dataesr/scientific-paragraphs-categorization huggingface dataset:
Background High-risk human papillomavirus (HPV) is a necessary cause of high-grade cervical intraepithelial neoplasia (grade 2 or higher, CIN2+). Simplified and rapid HPV DNA assays designed for use in resourcelimited settings have recently become available. methods We performed a systematic review and metaanalysis by searching Medline, Embase, Global Health and CINAHL databases for studies from 1 January 2004 to 25 February 2017 that reported the performance of careHPV or OncoE6 for the detection of histological CIN2+ in cervical cancer screening. We used bivariate models to estimate pooled sensitivity and specificity for CIN2+ and CIN3+. results A total of 29 657 women were included from seven studies evaluating the performance of careHPV for the detection of CIN2+ and four studies among 27 845 women for the detection of CIN3+.
Search We searched Medline, Embase, Global Health and CINAHL databases for publications in the English language from 1 January 2004 to 31 December 2013 using search terms for human papillomavirus (HPV), cervical intraepithelial lesions (CIN), squamous intraepithelial lesions (SIL), HPV screening; Diagnostics; Point of Care; Rapid; Near patient; method; test; evaluation; performance and comparison. Reference lists of review articles and all articles identified in the systematic search were checked. An updated search was performed on 25 February 2017. All abstracts were screened by one author (HK).
Sensitivity analysis Sensitivity analyses could not be performed due to lack of additional covariate data. Formal assessment of publication bias was not performed due to low number of studies. Data were analysed using Stata V. 14 (Stata Statistical Software). This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  18  and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. 19.
Discussion This population genomic analysis of C. neoformans var. grubii has revealed new biogeographic relationships and highlighted a complex history of hybridization events between groups. Analysis of genome-wide variation of 188 geographically diverse isolates greatly increases the resolution of the VNI, VNII, and VNB phylogenetic groups and precisely measures the level of genetic differentiation between isolates within each group and across geographic scales. These data support a much higher diversity of isolates in the VNB group compared to VNI and VNII isolates. Notably, we show that hybridization between these groups can result in genome mixing, suggestive of recent and ongoing meiotic exchange, and introgression of smaller regions between lineages have been identified and appear to perpetuate vertically  (Desjardins et al.
Acknowledgments We thank  Jose Muñoz  for helpful comments on the manuscript and the  Broad Institute Genomics Platform  for generating DNA sequences for this study. This project has been funded in whole or in part with Federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under grant number  U19 AI-110818  and by the  National Human Genome Research Institute  grant number  U54HG003067  to the  Broad Institute. Support to J. R. P. came from Public Health Service grants  AI73896  and  AI93257. J. R. and M. A. B. were supported by a  United Kingdom Medical Research Council (MRC)  grant awarded to M. C. F. , T. B. , and  T. S. H. (MRC MR/K000373/1 ). M. V. was supported by a  United Kingdom Natural Environment Research Council Ph.
Participants Participants were those taking part in a large multinational cohort study [the IMAGEN study: for further details, see 16]. Participants at age 14 (time point 1; T1) and their parents were recruited from secondary schools in 8 sites across the UK, Ireland, France and Germany. A total of 2225 parent-adolescent dyads completed the Development and Well-Being Assessment (DAWBA) online at T1; however, only 2215 of these pairs had data from the Dieting, Weight and Body Shape section (assessing ED symptoms) from at least 1 informant [43 dyads had data from only 1 informant (21 dyads with adolescent data only)]. 1607 parent-adolescent dyads also completed the DAWBA when the adolescent was aged 16 (time point 2; T2) (including an additional 2 pairs with incomplete baseline data); however, only 1604 pairs had data from the Dieting, Weight and Body Shape section from at least 1 informant (53 dyads with data from only 1 informant [25 dyads with adolescent data only]).
Procedure Interview, questionnaire, genetic and neuroimaging data were obtained from adolescents at age 14 (T1), and interview and questionnaire data were obtained online two years later at age 16 (T2). Interview and questionnaire data were collected from the parents at both time points. Only the responses on the Dieting, Weight and Body Shape section of the Development and Well-Being Assessment (DAWBA) interview were used here. The DAWBA interview was administered online, and height and weight were measured in person. Procedures were approved by the local ethics committees at each site, and were conducted in accordance with the Declaration of Helsinki. Written informed consent from the parents and written assent from the children were obtained prior to participation.
