dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are grateful to the staff of Medecins sans Frontieres and  Dr. Gervais Folefack  ( Ministry of Health ) in Akonolinga,  Dr. Philippe Le Gall  ( IRD ) and  Dr. Gae ¨tan Texier  ( CPC ) for their support in the initial stages of the fieldwork. We are thankful to  Myriam Benhalima ,  Afi Leslie Kaiyven ,  Francis E. Ntsonzock  and the staff of the  Mycobacteriology service at Centre Pasteur in Cameroon  for their invaluable help in different phases of the study. Thanks are due to the  IFORA  project, and more particularly  Dr. Herve  ´Chevillotte (IRD). We also thank  Kevin Carolan  and  Estelle Marion  for critical review of the manuscript and insightful discussions on the subject.
This process was repeated over multiple iterations, with comparison matrices populated by values drawn by Monte Carlo sampling from the Betapert distribution representing each pairwise-comparison, until a large number of consistent matrices and weights (at least 3000) were generated for each risk factor for each of the two objectives. The output was considered to represent the distribution of possible weights for each factor under consideration, given the authors' uncertainty in the factor's importance in relation to each objective. The process was automated using the VBA programming language (Microsoft Corp) and implemented in Microsoft Excel (2010).
Producing spatial layers Raster maps for the suitability criteria and constraints identified through the literature review were sourced and mapped in ArcMap 10 (ESRI, Redlands, CA). The source and structure of the spatial data used to represent suitability layers are given in Table  2. In the absence of data of sufficient accuracy to represent areas in which domestic pigs do not occur, a human population density of zero per km 2 (as defined by Landscan 2008, High Resolution Global Population Data Set, UT-Battelle, LLC, operator of Oak Ridge National Laboratory, US) was considered to provide the best evidence of areas in which domestic pigs cannot occur. This constraint layer and that representing the predicted home ranges of warthogs, the two bushpig species and giant forest hogs (as sourced from the African Mammals Databank (AMD)  [40] ), were converted into Boolean functions of 'suitable' and 'unsuitable' and assigned a value of 1 or 0, respectively.
Continuous values from each raster layer representing the suitability criteria (Table  2 ) and the Boolean constraints were extracted from each cell of a grid with a cell size 0. 8 × 0. 8 decimal degrees over the whole of mainland Africa and exported directly into Microsoft Excel, resulting in a dataset consisting of approximately 360,000 individual data points for each of the criteria. Suitability values were standardised to a common monotonic linear scale between 0 and 1 using the maximum score procedure  [39] , so that for linearly increasing criteria: x 0 ij ¼ x ij = x jmax And for linearly decreasing criteria: x 0 ij ¼ 1-x ij = x jmax Where x ij is the raw score for criterion j and x jmax represents the point at which its contribution to predictions of suitability becomes zero: all values above x jmax Lower and upper bounds of the distribution defined by the 90 th and 99 th percentile of values for Africa within area defined by model constraints; 2 Values describe the Mahalanobis distance from ideal habitat suitability: suitability decreases as distance increases (see  [40]  for full details).
Validation ASF outbreak report data from 2005 to September 2012 for mainland Africa were downloaded from the World Animal Health organisation WAHID system (OIE, 2012). When geographic co-ordinates for the location of a reported outbreak were not provided but a location name was given, 'Fuzzy Gazetteer' (isodp. hof-university. de/fuzzyg) was used to select the most likely coordinates for the location of the outbreak. Validation was performed on the mean suitability estimate for sylvatic cycles, domestic cycles, and a combination of the two. Given the non-independence of predictions for each objective (which were defined using some of the same criteria but with different weightings applied), the combined suitability estimate was derived by combining the mean value for each cycle using an ordered approach where the highest value for either objective was used to define the value at each pixel.
The latitude and longitude describing the geographical location of ASF outbreaks reported to the OIE is typically given at one or two decimal places, and represents the focus of an outbreak that can cover a wide geographic area: the validation dataset can therefore be expected to contain substantial amounts of georeferencing based error that could contribute to omission error. The partial ROC output is the ratio of the area under the curve (AUC) of the restricted ROC curve (i. e. the region where the omission error is less than E) against the AUC of the restricted null model. Bootstrapping to evaluate the statistical significance of the AUC ratios was performed by resampling 50 % of test points 1000 times from the pool of outbreak data, as described by Peterson et al. [43]. The value of E was set at 20 % to represent the likely high levels of geo-referencing error present in the validation set.
