dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 10. 0, http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Quality checked, paired-end reads were aligned to the mm9 reference genome assembly using STAR aligner  50. Reads were allowed to map across splicing junctions as per gencode mouse version M1 annotations  51. FeatureCounts 52 was used to calculate read counts covering each gencode gene. Then edgeR package  53  was used to normalise raw read counts over genes to counts per million mapped reads (CPM) using the TMM method. Low abundance genes with CPM < 1 in all cell types were filtered out and subsequently differential gene expression analysis was performed in edgeR with a negative binomial model. DEG across cell types were defined based on a 0. 05 false discovery rate (FDR) and a minimum expression threshold of log 2 CPM > 1.
Enrichment analysis of gene ontology terms in DEG was performed using DAVID version 6. 8  54. Signalling pathways and biological process networks significantly modulated by the DEG were identified using Metacore software suite licensed from Thomson Reuters Life Sciences Research. Connectivity map (CMap) analysis comparing RNA-seq gene expression profiles with gene expression signatures derived from public data was performed using the 'gCMap' package in R Bioconductor (version 1. 16. 0). The expression data sets have been deposited in NCBI GEO under the accession code GSE83436.
Acknowledgements We thank the  SIgN Flow Cytometry, Mouse, Functional Genomics and Bioinformatics cores  for experimental support, and  Alan Frey  and laboratory members for valuable suggestions on the manuscript. Funded by  Singapore A*STAR-SIgN  core funding (M. A. C. d. L. , F. Z. and M. P. ) and JCO grant  1431AFG104  (M. A. C. d. L. and J. -S. H. ),  NYUSM B. Levine postdoctoral fellowship (S. P. S. ) , the  Department of Medicine of NYUSM  (M. A. C. d. L. ) and  NIH/NIAID   R01 AI130343  (M. A. C. d. L. and J. J. L. ).
Provisions should be made so that this initial research platform may be further developed through a comprehensive 'interepidemic' action plan addressing and fine-tuning the response to any obstacles identified during the ZIKV research response (e. g. resolving regulatory and other bottlenecks, development of adaptable study protocols, strengthening ICT infrastructure for communication and information exchange, developing a training programme to enhance the local partners' capacity for laboratory and clinical research, developing a communication strategy for patient and public engagement, etc. ). A comprehensive data-management framework allowing the standardized collection, storage, analysis, and sharing of data should be implemented, initially focusing on Zika data but eventually evolving as a critical part of the preparedness research network.
Concept and methodology The project's work plan is built on 15 interoperating work packages (WP). The structural design of ZikaPLAN reflects the ambition to address the urgent research gaps (WP 1-8), identify short-and long-term solutions (WP 9-10), and build a single sustainable Latin American EID Preparedness and Response capacity, WP 11. Communications and consortium coordinated are handled in WP 12 and 13 respectively. The project collaborates and interlinks with the two other EU-funded Zika consortia, ZIKAction and ZIKAlliance on protocol development, data sharing, joint data-analysis plans, and biobanking, and aims to develop commonalities for the dissemination of research findings (WP 14-15). Figure  3  presents the schematic work plan by WP. Box 3 summarizes the primary research objectives of WP 1-12.
WP 2 neuro-Zika: neurological manifestations of Zika The 'Neuroviruses Emerging in the Americas Study' (NEAS) is a multicentre study in the Americas (NCT03206541) to establish a comprehensive registry of the clinical, radiologic, and paraclinical profile of patients with new onset of neurological diseases associated with Zika, dengue and chikungunya viruses (http://www. neasstudy. org/en/home/) with a network involving more than 10 hospitals in Colombia. NEAS was initiated by Prof. Carlo Pardo-Villamizar from Johns Hopkins University, US, and has already resulted in various publications  [15] [16] [17]. The NEAS network will be enhanced by various additional sites in Brazil. The main objective is to define the full spectrum of neurological complications due to ZIKV by using a case-control study design.
WP 8 'disease burden and risk assessment' This WP assembles a wealth of data obtained from various sources, including a longitudinal cohort study of 17,000 subjects aged 2-59 years in 14 different geographic locations in Brazil over 3 years, recruited through an ongoing Phase 3 trial by ZikaPLAN Partner Butantan. Seroprevalence studies will help to assess the asymptomatic to symptomatic ratio, the ageand gender-stratified attack rates, the clinical spectrum, and the impact of preceding dengue infections on the severity of illness. Documentation of the evolving epidemiology of ZIKV will be enriched by data from the Latin American network on birth-defect surveillance (ECLAMC). We will establish an 'International Committee for Birth Defect Surveillance Tools for Infection Disease Response Preparedness'.
