dataesr/scientific-paragraphs-categorization huggingface dataset:
6 AE 0. 8% (range 96. 8-99. 6%) between each other. Viral sequences from the two patients diagnosed with HIV in Marseille shared a mean identity of 98. 2 AE 0. 2% (range 97. 9-98. 4%) with those described by Leoz et al. 6, 8  As a comparison, the first best hit (GenBank accession number AB747469), which branches as an outgroup on the CRF56_cpx cluster and was obtained from a serum sample collected in the Philippines in 2011, and the second best hit detected in GenBank for these six sequences, showed a mean identity of 94. 5% and 92. 4%, respectively. At the amino acid level, identity between the RT sequences from the present study and those obtained by Leoz et al. ,  6, 8  and between these six sequences, ranged from 95. 9% to 99. 5%. Both case patients diagnosed with HIV in our centre were highly sexually active MSM.
We modelled the ecoclimatic niche of Culicoides imicola, a major arthropod vector of midge-borne viral pathogens affecting ruminants and equids, at fine scale and on a global extent, so as to provide insight into current and future risks of disease epizootics, and increase current knowledge of the species' ecology. Based on the known distribution and ecology of C. imicola, the species' response to monthly climatic conditions was characterised using CLIMEX with 109 spatial resolution climatic datasets. The species' climatic niche was projected worldwide and under future climatic scenarios. The validated model highlights the role of irrigation in supporting the occurrence of C. imicola in arid regions. In Europe, the modelled potential distribution of C. imicola extended further West than its reported distribution, raising questions regarding ongoing process of colonization and non-climatic habitat factors.
Geographical distribution of Culicoides imicola We compiled a geo-database of 1 381 individual presence records of C. imicola from published sources (Appendix S1 in File S1). To address problems with duplication and autocorrelation in assessing the goodness of fit, the individual point locations were transformed into 879 unique cells with 109 resolution. The 879 presence records of C. imicola were split into three subsets (Figure  1 ): 1) a training dataset to develop the model (Sub-Saharan Africa, 187 records), 2) a verification dataset, to adjust the model at unfitted locations (South-Eastern Europe, Middle East and Asia, 131 records) and 3) a validation dataset used to infer the strengths and limitations of the model (Western Europe and North Africa, 561 records). In selecting the distribution data sub-sets, we aimed to keep a maximum range of different climate categories in each zone, but also independence between zones with physical boundaries (Saharan desert, Red Sea and Adriatic Sea, cross-hatched areas in Figure  1 ).
We used the Ko ¨ppen-Geiger (KG) climate classification  [33]  and ensured that training, verification and validation zones shared similar climatic ranges, except for an equatorial climate, which was not represented in the validation zone. Africa was assigned to training because C. imicola was first described there (holotype from Kenya, Kieffer, 1913 cited in Borkent,  [34] ) and is considered as the historical distribution area  [35]. The validation dataset encompassed western Europe where the range of C. imicola's distribution is still under debate  [15, 36].
Climate data CLIMEX requires five climate variables: monthly values for minimum and maximum temperature, rainfall, and relative humidity at 9am and at 3pm. We used the CliMond 1975H_V1. 1 fine spatial resolution (109) global climate dataset  [37]. CliMond includes monthly climate data averages centred on 1975, so-called historical climate dataset, and a total of eight future climate scenarios: ie. for 2030 and 2070, for two global climatic models, CSIRO-MK3. 0 and Miroc-h, and two greenhouse gas emissions scenarios, A1B and A2 (4 future climate datasets for each year).
The rationale for considering grass growth is that it is necessary to support the vertebrate hosts of C. imicola. Cold Stress. We fitted a cold stress function to constrain survival to the coldest areas where C. imicola had been reported in traps. Model parameters and intermediate steps in model development, successively using training, verification and validation datasets (Figure  1 ), are detailed in Appendix S2 in File S1. The ''immature model'', fitted to the native range distribution data, was tested against the verification area (Figure  1 ), comparing the region where EI. 0 with occurrence records in South Eastern Europe, the Middle East and Asia. Where necessary, we adjusted the model during the verification step which resulted in a second model named the ''completed model''. This step allowed us to make adjustments for any enemy release effects  [48].
