554 (Thai2012), MG877555 (Ncal2013), MG877556 (Thai2013), and MG877557 (Gabon2012). Phylogenetic analyses. A background set of 51 full-length DENV genome sequences was obtained from GenBank to represent the different genotypes of DENV type 1  [48]. Genome sequences were aligned using ClustalW v. 2. 0. 12  [49]  and analyzed with RAxML v. 8. 2. 10  [50]  to generate the best-scoring maximum-likelihood (ML) tree of 10 runs with 100 thorough bootstrap replicates. The GTR + I + G nucleotide substitution model was chosen based on the lowest Bayesian Information Criterion (BIC) value using the JModelTest 2 software  [51].
The same procedure was applied to the eight DENV isolates. Phylogenetic trees were visualized using FigTree v. 1. 4. 3  [52]  and the ggtree R package  [53]. Phylogenetic comparative analysis of continuous traits was performed with the phyloSignal function of the phylosignal R package  [54]  using the Abouheif's Cmean, Moran's I, Blomberg's K and Pagel's Lambda methods  [55]. Empirical data analysis. A global likelihood function was used to model the probabilities of systemic mosquito infection over time for each virus isolate. Probabilities of systemic infection at each time point post virus exposure were estimated with a 3-parameter logistic model (Eq 1) assuming a binomial distribution of systemic infection status. f ðt; ðK; B; MÞÞ ¼ K=ð1 þ e À BðtÀ MÞ Þ Eq 1 Based on this function, the probability of systemic infection at a given time point (t) is governed by three parameters.
The daily rate of new systemic infections was obtained by calculating the first derivative of the cumulative systemic infection function. Comparison of systemic infection kinetics among DENV isolates was based on the Akaike Information Criterion (AIC). The global likelihood model was fitted to all 4,140 partitions of isolates. Isolates from one group were constrained to share the same parameter values. AIC values were calculated for all possible partitions of isolates with the same empirical data of systemic infection. The best isolate grouping was considered as the one with the lowest AIC score. An affinity matrix among isolates was derived from AIC values obtained from all grouping comparisons. Two isolates share a higher affinity if they are commonly grouped together in models with low AIC values.
The null hypothesis was the lack of difference in parameter values between polymorphic variants at each locus. It was rejected when p values were <0. 05 after Bonferroni correction for multiple testing. Geneious v. 7. 0. 6 (http://www. geneious. com, [60] ) was used to align and translate the viral genomic sequences. All statistical analyses were performed in the R statistical environment  [61]  and the R Markdown in provided as S3 File. Charts were generated with the ggplot2 package  [62].
In these simulations, mean values of K, B, and M were set to their respective experimentally determined estimates, whereas standard deviation was the same as in the first set of simulations (S1 Table ). Epidemics caused by each DENV isolate were examined in 100 independent replicate simulations. Each simulation was initiated with one infected human at t = 0, then each simulation was run for 400 days. The code of simulations is available at https://github. com/slequime/ArboEpiSim. In silico data analysis. For each replicate of a given parameter combination, the total number of humans infected during the outbreak was recorded. Two outputs derived from the simulation data were examined: (i) the proportion of failures to initiate an outbreak and (ii) the mean number of secondary human infections across replicates.
Failure to initiate an outbreak was defined as the lack of secondary infections over the length of the simulation following initial introduction of a single infected human. Failures to initiate an outbreak were filtered out before calculating the mean number of human infections. For each analysis, a stratified random splitting was performed on the data with the createDataPartition function from the caret R package  [72]  to obtain training and test subsets of the data with a balanced number of sample for each parameters combinations. The training set was composed of 70% of the data. We performed random forest regression with optimized parameter settings provided by the train function of the caret R package  [72]. A total of 500 trees were used for both analyses with two and four variables or interaction between variables randomly tried at each split.
