dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical and analytical methods For the spatiotemporal description of the epidemic, rainfall data were aggregated weekly and graphically represented in parallel with cholera morbidity. Cholera attack rates were calculated and mapped, by prefecture and sub-prefecture, for various time periods using shapefiles of administrative divisions obtained from the HealthMapper application (WHO, Geneva, Switzerland) and Quantum GIS v1. 8. 0 (QGIS Geographic Information System, Open Source Geospatial Foundation Project, available at: http:// qgis. osgeo. org). MLVA-based genotypes were compared at each of the 6 VNTR loci. Genetic relatedness between the strains was first assessed using eBURSTv3 (http://eburst. mlst. net/), which aims to identify the founding genotype. A simple network of all possible links between genotypes was also assembled using Gephi.
0. 8. 1 beta software (https://gephi. org/). Molecular epidemiology analyses were completed via the sequential mapping of each genotype by month at the prefecture level. After the first whole-genome sequence was obtained with the GS FLX+ System, proteins were predicted using Prodigal software (http://prodigal. ornl. gov/). Data was then annotated employing the GenBank database (http://www. ncbi. nlm. nih. gov/genbank) and the Clusters of Orthologous Groups database using BLASTP with an E-value of 10 25. Allelic polymorphism of the cholera toxin B subunit and other virulence factors was characterized by comparing the obtained sequence with the genome description of V. cholerae strains available in GenBank and recent literature. Based on the reference strain El Tor N16961 (GenBank accession numbers: AE003852.
1 and AE003853. 1). PCR thermal cycling conditions for all assays: 95uC for 5 min; followed by 30 cycles of 95uC for 30 sec, 58uC for 30 sec and 72uC for 45 sec; 72uC for 5 min. doi:10. 1371/journal. pntd. 0002898. t001 For phylogenetic analyses, the paired-end read data obtained with the HiSeq Illumina System and sequence data from 198 previously sequenced strains available in the NCBI SRA database were mapped to the reference N16961 El Tor strain (NCBI accession numbers AE003852 and AE003853) using SMALT software (http://www. sanger. ac. uk/resources/software/smalt). A whole-genome alignment was obtained for each strain in this analysis, and SNPs were called using the approach described by Harris et al. [19]. The reads that did not map to the N16961 genome were filtered out during SNP calling, and any SNP with a quality score less than 30 was excluded.
A true SNP was only called if there were at least 75% of the reads at any heterogeneously mapped ambiguous sites. High-density SNP clusters indicating possible recombination sites were excluded using the methodology previously described by Croucher et al. [20]. Maximum Likelihood phylogenetic trees were estimated using the default settings of RAxML v0. 7. 4  [21]  based on all the SNPs called in the manner explained above. M66 (accession numbers CP001233 and CP001234), a pre-seventh pandemic strain, was used to root the final phylogenetic tree of the seventh pandemic strains  [13]. FigTree (http://tree. bio. ed. ac. uk/ software/figtree/) was used to visualize and order the nodes of the phylogenetic tree.
Spatiotemporal epidemic progression Taking into account both the national database and patient line list, this epidemic was responsible for an estimated 7,743 suspected cases (global attack rate: 6. 3 cases/10,000 inhabitants) and 138 deaths (case fatality ratio: 1. 8%). The initial case was reported on February 2, 2012 (epidemiological week 5) in the midst of the dry season (Figure  1 ). The weekly number of new cases remained below 100 until July. The epidemic then peaked in August, 5 months after the onset of the rainy season, with nearly 1,188 new cases recorded during week 34. Cholera incidence began to markedly decline in September. The final case was recorded on December 11, 2012, and the Minister of Health officially declared the end of the epidemic on February 6, 2013. Overall, the capital of Conakry reported 4,642 cases (25.
Acknowledgments The authors would like to thank all the staff who took part in patient care, field investigations, data reporting as well as sample collection, transport, processing and analysis. In particular, the authors are indebted to  Leonard Heyerdahl  ( AMP, France ), who took part in organizing the prospective sampling of clinical cholera samples. This study wouldn't have been possible without  H Diallo  ( INSP, Guinea ) who performed initial vibrio cultures, the staff of  HIA Laveran  who cultured and performed the  MLVA in Marseille and the staff of Aix-Marseille Universite  ´who sequenced and analyzed the V. cholerae clone. The authors are also grateful to  Bertrand Sudre , who provided critical comments on the manuscript.