Each model was validated by calculating its sensitivity score: the proportion of records falling in locations modelled as being climatically suitable (EI. 0). In addition, the degree of departure from random prediction was tested with the exact one-tailed binomial statistic  [32]  which measures the statistical significance of sensitivity scores given the modelled prevalence, ie. the proportion of the model universe that is climatically suitable. A useful model should have a high sensitivity score reflecting that recorded locations are modelled as being climatically suitable, and a significant binomial statistic score ensuring the model projection does not cover an excessive proportion of the study area. The completed model was then run using the global dataset under historical climate and future climate scenarios.
For the three datasets, the completed model explained the presence of C. imicola in 833 grid cells at 109 resolution and the addition of irrigation component explained an additional 38 records in arid areas (Appendix S2 in File S1). Only 8 occurrence records were not fitted in the composite irrigation model (Appendix S2, Figure  SI4b  in File S1). Under the natural rainfall future climate scenario, C. imicola's projected potential distribution for climate scenario A2, generated by CSIRO-MK3. 0 model, projected in 2070 (Figure  3 ) expands into areas that are presently too cold for persistence, and contracts from areas that are set to become more xeric. Similar changes in ecoclimatic suitability patterns were observed with other climate projections (ie. 2030 projections, A1B climatic scenario or Miroc-h climate model, see Figure  SI5  and SI6 in Appendix S3, in File S1).
imicola in xeric regions, without creating a risk map that indicated that vast areas of desert were climatically suited. The few location records in Figure  2  that fall in apparently climatically unsuitable locations are most likely the result of populations that are able to persist by virtue of small livestock watering points not captured by the irrigation dataset of Siebert et al. [42] , perhaps signalling a scaling limitation regarding the use of this data. Agencies with biosecurity responsibilities in xeric regions may therefore wish to consider the irrigation climatic risk scenario independently of the irrigation locations indicated in Siebert et al. [42]. This risk map could indicate climate suitability for C. imicola in areas where soil moisture availability is not well modelled using natural rainfall and crop irrigation is not captured in the dataset of Siebert et al.
This work was jointly funded by the  Project  ' 'Climate Change and Impact Research: the Mediterranean Environment (CIRCE) '',  European Commission 's  6th Framework Programme  and also by  HarvestChoice  project with support of the  Commonwealth Scientific and Industrial Research Organization (CSIRO) , the  University of Minnesota  and the  Bill and Melinda Gates Foundation. This study was partially funded by  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext000 (http://www. edenext. eu). The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission. H. Guis ,  T. Balenghien  and  C. Garros  were funded by the  Ministe  `re de l'agriculture, de l'agroalimentaire et de la foret.
Image Analysis FVHs were defined as focal, tubular, or serpentine hyperintensities in the subarachnoid space relative to CSF 1 and corresponding to the typical arterial course. Axial FLAIR and DWI were reviewed together to assess a FVH-DWI mismatch. FVH-DWI mismatch was considered present when FVHs extended beyond the boundaries of the cortical DWI lesion (ie, when Ն1 FVH was facing the isointense cortex on DWI). FVH-DWI mismatch was considered absent when there was no FVH or when all FVHs were facing the hyperintense cortex on DWI. Two radiologists (L. L. and C. O. , with 3 and 17 years' experience in stroke imaging, respectively) independently reviewed the image datasets on a dedicated workstation for FVH-DWI mismatch evaluation. They were aware that all included patients had a proximal MCA occlusion; they had access to pretreatment DWI, MR angiography, and T2* images; but they were blinded to PWI, clinical data, and follow-up.
Discordance between observers was resolved by consensus. One neuroradiologist (L. L. ) reviewed the T2* images to ensure that FVHs did not correspond to blood clots and to evaluated FLAIR image quality (good-to-excellent or diagnostic quality despite artifacts). Pretreatment (DWI 1 ) and follow-up DWI (DWI 2 ) lesions were segmented to compute initial and final DWI lesion volumes as detailed elsewhere,  18  and DWI 1 lesion extent was evaluated semiquantitatively by using the 10-point DWI-ASPECTS. Relative infarct growth was defined as Volume DWI2 /Volume DWI1 ϫ 100. Critically hypoperfused tissue, defined as time to maximum value of the residue function (Tmax) Ͼ 6 seconds, was segmented from PWI maps (BrainStat AIF, READY View software; GE Healthcare). The PWI-DWI mismatch volume was defined as the Tmax Ͼ6-second volume without DWI 1 changes.