The last column identifies the small number of samples that were tested by PCR instead of FFA. (TXT) S3 File. R Markdown document of the study. The file provides code lines used to analyze the experimental data and perform data visualization. The R code lines only refer to the procedure of systemic infection modeling and the AIC-based method to compare systemic infection across isolates. This document can be opened in Rstudio  [73]  with S2 File as the input file. (RMD) S4 File. Simulated effect of mosquito infection kinetics on dengue outbreaks. The interactive bubble plot represents the probability and size of dengue outbreaks across simulation replicates as a function of the three parameters (K, B, M) describing the kinetics of systemic mosquito infection. The size of the bubble is proportional to the percentage of successful outbreaks (>1 infected human).
This work was supported by  Agence Nationale de la Recherche  (grants  ANR-16-CE35-0004-01  and  ANR-17-ERC2-0016-01 ), the  French Government 's  Investissement d'Avenir program Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ), the  City of Paris Emergence(s) program in Biomedical Research and U. S. National.
De novo genome assembly and whole genome alignment Reads preprocessing and de novo genome assembly of the reference isolate with a hybrid strategy were carried out at BGI. Illumina reads with adapter contaminations, or with more than 10% of ambiguous bases or with more than 40% of low quality (Q score < 20) bases were discarded by SOAPnuke v. 1. 5. 2 (developed by GBI). The quality of processed reads was checked with FASTQC v. 0. 11. 2 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). PacBio polymerase reads were processed by SMRT® analysis package v. 2. 3. 0 to filter out reads with quality less than 0. 8, to remove adapters and to extract subreads with length of at least 1000 bp. The resulting subreads were self-corrected by Falcon Genome Assembly Tool Kit v. 0. 3. 0 and assembled into contigs by Celera Assembler v.
8. 3  [20]  followed by single base corrections using SOAPsnp v. 1. 05 [21]  and SOA-PInDel v. 1. 08 [22]  pipelines with HiSeq PE reads. Scaffolding was performed by SSPACE-LongRead  [23]  using HiSeq MP reads. Filtered reads of re-sequenced isolates were assembled de novo by VelvetOptimiser v. 2. 2. 5  [24]. The optimized k-mer, expected coverage and coverage cutoffs were obtained through iterative process for various k-mer (37 to 67). Gaps were minimized by GapFiller v. 1. 10 [25]  using original reads via 15 iterations (-m 50, -o 3). The assembled contigs of re-sequenced isolates were aligned to the reference assembly by NUCmer in MUMmer v. 3. 23  [26]  and filtered by delta-filter utility program to keep one-to-one best mapping of reference to query with at least 1000 bp. The alignments were visualized with dotplots generated by mummerplot and summarized by show-coords utility program in MUMmer package.
Transposable elements (TEs), simple sequence repeats (SSRs), gene prediction and annotation TEs in reference assembly were predicted using RepeatScout v. 1. 0. 5  [27] , Nseg  [28] , TRF  [29]  and classified with REPCLASS v. 1. 0. 1  [30]  and tblastx against Repbase Update database v. 29. 8. 2016. Full-length long terminal repeat (LTR) retrotransposons were identified by LTRharvest  [31]  and LTRdigest  [32]. All detected classified TEs were soft-masked by RepeatMasker v. 4. 0. 6 (http://www. repeatmasker. org/) (Additional file 2: Method). SSRs were scanned by SciRoKo v. 3. 4  [33]  with perfect MISA-mode (default setting). SSR motifs were standardized by grouping motifs in different reading frames and corresponding reverse complements together. The distribution of SSRs relative to protein-coding genes (CDS, introns, 200 bp up-and downstream of CDS, 200-500 bp up-and downstream of CDS and > 500 bp up-and downstream of CDS) were inspected with BEDtools v.