, 2009a, b)  to build the meteorological forcing data set (referred to as the DATE method). This method has the benefit of following the large-scale chronology of meteorological conditions from the ALADIN RCM, but it introduces discontinuities in meteorological variables every day between two analogue days. In this study, an alternative approach was taken, fully relying on the chronology of meteorological conditions from the reference period 1960-1990 and applying corrections to this continuous time series based on a quantile-mapping method (Deque, 2007). Firstly, for each simulation and each ALADIN grid point, meteorological daily fields are compared with daily data from the ECMWF ERA-40 reanalysis  (Uppala et al. , 2004) , and a date with analogue weather conditions is identified through an appropriate distance.
The series of analogue dates is then used to extract corresponding meteorological data from the SCM-ERA40 meteorological reanalysis  (Durand et al. , 2009a ) that we call EMx CS DATE , with x = 6, 7 or 9 and CS being the SRES scenario, namely A1B, A2 or B1. Percentiles of each meteorological variable for each scenario and time period are then computed based on each collection of SCM-ERA40 data for the corresponding selected dates. Percentiles for the same variables but for the entire SCM-ERA40 time series were also computed. For consistency, the SCM-ERA40 data series used were limited to the 1960-1990 period, both for the data search and for the percentile calculation. These percentiles (at rank α) are denoted as q α (EMx SC DATE ) and q α (SCM ERA40 ), respectively, and are used to produce ranked differences, at the same α percentile value, between the different RCM projections (as a series of dates) and the statistics of meteorological variables into the SCM-ERA40 record.
Simulated SAFRAN and Crocus data from the daily series on the massif scale were used to derive anomaly series on the nine larger spatiotemporal scales corresponding to those studied for the reference period. Finally, the nine CI regression models obtained over the reference period were fed with these projected snow and meteorological data (after suitable standardization), using appropriate weighting coefficients (Tables  1 2 3 ) and leading to projected values of the avalanche activity index CI for the two future periods of annual and seasonal avalanche activity indexes for the nine regions/seasons considered. For example, Fig. 3  presents the distribution of annual and seasonal values of the CI regression model during the reference period and the two time periods considered in the future on the scale of the entire French Alps.
Acknowledgements. This work was mainly done in the framework of the joint  Météo France-Irstea ECANA  project (http://www. avalanches. fr/projet-ecana/)funded by the  French Ministry of the Environment (Risk Division-DGPR ). It has also benefited from the results of the  ANR   SCAMPEI  project (http://www. cnrm. meteo. fr/scampei/presentation_scampei/). The authors are grateful to  C. Fierz ,  C. Marty  and  H. Gudmundsson  whose comments and work helped us to produce a better paper. Edited by:  G. H. Gudmundsson.
Participants, software, and apparatus Seventeen naive observers (nine women; age range 19-24 years) participated in the experiment. Partici-pants provided written informed consent. The experiment was conducted with approval of the Aix-Marseille Université ethics committee. The right eye was recorded with an EyeLink 1000 (SR Research, Mississauga, ON, Canada), a video-based eye tracker sampling at 1000 Hz. Stimuli were presented on a 21-in. ViewSonic p227f CRT monitor (1024 • 768 px, 100 Hz) with OpenSesame  (Mathoˆt, Schreij, & Theeuwes, 2012) /PsychoPy  (Peirce, 2007).
Acknowledgments The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. 622738. SM and JG were supported by  ERC  grant  230313  (http://erc. europa. eu/). ESD was supported through  European Union   FP7 Marie Curie ITN grant   606901. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare no competing financial interests. We thank  Virgile Luong  for his help in collecting the data. Author contributions: All authors contributed to the study design and contributed to writing of the manuscript. SM developed the experiment and analyzed the data.
RNA-seq was used to study the response of Desulfovibrio hydrothermalis, isolated from a deep-sea hydrothermal chimney on the East-Pacific Rise at a depth of 2,600 m, to various hydrostatic pressure growth conditions. The transcriptomic datasets obtained after growth at 26, 10 and 0. 1 MPa identified only 65 differentially expressed genes that were distributed among four main categories: aromatic amino acid and glutamate metabolisms, energy metabolism, signal transduction, and unknown function. The gene expression patterns suggest that D. hydrothermalis uses at least three different adaptation mechanisms, according to a hydrostatic pressure threshold (HP t ) that was estimated to be above 10 MPa. Both glutamate and energy metabolism were found to play crucial roles in these mechanisms. Quantitation of the glutamate levels in cells revealed its accumulation at high hydrostatic pressure, suggesting its role as a piezolyte.
