dataesr/scientific-paragraphs-categorization huggingface dataset:
All 1H NMR spectra were obtained on a Bruker DRX-600-Avance 1H NMR spectrometer operating at 600. 13 MHz for 1H resonance frequency using an inverse detection 5 mm 1 H-13 C- 15 N cryoprobe attached to a CryoPlatform (the preamplifier cooling unit). The 1H NMR spectra were acquired at 300 K using the Carr-Purcell-Meiboom-Gill (CPMG) spinecho pulse sequence with pre-saturation, with a total spin echo delay (2nτ) of 240 ms to attenuate broad signals from proteins and lipoproteins. A total of 128 transients were collected in 32,000 data points using a spectral width, a relaxation delay and an acquisition time of 20 ppm, 2. 5 sec, and 2. 28 sec, respectively. The spectra were Fourier transformed by multiplication of the free induction decay FIDs by an exponential weighting function corresponding to a linebroadening of 0.
3 Hz. All spectra were manually phased and baseline corrected, and referenced to 3-trimethylsilylpropionate TMSP using the Bruker TopSpin 2. 1 software (Bruker, GMBH, and Karlsruhe, Germany). Data were reduced using the AMIX software (version 3. 9, Bruker, Rheinstetten, Germany) to integrate 0. 01 ppm wide regions (i. e. , buckets) corresponding to the δ 9. 00-0. 70 ppm region for plasma samples. The 5. 10-4. 50 region, comprising water resonances, was excluded. To account for differences in the amount of used material, each integrated region was normalized to the total spectral area  [16]. Spectral assignment was based on matching data to reference spectra in a home-made reference database, as well as with others database (http://www. bmrb. wisc. edu and http://www. hmdb. ca), and reports available in the literature  [17, 18].
Statistical analyses General purpose of statistical analyses. Statistical analyses were performed with the R software  [19]. First of all, the phenotypic and metabolomic data were adjusted for undesirable batch and sex effects. All the subsequent statistical analyses were made on the surrogate adjusted variables. Analysis of variance was done with the Type III ANOVA. The metabolomic data and metadata that were used in this study are provided in the S1 Data File. The present study aimed to assess and predict the sensitivity of sire families to HS encountered in TROP region using metabolomic records obtained in TEMP conditions. As shown in Fig  1 , three main steps were taken: 1. We firstly quantified the actual average sensitivity of SF to TROP climate using phenotypic traits. 2. In a second step, we selected the 4 SF that showed extreme degrees of sensitivity (2 SF) and robustness (2 SF) to HS to build a predictive classification model using the profiled metabolomes in TEMP climate and sPLS-DA method.
From these t-tests, a 10 sires x 3 traits matrix of the t statistics values (S1 Table ) was built. For convenience, the "t" statistics were written t ADFI , t BW and t TR for ADFI, BW and RT, respectively. Thus, each SF was characterized by three unit-free variables. We performed a principal component analysis (PCA) on this matrix to assess the components of SF differences in HS sensitivity by considering performance (t ADFI and t BW ) and thermoregulation (t RT ) related traits. We retained the first component of the PCA (PC) as indicator of sensitivity to HS, the scores of each SF for the first PC being considered as a composite index of familial sensitivity (I sens ). Predictive classification model. In order to predict sensitivity to TROP climate, our strategy was to use 1H RMN metabolomic data obtained in TEMP climate to predict the SF sensitivity status to TROP climate.
The most extreme SF regarding sensitivity were identified based on I sens values. The metabolomics data of the pigs from these SF in TEMP climate were used to train a classifier that identified the most predictive buckets of sensitivity. The model was validated using cross validation (CV). Details about the different steps of this analysis are described below. According to the index of sensitivity, we built two extreme groups: 1) a robust group (n = 102 pigs) composed of the 2 most robust SF according to their I sens and, 2) a sensitive group composed of the 2 most sensitive SF according to their I sens (n = 121 pigs). The metabolomic data of robust and sensitive groups in TEMP climate were used as a calibration set in a multivariate discriminant analysis performed via sPLS-DA, discriminating robust/sensitive families.
Contrary to a classical PLS-DA, sPLS-DA, as described and implemented in the R package mixOmics, performs variable selection on the basis of a soft thresholding that approximates the Lasso penalization  [22]. The purpose of this variable selection is to remove the less informative buckets and to make the interpretation of the model easier. To choose the optimal number of buckets to select, we used the method described by Lê Cao et al (2011)  [23]. We set up the tuning process until obtaining the minimum number of buckets between 5 and 20 that allowed the lowest balanced error rate (BER). At the default decision threshold of sPLS-DA (0. 5), BER is an average error rate of prediction. BER is a suitable metric of a model's inaccuracy in case of imbalanced groups. The sensitivity of the model (true positive rate) is the number of animals truly predicted as sensitive to HS over the actual number of animals sensitive to HS.
