dataesr/scientific-paragraphs-categorization huggingface dataset:
The sensitivity analyses relax the group of assumptions 1), and while informative, we stress that we would need better data not better models to improve our estimate of the probability of transmission of T. cruzi to human. From prevalence (Eq. 6, 2b): Teya: 0. 26% , Sudzal: 0. 68% , Merida: 0. 26%  [29, 30, 31]  Data are presented as mean 6 SE (when available in the original study). Entomological data were combined to estimate the number of potentially infectious contact per person per year (PIC).
M1 and M2 refer to the triatomine collection methods and the corresponding correction factors defined in the main text. a Vector densities were further corrected for seasonality using monthly variations in infestation of T. infestans  [32]  or b for the seasonal infestation pattern of T. dimidiata  [21]. c The biting rate was estimated and corrected for seasonal variations according to  [33]. Incidence was either measured directly or derived from prevalence using data on children or on all age-categories at the house or village scale. insect collection method as described above. However, in 3 case studies (datasets 6, 11 and 12), bugs were collected using a third method; M3 -a timed-manual collection by trained personnel without insecticide spraying. We considered this method to have an intermediate efficacy, and we thus set the correction factor to 6 and varied its value within a range set to its value 61.
Entomo-epidemiological datasets Estimate of the probability of transmission (Datasets 1-5). Estimating the probability T of transmission requires detailed entomological and epidemiological data, which are only available from long-term field studies on specific biological systems. We thus re-analyzed the data presented by  [11] , and extensively reviewed the literature in the field of Chagas disease to identify additional systems which have been consistently investigated in the field over the last 10-15 years so that all entomological and epidemiological data required to get estimates of the probability of transmission were available (Table  1 ,  [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] ). These studies involve four vector species (Triatoma infestans, T. longipennis, T. barberi and T.
dimidiata) from five locations in Argentina and Mexico. They described various entomological settings with corrected vector density varying from less than 10 to almost 300 triatomines per house, vector infection rate ranging from 10% to 58%, and vector feeding frequency on human of 1% to 46%. Overall, those entomological and epidemiological heterogeneities combined to produce substantial variations in the number of potentially infectious contacts that ranged from 3 to 340 contacts per human per year which, as expected, correlated with annual incidence of infection that ranged from about 0. 1% to 20% (Table  1 ). The first dataset provided all the entomological and epidemiological information at the house scale, allowing to calculate a log likelihood function (Eq. 4) and to derive an estimate and confidence interval for the probability of transmission T.
In all the remaining datasets, some entomological (datasets 4-5) and/or epidemiological (datasets 2-5) data were only available at the village scale, so that only point estimates could be derived. When T. cruzi incidence was given in the original study (dataset 3), the estimate was calculated using Eq. 5, while when prevalence was measured in adult (datasets 4-5) or in children (2), the point estimate of T was calculated from Eq. 6 and 7-8, respectively. Predicting human prevalence (Datasets 6-14). To evaluate whether our estimates of the probability of transmission T would allow predicting the prevalence of infection in humans from limited entomological knowledge, we selected nine independent case studies reporting data on the average triatomine density and infection rate, and on human prevalence of infection (Table  2 ,  [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] ).
Again, these data were obtained by careful screening of the literature. Estimates of the biting frequency and proportion of bites on human were not available in those studies, and we thus used values from dataset 1 to 5 according to the species of interest (i. e. T. infestans. vs. others) and the habitat of the vector (i. e. domicile. vs. peridomicile). These new datasets covered 7 countries  Basic entomological data (vector density and infection rate) and the estimate of the probability of transmission were used to predict human prevalence. Vector densities (average per house) were corrected according to the collection methods used (M1, M2 and M3) as defined in the main text. Density and infection rates were available for domestic (D) or peridomestic (P) habitats. Mean infection rates are given for the villages.