The results from our partners and this work package, further enhanced by literature reviews, will feed into mathematical modeling to document and predict burden of disease in Latin America. Complemented by data from GeoSentinel, Healthmap, and Bluedot, we will analyze the geographic spread of Zika infections beyond Latin America. Much of this will involve modeling spread via air passengers  [19, 20]. We will apply previously developed models for the risk of dengue spread via air passengers  [21] [22] [23]  to model the spread of ZIKV. We already modeled the risk of ZIKV infection during the Olympics  [24, 25]  and provided input to the WHO IHR committee not to cancel the Olympics. We also published the most likely time of ZIKV introduction into Brazil based on mathematical modeling  [26].
To this purpose, we will work together with ZIKAlliance and ZIKAction in order to leverage synergies, in particular with regard to the clinical cohort studies. A specific platform will be set up to enhance the outreach to expand to new sites and to develop into an independent research network beyond the project time. This is called REDe (the Spanish and Portuguese term for network), which will be a shared and open space for information exchange and releasing research documents such as protocols and operating procedures, to ensure standard outcome measures are used, to optimize data sharing, and also to disseminate the research tools in order that others may use and adapt them. REDe is hence a research-capacity development and knowledgeexchange space for all three EU-funded Zika consortia and their clinical sites.
WP 14 Zika data share: harmonization of data and roadmap to data sharing The aims of the work package are (1) to ensure harmonization of protocols and standardization of tools (including data dictionaries) for the multicentre pregnant women and children cohort studies across the three EU-funded Zika consortia (ZikaPLAN, ZIKAction, ZIKAlliance); (2) to conduct data sharing within the three consortia and to provide a roadmap toward wider data sharing. The pregnant women cohort is harmonized between ZIKAction and ZIKAlliance (as both recruit asymptomatic and symptomatic pregnant women), whereas ZikaPLAN recruits only women symptomatic ZIKV infection during pregnancy. The data garnered through these combined cohorts will also feed into the WHO-led 'individual participant data metaanalysis (IPD-MA)', which will combine de-identified, participant-level cohort data to identify and quantify the relative importance of different predictors of congenital Zika syndrome (CZS) (www.
Acknowledgments We acknowledge all our consortium members and many other members who participate in the ZikaPLAN project in different capacities:  John Kinsman ,  Joacim Rocklöv ,  Niklas Arnberg  ( Umeå University, Sweden );  Hannah Kuper ,  Chris Drakeley ,  Laith Yakob ,  Michael Gaunt  ( London School of Hygiene and Tropical Medicine, UK) ;  Susan Barnett ,  Julia Edgar ,  Christopher Linington  ( University of Glasgow, UK );  Joan Morris  ( Queen Mary University London, UK );  Helen Dolk ,  Maria Loane  ( University of Ulster, UK );  Susanne Kaptein  ( University of Leuven, Belgium );  Bart C. Jacobs ,  Pieter A. van Doorn  ( Erasmus University Medical Centre Rotterdam, Netherlands );  Claudia Romero-Vivas  ( Universidad del Norte, Colombia )  Lyda Osorio ,  Beatriz Parra ,  Andreas Zea-Vera ,  Carlos-Pardo Villamizar  ( Universidad del Valle, Colombia );  Sebastian Quesney ,  Koren Wolman-Tardy  ( Fondation Meriux, France );  Lisa Ng ,  Lance Turtle ,  Anu Jacob ,  Michael Griffiths ,  Julian Hiscox  ( University of Liverpool, UK );  Alessandro Sette  ( La Jolla Institute, USA );  Aravinda de Silva ,  Stefan Matz  ( University of North Carolina at Chapel Hill, USA );  Ralph Huits ,  Kevin K.
Acknowledgements This study is currently being funded by a grant from the  Canadian Institutes for Health Research  (CIHR # 43854 ). We also wish to acknowledge the  CEA (Commissariat al'E ´nergie Atomique et Aux E ´nergies Alternatives, France), and Institut de Recherche Technologique NANOELEC (IRT Nanoelec ,  Programme d'investissement d'avenir , francais), who also financially supported the development of the approach. Finally, our thanks to  UniteMixte Internationale-LN2 (Laboratoire Nanotechnologies et Nanosystemes)  for its support and interest towards the impact and the pursuit of this work by its Ethics, Uses and Society axis.