2. 26. 0  [34]. Protein-coding genes were identified using the modified fungal genome annotation pipeline as described by previous studies  [35, 36]. After removing adapter contaminations, low quality reads (Q score < 20) and rRNA remnants, remaining RNA-seq reads were constructed into transcripts using both de novo and genome-guided methods (jaccard_clip option on) in Trinity v2. 3. 2  [37]. For the genome-guided method, RNA-seq reads were first aligned to the genome assembly by GSNAP v. 2014. 7. 21  [38]  to generate a BAM file as the input. The resulting transcripts from these two modes were combined and aligned to the genome assembly with GMAP v. 2014. 7. 21  [39]  incorporated in Program to Assemble Spliced Alignments (PASA) pipeline v. 2. 0. 2  [40]  to build a complete set of transcripts.
Homologs from predicted proteins of H. irregulare TC 32-1 available in Mycocosm at Joint Genome Institute (JGI) portal (http://genome. jgi. doe. gov/Hetan2/Hetan2. home. html) were searched in the soft-masked assembly by Exonerate v. 2. 2. 0  [41]  with protein2genome model. GeneMark-ES v. 4. 21  [42]  with self-training algorithm, fungal branch point model was utilized for ab initio gene predictions. All evidences generated above were sent into EVidenceModeler v. 1. 1. 1  [40]  to construct weighted consensus gene structures. The weights were set as 1, 5, and 10 for ab initio predictions, homology-based predictions, and transcripts, respectively. A subset of 806 complete gene models with exact exon-intron boundaries as PASA-constructed transcripts and a support from exonerate-based homologs were manually selected for training Augustus v.
3. 2. 2  [43]  and SNAP v. 2013. 11. 29  [44]. The trained programs were used to predict genes ab initio with and without evidence from GSNAP mapping, Exonerate protein alignments and identified known repeats. Thereafter, all predicted gene models were combined into consensus gene structures by EVidenceModeler with the weights of 2, 3, 5, and 7 for ab initio gene predictions without and with hints, homology-based predictions and transcripts, respectively. The complete gene set was subjected to PASA to add UTR annotations based on transcript alignments. The completeness of predicted gene set was assessed by Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis using 1438 fungal single-copy ortholog profiles  [45]. The predicted protein sequences were functionally annotated with BLAST2GO v.
4. 0. 7 pipeline  [46]  (default value). Protein sequences were first blastp against the nonredundant protein database in NCBI (threshold Evalue ≤1e-5). Hits were used to assign Gene Ontology (GO) terms to each protein sequence. InterProScan v. 5. 15-55  [47]  was performed to search protein sequences against a collection of databases including Gene3D v. 15. 0 to identify present domains and motifs. Meanwhile, available corresponding GO terms for each hit were also retrieved and merged to existing GO annotations from BLAST2GO. CAZymes were annotated by dbCAN v. 5. 0 with HMMER3 (default setting)  [48]. The whole genome was visualized with Circos v. 0. 69. 5  [49].
Secretome prediction Secreted proteins were identified using SignalP v. 4. 1 (sensitive mode)  [50]  for signal peptide prediction, TargetP v. 1. 1  [51]  for subcellular locations and TMHMM v. 2. 0  [52]  for transmembrane (TM) domain detection. Proteins with more than two TM domains or with one TM domain not overlapping with signal peptide (helix of at least 18 amino acids not in the first 60 amino acids) were excluded. The predicted secretome was further searched in MEROPS v. 11. 0  [53]  and PeroxiBase v. 2004-2015  [54]  with blastp (Evalue ≤1e-4) for proteases and peroxidases, respectively. Potential virulence-related proteins were identified by blastp against pathogen-host interaction database (PHIbase) v. 4. 2  [55]  (E-value ≤1e-5).
Genome mapping, variant calling and genome-wide nucleotide polymorphism analysis Processed reads of re-sequenced isolates were mapped to the reference assembly by BWA-MEM v. 0. 7. 12  [56]. Duplicates were removed by Picard MarkDuplicates v2. 1. 1 (http://broadinstitute. github. io/picard/). SNPs and small insertions/deletions (InDels) collectively termed variants were called by Genome Analysis Toolkit (GATK) v. 3. 6  [57]  HaplotypeCaller and GenotypeGVCFs, followed by hard filtering with VariantFiltration (generic filter recommendations of GATK plus DP > 200. 0, DP < 10. 0). Nucleotides flanking identified SNPs were retrieved by BEDtools. Variants exclusive to each isolate were pinpointed by VCFtools v. 0. 1. 15 [58]. The impacts of variants on different types of reference genomic regions (intergenic regions, exons, introns, 5'UTR, 3'UTR and exon-intron splicing sites) were predicted with SnpEff v.