Estimating the probability of Trypanosoma cruzi transmission to human We first determined the probability of T. cruzi transmission using dataset 1 which includes data collected at the scale of the household  [11]. A profile likelihood was drawn as a function of the probability of transmission (Figure  1 ). Such profile provided a maximum likelihood estimate of T~5:8|10 {4 per contact with infected bugs, which was close to the original point estimate of 9|10 {4 obtained with the same data. The profile likelihood also provided a confidence interval around this estimate, which was 2:6 ; 11:0 ½ |10 {4 per contact, which means that on average 900-4000 contacts with infected vector are needed for a host to become infected (Figure  1 ). This confidence interval was confirmed by the sensitivity analysis to variations in entomological raw data (P vj , b j , F j and N vj ).
The 95% range of the sensitivity estimates was 3:1 ; 14:9 ½ |10 {4 per contact and the corresponding distribution remained well within the confidence interval derived from the likelihood approach. Only 10% of values from the sensitivity analysis felt outside of the 95% likelihood confidence interval. From the other four datasets, where entomological and/or epidemiological data were provided at the village scale, we derived additional point estimates of the probability of transmission of T. cruzi to human. These estimates varied from 1:5 and 1:7|10 {4 for T. infestans in Argentina and 2:0|10 {4 for T. longipennis and T. barberi in Mexico, to 9:2 ; 3:5; 9:1 ½ |10 {4 for T. dimidiata in Mexico (Figure  2 ). They all lied within or very close to the confidence interval derived above from dataset 1 indicating that there is no major difference between the maximum likelihood estimates of T and these four estimates.
The estimate for dataset 4 can be viewed as a species-averaged probability of transmission as the data do not allow to distinguish infection from either T. longipennis or T. barberi. The sensitivity analyses to variation in the entomological raw data (P vj , b j , F j and N vj ) further confirmed the high consistency of those results. The distribution of estimates obtained for the other two datasets on T. infestans were slightly less dispersed than in the first data set (Figure  2A  and B ) with 95% of the values ranging in 0:7 ; 3:8 ½ |10 {4 for dataset 2, and in 0:9 ; 4:5 ½ |10 {4 for dataset 3. The distribution obtained for T. longipennis and T. barberi (dataset 4) in Mexico were slightly broader with 95% of the estimates found within 0:9 ; 6:2 ½ |10 {4 (Figure  2C ). Finally the distributions obtained for T.
dimidiata in Mexico (dataset 5), were the most variable since 95% of the values laid within 5:0 ; 22:6 ½ |10 {4 , 1:7 ; 13:0 ½ |10 {4 and 5:1 ; ½ 21:0|10 {4 for the villages of Teya, Sudzal and the city of Merida, respectively (Figure  2D, E  and F ). Nevertheless, all estimates were still very consistent with the likelihood-based confidence interval. According to our analysis, including a likelihood estimation with confidence interval, point estimations and sensitivity analyses, the probability of stercorarian transmission of T. cruzi to human is estimated to be in the range 10 {4 -2|10 {3 per contact.
Dataset Species and location Observed human prevalence Predicted human prevalence [95% CI] p-value pressure on T. cruzi may be too weak given its potential for direct transmission which is known to be of evolutionary and epidemiological importance in opossum  [16, 70]  as well as in human either because of oral or maternal transmission  [13]. Our quantitative assessment of the probability of transmission of T. cruzi offers new opportunities to tackle these key ecoevolutionary questions, as it allows quantifying standard epidemiological measures such as R 0 or related quantities  [3, 4, 71]  which have been consistently missing in the epidemiology of Chagas disease [6, but see 72], while they are central tenets of the study of the epidemiological dynamics of malaria  [4] , dengue  [17]  and others human and livestock vector-borne diseases  [73].
This investigation received financial support from the  UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases (TDR) /  International Development Research Center (IDRC) , Project # A90276  and the  European Union   EU-FP7-PEOPLE-Intra-European Fellowship for career development (IEF)  grant no. 253483. For part of this study, PN received funding from the  European Union Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement no. 278433-PREDEMICS  and thanks the  MRC Centre for Outbreak Analysis and Modelling  for support. This work was performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Universitede Lyon , within the  program ''Investissements d'Avenir''  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
4 Per house c : 0. 36 0. 04 Average from c : 0. 4 Average from c : 0. 2 Average from c : 0. 2 Vector infection rate (P vj ) Per house 37%65 Per house: 10%63 Per house: 40%65 Average for the village: 46% Average for the village: Teya: 14%, Sudzal: 27%, Merida:48% Corrected vector density (N vj ) Per house M1,a : 287647 Per house M1,a : 140632 Per house M1,a : 80615 Average per house M1 : 45 : Average per house M2,b Teya: 40, Sudzal: 20, Merida: 1 Species and location T. infestans, Gran Chaco, Argentina T. infestans, South of Gran Chaco, Argentina T. infestans, Gran Chaco, Argentina T. longipennis and barberi, Jalisco, Mexico T. dimidiata, Yucatan, Mexico Dataset 1 2 3 4 5.