Sequencing The libraries were amplified with the SOLiD EZ Bead Amplifier. The samples had previously been prepared using the SOLiD EZ Bead Emulsifier, which allows the mixing of the aqueous phase containing the SOLiD EZ Bead, library template, and primers with the oil phase (Oil Master Mix). The preparation was then amplified by PCR (amplifier software scale E80). After the run was complete, the samples were purified using the SOLiD EZ Bead Enricher according to manufacturer's instructions. The P2-enriched beads were then concentrated, and the 39ends were modified as described in the manufacturer's protocol. Paired-end stranded RNA-seq with ribo-depleted total RNA was performed using a 5500 xl SOLiD high-throughput sequencer (Life Technologies). The RNA-seq data have been deposited in the NCBI Gene Expression Omnibus (GEO, http://www.
Bioinformatic data analysis The obtained reads were mapped to the D. hydrothermalis AM13 genome sequences (MicroScope accession number: DE-SAMv2_DESAMv2) using the Life Technologies Bioscope Genomic Analysis Software. To estimate the level of transcription for each gene, the number of reads that mapped within each annotated coding sequence (CDS) was determined using the multiBamCov program from the BEDTools suite  [14]. For each pressure condition, the data obtained from the two independent cDNA libraries (replicates 1 and 2) were added. To enable comparison of the expression levels between both different RNAseq experiments and different genes within the same experiment, it was necessary to normalize the read counts. The R package DESeq was used to analyze the differential expression of genes between the different culture conditions  [15].
Metabolite extraction For intracellular glutamate quantitation, cultures were performed as described above for RNA preparation. Cells were immediately harvested from 2 ml cultures by centrifugation at 15,000 6 g for 3 min and then washed with 1 ml of 200 mM NaCl solution. The pellet was resuspended with 0. 5 ml boiled water and incubated for 15 min at 100uC, followed by incubation on ice for 10 min. The extract was centrifuged at 15,000 6 g for 5 min to eliminate cell debris, and the supernatant was snapfrozen and stored at 280uC. Intracellular glutamate was measured using the fluorometric Glutamate Assay kit (Abcam, UK). Samples were plated in 96-well black microplates (Costar 96-well Assay Plates, Black Polystyrene) for fluorescence readings on a TECAN Infinite M200 plate reader, and data were obtained using the ''icontrol 1.
The extract was then centrifuged for 10 min at 15,000 6 g. The supernatant was harvested and TCA was neutralized to pH 6 by the addition of 600 ml of 20 mM Tris-acetate, pH 7. 75. Luminescence was measured using a Luminoskan Ascent plate reader (Thermo Scientific), and the data were treated using the Ascent software v. 2. 6 (Thermo Scientific).
1 and 10 MPa, with a second-level response occurring between 0. 1 MPa and a hydrostatic pressure threshold above 10 MPa, hereafter referred to as HP t. The distribution of the differentially expressed genes according to the COGs database is shown in Figure  4. A large portion (18. 5%) of the differentially expressed genes did not belong to any COG class. The genes belonging to the amino acid transport and metabolism (E), signal transduction mechanisms (T), and energy production and conversion (C) classes were the most abundant (24. 6%, 15. 4%, and 12. 3%, respectively). Because these three categories represent only 8. 3%, 9. 4%, and 6. 8% of the predicted CDSs, respectively, in the whole D. hydrothermalis genome (MicroScope accession DESAMv2_DESAMv2), these categories are likely to be important for pressure adaptation.
Because our data suggested glutamate accumulation in D. hydrothermalis at high pressure, we measured the amount of glutamate in cells. The concentration of intracellular glutamate in cells collected during the exponential phase is shown in Figure  5A. The intracellular glutamate concentration was 82. 3 (612. 2) nmol/ mg prot in cells grown at atmospheric pressure, whereas a concentration of 185. 6 (63. 5) nmol/mg prot was observed in cells grown at 26 MPa, corresponding to a 2. 25-fold increase. An intermediate concentration (142. 2 (624. 9) nmol/mg prot) was observed when cells were cultured at 10 MPa. These data show that glutamate accumulates within cells at high hydrostatic pressures and that glutamate accumulation increases with increasing hydrostatic pressure.
