dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction and reverse transcription, quantitative PCR, and native chromatin immunoprecipitation As the genome annotation of S. mansoni has mostly been done through prediction software, we performed RNA-Seq on cercariae to unambiguously identify TSS and exon-intron structures. Since published RNA extraction protocols for cercariae did not yield RNA suitable for Illumina sequencing, we developed the following method: 5,000 cercariae were ground with glass beads in liquid nitrogen for 5 minutes, resuspended in 0. 5 mL TRIzol (Invitrogen), and 0. 1 mL of chloroform was added. The tube was shaken vigorously for 1 minute and left at room temperature for 3 minutes, and was then centrifuged at 12,000×g for 15 minutes at 4°C. The upper phase was transferred to a new RNase-free tube, an equal volume of 70% ethanol was added and the tube was vortexed.
The flow cell was clustered using Single Read Cluster Generation Kit (Illumina Inc. , USA), according to the Illumina SR_amplification_Linearization_ Blocking_PrimerHyb recipe. The flow cell was loaded onto the Illumina HiSeq 2000 instrument following the manufacturer's instructions and sequencing was performed with the 50 cycles, single read, indexed protocol. Image analyses and base calling were performed using the HiSeq Control Software (HCS) and Real-Time Analysis component (RTA). Demultiplexing was performed using CASAVA (Illumina). The quality of the data was assessed using fastqc from the Babraham Institute and the Illumina software SAV (Sequence Analysis Viewer).
The higher the quality score is, the lower is the probability that a read can map elsewhere in the genome. By filtering mapped reads with a score of 255 (the highest possible) in samtools  [30]  (samtools view-hS-q 255), we kept reads with a single match on the genome. In Bowtie 2. 1 score 255 was abandoned and filtering of uniquely mapped reads was done with "samtools view-Sh-q quality value 40-42-F 0x0004 -| grep-v XS:i. ". SAM alignment files were converted into the bed format with pyicos  [31]  and sorted with sortBed-i of the bedtools suite  [32]. For peak identification an equal number of random lines in the bed-file was chosen for each biological replicate (Table  1 ). Although comparative analysis software usually include a normalization step to handle the different numbers of mapped reads in conditions or replicates when comparing samples, we found that the random sampling step drastically reduces the amount of false positives without lowering the sensitivity.
To compare ChIP-Seq and qPCR results, coverage was calculated with coverageBed of the bedtools suite for the regions whose boundaries were defined by the corresponding qPCR primer positions and divided by the coverage in the alphatubulin reference locus (Smp_scaff000423:21,107-21,289). Spearman rank correlation gave values > 0. 8, R2 was 0. 64 for H3K4me3 and 0. 59 for H3K27me3 indicating that both methods gave comparable results (S1 Fig). For RNA-Seq, reads were aligned to the genome with TopHat v1. 4. 1 with default parameters and exon-intron structure was reconstructed with cufflinks v1. 3. 0 evoking-q-I 40000-F 0. 100000-j 0. 150000-p 8 on each library and data were merged and linked to the gene prediction of the Sanger Centre (ftp://ftp. sanger. ac. uk/pub/pathogens/Schistosoma/mansoni/ genome/Assembly-v5/) with Cuffmerge v1.
Comparative ChIP-Seq analysis To identify peaks that were different between cercariae and adults, we used the DiffBind library 1. 6. 1  [36]  available on Bioconductor  [37]  for R version 2. 15. 1. As input, we used the randomly sampled aligned reads that also served as inputs for PeakRanger and the regions file generated with PeakRanger, all in bed format. Diffbind allows for replicates and "input" negative control to be taken into account to search for differential peak distribution between conditions. For H3K9ac, PCR duplicates were removed with pyicos, which improved sample autocorrelation. For H3K4me3 all differences were inspected on GBrowse. For the other histone isoforms, one hundred regions identified by DiffBind were randomly sampled with a custom Perl script and verified by visual inspection.
For the analysis of chromatin structure around repetitive sequences, reads were aligned to a fasta file containing all 3,145 known repeats of S. mansoni as consensus sequences (available http://methdb. univ-perp. fr/downloads/) [39]  using Bowtie2  [29]  evoking parameter-k 2 and parameter preset "sensitive". Unique matches were filtered using samtools  [30]  (mapping quality mapq = 255). In order to obtain identical effective library sizes for each sample, the same number of aligned reads was randomly sampled for each of the ChIP experiments (Table  1 ). After the counts had been normalized, they were compared between samples using the DESeq package of the R software  [40]. This package uses the negative binomial distribution to calculate the P-value for each element between two compared samples and find elements that present significant differences in their read enrichment levels.