Nucleotide sequence accession number The S. agalactiae 2-22 complete genome sequence is available from DDBJ/GenBank/EMBL under accession number FO393392. Draft genome sequences of strains 90-503, SS1219, CF01173, SS1014, SS1218 and 05-108A are available under accession numbers CAPZ01000001-CAPZ01000082, CAQA01000001-CAQA01000070, CAQB 01000001-CAQB01000131, CAQC01000001-CAQC01 000099, CAUB01000001-CAUB01000070 and CATH01 000001-CATH01000087 respectively.
Annotation methods Annotation of strain 2-22 was performed using the CAAT-box environment as previously described  [23]. Briefly, coding sequences (CDS) were defined by combining Genmark predictions with visual inspection of each open reading frame (ORF) for the presence of a start codon with an upstream ribosome-binding site and blastp similarity searches on Uniref 90, Trembl and Swissprot databases. Function predictions were based on blastp similarity searches and on the analysis of motifs using the PFAM databases  [82]. For the six other strains putative CDS were determined using Artemis  [83]. Predicted CDS were tested in reciprocal pair-wise blastp comparison against NEM316, A909 and 2-22 sequences. ORFs longer than 100 codons or encoding a protein > 90% identity by blastp with a protein annotated in NEM316, A909 or 2-22 were retained for further analyses.
Whole genome sequence comparisons Determination of insertion/deletion events in ST260-261 strains compared to strain A909 [GenBank: NC_007432] was performed by combining blastn and nucmer analyses  [40]. Each indel was individually inspected to differentiate possible misalignments from true indels and studied for the presence of repeat sequences at its extremities. We also aligned to A909 sequence the genome sequences of three S. agalactiae strains of human and bovine origins (NEM316, GenBank: [NC_004368]; 2603V/R [GenBank: AE009948] and FSL S3-O26/AEXT01000000 [GenBank: AEXT00000000]  [24] ). These alignments were used to produce tables of SNPs, indels and gaps relative to A909 genome sequence. Only indels that occurred internally to Velvet contigs in draft genome sequences and were repetitively observed for all strains of the same sequence type were taken into account.
The oligonucleotide design was carried out with the OligoArray server  [84]  (http://berry. engin. umich. edu/ oligoarray/). The microarray was manufactured by Agilent Technologies. Only probes with 100% identity to A909, CF00173, 2-22 and SS1219 sequences were taken into account in the data analysis (ie 4401 probes on 1389 genes). Analysis was performed as described  [85]. The transcriptome data are MIAME compliant and have been submitted to the ArrayExpress database maintained at www. ebi. ac. uk/microarray-as/ae/ under the Accession Numbers E-MEXP-3828, E-MEXP-3830 and E-MEXP-3829.
Acknowledgements We specially thank  Drs J. Hawke ,  B. Beall  and  E. Duchaud  for providing us the GBS strains used in the study. We thank  Drs. E. Souche  and  P. Lechat  for their help in bioinformatics analysis and  Pr P. Trieu-Cuot  and  Dr. P. E. Douarre  and  R. Guérillot  for helpful discussions. This work was supported by the  French National Research Agency  (grants  ANR-08-GENM-027-001 ) and the  LabEx  project  IBEID. The  Institut Pasteur Genopole  is a member of  France Génomique  ( ANR10-IBNS-09-08 ).