The measured ADP/ATP ratio was 0. 10 (60. 08) in cells grown at 26 MPa and 0. 22 (60. 11) and 0. 44 (60. 13) when cells were cultured at 10 MPa and 0. 1 MPa, respectively. These data show that the phosphorylation process is more efficient under in situ hydrostatic pressure conditions (26 MPa) than at lower pressures.
Type V is unclassified ORF1s  [11]. The ORF1 classification is based on a sample of 14 ORF1s from 10 LINE clades  [11]. Clade allocation was based on the Repbase sequence title, which theoretically indicates the clade that the element belongs to  [17]. Here we explore the structure and evolution of the   [7, 9, 10]. LINE clades were first assigned to five groups (R2, L1, RTE, I and Jockey) by Eickbush and Malik  [8]. Groups are called superfamilies in the TE classification paper by Wicker et al. [7]. The ORF1 is not present in some RTE clades (shown with a dashed outline). In this paper, all Jockey superfamily/group full-length sequences from the Repbase database were assigned to three lineages based on an APE-RT phylogeny (see Figure  3 ). Subgroups were identified within the three lineages and ORF1 structures (see Figure  2 ) mapped onto the phylogeny (see  Figures 4, 5 and 6).
ORF1s of Jockey superfamily/group elements in more depth within a phylogenetic framework. We used all full-length Jockey superfamily/group sequences from the Repbase database  [18]  for two reasons. First, Repbase is the most comprehensive and widely used TE database. Second, many entries are consensus sequences, allowing us to examine a wide range of elements. We examined 448 full-length Jockey superfamily/group elements. ORF1 structures were determined by multiple alignment and HMM-HMM comparison against three protein databases. The structures were then mapped onto an APE and RT phylogeny. We identified ORF1 types in clades where they had been not previously described. We also identified structural variations of the ORF1 types. We propose that there has been ORF1 domain shuffling in Jockey superfamily/group elements, and that in some instances entire ORF1s may have been horizontally acquired.
The ORF1 structure schematic shows coding domains 5' to the endonuclease identified in this publication and are drawn to scale. Domains not always present are shown with a dashed outline. Red: CCHC, gag-like Cys 2 HisCys zinc-knuckle; green: CTD, C terminal domain; yellow: coiled-coil domain; purple: esterase; pink: PHD, plant homeodomain; blue: RRM, RNA recognition motif; lilac: zf/lz, zinc finger/leucine zipper. The hatched CC, RRM + CTD domains indicate transposase 22, the RCSB Protein Data Bank entry 2yko and Pfam entry PF02994. A key to all the domains is shown in Figure  6. the Repbase database  [18]. These were classified by Repeatmasker as 536 CR1, 422 L2, 54 RexBarber, 206 Jockey, 20 L1 and 1 R1 type sequences. The L1 and R1 sequences were removed. Only one complete RexBarber sequence was found, so this was also removed.
Robustness of the nodes was estimated by 500 bootstrap replications. Only bootstrap values for the three lineages are shown. Lineages are delineated by alternate light and dark grey shading using FigTree  [21]. Details of the ORF1 types identified in the three lineages are shown in Figures  4, 5  and 6. an unclassified ORF1, because we were able to identify less than 10% of the entire ORF. Type I has at least one RRM domain immediately upstream of a CCHC zinc knuckle  [11]. In our analysis, this ORF1 type was found in all Jockey lineage elements, CR1 subgroup 3 and L2 subgroups 2, 8 and 10 (Figures  2, 4, 5, 6  and Table  1 ). Type II is found in the human L1 element and has a CC domain, a single RRM domain and a CTD domain  [11]. In the Research Collaboratory for Structural Bioinformatics (RCSB) and the Protein families (Pfam) databases the three domains are submitted as a single entry, transposase 22 (2yko_A and PF02994, respectively).
Domains are color-coded, details are shown in the key. CCHC, gag-like Cys 2 HisCys zinc-knuckle; CTD, C terminal domain; PHD, plant homeodomain; RRM, RNA recognition motif; zf/lz, zinc finger/leucine zipper. Transposase 22 refers to the RCSB Protein Data Bank entry 2yko_A and Pfam entry PF02994, the L1ORF1 protein composed of a coiled-coil, RRM and CTD domain  [24]. Red asterisks indicate single sequences within a subgroup from a different phylum. In Figure  4  (L2 lineage) these are a single Branchiostoma floridae (Chordata) sequence in subgroup 6 and a single Capitella species (Annelida) sequence in subgroup 3. In Figure  5  (Jockey lineage) this is a single Drosophila sequence in subgroup 2. Average percent pairwise nucleotide identity of the RT domain for each subgroup, estimated using Geneious  [25].