Thus, individuals who may engage in these behaviours although they did not endorse the screening questions would not have been accurately represented. However, post hoc exploration of the endorsement of entry questions yielded similar findings to our GEE models, whereby entry questions were more frequently endorsed in girls compared to boys (by both informants) and by adolescents compared to parents (for both sexes), and this did not vary widely between time points: (adolescents: 58. 0-58. 5% girls and 23. 1-29. 7% boys; parents: 20. 9-25. 2% girls and 5. 3-9. 6% boys). Secondly, there were insufficient data to investigate differences in symptom severity. Finally, parental weight and parental history of an ED were not assessed. Parents experiencing difficulties with eating or weight regulation may not be as good at noticing eating problems in their children  [32].
Further support was provided by Grants from: ANR (project AF12-NEUR0008-01-WM2NA, and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MIL-DECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface Grant), Paris Sud University IDEX 2012; the National Institutes of Health, USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium Grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. Savani Bartholdy is supported by a PhD studentship from the National Institute of Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley National Health Service (NHS) Foundation Trust and King's College London (KCL).
First, we performed in vitro experiments and synthesized 29 candidates for beta-lactamases further expressed in Escherichia coli (see methods). We detected a beta-lactamase activity in 20/29 (69. 0%) of the tested candidates (Fig. 1B , see methods for the selection of candidates). The mean amino acid identity with known beta-lactamase of the 20 beta-lactamases successfully predicted by PCM and validated in vitro was 28. 2% (range 19. 4%-82. 6%, Supplementary Table  1 ). Then, we used a functional metagenomics dataset from soils.
Discussion Altogether, the results of this study support that the vast majority of ARDs from the intestinal microbiota could be considered as hosted by commensal bacteria, and that their transfer to an opportunistic pathogen is a rare event. We provide several findings to support this assumption: 1) we could assess the diversity of ARDs in the intestinal microbiota with a novel method that could predict functions for proteins which sequences were besides distant to known proteins, and confirmed that ARDs from the intestinal microbiota were distant from known ARDs from pathogens, 2) this method was validated by gene synthesis and application on an external, validation metagenomic dataset, 3) the majority of pdARDs showed no significant signature of genetic mobility, 4) this stability was illustrated by that we could stratify subjects into 'resistotypes', 5) we observed that gene richness, otherwise associated with a healthy status 19   , was positively correlated to the abundance of ARDs in subjects not exposed to antibiotics.
Our results call for further experiments to identify protective species with ARDs of low human risk concern  27  that could potentially be included in synthetic microbiota as "bodyguards" for the bacteria of interest. Our study has limitations, though. As mentioned earlier, metagenomic sequencing only covers the dominant bacteria, so that we could not cover the ARDs present in subdominant bacteria. Hence, we cannot rule out that the subdominant bacteria could be a reservoir of ARDs that could be transferred to pathogens. Besides, the method we used to identify distantly related proteins is based on homology modelling and takes advantage that proteins sharing the same function have more similar structures than amino acid sequences. While PCM showed excellent results on a soil dataset and on synthesized beta-lactamases, we nonetheless acknowledge it remains a prediction tool Indeed, two proteins sharing similar structures do not necessarily share the same functions and PCM shall yield false positive (as observed in the functional validation of synthesized beta-lactamases) so the number of ARDs in the intestinal microbiota may have been overestimated.
Constitution of the databases of antibiotic resistance determinants An ARD may be defined as a gene that decreases susceptibility to antibiotics when it is present or that increases susceptibility to antibiotics when it is lost 11. This definition excluded intrinsic efflux pumps, target genes (in which mutations can confer resistance to some antibiotics) and genes involved in the regulation of antibiotic resistance genes. Amino acid sequences of functionally characterized ARDs from the major antibiotic families in human therapeutics (beta-lactam, aminoglycosides, tetracyclines, trimethoprim, sulfonamides, macrolides-lincosamides-synergistines, fluoroquinolones, fosfomycin and glycopeptides)  38  were obtained from the following antibiotic resistance databases: Resfinder   1 ). The recently described plasmid-mediated colistin resistance mcr-1 gene  41  could not be included because of the lack of a reliable PDB template at the time of the study.