Analysis of purified monocytes from the same volunteers confirmed a strong eQTL, the most significant association was to rs375984 (P ¼ 3. 2 Â 10 À11 ; Supplementary Figure  S2 ). To further resolve this, we imputed 19 129 additional SNPs within 250 kb, which revealed three more strongly associated variants in the first intron of ZFP57 in perfect LD (rs416568, rs365052 and rs2747431, P ¼ 4. 6 Â 10 À52 ; Figure  1c  and d ). We determined the functional genomic landscape for these eSNPs using data from the ENCODE project. 8 Analysis of DNase-seq and ChIP-seq data sets resolved rs365052 is a candidate regulatory variant located in a DNase I hypersensitive site with evidence of CTCF binding (Figure  1d ). Analysis of the eQTL by HLA type showed association with HLA-A*01 and *23, but was not more informative than SNP markers (Figure  2 ).
For the two most common ancestral haplotypes among Europeans, we found that volunteers with a copy of HLA-A1-B8-DR3 (n ¼ 19) had higher expression of ZFP57 compared to HLA-A3-B7-DR15 (n ¼ 12; Mann-Whitney, Po0. 0001). We investigated whether the genetic variants identified here as associated with ZFP57 expression may be significant in common disease given the many disease associations reported involving the MHC class I region. We interrogated GWAS data sets and found intersection of ZFP57 eSNPs variants with reported disease associations involving malignancy, HIV/AIDS and autoimmunity (Table  1 ). These included nasopharyngeal carcinoma and prostate cancer, the latter involving disease risk based on gene-gene interaction with the tumour suppressor gene NKX3-1. Associations were also noted involving HIV-1 viral set point and disease progression to AIDS.
ACKNOWLEDGEMENTS We are grateful to the volunteers, to  Stephen Leslie  and  Alexander Dilthey  for HLA-type imputation and to  Tomas Malinauskas  for assistance with amino-acid analysis. We thank  Peter Humburg  and other members of the  Knight Lab  for useful advice and discussion of this work. This study was supported by  MRC Studentship (KP) ;  Wellcome Trust  ( 074318 JCK, 088891 BPF, 075491/Z/04  core);  European Research Council  ( 281824 JCK );  MRC  ( 98082 JCK ); Alliance  Franco-British Partnership  (JCK, CV);  NIHR Oxford Biomedical Research Centre.
To scale up and understand the whole system, we need new approaches, data types and services. Access to these larger data resources are largely to be found through informatics, but the application of those resources will be made by domain specialists. Our ultimate goal is an understanding of the whole Earth system, so we must retain a broad range of biodiversity monitoring sites, but at the same time we should also focus research effort on key model ecosystems where we can achieve the intensity of outcomes the biomedical research community has with the model organism approach. Only by looking at vast databases that describe the whole of the system will we be able to understand the big picture, find correlations and patterns of activities. Knowing how such patterns and processes of biodiversity change will further help in more targeted experimentation, resulting in new key datasets.
In "Evolutionary Informatics: Unifying knowledge about the diversity of life"  [18] , Parr et al. propose the grand goal to "Link together evolutionary data across the great Tree of Life by developing analytical tools and proper documentation and then use this framework to conduct comparative analyses, studies of evolutionary process and biodiversity analyses". Five challenges to realising that goal are also discussed. In this white paper we must establish a stronger focus and a direction to guide the development of biodiversity informatics in Europe over the next decade whilst at the same time allow for serendipity. Clearly, the rate of change in the technology environment around us is dramatic: tools like Facebook, Twitter, Foursquare, Google (Earth/Scholar/. ), Mendeley and Dropbox have penetrated our working lives and techniques like MapReduce  [19]  impact greatly our ability to manipulate and analyse massively large datasets.
Smartphones, digital cameras, GPS positioning and progress in geospatial analysis offer possibilities for 'apps' and techniques that were hardly imagined just a few years ago. Workflows as a tool for in-silico processing of data and the concept of Virtual Laboratories where scientists carry out digital experiments, hardly known few years ago, offer today enormous opportunities in virtually reproducing our environment. Similarly in genomics, the rapidly decreasing costs of sequencing technologies combined with the emergence of increasingly sophisticated alignment and inferencing algorithms is leading to huge increases in our knowledge of life as a system. This 'disruptive' innovation trend will continue with 'cloud' , 'big data' , 'linked data' and 'open access' leading to new ideas, products and services.