Acknowledgements. The authors wish to thank the staff of the  Nature Reserve of Py (Claude Guisset  and  David Morichon , in particular) for their hospitality and valuable help in the fieldwork. The study was partly supported by  AGAUR  ( 2014 SGR 1241 ). A. J. S. Bakhoum  has a post-doctoral fellowship (No. CE/01/2013 ) from the '' Collectivité Territoriale de Corse -Direction de l'Enseignement Supérieur et de la Recherche ''.
Microsatellite typing (MLMT) has also been applied to population data for fine-scale intra DTU genetic analysis  [31, 32, 33]. Multilocus sequence typing (MLST), originally developed for bacterial species typing, has now been applied to a wide range of prokaryotic  [34, 35, 36, 37]  and increasingly eukaryotic microorganisms  [38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]. The technique typically involves the sequencing and concatenation of six to ten internal fragments of single copy housekeeping genes per strain  [49]. Data are often hosted on interactive open access databases such as MLST. net for use in the wider research community. A major advantage of MLST analysis is that sequence data are unambiguous, minimizing interpretative errors. In this context, the MLST approach represents an excellent candidate to become the gold standard for T.
org/tritrypdb/), using the primer sequences as well as the fragment sequences as query, displayed single matches in all cases. Chromosome location, primer sequences and amplicon size for each target are shown in Table  2. Nucleotide sequences for all the analysed MLST targets are available from GenBank under the following accession numbers: JN129501-JN129502, JN129511-JN129518, JN129523-JN129524, JN129534-JN129535, JN12954 4-JN129551, JN129556-JN129557, JN129567-JN129568, JN129 577-JN129584, JN129589-JN129590, JN129600-JN129601, JN12 9610-JN129617, JN129622-JN129623, JN129633-JN129634, JN1 29643-JN129650, JN129655-JN129656, JN129666-JN129667, JN129676-JN129683, JN129688-JN129689, JN129699-JN1297 00, JN129709-JN129716, JN129721-JN129722, JN129732-JN12 9733, JN129742-JN129749, JN129754-JN129755, JN129765-JN1 29766, JN129775-JN129782, JN129787-JN129788, JN129798-JN 129799, JN129808-JN129815, JN129820-JN129821, KF889442-KF889646.
Additionaly, we used T. cruzi marinkellei as outgroup. Sequence data of the selected targets for T. cruzi marinkellei were obtained from TriTrypDB (http://tritrypdb. org), under the following accession Ids: TcMARK_CONTIG_2686, TcMARK_-CONTIG_670, TcMARK_CONTIG_1404, Tc_MARK_2068, Tc_MARK_3409, Tc_MARK_5695, Tc_MARK_9874, Tc_MA RK_515, Tc_MARK_4984, Tc_MARK_5926, Tc_MARK_ 8923, TcMARK_CONTIG_1818 and Tc_MARK_2666.
Data analysis MLST data were analysed with MLSTest software (http://ipe. unsa. edu. ar/software) [52]  with the objective of identifying the most resolutive and minimum number of targets for unequivocal DTU assignment and potential fine scale characterisation. MLSTest contains a suite of MLST data specific analytical tools. Briefly, single nucleotide polymorphisms (SNPs) were identified in all loci in MLSTest alignment viewer. Typing efficiency (TE) was calculated using the same software. TE for a determined locus is calculated as the number of identified genotypes divided by the number of polymorphic sites in this locus. Additionally, discriminatory power, defined as the probability that two strains are distinguished when chosen at random from a population of unrelated strains  [53] , was determined for each target (Table  3 ).
5, see  [53]  and MLSTest 1. 0 manual at http://www. ipe. unsa. edu. ar/software for further details). Statistical support was evaluated by 1000 bootstrap replications. Overall phylogenetic incongruence among loci (by comparison with the concatenated topology) was assessed by the Incongruence Length Difference Test using the BIO-Neighbour Joining method (BIONJ-ILD,  [55] ) and evaluated by a permutation test with 1,000 replications. Briefly, the ILD evaluates whether the observed incongruence among fragments is higher than that expected by random unstructured homoplasy across the different fragments. A statistical significant ILD p value indicates that many sites, in at least one fragment, support a phylogeny that All combinations from 2 to 12 fragments were analysed using the scheme optimisation algorithm in MLSTest which identifies the combination of loci producing the maximum number of diploid sequence types (DSTs).