Gene classification Mapping coverage of re-sequenced isolates over the reference gene models was calculated with BEDtools. Coverage breadth was expressed as percentage of nucleotides with minimum one read aligned. Genes were defined as core genes when shared by all isolates with minimum coverage breadth of 80%. Remaining genes exhibited deletions including complete deletions (coverage breadth ≤10%) or partial deletions (coverage breadth < 80% but > 10%) in subset of re-sequenced isolates. Genes only present in the reference isolate were called reference-specific genes. Novel genes relative to the reference were identified by de novo assembling unmapped reads using VelvetOptimiser, predicted using referencetrained Augustus and annotated by blastp against NCBI non-redundant protein database (E-value ≤1e-5), BLAS-T2GO and InterProScan v.
In brief, S15 possesses a battery of CAZymes for both plant and fungal cell wall degradation and modification with more secreted proteins tailored to lignin degradation. Moreover, 19 proteins such as catalases, haloperoxidases, and thioredoxins required for encoding ROS-scavenging system  [64]  and putative secreted proteases mainly from Aspartic and Serine families were also found (Additional file 11: Table  S5 ). To further associate various secreted proteins to fungal virulence, pathogen-host interaction database (PHIbase), containing information on experimentally validated virulence factors in bacteria, fungi and oomycetes, was searched. In total, 211 secreted proteins have hit annotations of "reduced virulence", "loss of pathogenicity" or "effector_(plant_avirulence_determinant)", of which 133 proteins were affiliated to CAZymes, peptidases and peroxidases.
The remaining genes had no hits in searched databases and were not shown. For the core genes, 1457 and 1456 genes fall into highly conserved and divergent gene categories, respectively. There were various significantly enriched GO terms engaged in essential biological processes in the conserved genes (Additional file 20: Table  S11 ), in contrast to the divergent genes, which exhibited no significantly overrepresented GO terms. The most abundant GO term (biological process category) in divergent genes was oxidation-reduction process (GO:0055114) (123 genes) and the most numerous domains were relevant to transcription factors (TFs) such as Zinc finger of C2H2 type, GATA type, and NF-X1 type and Zn(2)-C6  fungal-type DNA-binding domain  [69]. Secreted protein coding genes affiliated to the highly conserved genes (89 genes) were significantly associated with main GO terms (assigned to at least 10 sequences) of carbohydrate metabolic process (GO:0005975) and hydrolase activity, hydrolyzing O-glycosyl compounds (GO:0004553).
Highly divergent secreted proteins (123 genes) displayed significant overrepresentations of main GO terms of oxidation-reduction process (GO:0055114), carbohydrate metabolic process (GO:0005975) and heme binding (GO:0020037) (Additional files 21 and 22: Table  S12   a denotes the proteins having PHI-base database hits b denotes the secreted proteins kb vs 3. 84 nonsynonymous SNPs/kb). Nonetheless, the differences were not significant in either comparison (Mann Whitney test, P = 0. 9803; permutation t-test, P = 0. 07449 respectively). To check the gene number variation among the resequenced isolates, a gene set with complete or partial deletions in one or more of the isolates was collected. In analogy to the divergent core genes, no significantly enriched GO terms were found. Genes encoding TFsrelated domains and those involved in oxidationreduction process (GO:0055114) were also prevalent.