Negative references For each ARD family, the COGs/NOGs attributed to candidates, that were not the COGs/NOGs of the reference dataset were further considered (Extended Data Fig. 2 ). We assumed that it reproduced the errors of functional assignment likely to be generated in sequence-only annotations. The hits from the Blast search against the eggNOG v3 database were added to the negative reference dataset. A manual curation step was performed in order to ensure that no references were eventually included in the negative references.
Pairwise comparative modelling The concept of pairwise comparative modelling (PCM) is shown in Extended Data Fig. 1  and 2 , and the framework is available at https://github. com/aghozlane/pcm. Each candidate was subjected to homology modelling with reference templates and negative templates, generating two 3D-structures for each candidate (Fig  1A ). The main idea is that if the sequence is really associated to the reference fold, its model must be significantly different from the ones obtained with the negative structural template. Homology modelling was performed by PCM in seven main steps: 1. Three structural templates were identified by Blastp (among the lists produced as described above) that shared the highest homology with the candidate protein.
5. The best model out of a hundred produced by MODELLER (based on the Dope score) was considered for structure assessment analysis using ProQ  51  and Prosa-web estimates the degree of superposition of the residue between two structures. The differences (delta) between the scores determined from each modelling path (with the reference set or the negative set) were calculated and used for the PCM machine learning program (see below). For one given candidate, the PCM whole process took an average of 8 CPU-hours (30 minutes on 16 CPUs).
Validation of the method with a functional metagenomic dataset In order to assess the performances of PCM, we took advantage of the publication by Forsberg et al. , RNA methylases, Tet(M), Tet(X), Qnr, Sul and DfrA, using the supplementary table 2 of the paper. Inserts with no ARDs were removed (n=269). Also, inserts selected on cycloserine (n=868) and chloramphenicol (n=129) were not considered here. Fourteen inserts which contained more than one ARD that could be predicted to confer resistance to the antibiotic used for the screening (e. g. ; two beta-lactamases) were not considered in this analysis. Eventually, 1,658 inserts containing no identified ARDs or ARDs that did not confer resistance to the antibiotic used for selection were discarded and 294 containing efflux pumps (not considered in this study).
Thus, the validation set was made of 1,423 inserts (containing a resistance gene) for a total of 3,778 genes. Class B1-B2 and class B3 beta-lactamases were modelled separately then doublets were removed and results were shown as class B beta-lactamases as the precise class annotation was not provided in the dataset. In total, 1,390 unique hits were found during the initial screen of PCM, of which 1,374 were predicted as ARDs (Supplementary Table  5 ). Among the 33 ARDs not included for PCM, 12 were not considered because they were undersized and 10 because they were oversized. No hits for AAC(2), ANT, Qnr or Sul were found. The mean identity shared with reference ARDs was 37. 6% (range 18. 8-94. 5). Overall, the sensitivity and negative predictive value were 96. 6% and 98. 0%, respectively.
In comparison, only 8 ARDs would have been identified by a conventional method (combination of Hmmsearch, Blastp and SSearch with both a minimal identity with a reference ARD and coverage over or equal to 80%). Conversely, Resfams  27  that was specifically designed to identify ARDs from functional metagenomic datasets showed similar sensitivity to PCM with 1,346 correct predictions of ARDs (94. 6% sensitivity).
Searching for signatures of mobile genetic elements nearby the predictions of ARDs Mobile genetic elements (MGE) -associated proteins were searched in the genetic environment of the pdARDs. First, homologues of insertion sequences were searched in the redundant gene catalog, contigs where an ARD was predicted (n=908,888) using ISfinder  33  and blastp (query size threshold 150 amino acids, e-value 1E -30 , identity threshold 40%) against the ISfinder database. Furthermore, conjugative elements were searched among the same gene set (n=908,888) with Conjscan 34 , using the default parameters and the filters recommended by the authors (best e-value<0. 001 and sequence coverage of at least 50%). A pdARD was considered to be linked to a conjugative element when at least two distinct elements and/or one mob element were found in the contig(s) of its homologues (irrespective of the genetic distance between the pdARD gene and the MGE-associated gene).