PCR amplification and sequencing All 13 gene fragments were successfully amplified using identical PCR reaction conditions (see methods) which generated discrete PCR fragments. PCR amplifications of the 13 targets were applied to an extended panel of 90 isolates obtaining more than 98% of positive PCR and amplifications produced strong amplicons and an absence of non-specific products (data not shown). We obtained amplicons of the expected length for all the assayed targets and for all the examined strains. Amplification for various DNA template concentrations was assayed via serial dilution. No difference in PCR amplifications were obtained when DNA concentrations from 20 to 100 ng were used. A total of 5,121 bp of sequence data were analysed for each strain (Table  2 ). There were no gaps in sequences.
We assessed whether the outlier for TcV (Mn cl2) and the low bootstrap observed for TcVI (applied to all 13 fragments) was due to incongruence among fragments. The thirteen fragment dataset was significantly incongruent (BIONJ-ILD p-value,0. 001) for at least one partition which was corroborated using NJ-LILD with a permutation test and 500 replications. Significant incongruence (pvalue,0. 05 after Bonferroni correction) was detected in the TcV and TcVI nodes. Incongruence was likely due to strains within DTUs TcV and TcVI demonstrating apparent loss of heterozygosis (LOH) in the Met-II fragment. Excluding Met-II, the p-value for ILD was not significant (BIONJ-ILD p-value = 0. 33), and the bootstrap values for TcV and TcVI exceeded 85%, furthermore tree topology was congruent with expected DTU assignment.
For example, hybrid isolates TcV would be assigned to TcII based on TcMPX sequencing due to apparent LOH. Despite this LOH the TcMPX locus was included in the 4 target scheme to increase bootstrap support in differentiating between TcV from TcVI. Although MLST has been successfully applied to other diploid organisms including Candida albicans, the potential for heterozygous alleles complicates typing schemes. In the present work, two methods to handle heterozygous sites, SNPs duplication and average states algorithms, produced broadly similar results with SNP duplication producing marginally higher bootstraps due to the physical duplication of informative sites. Here we decided to implement the average states methodology to derive genetic distances and phylogenies. Both approaches can be found in the software MLSTest  [52]  producing results that enable resolution at the DTU level and an associated DP of 1 for the panel tested.
1047053511543. 60 35 CGTGGCACTCTCCAATTACA (20) 360 21 321 AATTTAACCAGCGGGATGC (19) PDH Tc00. 1047053507831. 70 40 GGGGCAAGTGTTTGAAGCTA (20) 491 21 451 AGAGCTCGCTTCGAGGTGTA (20) GTP Tc00. 1047053503689. 10 12 TGTGACGGGACATTTTACGA (20) 561 21 521 CCCCTCGATCTCACGATTTA (20) SODA Tc00. 1047053509775. 40 21 CCACAAGGCGTATGTGGAC (19) 300 20 263 ACGCACAGCCACGTCCAA (18) STPP2 Tc00. 1047053507673. 10 34 CCGTGAAGCTTTTCAAGGAG (20) 409 21 369 GCCCCACTGTTCGTAAACTC (20) Met-II TC00. 1047053510889. 280 6 TCATCTGCACCGATGAGTTC (20) 700 51 389 CTCCATAGCGTTGACGAACA (20) *Gene fragments included in the 7 loci MLST scheme; { Gene fragments included in the reduced 4 loci MLST scheme; a Gene ID: GenBank access number for the complete gene in the CL-Brener strain; b Amplicon size refers to the sequence size of the gene fragment including the primers regions; c 59 starting position: indicates the position where the analyzed sequence starts, counting from the first base of the amplicon; d Fragment Length refers to the sequence length used for the analyses (the analyzed fragments do not include the primer regions).
Nucleotide sequencing and sequence analyses The amplified 348-bp fragments were excised from the gel and purified using the Wizard SV Gel and PCR Clean-Up System (Promega; catalogue number A9281). The purified products were directly sequenced for both strands with nested PCR primers using the CEQ 8000 Genetic Analysis System and dye-labelled dideoxy termination method (DTCS QuickStart sequencing reagent; Beckman Coulter, Fullerton, CA, USA). Individual sequences were proofread and edited using CEQ 8000 software (Beckman Coulter), and merged into composite sequences with SeqMan software (Lasergene v. 7; DNASTAR Inc. , Madison, WI, USA). Different sequences were used to root the phylogeny and were chosen as the best hits to our sequences in GenBank. Phylogenetic inference was conducted for the combined concatenated dataset.
jModelTest version 2. 0  25  was used to infer the best-fit model of evolution, based on hierarchical loglikelihood ratio tests comparing successively complex models. The evolutionary history was inferred using the maximum likelihood method based on the generalized time-reversible model (GTR+G). The tree topology search was done using the Best of NNIs and SPRs algorithm, over 100 bootstrapped replications of the dataset. The analysis involved 260 nucleotide sequences. Evolutionary analyses were conducted in PhyML v3. 0, 26 run on the Phylemon 2. 0 platform. 27.