Acknowledgments This work was financially supported by the project  Nano-OSCAR  from the  "Agence Nationale de la Recherche ". NBJ is indebted to  Nancy CHU , " Hôpital Central, Chirurgie Orthopédique et Traumatologie" (INSERM to Hospital Interface Contract ). We are indebted to  Lionel Thomas  and  Jean-Michel Gallone  from the  IPHC, Strasbourg , for the micro-CT analysis. We thank  Jens Kelm  for his collaboration in MTs preparation. We are also indebted to  Dr Nadia Messadeq  and  Jean-Luc Weickert  for their help with SEM. We also thank  Victor Sebastian  and  Carlos Cuestas  for their.
Methods A bibliographic search was performed by querying MedLine (PubMed last access 06/11/2016) using the keywords "America AND snake Ã AND [envenom Ã OR antiven Ã ]". From a total of 4,514 references, 187 concerned the epidemiology and/or management of snakebites in the Americas. Furthermore, websites regarding i) the epidemiology of snakebites (using the words "health surveillance", "surveillance bulletin", "epidemiology surveillance", "snakebite envenomation", "snakebite death"), ii) population demography (using the words "population demography") and iii) administrative and environmental geography (using the word "map") were identified using the Google search engine for each of the countries of America and using the official language of each country (English, Spanish, Portuguese, French and Dutch).
Access to these websites was made between September 2010 and December 2016. The list of the websites and the last access date to each are mentioned in Table  1. However, a few websites were closed during this period and sometimes replaced by new ones, the use of which was often restricted by a password. All the data were transferred and analyzed using Excel software. The trend curves and R 2 , the coefficient of determination that is the square of the coefficient of correlation indicates the extent to which the dependent variable is predictable, were calculated through Excel. The comparisons were made using parametric tests (t-test, χ 2 and Pearson correlation) or nonparametric (Mann-Whitney), depending on the distribution of studied variables and number of cases/groups. The significance level was equal to 0.
05 and the means were expressed using a 95% IC. Statistical analyzes were performed using the BiostatTGV online software (http:// marne. u707. jussieu. fr/biostatgv/). Topographic, physical and political maps were taken from the World Atlas of Wikimedia (https://commons. wikimedia. org/wiki/Atlas_of_the_world) and drawn on the basis of the data obtained in this study.
Brazil The notification of snakebites is performed for a long time in Brazil but the results are online only since 2001. According to Chippaux  [20]  from the data reported by the health facilities and available online on the site of SINAN which is the main Database on causes of morbidity and mortality available online since 2001  [21] , the average number of snakebites was about 27,200 per year (15 per 100,000 population) with more than 115 deaths (0. 06 100,000 inhabitants) during the period 2001-2012. The geographical distribution showed a clear predominance in northern Brazil, especially in the Amazon (Fig  4 ). The seasonal distribution of bites was more pronounced in the summer, particularly in the southern regions (Fig  5 ). The incidence by age group varied greatly from region to region.
These figures were therefore overestimated, which can be https://doi. org/10. 1371/journal. pntd. 0005662. g023 explained by the highly biased epidemiological source of information. Indeed, most authors who publish epidemiological or clinical studies on snakebites report facts upon regions with high incidence-or severity-of envenomation that are often poorly representative  [60]. Nevertheless, the general incidence is much lower than in Asia or Africa [1; 2; 61], excluding for particular regions such as the Amazon. However, mortality remains moderate, except in enclosed or poorly equipped areas. Most of the data collected in this study comes from the Ministries of Health of the concerned countries. Until now, epidemiological surveys were needed to obtain information that was most often limited geographically according to the constraints and choices of the investigators.