57. Laplace criterion was used to define optimal number of clusters as described on oral and faecal microbial dataset. By analogy with the term enterotype, we chose to name as "resistotype" a cluster of subjects based on their similarity of their faecal relative abundance of pdARDs. Chi-squared test was used to assess the associations between resistotypes and enterotypes. Rarefaction analysis at one million read was done to determine the gene richness per samples. RLQ analysis  59  was conducted to assess the associations between the relative abundances of pdARDs, their characteristics (family, size of the cluster of associated genes [CAG]) and those of subjects (enterotypes, resistotypes, gender, body mass index [BMI], age). Of note, we excluded the patients suffering from inflammatory bowel disorders from this analysis.
Metagenomic sequencing and mapping. Total faecal DNA was extracted  51, 52  and sequenced using SOLiD 5500 wildfire (Life Technologies) resulting in a mean of 68. 5M sequences of 35-base-long single-end reads. High-quality reads were generated with quality score cut-off >20. Reads with a positive match with human, plant, cow or SOLiD adapter sequences were removed. Filtered high-quality reads were mapped to the MetaHIT 3. 9M gene catalog  25  using the METEOR software 64. The read alignments were performed in colorspace with Bowtie software (version 1. 1. 0) 65 with options: -v 3 (maximum number of mismatch) and -k 10000 (maximum number of alignment per reads). The gene abundance profiling table was generated using a two-step procedure. First, the unique mapped reads (reads mapping to a unique gene from the catalogue) were attributed to the corresponding genes.
MetaGenomic Species (MGS) MGS are co-abundance gene groups with more than 700 genes, and can thus be assumed as part or complete bacterial species genes contents. 741 MGS were delineated from 396 human gut microbiome samples 25. In this study, the relative abundance of MGS were determined as the median abundance of 90% of the genes composing each cluster. MGS taxonomical annotation was updated by sequence similarity using NCBI BLASTN, when more than 50% of the genes matched the same reference of NCBI database (December 2014 version) at a threshold of 95% of identity and 90% of gene length coverage to get the species annotation. 25.
//www. lahey. org/studies/), RED-DB (http://www. fibim. unisi. it/REDDB/), Marilyn Roberts' website for macrolides and tetracycline resistance (http://faculty. washington. edu/marilynr/) and from functional metagenomics studies 5, 6, 31. Nonredundancy of the reference ARDs was assessed with CD-HIT v4. 5. 7 The final database was manually curated. The cluster of orthologous genes (COG) of each member of the reference dataset was assigned from the v3 eggNOG database The mean size of the reference dataset sequences was determined. In total, we collected 1,651 non-redundant amino acid sequences spanning 20 ARDs families: Class A beta-lactamases (Blaa), class B1-B2 beta-lactamases (Blab1), class B3 betalactamases (Blab3), class C beta-lactamases (Blac), class D beta-lactamases (Blad), aminoglycosides acetyltransferases (AAC) AAC(2), AAC(3)-I, AAC(3)-II, and AAC(6), aminoglycosides nucleotidyltransferases (ANT), aminoglycosides phosphotransferases (APH), 16S rRNA methylases, Tet(M), Tet(X), type A dihydrofolate reductase (DfrA), dihydropteroate reductase (Sul), erythromycin ribosome methylase (Erm), quinolone resistance proteins (Qnr), fosfomycin resistance proteins (Fos), and D-Ala -D-Lac/Ser ligases (Van) (Table.
was used to compute the classifier. Ten-fold stratified cross validation (re-sampled 100 times to obtain more stable accuracy estimates) was used to partition the data into a training set and a test set. The LASSO hyper-parameter was optimized for each model in a nested 5-fold cross-validation on the training dataset using the area under curve (AUC) as model selection criterion. From the 100 times re-sampled ten-fold cross validation, receiver operating characteristic (ROC) analysis was used to evaluate the model performances using the median AUC. Coefficients extracted for each modelling or alignment score were also evaluated for their stability throughout the computed models. The PCM score was the ratio (expressed as a percentage) between the numbers of time a candidate was classified as a reference and the number of bootstraps.