Swine HEV detection Faecal samples (19 pools) collected from five swine farms with pigs aged between 2 and 4 months were subsequently analysed by RT-PCR to detect the presence of HEV. The analysis of nested PCR products revealed six positive stool samples (Table  4 ). The virus was detected in pooled samples collected from pigs of different ages: 2. 5 months old (farms B and E), 3 months old (farms C and A), and 4 months old (farm D). To characterize the viral strains circulating in the farm-pig populations, two of the positive PCR products were sequenced and analysed. A 347-nucleotide sequence (roEF5; GenBank accession number KM058114) and a 341nucleotide sequence (roFPR4, GenBank accession number KM042904) were obtained.
All faecal samples were collected from swine without clinical symptoms. Swine HEV RNA was isolated from faecal samples collected from 2to 4-month-old pigs. These results are similar to previously published data, confirming that HEV is circulating in all farms and in all age groups, from weaners to fatteners, and that pigs that are close to slaughter age can still be HEV-infected. 41 Romanian swine HEV isolates were grouped into genotype 3 by phylogenetic tree analysis and were closely related to the swine and human HEV isolates identified in European countries. Analysis of the sequences (Fig. 1 ) revealed that, at the amino acid level, the partial capsid protein sequence (ORF2) of strain roEF5 presented a maximum 92% identity with a French human isolate (GenBank accession number EU495231) and 89% identity with a swine HEV isolate from wild boar in Germany (GenBank accession number KF303501.
1), a French human strain (GenBank  accession number GQ427014), and a human strain isolated in Italy (GenBank accession number HM446630. 1). Phylogenetic analysis revealed that this swine HEV had 91% identity with six swine strains isolated in Bolivia (GenBank accession numbers JQ424439. 1, JQ424440. 1, JQ424437. 1, JQ424442. 1, JQ424441. 1, and JQ424438. 1) and 90% identity with three swine strains isolated in Japan (GenBank accession numbers AB471981. 1, AB471980. 1, and AB292650. 1). Similar results were obtained for pig livers in Germany,  42  where 4% of the samples tested were found to be positive for HEV. Sequence determination and phylogenetic analysis classified the isolate strains as HEV genotype 3. In Italy, HEV RNA was detected in 31 out of 48 pigs (64. 6%). 43 The virus was found at a statistically higher rate in the 3-to 4-month-old pigs (95.
0%) than in the 9-to 10-month-old ones (42. 9%). According to the nucleotide sequence phylogenetic tree of the ORF2 region (341 nucleotides) of the other isolated strain roFPR4, it was very closely related, presenting a maximum identity of 97% with a human isolate from the Netherlands (GenBank accession number JQ929109) and an identity of 93% with one human isolate from Hungary (GenBank accession number EF530667). The sequence identity of the analysed ORF2 fragment within the roFPR4 and the other strains from GenBank was in the range of 91-97%. Because of the lack of published studies describing human HEV circulation in Romania, we used human and swine HEV isolated in neighbouring European countries in our phylogenetic analysis. In the Netherlands, different swine HEV strains were characterized and typed as genotype 3, showing close genetic similarity to European human strains.
Figure1. Phylogenetic tree analysis of the Romanian isolates of swine HEV using the maximum likelihood method based on the generalized time-reversible model. The analysis involved 260 nucleotide sequences. Evolutionary analyses were conducted in PhyML v3. 0 run on the Phylemon 2. 0 platform. Human and swine HEV included for comparison are indicated with their GenBank accession number, host, and country where the strain was isolated. The final number included in parenthesis refers to strain identification from polyphyletic clades listed in the Supplementary Material.
3 kb. After trimming bad-quality Illumina runs, the sequences were assembled by Genostar (Montbonnot, France) in 3,744 contigs with the CLC Genomics Workbench version 6. 0. 2 (http://www. clcbio. com /products/clc-genomics-workbench). PacBio RSII reads were then used to generate scaffolds from the contigs using the softwares BLASR (2) and SSPACE Premium scaffolder version 2. 3 (3). Subsequently, gaps were closed with GapFiller version 1. 10 (4). Finally, 176 scaffolds were obtained with a mean size of 246,804 bp, representing a total length of 43. 4 Mbp. The genome annotation was performed by Genostar; prediction of coding DNA sequences (CDSs) was performed with Augustus version 2. 5. 5  (5) , trained on an algorithm optimized for Neurospora crassa. The 10,919 putative CDSs identified were annotated by BLASTP (6) against the TrEmbl database (7).