Acknowledgments The authors are grateful for technical support by  Anne Rognon ,  Jean-François Allienne ,  Nathalie Aranciabia ,  Rémi Emans ,  Bernard Dejean  and  Claire Picard. We thank  Ivonne Heinze  for her diligent and skillful technical assistance in Illumina sequencing. We also thank the  French National Research Agency  for providing financial support to this project ( ANR INVIMORY ).
The study was funded by the  Agence Nationale de la Recherche , contract  ANR 2010 BLAN 1720 01  (EPIGEVOL) (http://www. agence-nationalerecherche. fr/programmes-de-recherche/recherchesexploratoires-et-emergentes/blanc-generalite-etcontacts/blanc-presentation-synthetique-du-projet/?  tx_lwmsuivibilan_pi2[CODE]=ANR-10-BLAN-1720 ). /.
Here, the model brings new elements to the field. Specifically, we evaluated whether including the seasonal host population dynamics and contact pattern that can be inferred from the knowledge of the species could result in the seasonal epidemiological variations that have been documented in the field  [17]. We performed a sensitivity analysis of this model (i. e. , we studied the effect of parameter variations on model outputs) to identify key parameters which are potential control points of the biological system and also sources of model uncertainty. Moreover, we produced first estimates of epidemiological parameters, namely transmission and infection-related mortality rates, and tested which were the most important ones for viral dynamics. For this, we used data from a long-term demographic and epidemiological survey of a Pyrenean chamois population at Orlu, France.
Parameter estimation and model analysis The demographic parameters (Table  1 ) were calibrated by integrating knowledge from unpublished and published data on the focal population and by using experts' knowledge. The mortality rate of juveniles and the fertility rate of subadult females are known to be strongly influenced by density-dependence processes in ungulate species  [23]. We therefore used a sigmoid function to continuously represent variations in these rates using explicit variables (d: strength of density dependence, K: carrying capacity, N: total population size), (equations are given in Additional file 3). Several epidemiological parameters (Table  1 ) were calibrated by integrating knowledge available on Pyrenean chamois populations. When parameters were unavailable for wild populations of ungulates, we used data from experiments on domestic species (mainly sheep).
Parameters that could not be obtained from Pyrenean chamois were disease-related mortality of P animals and transmission coefficients by both P and T individuals. For the first simulations, the probability of diseaserelated mortality for transiently-infected individuals was calculated using data from Pioz et al. (unpublished data). The survival of P animals was based on a cattle study  [34] , and corresponds to a half-life of 6 months. For the transmission coefficients, the only information available concerned cattle, and we chose as priors the parameters estimated for Bovine Viral Diarrhea Virus (BVDV) in this species  [34]. As initial conditions, the composition of the population (age and sex) was determined on the basis of the stable age structure predicted by simulations without introduction of the pathogen, and population size was based on the yearly censuses.
Moreover, as the exact year of virus introduction was not known, several options were tested from 1990 to 1993. To assess the importance of PI individuals in the epidemiological dynamics, we performed an additional estimation considering a model without persistently infected animals. In this case, the range used for β T was larger [0. 001, 0. 6], β P value was 0, and the risk of abortion when infection occurs during the first half of pregnancy was 100%. Lastly, to evaluate the impact of assuming homogeneous or heterogeneous contacts, we performed an additional estimation considering homogeneous contacts all year long. We used the chi-square distance to compare the observed and simulated data. Two sets of data were used: seroprevalence over the last eleven years, which corresponds to an endemic period, considering only animals tested for antibodies and viral antigen, aggregated by age classes and sampling seasons, and the estimates of population size over time.
Acknowledgements This work was carried out with the financial support of the  Office National de la Chasse et de la Faune Sauvage ,  INRA  and VetAgroSup. We thank  Pierre Menaut  and  Philippe Gibert  for their long-term implication in collecting field data and in epidemiological survey, respectively. We also thank the  DIR Sud-Ouest , with special thanks to  K. Foulché  and his considerable involvement in the study site.