20) and were further excluded from the model with P Ͻ. 10. The OR and 95% CI were obtained. A 2-tailed P-value Ͻ. 05 was considered significant (SPSS for Windows, Version 19. 0; IBM, Armonk, New York). Sensitivity, specificity, and positive and negative predictive values of the FVH-DWI mismatch for the detection of PWI-DWI mismatch were computed.
Camels are not the usual targets of zoonotic surveillance programs, but these recent examples highlight the interest of focusing future viral metagenomic studies on other animal species interacting with humans if one considers their ability to transmit human infectious agents by crossing the species barriers between animals and humans. Metagenomics is thus a promising tool for the detection of new viral species that could potentially be a threat for human health. However, it yet suffers several pitfalls when considering new/highly divergent viral genomes. Indeed, the taxonomic assignation of reads generated by NGS techniques is only based on the comparison of sequences or patterns with previously described sequences present in databases. As a consequence, completely new or highly divergent viral sequences might be difficult to identify and subsequently there is a high risk to miss the detection of important viral pathogens.
Transposable elements identification and novel coding gene discovery The percentage of interspersed repeats identified with the Repeat Masker [21] in the intergenic transcripts was about 34. 5%. Moreover, SINEs and LINEs were the most abundant repetitive elements identified (14. 1% and 14. 9%, respectively) (Table  S3 ). With the aim of improving the current porcine genome annotation, we took into account the intergenic transcripts identified with cufflinks (a mean of 4,440 transcripts) to determine whether these transcripts potentially codified for proteins. A total of 2,372 putative proteins were predicted by Augustus  [22]  corresponding to non-annotated transcripts of the Sscrofa10. 2 genome assembly version. Among the 2,372 novel predicted proteins, only 1,406 (59. 2%) had at least one orthologous gene identified with BLASTP option of Blast2GO, representing a total of 577 known genes (Table  S4 )  [23].
These proteins corresponded to: 720 Sus scrofa, 17 Homo sapiens and 247 Bos taurus in silico predicted protein, and 476 Sus scrofa, 933 Homo sapiens, and 403 Bos taurus known proteins. The pig species was the only one showing a higher percentage of computationally predicted protein (60. 2%) in comparison to known proteins (39. 8%). Moreover, 918 of the predicted novel proteins were successfully annotated with Blast2GO  [23]. To summarize the functional annotation, a GO Slim analysis was performed. The most relevant molecular functions identified were ''protein binding'' (25%), ''ion binding'' (19%), ''nucleic acid binding'' (17%), ''small molecular binding'' (10%) and, interestingly, ''lipid binding'' (2%). These new transcripts were mainly involved in the following biological processes: ''primary metabolic process'' (9%), ''cellular metabolic process'' (9%), ''macromolecule metabolic process'' (8%) and ''regulation of biological process'' (7%).
Differential gene expression analysis A high correlation (r = 0. 98, P-value ,2. 2610 216 ) between H and L groups in the mean gene expression was found, showing that most of the genes had a similar behaviour. A total of 11,945 transcripts were used to perform the differential expression analysis after filtering. Using EdgeR program  [24] , 314 genes were identified as significantly differentially expressed between H and L groups. Whereas, employing DESeq  [25] , 208 genes were detected. Figure  1  shows the P-value distribution and how among the transcripts accepted as differentially expressed the selected cutoff of P-value ,0. 01 is clearly departing from the expected P-value (equivalent to a FDR #0. 12). A total of 131 genes (Table  S5 ) overlapping in both analyses were selected as differentially expressed between H and L groups and, thereafter, used for pathway analysis (Figure  2 ).
In addition, three of the differentially-expressed genes in our analysis (ATF3, ENAH and SLPI) were also identified in a muscle microarray study of extreme animals for FA composition from the same backcross  [27]. Other genes such as DNAJA4, ANKRD1, MYH10 and TNFRSF12A were also common, but they were only detected by the DESeq program  [25]  in the RNA-Seq data.