A function was inferred for a given CDS when the deduced protein sequence shared at least 80% similarity and 40% identity with a protein of known function in the TrEmbl database. A putative function was attributed to the remaining CDSs by functional domain searches through the Pfam database  (8). Thus, a function was assigned to 8,818 out of the 10,919 CDSs (80. 75%). For 813 CDSs, the functional annotation was refined by attribution of the enzyme classification numbers. Genomic data will greatly improve our comprehension of the pathogenic mechanisms underlying scedosporiosis and will help our understanding of the low susceptibility of Scedosporium apiospermum to currently available antifungal drugs  (9). Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number JOWA00000000.
ACKNOWLEDGMENTS The authors would like to thank  Dr. Sylvain Larrat ,  Dipl. ACZM  for reviewing this article,  Delphine Grosset , Visual Conception Communication Creator for her assistance with Figure 4 drawing. Drs. Jean-Luc Wargny  and  Sandrine Goldman Mottet  for discussing the case, and the technicians of the  Clinique Vétérinaire de la Gare of Taverny, France , for their technical support.
Animals and breeding kennels A total of 266 puppies (60 litters) from 29 French breeding kennels were included in this study between May and September 2009 (mean of 9 puppies included per kennel; range: 2-18). Puppies were between 5 and 14 weeks of age (mean: 7. 8 weeks of age) (Fig. 1 ). These breeding kennels were randomly selected from a data base of breeders registered at Alfort Veterinary School for training programs. Only puppies with a normal clinical examination were included (puppies with clinical signs of prostration, dehydration and/or anorexia were excluded of this study). For each kennel, data concerning environmental factors (number of puppies sold per year, and litter size for each puppies included), management of the kennel and puppies (number of meals distributed per day, access to outdoor, vaccination) and puppies' characteristics (age, breed, sex), were collected.
Acknowledgements This work was supported by the  French 'Région Centre'. Juan Salazar-Ortiz  was supported by a grant from  CONACYT (Mexico). The authors wish to thank  Dr. P. Chemineau  ( INRA ) for his encouragement; the  UEPAO technical staff for taking care of the pony mares in the experimental stables of INRA ;  Mr. G. Duchamp , who performed or supervised the follicular punctures;  Dr. A. F. Parlow  ( NIH, USA ) for the eGH, eFSH and eLH standards;  Dr. Y. Combarnous  and  Dr. C. Cahoreau  for the antibodies against eCG and eFSH;  Dr. J. Simon  for his help with the insulin and glucose assay methodologies;  Mrs. M. Ottogalli  and  M. Leveau  for their help with the hormone assays;  Ms. A. L. Laine  and her staff from the  Laboratory of Hormonal Assays  for the P4 assays; Pr. R. J. Scaramuzzi ,  Dr.
, 1998). In France, a risk-based MI for poultry is currently implemented in major slaughterhouses  (Allain, Le Bouquin, Donguy, & Magras, 2013). Data including mortality, veterinary treatments and diseases are collected at farm level for each poultry batch and transmitted 48 h before slaughtering. Results of the inspections are fed back to the farmers and to the animal health officials if a problem of welfare and/ or animal health is detected. This integrated system is based on the use of control indicators defined for each inspection step with the setting of alarm thresholds. This system enables a complete traceability of a processed batch with the record of alarm signals raised during the MI process and of the management measures applied. The information are fed back to producer and, if needed, to the Veterinary Authorities in charge of the inspection of animal health and welfare at farm level.
The number of viable cells in culture was quantified using the CellTiter-Glo ® Luminescent Cell Viability assay (Promega) according to the manufacturer's instructions. Briefly, CellTiter-Glo ® (100 l) substrate was added to each well, plates were placed on an orbital shaker to induce lysis and subsequently incubated at room temperature (10 min). Luminescent signal was measured using the Fluostar Optima at 405 nm. The background value (medium only) was subtracted. The quantity of live cells was calculated as a percentage of the control (cells without drug). The half-maximal inhibitory concentrations (IC 50 ) were calculated using Sigmaplot12. 5 (Systat Software, Inc. ). Two independent experiments were conducted for each strain. Female Swiss mice (CD1) were purchased from Charles River (L'Arbresle, France) and used for experiments at eight weeks of age.