PerR protein was detected by using a rabbit anti-PerR antibody at a 1:2,000 dilution as the primary antibody, goat anti-rabbit horseradish peroxidase (HRP)-conjugated IgG at a 1:150,000 dilution (Sigma-Aldrich) as the secondary antibody, and the SuperSignal West Pico reagent (Thermo Scientific) as the HRP substrate. Survival in the presence of H 2 O 2 was assessed by incubating exponentially growing cells in the presence or absence of 10 mM H 2 O 2 for 30 min  Accession number. The annotated sequence of pMaORI has been deposited in GenBank under accession number KP784428.
Craig Venter Institute (http://gsc. jcvi. org/). The draft genome se- quences of strains 200901116 (4,135,276 bp, 84 contigs) and 200901122 (4,161,553 bp, 85 contigs) have been deposited in GenBank under accession numbers AKWB00000000 and AKWM00000000, respectively. The GϩC content of these strains is 39. 5 mol%, which is within the range of 35 to 45 mol% reported for members of the genus Leptospira  (9). Comparative genome analysis was performed with the MaGe interface in the SpiroScope database (https://www. genoscope. cns. fr/agc/mage) (18). The genomes of strains 2009001116 and 200901122 are highly similar, as demonstrated by the high number of genes they have in common; they have 3,501 coding sequences (CDSs) with an average pairwise amino acid identity of Ͼ99% in common. In comparison, by the same criteria, strain 200901116 has only 34 CDSs in common with L.
interrogans strain Fiocruz L1-130 and 194 CDSs in common with L. borgpetersenii strain L550, with Ͼ99% amino acid identity. By analyzing genome disparity, we found a unique region of approximately 52 kb carried by strain 200901116 but not strain 200901122 at approximate positions NT440186 to NT492268 (Fig. 1 ). Most of the genes in this region encode hypothetical proteins. The genes identified in this region include phage-related gene homologues coding for integrase, late control protein D, portal protein, GpA terminase, bacteriophage resistance factor (PF05565 family), and a toxin (RelE-like family)-antitoxin (Xre-like family). Another gene (AKWB270011) in this region codes for a putative protein of 234 amino acids containing an N-terminal helix-turn-helix domain (25 to 76 amino acids), which is typically associated with DNA binding.
ACKNOWLEDGMENTS We thank  J. Vinetz  and  D. Fouts  for their permission to use the draft genomes that are part of a project funded with federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services  under contract  HHSN272200900007C. This work was supported in part by  National Science Foundation  grant  IIA-1159099  (C. J. P. ) and the  Institut Pasteur (M. P. ).
The host cell cycle was therefore analyzed after SB203580 treatment. Epithelial cells (MDBK, m-ICcL2 and CLEC-213) were treated overnight with SB203580 (25 μM) but no change in cell cycle phase was measured in our experimental conditions (Fig. 2C  (CLEC-213) were pre-treated with SB203580 (25μM) overnight and washed, pretreatment did not modify host cell invasion (S3 Fig. panel A ) leading to the hypothesis that this inhibitor might be less potent on chicken MAPK than on the mammalian p38 MAPK. These data suggest that the decrease in cell invasion in the presence of SB203580 is mostly due to a direct effect on the parasite itself which would be consistent with the higher IC50 found with the chicken cell line compared to the mammalian cell lines. Overall, these data suggest that the inhibitor may have a variable effect on the host cells depending on species but may affect mostly parasite p38 MAPK homologues.
E. tenella possesses putative p38α MAPK homologues We next investigated if the decrease in cell invasion resulted also from a direct effect of the inhibitor SB203580 on the parasite. We first sought for p38α MAPK homologues in the E. tenella database (www. toxodb. org). Blast of Mus musculus p38α MAPK (MAPK14; NM_001168508) protein on E. tenella protein database led to the identification of E. tenella putative kinases (accession number ETH_00035055, ETH_00005380, ETH_00028715, ETH_00004310, ETH_00004180) presenting 32-42% identity and that are mainly Cyclin dependent like kinases (CDKs), MAPK, Glycogen synthase kinases, CDK-like kinases (CMGC kinases, Table  1 ). Multiple sequence alignment of these kinases clearly revealed the eukaryotic protein kinase catalytic domain described by  Hanks (2003)   [17]  (Table  2 ).