Functional analysis With the aim of having a more complete functional view of our differentially-expressed genes in the H and L groups, we used Babelomics  [28]  and Ingenuity Pathways Analysis  [29]  programs, who have related capabilities but use different databases. The top canonical pathways overrepresented according to IPA were related with nitric oxide signalling in the cardiovascular system The percentage of each FA, relative to the total FA doi:10. 1371/journal. pone. 0099720. t001 (7 genes, P-value = 7. 75610 27 ) and endothelial nitric oxide synthase signalling (eNOS, 8 genes, P-value = 2. 34610 26 ). On the other hand, using Babelomics we observed an overrepresentation of lipids and lipoproteins metabolism (6 genes, P-value = 1. 64610 23 ), and also the peroxisome proliferator-activated receptors (PPAR, 4 genes, P-value = 7.
Mapping and annotation We ran FastQC  [76]  for the quality control. Indexed reads were then mapped to the reference pig genome version 10. 2 (Sscrofa10. 2) and the annotation database Ensembl Genes 67  [77]  using TopHat v2. 0. 1  [19]  with an allowance of two mismatches for each read. The resulting bam files containing the aligned sequences, were subsequently merged with Samtools  [78]. Reads were annotated using the intersectBed option of BEDtools  [79]. Cufflinks v2. 0. 2 program  [20]  was used to assemble the transcripts with a minimum of 10 reads per alignment. Finally, Samtools  [78]  was employed to compute descriptive statistics.
Gene expression quantification and differentialexpression analysis The number of reads mapping to each gene was determined with the comp-counts option in Qualimap v5. 0 program  [80]. We discarded those genes with a group mean less than 20 counts. We calculated the Pearson correlation coefficient between the mean expression values of the H and L group using the cor. test function of R. For the differential expression analysis we used DESeq  [25]  and EdgeR  [24]  packages implemented in R. We considered as differentially expressed between H and L groups those genes identified by both programs (DESeq and EdgeR) with a fold change $1. 2 and P-value #0. 01, the same parameters used in Corominas et al. [5] , these case for both programs. FDR was calculated using the R package qvalue  [81].
Transposable elements and orthology analysis We used RepeatMasker version open-3. 3. 0 [21] with the rm-20120418 database in order to identify repetitive and transposable elements in the pig muscle transcriptome. We used ''quick search'' and ''pig'' species options and the Search Engine NCBI/ RMBLAST. Intergenic expressed regions not annotated in the Sscrofa10. 2 version assembly were identified using Cuffcompare  [82]  and extracted using our own Python and R scripts. Novel putative proteins were predicted with Augustus program  [22]. Afterwards, using Blast2GO  [23] , we mapped and annotated the novel protein coding genes. Using BLASTP option (E-value hit filter 1. 00E-6, annotation cutoff 55, gene ontology (GO) weight 5 and HSP-hit coverage cutoff 0) we checked their orthology with already annotated proteins in Homo sapiens, Bos taurus and Sus scrofa protein databases.
Gene ontologies and pathways The Ingenuity Pathways Analysis software  [29]  and FatiGO tools from Babelomics 4. 3  [28]  were used to identify the most relevant biological functions and pathways in which the differentially-expressed genes (between the H and L groups) were involved. IPA, which uses its own private databases, allowed us to identify biological relevant information, identifying overrepresented pathways using the BH multiple testing correction (FDR) at P-value ,0. 05, and generating biological networks. For FatiGO, we used KEGG  [83]  and Reactome  [84]  databases setting the cutoff FDR ,0. 1. The Mouse Genome Database (MGD)  [26]  was used in order to identify how mutant alleles driven in mice for the 18 identified genes common in GWAS and RNA-Seq analysis affected the phenotype.
Prostate cancer mortality projections at the nationwide and regional levels to the year 2025 are carried out in this ecological study that is based on an analysis of Brazilian trends between 1996 and 2010. The predictions were made for the period 2011-2025 utilizing the Nordpred program based on the period of 1996-2010, using the ageperiod-cohort model. A significant increase was observed in the Brazilian rates between 1996 and 2006, followed by a non-significant decrease. The projections indicate a decrease in rates at a national level as well as for the Central, South and Southeast regions. Increases are expected for the North and Northeast regions. In conclusion, a reduction in the mortality rates for prostate cancer in Brazil is expected to the year 2025, as well as for the Central, South and Southeast regions.