Optical sections were collected at 0. 5 μm intervals along Z-axis using a Leica SP8-UV microscope, Montpellier Rio Imaging (MRI) facility. Relative 3D distances between FISH signals were analyzed in approximately 100 to 120 nuclei per 3D stack in T1 and T2, and in approximately 10 to 12 nuclei per 3D stack in the germline, using the Metamorph software (Universal Imaging Corp. ). The distance distribution between the two probes was obtained by pooling replicates for each condition. The classes were arbitrarily defined with a 500 nm range. In order to measure statistical significance between two distributions using a two-tailed Student's t-test, the normality of each distribution was assessed with Shapiro-Wilk test (P>0. 05). To estimate the frequency of homologous unpairing, homologous loci were considered unpaired when the distance between the centers of their FISH spots was higher than 500nm.
For the anti-Polycomb FISH-immuno, the colocalization levels between 37B and Polycomb foci were acquired and measured as follows: 3D stacks were collected from 4 different embryos at stage 14-15 in T1 and T2 embryonic segments. Optical sections were collected at 0. 3 μm intervals along Z-axis using the Leica SP8-UV microscope. Raw images from 3D stacks were deconvolved with Huygens Professional version 16. 10 (Scientific Volume Imaging, The Netherlands), using the CMLE algorithm. After deconvolution, the FISH signals and Polycomb immunostaining were segmented in 3D using Imaris software (Bitplane, Switzerland). The centers of mass of the FISH signals were calculated and tested for their colocalization with the Polycomb segmented objects, using the XT module of Imaris. The intensity of the Polycomb staining in the centers of mass of Polycomb colocalized FISH objects was measured and normalized by the mean Polycomb intensity (measured in Polycomb segmented objects) for each stack.
Genomic DNA sequencing For each line, genomic DNA was extracted and purified from 150 adult female flies using the Gentra Puregene Cell Kit (Qiagen) following the manufacturer's instructions. Wholegenome overlapping paired-end (2x125 and 2x165 for Oregon-R and Canton-S flies, respectively) sequencing data were generated in three biological replicates. Library preparation and gDNA sequencing was performed by FASTERIS SA. Fastq files were aligned against the dm6 Drosophila reference genome using Bowtie2 v2. 2. 0 47 with standard parameters. Reads mapping to unplaced or unlocalized scaffolds were discarded, and BAM files containing uniquely aligned reads were pooled within each line using Picard (see URLs section) and sorted and indexed using SAMtools v1. 1  48. SNPs were called using BCFtools v1.
2. Line-specific SNPs were annotated with respect to genomic features and coding sequences using R 3. 3. 0/BioC and the VariantAnnotation package v1. 18. 7  49  , and subjected to gene ontology analysis using Gorilla 50 with a p-value cutoff of 0. 05. Structural variations (SVs) detection was carried out using the DSVD pipeline as previously described  32. Seedbased alignment was performed using Bowtie2 v2. 2. 0 with parameters -3 95 -N 1 -L 30 -k 11. Sixteen different SV types, including small insertions and deletions, inversions and complex chromosomal rearrangements, were considered. Detected events were then filtered by discordant coverage, and SVs with a weight of 1 were discarded. All remaining linespecific high-confidence SVs were annotated to genomic compartments and coding sequences.
For small RNA sequencing data, reads ranging between 20-29 nt length were further extracted and analyzed separately. Data was visualized using the IGV browser  51. Read counts to different elements of the transgene were calculated using bedtools and a custom bash script. For gene expression analyzes, counts for uniquely mapping reads to a manually curated annotations of protein-coding genes and non-coding RNAs obtained from RefSeq, miRbase, RFAM and Ensembl were calculated using a custom python script. Differential expression analyzes were performed using the DESeq2 R package 54.
Acknowledgments This study benefited from the  CNRS  human and technical resources allocated to the  ECOTRONS Research Infrastructure as well as from the state allocation 'Investissement d'Avenir' AnaEE-France   ANR-11-INBS-0001. We thank  J. Roy ,  S. Devidal ,  A. Milcu ,  D. Landais ,  O. Ravel  and  A. Faez  for help at the Ecotron-CNRS Facility in Montpellier;  J. Foucaud ,  B. Serrate  and  A. Rombaut  for help with conducting experiments on environmental effects in CBGP;  J. -M. Chang  and  V. Loubiere  for technical support;  M. Siomi  ( Keio University ) for providing the anti-Aubergine 4D10 antibody; Montpellier Ressources Imagerie facility MRI- IGH  for microscopy support. F. C. was supported by the  Fondation pour la Recherche Medicale (FRM). F. B. was supported by  CNRS. F. Comoglio was supported by  ETH Zurich.