c ORF1 type (I-V) identified for each subgroup, based on ORF1 types described by Khazina and Weichenrieder  [11]. Subtypes (A, B and C) are used to show the diversity of ORF1 structures within types identified in this paper. d Domains identified within the ORF1 by HMM-HMM comparision  [26]  or by Pcoils  [23]. CC, coiled-coil domain; CCHC, gag-like Cys 2 HisCys zinc-knuckle; lz, leucine zipper; PHD, plant homeodomain; RRM, RNA recognition motif; Tnp22, transposase 22, (RCSB Protein Data Bank entry 2yko_A and Pfam entry PF02994), which is the L1ORF1 protein composed of a coiled-coil, RRM and CTD domain  [24] ; zf, zinc finger. e Minimum length of the inferred amino acid sequence for each domain. f Average percent pairwise inferred amino acid identity for each domain, estimated using Geneious  [25].
g Top hits starting with 'PTHR' are from the Panther Classification System, all other top hits are from the RCSB Protein Data Bank. h Probability reported by HHPred  [26]. were concordant with our phylogenetic analysis and Repeatmasker type except for the L2 sequences (Figure  3 ). These were split into four clades, Daphne, Kiri, L2 and L2B, by the RTclass1 tool (Figure  4 ). For these four clades, the Repbase sequence names did not consistently reflect clade assignments [see Additional file 1].
Conclusions We inferred a phylogeny based on the APE and RT domains for full-length Jockey superfamily/group elements from the Repbase database. ORF1s structures were mapped onto the ORF2 phylogeny. All Jockey lineage elements have the same type of ORF1, with one to two RRM domains upstream of three CCHC domains. In contrast, in the L2 and CR1 lineage elements, all five ORF1 types are found, with no one type of ORF1 predominating. The structure of these ORF1s is indicative of domain shuffling. The PHD domain is much more prevalent than previously suspected; it was identified in four ORF1 types in many subgroups within the L2 and CR1 lineages and both upstream and downstream of the RRM domain. There was also evidence of reticulate evolution and possibly horizontal transfer of entire ORF1s. The ORF1 of the CR1 subgroup 3 and L2 subgroup 2 is unusual, a Jockey like ORF1 with a PHD domain upstream of the RRM domains.
Methods Sequence retrieval from Repbase, Repeatmasker classification and alignment All 1,249 sequences from the Jockey superfamily/group  [9]  were downloaded from the Repbase database in April 2014  [18]. The two lungfish sequences were taken from Metcalfe et al. [41]. The sequences were classified into 'type' by screening against a database of transposable element encoded proteins as implemented by the webbased Repeatmasker program  [19]. Sequences were then conceptually translated, aligned using ClustalW as implemented in BioEdit and adjusted by eye  [42]. Incomplete sequences were removed.
Open reading frame 1 analysis For each subgroup identified the region 5' to the endonuclease domain and 3' to the 5' UTR was extracted as an alignment. For simplicity's sake this region will be referred to 'ORF1', although some domains identified are most likely at the beginning of ORF2 or are at the 5' end of a single ORF. The beginning of the ORF1 was identified by a methionine and checked against the Repbase EMBL file if the translation was available. Each subgroup was analyzed for similarity to known domains using HMM-HMM comparisons as implemented in HHpred  [22]  against the following databases, the RCSB Protein Data Bank  [43]  as at 27 December 2012, the Pfam database  [44]  as at 2 December 2011 and the Panther Classification System  [45]  as at 1 May 2012. For each region the top hit was taken as the hit with the highest probability, or the hit with the highest coverage with a high probability (>85%).
For sequences with top hits against the RCSB Protein Data Bank  [43] , the RCSB record was checked to determine the type of the domain identified. For RRM domains, the publication associated with the top hit at the RCSB Protein Data Bank  [43]  was used to find the RNP consensus sequences. The JnetPred secondary structure prediction software  [46]  as implemented in JalView  [47]  was used to identify beta-sheets and alpha-helices. Pcoils  [23]  was used to infer coiled-coil domains and to confirm the position of coiled-coil domains in transposase 22 domains. For each subgroup the pairwise percent identity at the amino acid level for the ORF1 was estimated using Geneious  [25].