The GEO BON system will have a Service Oriented Architecture (SOA) and will be built largely from contributing systems that have their genesis at regional, national or sub-national scales. At the European level, the planned ESFRI LifeWatch research infrastructure  [25] , along with EBONE  [26]  and EU BON  [27]  projects, eventually forms the European contribution to realising GEO BON. Key among the technical objectives of GEO BON is the need to promote the use of multidisciplinary interoperability standards, and to define and update interoperability solutionsapplying the System of Systems approach promoted by GEOSS. GEO BON will also help to promote data publication principles in support of full and open availability of data and information, recognising relevant international instruments and national policies and legislation.
Such an enhanced integration of all related information, including raw data, processed data, algorithms (code, workflows) and publications can be achieved through the implementation of an effective biodiversity informatics infrastructure: a shared and maintained multi-purpose network of computationally-based processing services sitting on top of an open (published, registered and linked) data domain. Together, these deliver a stable, broad portfolio of biodiversity information and analytical services that can be used by user communities to investigate problems of interest. The vision is to develop the concept of 'services' delivering either data or analysis of information using a small set of interchange standards. New services can be introduced into such an environment and be generally accessible without special effort.
Section 1: the fundamental backbone (getting the basics right) Why are names important? 1. Until the recent application of molecular technologies to biodiversity studies, almost all information has been labelled with scientific names. Names have a special significance to link information elements and as such, it is important to use them knowingly and to build tools that work with names  [36]. As they reflect concepts that change between individuals and over time, names may refer to many different concepts, making them equivocal identifiers. In addition, information is often available only in local databases. The challenge is to find it, harmonise the way it is accessed and make it available in computer-readable formats. Nomenclature, taxonomy, taxa and their biology together constitute a large challenge requiring novel infrastructure and change of usual practices by stakeholders.
Biodiversity science needs to adopt a system of persistent and universally unique identifiers (PIDs or UUIDs) that will allow resources to be located and linked. An identifier can be attached to a resource of any kind, including data (e. g. specimens in a collection), taxonomic concepts, genetic sequences (e. g. INSDC accession numbers), publications (e. g. DOIs), or data sets and services, (e. g. workflows, computational services or computer code). Identifiers must be stable and unique but they should also: conform to some widely-used syntactic definition; their initial part should be consistent (e. g. http:// or DOI:), so that they can be recovered in a freetext search; ideally, be resolvable (resolution = where to find a particular resource); be archived together with the resource in a sustainable manner, ideally in multiple locations (if the GUID is not resolvable, the resource can be found by searching for the GUID).
e. an authoritative master copy), in practice they are often unable to deliver the speed of response necessary for usability and creating a local copy (a snapshot of a dynamic resource) or developing an independent resource is often the only realistic solution available. Local copies are often the only practical solution for computation but there is no mechanism by which alterations to the primary resource can be effectively propagated to all copies. This inevitably leads to differences between copies. Copies should, therefore, be used over a short timeframe and, if necessary, refreshed. A feedback mechanism is required so that a data user can report an inconsistency to the data owner and, where a correction is suggested, can be easily incorporated into the original dataset. Automated workflows crucially require Web services but working with large datasets in networked services poses technical challenges in the ability to move large volumes of data, the provision of suitable search facilities that minimise the number of host-client interactions, and the bandwidth necessary to keep response times short.
Centralised services, such as VertNet  [51]  assemble large collections of data in a common structure, submitted by individual data creators. The benefit that this brings is economy of scale and the ability to tune the performance of the system. In the context of data cleaning, for example, having the data centrally makes it much easier to compare across data sets and discover inconsistencies. The gain of economy of scale is very important since once a given type of error is identified, rules can be applied for cleaning across the whole data set, therefore avoiding overloading remote services. Once established, though, it is difficult to change the structure and change the purpose, but for large-scale data generation systems, logically centralised services offer significant advantages. One drawback, however, now receiving attention in the genomics and other fields is the issue of time taken to move large datasets to where the resources for computational analysis and modelling are located.