SB203580 decreases parasite motility As we identified putative parasite kinase homologues to the mammalian p38α MAPK, we sought for some direct effect of SB203580 on the parasite by using a host cell free system  [32]. Here, we determined parasite motility by two different methods that led to the same conclusion. We first stained trails of parasite proteins left on glass surfaces (Fig. 3A ) with a mouse polyclonal antibody against E. tenella sporozoites. Secondly, we counted motile sporozoites by videomicroscopy (Fig. 3B ). In both experiments, SB203580 induced a strong inhibitory effect on parasite gliding motility; however, this inhibitory effect is quickly reversible after chasing the inhibitor. Altogether, those data clearly show a decrease in parasite motility in the presence of SB203580 suggesting a direct effect of this inhibitor on the parasite.
Sporozoites in PBS at 4°C were used as a negative control. (B) Effect of SB203580 on micronemal protein secretion. This was evaluated by western blotting in the supernatants and parasite lysates. Profilin was used as a control of the number of sporozoites. Quantification of secreted micronemal proteins in supernatants was performed using Bio-Profil Bio-1D++ software and is represented by histograms compared to the micronemal protein content in sporozoites. Data are representative of 2 experiments. doi:10. 1371/journal. pone. 0116509. g004 concentrations to carefully determine the doses that were not cytotoxic for both epithelial cell and parasite. In these optimized conditions, the inhibitors of p38 MAPK (SB203580) and JNKII (SP600125) pathways decreased cell invasion by E. tenella in a dose dependent manner (IC50 for SB203580 is 9-11.
tenella enters epithelial cells. However, few experiments performed with a chicken epithelial cell line suggest that the SB203580 may mostly act on parasite kinases as pretreating chicken epithelial cells with this inhibitor did not modify host cell invasion by Eimeria tenella. In Apicomplexa parasites, several kinases have been described  [45]  and play an important role in parasite development and virulence  [46] [47] [48] [49] [50]. Then, as the effect of the host cell in parasite entry was determined to be about 20-34%, we hypothesized that the major effect of the inhibitor SB203580 could be on the parasite itself by acting on parasite p38 MAPK homologues or closely related kinases. We then sought for p38α MAPK homologues in the E. tenella database and identified five E. tenella kinases (Table  1 ) with 32-42% amino acids identity and possessing, as for the p38 MAPK, an ATP binding site that could be targeted by the SB203580 inhibitor.
Cell cycle For cell cycle experiments, 10 6 epithelial cells (MDBK or m-ICcL2) were placed in a 6-well plate. First, cells were treated overnight with SB203580 (25 μM). Cells were trypsinized, washed and fixed with 70% cold ethanol. After fixation, cells were treated with RNases (0. 2 mg/ml, Sigma) for 1 h at 37°C, stained with propidium iodide (50 μg/ml) and analyzed by flow cytometry (Moflo Beckman Coulter, Fort Collins, Colorado, USA). The percentage of cells in each phase was assessed by using the software MultiCycle for windows Phoenix flow system, Inc. San-Diego, CA, USA.
Acknowledgments This work was initiated with our colleague  Dr. Fabien Brossier  who sadly passed away on January 11 th , 2014. We thank  Alain Vandewalle  ( UFR de Médecine Bichat ) for providing us the cell line m-ICcL2. The authors are also grateful to  Janene Bumstead ,  Damer Blake  and  Fiona Tomley  ( Royal Veterinary College, University of London ) who kindly supplied the Wis strain of E. tenella, the MDBK cell line and micronemal protein antibodies and to  Evelyne Esnault  and  Pascale Quéré  for the CLEC-213 cell line. We thank  Laetitia Fragnet-Trapp  ( INRA Nouzilly-UMR1282 ) for helpful discussions on the cell cycle phases. We also thank  Sébastien Lavillatte  and  Edouard Guitton  ( INRA Nouzilly-PFIE ) for their help in the animal facilities.
Procedure We conducted a paper-and-pencil survey in five countries: Belgium, France, Germany, Sweden and Switzerland. The study was part of the European research project MINAPIG (www. minapig. eu). In all countries but Switzerland, the questionnaire was sent to farmers who agreed to participate in a crosssectional study about antimicrobial usage. Farmers were invited by telephone, mail, e-mail, or directly by their veterinarian to participate in a study about antimicrobial usage in pig farming, which involved a farm visit by one of our interviewers. 2 In Belgium, farmers' contact details originated from a subscription list for a newsletter for pig farmers from the University of Ghent. Farmers' addresses originated from a database from the National Pig Institute in France and from the Animal Health Service in Sweden.