Method This is an ecological study for which the data were obtained from the Brazilian Ministry of Health web page (Departamento de Informática do SUS. Informações de Saúde. Estatísticas Vitais. http://www2. datasus. gov. br/DATASUS/ index. php?area=0205, accessed on 02/Jul/2013) along with population data from 1996 to 2010. All deaths registered as prostate cancer (ICD-10 code: C61) were included in the analysis. The quality of death certificates is comparable to those of developed countries, and the proportion of ill-defined death causes is lower than 13% for the overall country and lower than 6% for the capital cities (Global Health Observatory Data Repository, World Health Organization. Demographic and Socioeconomic Statistics: Census and Civil Registration Coverage. Data by Country. http://apps.
The analysis starts with a minimum number of joinpoints and compares whether one or more are significant to the model 8. Birth-cohort analysis was carried out graphically and dividing data in fiveyear periods. Predictions were made for the periods 2011-2015, 2016-2020 and 2021-2025, using the ageperiod-cohort model of the Nordpred program (Cancer Registry of Norway, Oslo, Norway), written in the statistical program R. Data were pooled into five-year blocks and the age group limit considered for analysis was the one with more than 10 cases in the combined periods. Predictions were made by geographical region and the pooled results will be referred to as total presented by age ranges (0-54, 55-74 and ≥ 75 years of age). As prostate cancer is more common in individuals over the age of 55, age groups were employed following Moller et al.
Results In the period 1996-2010, 139,349 deaths were registered in the Brazilian mortality database as being from prostate cancer. The Joinpoint analysis shows an initial increase in mortality rates, followed by a stable plateau after the year 2006. The same trend is found by region, with an exception for the North region, where significant increases were verified for the entire period. In the remaining regions, the initial increase occurred until the years 2005 and 2006, followed by a decrease in rates. However, the APC were not statistically significant in the second segment (Table  1 ). Trend analysis for mortality by birth cohort shows an increment in rates for individuals in the age groups of 75-79 and 80-84 years of age, with a change in trends and stability for younger cohorts starting from 1933 (Figure  1 ).
The evolution observed in the rates of this study for the more developed regions of Brazil is similar to that found in the majority of European countries and the USA. In these countries, mortality rates have declined during the last two decades, probably due to early diagnoses facilitated by the PSA testing and improvements in prostate cancer treatment  4, 5, 20. Such a trend has also been observed in other South American countries, as is the case of Chile and Argentina. Nevertheless, it is worth highlighting that in these countries, the adjusted rates are higher than the Brazilian rates, with changes occurring earlier, at the end of the 1990s (International Agency for Research on Cancer; World Health Organization. Cancer Mortality Database. http://www-dep. iarc. fr/whodb/ whodb. htm, accessed on 30/Jul/2013)  21, 22.
Phenotyping of gd T Cells by Flow Cytometry mAbs directed against the following molecules were purchased from Beckman-Coulter: TCR-Cd, TCR-Vd2, and CD16. mAbs directed against CD3, perforin, and granzyme B were from BD Biosciences. Staining was performed on gd T cell lines as previously described  23  and analyzed on a FACScanto cytometer (BD Biosciences) with the use of the FACSDiva software, version 6. 1. 2 (BD Biosciences). At least 10,000 T lymphocytes were analyzed for six-color staining. Permeabilization and fixation of cells for intracellular staining were performed as described elsewhere. 32.
ACKNOWLEDGMENTS We thank the  Bordeaux Imaging Center  for microscopy analysis, the  SFR TransBioMed Flow Cytometry facility  for phenotyping analysis and cell sorting, the technicians from the  Laboratory of Immunology and Immunogenetics at Bordeaux University Hospital  for their expert assistance,  Catherine Rio  for clinical data collection, the nurses of the  Department of Nephrology  for patient care, the  Etablissement Français du Sang Aquitaine Limousin  for providing blood samples, and  M. Capone  and  C. Behr  for thoughtful discussions. This work was supported by the  Fondation pour la Recherche Médicale  ( DEQ20110421287 ) and the  Agence Nationale de la Recherche  ( ANR-12-BSV3-0024-02 ). TB was supported by  CNRS  and  CHU Bordeaux.