in 2014 where the ARD content of different North American soils was analysed using functional metagenomics The screening of the clones was performed on aztreonam, chloramphenicol, ciprofloxacin, colistin, cefepime, cefotaxime, cefoxitin, D-cycloserine, ceftazidime, gentamicin, meropenem, penicillin, piperacillin, piperacillin-tazobactam, tetracycline, tigecycline, trimethoprim and trimethoprim-sulfamethoxazole (cotrimoxazole). For the present study, we collected the nucleotide sequences of the inserts deposited on Genbank (KJ691878-KJ696532). The sequence translation of the open reading frames was performed by Prodigal (using default parameters) A total of 4,654 sequences of inserts were collected, in which 12,904 amino acid sequences were predicted. We then searched for ARDs belonging to the relevant ARD families according to the antibiotics used for the screening of the clones: beta-lactamases (all classes), APH, ANT, AAC(2), AAC(3)-I, AAC(3)-II, AAC.
66(http://shaman. c3bi. pasteur. fr/,data are available at https://github. com/aghozlane/evotar). The relationship between richness and the abundance of ARDs was assessed by the Spearman correlation test. The statistical threshold was set at a p-value of 0. 05. The p-values were represented by a single * when 0. 01<p<0. 05, ** when 0. 001<p<0. 01 and *** when p<0. 001. Illustration of the concept of "Pairwise Comparative Modelling" (PCM) with Qnr proteins (panel A). A1: Qnr protein structure (2W7Z) obtained from the PDB database. A2: A candidate protein (MC3. MG361. AS1. GP1. C32174. G14) for Qnr modelled with a reference Qnr structural template. This protein had 35. 7% amino-acid identity with the closest Qnr protein (QnrB4). A3: The same candidate protein (MC3. MG361. AS1. GP1. C32174. G14) for Qnr this time modelled with a Qnr negative reference template.
Acknowledgements The authors are deeply grateful to the  GENOTOUL (Toulouse, France) ,  GENOUEST (Rennes, France) ,  ABIMS (Roscoff, France) ,  MIGALE  (Jouy-en-Josas) and TGCC- GENCI  ( Institut Curie ) calculation clusters. The authors also warmly thank  Bruno Perichon  ( Institut Pasteur, Paris, France ) for providing ARD sequences from  Acinetobacter baumannii ,  Patricia Siguier  ( CNRS, Toulouse, France ) for helping the search of insertion sequences with ISfinder,  Julien Guglielmini  ( Institut Pasteur, Paris, France ) for his assistance in finding conjugative elements and  Stevenn Volant  ( Institut Pasteur, Paris, France ) for the design of the statistical model in SHAMAN.
10. 40. 1040 //www. lahey. org/studies/), RED-DB (http://www. fibim. unisi. it/REDDB/), Marilyn Roberts' website for macrolides and tetracycline resistance (http://faculty. washington. edu/marilynr/) and from functional metagenomics studies 5, 6, 31. Nonredundancy of the reference ARDs was assessed with CD-HIT v4. 5. 7 The final database was manually curated. The cluster of orthologous genes (COG) of each member of the reference dataset was assigned from the v3 eggNOG database The mean size of the reference dataset sequences was determined. In total, we collected 1,651 non-redundant amino acid sequences spanning 20 ARDs families: Class A beta-lactamases (Blaa), class B1-B2 beta-lactamases (Blab1), class B3 betalactamases (Blab3), class C beta-lactamases (Blac), class D beta-lactamases (Blad), aminoglycosides acetyltransferases (AAC) AAC(2), AAC(3)-I, AAC(3)-II, and AAC(6), aminoglycosides nucleotidyltransferases (ANT), aminoglycosides phosphotransferases (APH), 16S rRNA methylases, Tet(M), Tet(X), type A dihydrofolate reductase (DfrA), dihydropteroate reductase (Sul), erythromycin ribosome methylase (Erm), quinolone resistance proteins (Qnr), fosfomycin resistance proteins (Fos), and D-Ala -D-Lac/Ser ligases (Van) (Table.