In Germany, farmers were recruited through consultancy circles for pig farmers and through veterinary practices. Our aim was to visit 60 farms in each country, which had at least 100 sows and at least 500 finishers per year. Because it was expected that it would be difficult to reach this final sample size, all farmers were approached in Belgium, Germany and Sweden, without doing a random selection of addresses and thus resulted in convenient samples that may not be representative of pig farmers in each respective country. Since we had access to a national database that includes most pig farmers in France, we randomly selected addresses of pig farmers living in the north-west of France, where 75% of the country's pig production is concentrated. After deleting cases of farmers who did not return our questionnaire, this resulted in the following sample size per country: 46 farmers in Belgium (response rate: 68%), 56 in France (response rate: 71%), 54 in Germany and 59 in Sweden.
3 Farmers were asked to complete our seven page questionnaire before the farm visit so that the interviewer only had to collect it. Data collection took place between November 2012 and December 2013. In Switzerland, a different procedure was followed: farmers who had participated in a study by the VetSuisse Faculty of the University of Zurich were sent the exact same paper-and-pencil questionnaire as was used in the other countries. Farmers were not visited; the questionnaire was sent and returned by postal mail and thus included a stamped and addressed return envelope. Two reminders were sent to farmers who had not yet replied, three weeks apart. This resulted in a final sample of 66 farmers (response rate: 73%).
Data analyses The analyses were done in IBM SPSS Statistics Version 22  (IBM Corporation, 2013). All farmers who returned the questionnaire were included in the following analyses. In cases of missing data on a single item or scale of a case, the full case was excluded from the respective analyses (i. e. , listwise deletion). The questionnaire items were a priori grouped based on their content to form overarching constructs. We then calculated Cronbach's ˛ to investigate the internal reliabilities of the constructs and computed the mean scores of the grouped items per farmer (Table  1 ). All following analyses were conducted on the mean scores of the scales per farmer, unless stated differently. We conducted several analyses of covariance (ANCO-VAs) to examine whether farmers from different countries differed in their level of worries, antimicrobial usage, benefits of antimicrobials and contributions by veterinarians.
Belgian farmers were significantly younger than Swiss and Swedish farmers (Table  2 ). Moreover, the herd sizes, based on the number of sows, of the participating farms differed between the countries (Table  2 ). 5 Belgian and German farmers had significantly more sows than Swedish and French farmers. For the Swiss sample, we only had access to the number of fattener places (M = 326, Mdn = 220, IQR = 88-528). We included gender and age as covariates in the analyses below, unless stated otherwise. Number of sows at the farm was not included as a covariate as we did not have these data for the Swiss participating farmers. 6  The average numbers of sows of our samples were compared to those of farmers in their respective countries. Overall, the farmers participating in our survey appeared to have larger herds (Table  2 ) than the average number of sows per farm in the respective countries; the average number of sows per farm in Belgium in 2013: 210, in France in 2011: between 152 and 257 (depending on the type of farm), in Germany in  2013 : 145, and in Sweden in 2013 : 190 (Belgian FPS Economy et al.
We believe that putting the pig at the center of this journal is unique and will serve both our favored animal species and our professions. Being open access (http://www. porcinehealthmanagement. com/authors/instructions), all articles published in PHM by BioMed Central [4] are freely and globally accessible online and thus available to readers at no cost. Articles can thus reach a larger readership than with a subscription-based journal. Open access also allows rapid publication of reviews, articles and of new clinical data, with the opportunity to upload videos, photos, and datasets. We believe that the open access format offers many advantages relative to traditional publication strategies, as articles will be published online very shortly after editorial acceptance which, markedly decreasing the time for distribution.
Acknowledgements The authors would like to acknowledge the veterinarians  Tania Aeriniotaki ,  Athina Tsokana ,  Dimitris Papaioannou ,  Niki Lemoni ,  Christos Ktenas  and the biologist  Despina Migli  for their valuable assistance. The authors are also grateful to  Paolo Matteo Accettura  and  Francesca Laiacona  for their support in the laboratory activities.