B. B. was supported by the  Sir Henry Wellcome   Postdoctoral Fellowship  ( WT100136MA ). Research of R. P. was supported by the  FP7 European Network of Excellence EpiGeneSys , the  Swiss National Science Foundation  and  ETH Zurich. M. N. and A. A. were supported by  NIH  r01 grant  GM097363. Research in the laboratory of G. C. was supported by grants from the  European Research Council  ( ERC-2008-AdG No 232947 ), the  CNRS , the  FP7 European Network of Excellence EpiGeneSys , the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  676556  (MuG), the  Agence Nationale de la Recherche , the  Fondation pour la Recherche Médicale , the  INSERM , the  French National Cancer Institute (INCa)  and the  Laboratory of Excellence EpiGenMed.
3. 3. 2 (R Core Team, 2016), using the contributed package glmmADMB v. 0. 8. 0  (Fournier et al. , 2012). The numbers of aphids or parasitoid mummies per cm of plant stem length were fitted using a negative binomial distribution with log link, testing for the effects of host genotypic diversity, parasitoid reproductive mode, and count (i. e. , week after the start of the experiment), as well as their interactions. Cage was included in the models as a random effect to account for the nonindependence of repeated counts from the same cages. A time-lagged, negative correlation between the change in parasitoid density and the change in host density would be a signature of parasitism affecting host population growth. Therefore, we used a linear mixed model to predict the change in estimated aphid density between two counts in all cages by the change in estimated parasitoid density in the same time interval as well as the change in parasitoid density in the two previous time intervals (1-week lag and 2-week lag), again including cage as a random effect.
We took the proportion of mummies among all aphids initially exposed to parasitoids as our estimate of susceptibility to parasitism. These proportions were arcsin square-root transformed and analyzed with a linear mixed model (R package lme4;  Bates, Maechler, Bolker, & Walker, 2015) , testing for the fixed effects of H. defensa infection, parasitoid line, and their interaction, as well as for the random effects of block, aphid clone (nested within H. defensa infection), and the aphid clone × parasitoid line interaction. p Values for the fixed effects were calculated using F tests with Satterthwaite's approximation, and p values for the random effects were calculated based on likelihood ratio chi-square tests using the lmerTest library in R  (Kuznetsova, Brockhoff, and Christensen, 2015).
ACKNOWLEDGEMENTS We thank  L. Breithut  for help with aphid and parasitoid genotyping and  P. Rodriguez  for the skillful maintenance of insect cultures. We also thank  N. G. Hairston  for insightful comments on the experiment, and two reviewers for useful suggestions and corrections. This study was supported by the  Swiss National Science Foundation  (SNSF Professorship nr. PP00P3_146341  and Sinergia grant nr. CRSII3_154396  to CV).
Pyrosequencing Data Analyses To minimize the effect of random sequencing errors  (Huse et al. , 2007; Kunin et al. , 2010) , pyrosequencing reads analyses and filtering were performed from the sff file using the Mothur pipeline  (Schloss et al. , 2009). Sequences (i) shorter than 200 bp, (ii) containing homopolymers longer than 8 bp, (iii) that aligned to the incorrect region within the 16S rRNA gene (Silva release 119, www. arb-silva. de), (iv) identified as chimeras using Uchime algorithm  (Edgar et al. , 2011; Schloss et al. , 2011) , and (v) affiliated to Archaea when Bacteria was targeted and inversely were removed from the dataset. Using the tag combinations, sequences were assigned to their respective sample. Sequences were pre-clustered using the Mothur pipeline, as recommended to reduce potential pyrosequencing errors  (Schloss et al.
, 2009). Then samples were normalized and all the following analyzes were performed on the same number of sequences per sample for the bacterial (1,096 sequences) and the archaeal (1,413 sequences) regions. Taxonomic assignments of the reads were performed as previously described in detail in  Cruaud et al. (2015)  using the Mothur version of the Bayesian classifier  (Schloss et al. , 2009)  on both SILVA database (release 119, 464,618 sequences of Bacteria and 18,797 sequences of Archaea, www. arb-silva. de) and an in-house database composed of deep-sea marine sediment sequences from reference publications updated and completed for this study using the SILVA database release 119, as described in  Cruaud et al. (2015)  (2,860 bacterial and 978 archaeal sequences, Table  S4 ). Statistical analyses were performed using the software environment R (v.