Availability of data and materials The datasets supporting the conclusion of this article are included within the article and its additional files, as well as in the NCBI SRA (https:// www. ncbi. nlm. nih. gov/sra/) under Bioproject PRJNA400846 (https:// www. ncbi. nlm. nih. gov/bioproject/) and SRP116620. The Whole Genome Shotgun project of the H. parviporum 96026 has been deposited at DDBJ/ ENA/GenBank (https://www. ncbi. nlm. nih. gov/genome/) under the accession PDUQ00000000. The version described in this paper is version PDUQ01000000.
scaffold38. 32 a Short chain dehydrogenase GO:0016491;GO:0008152 IPR002347; Short-chain dehydrogenase/reductase reductase IPR016040 SDR; NAD(P)-binding domain evm. scaffold38. 33 Hypothetical protein GO:0016020 - Alpha/Beta hydrolase fold evm. scaffold51. 36 b - - - - evm. scaffold51. 42 Glucose-6-phosphate GO:0004347;GO:0006094; IPR001672; Phosphoglucose isomerase (PGI); isomerase GO:0006096; IPR035476; Phosphoglucose isomerase, SIS domain 1; IPR035482 Phosphoglucose isomerase, SIS domain 2 evm. scaffold51. 44 b Hypothetical protein - - -.
Acknowledgments Fieldwork was financially and logistically supported by the  Institut Paul-Emile Victor  ( IPEV Programme 109 ) and the  Terres Australes et Antarctiques Francaises. S. Dupont was supported by a grant from the  Conseil General des Deux-Sèvres  and the  Region Nouvelle-Aquitaine. We are indebted to  Maxime Passerault ,  Thibaut Lacombe ,  Joan Ferrer Obiol ,  Alexandre Corbeau ,  Florian Orgeret  and  Elie Gaget  for their assistance in the field and to  D. Besson  for managing the long-term database.
Framework Chemical emissions from the use of CPs were estimated for each country as well as for every single WWTP using Equation (1). Population data was hereby taken from census data. The different terms of Equation (  1 ) are described in Section 2. 2 (A P,c and F PU,P,c ), Section 2. 3 (F mass,X,P and F X,P ), and Section 2. 4 (N c,i and E X ). The parameter uncertainty in A P,c , F mass,X,P and E X is propagated into M X,i using Monte Carlo simulations (Section 2. 5). M X;i ¼ 365, X m P¼1 F mass;X;P ,F X;P ,ð1 À E X Þ X n.
Consumer data collection and treatment The amount of CP used per person was derived from consumer habits studies reported in scientific literature. To retrieve this information, a literature search was performed using Web of Science, combining the keywords "consumer", "population", "use", "habit", "amount", "personal care product", and "exposure". The search was limited to articles in English. Articles were selected if they provided information on either the amount of product used per day and the frequency of use, and/or the amount used per application. Studies aggregating the results over the entire EU or considering only one specific consumer group (e. g. pregnant women) were not selected. As a result, the analysis focused on four countries: the USA  (Loretz et al. , 2006 (Loretz et al. , , 2008)) , the Netherlands  (Biesterbos et al.
Total market estimates Our final chemical emission load estimates cannot be validated by measurements, since they represent the emissions resulting from the use of only three CPs. A sense-check of our approach is however possible by comparing the estimated country-level product consumption from consumer survey data with total market estimates. Product use data from 2011 were obtained from the market research company Euromonitor. Euromonitor analysts use a combination of various sources of information (e. g. desk research, trade surveys, store checks and global and local company analysis) to derive sale volumes  (Euromonitor, 2015). Data for specific product types were grouped as shampoo (standard shampoo, salon hair care and men's hair care), bodywash (men's bath and shower and body wash shower gel), and conditioner (single type).
The coordinates, type of treatment, and served population of the recorded WWTPs are provided. 2. 4. 1. 2. France and The Netherlands. The Urban Waste Water Treatment Directive (UWWTD) asks European Countries to report on the status of their wastewater treatment  (European Commission, 2016). The database resulting from this regulation is available online  (European Environmental Agency, 2016). It includes, among others, information on the location of every WWTP, the type of WWTP, and the incoming load in population equivalent (p. e. ). This value was assumed to represent the number of persons connected to the WWTPs.