Discussion Preventing the adverse effects of HS on animal productivity will be an increasingly challenging goal for many livestock production systems in the future. Data obtained in the present study confirm the negative impact of hot and humid tropical environment on growing-finishing pig performances, with a significant reduction of voluntary feed intake leading to subsequent negative effects on growth  [28]. In the present study, ADG measured between 11 and 23 weeks of age was reduced by about 10% in TROP compared to TEMP (759 vs. 836 g/d). This result is in agreement with previous results published by  Gilbert et al (2012)  (-11%; 680 vs. 780 g/d)  [29]. Based on the average performance of sire progeny, the ranking of the sires changed according to the climatic environment. In other words, this suggests that the best sires in TEMP environment would not be the best ones in TROP conditions.
Acknowledgments The authors are grateful to  K. Benony ,  D. Beramice ,  B. Bocage ,  M. Bructer  et  F. Silou  from the experimental unit  INRA-PTEA ,  F. Meslier ,  J. Bailly ,  P Epagneaud  and  C. Lebourhis  from the experimental unit  INRA-GenESI ,  K. Fève  and  Y. Labrune  from  INRA GenPhySe  for their technical assistance. MetaToul is part of the  French National Infrastructure for Metabolomics and Fluxomics MetaboHUB- AR-11-INBS-0010  (www. metabohub. fr). We thank the staff members of MetaToul core facilities for technical assistance.
Production of the data used in the present study was funded by the  French National Agency of Research (ANR PigHeaT ,  ANR-12-ADAP-0015  to DR). SD was supported by a postdoctoral fellowship from the  Region Bretagne  ( Dispositif « Strate  ´gie d'attractivitedurable » 2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The number of complete phage genomes available in GenBank is low. Despite the recent discovery of the importance of prophages in the diversity of H. pylori  11  , they remain poorly characterized. The lack of information on bacteriophages of H. pylori prompted this study. Based on the presence of the prophage integrase gene we determined that an estimated 20% of H. pylori strains carry prophages  11, 33. Based on PCR screening, we compiled a collection of H. pylori strains carrying prophages  33. We therefore undertook a more holistic approach, using the next generation sequencing (NGS) method to study the full genome of strains (Whole-genome sequencing) from this collection as well H. pylori strains presenting prophages found in public databases. This information allowed us to identify phage sequences, which were then used for comparative genomics.
High-throughput genome sequencing was performed using a HiSeq 2500 machine (Illumina Inc. ), and the 100 bp short read paired-end data was de novo assembled using Velvet (version 1. 2. 08)  64. The VelvetOptimiser script (version 2. 2. 4) was run for all odd k-mer values from 21 to 99 (several assemblies using different k-mer sizes were run), with all program settings unchanged apart from a minimum output contig size set to 200 bp and the scaffolding option switched off. All genomes were annotated using the RAST server (http://rast. nmpdr. org/)  37 , the NCBI Prokaryotic Genomes Annotation Pipeline version 2. 3. and PHAST web server  38. The respective trimmed reads were submitted to the Sequence Read Archive (SRA). Assembly of prophage genomes. For prophage identification two strategies were taken.
The annotation of prophage genomes was carried out further using Phages v. 1. 0 (http://www. phantome. org/PhageSeed/Phage. cgi?page= phast), and RAST  37. The annotation of coding sequences (CDS) found by the three different methods were compared. The annotation of both H. pylori India7 (accession number CP002331) and Cuz20 (CP002076) prophages, as well as that of the Helicobacter 1961P (NC_019512. 1), KHP30 (NC_019928. 1), KHP40 (NC_019931. 1), phiHP33 (NC_016568. 1) phages, were used for comparative purposes. The annotated prophages were aligned using the progressive Mauve algorithm software (version 2. 3. 1)  70 , to check the order of the CDS in the prophage genomes and the existence of a consensus sequence. In order to infer phylogenetic relationships among prophages, the intact genomes of the 23 prophages identified in the present study, were aligned using MAFFT version 7  71  together with other six phage Helicobacter genomes available at public databases (1961P, KHP30, KHP40, phiHP33, H.
pylori India7, and H. pylori Cuz20) as well as with the H. acinonychis (accession number NC_008229. 1) prophage used as an outgroup. A nucleotide Neighbour-joining phylogenomic tree was constructed using the MEGA (Molecular Evolutionary Genetics Analysis) 6. 0 software  72  , with distances estimated using the Kimura two-parameter model  73. Considering the huge genomic diversity observed among all prophage genomes as well as their different lengths, both complete and pairwise deletion options were used. While the former removes all sites containing missing data or alignment gaps before the distance estimations begin, in the pairwise-deletion, option sites are only removed during the analysis as the need arises. Branching significance was estimated using bootstrap confidence levels by randomly resampling the data 1,000 times with the referred evolutionary distance model.
RDP4 simultaneously applies different methods for detecting and characterizing individual recombination events that are evident within a sequence alignment without any need for predefined sets of non-recombinant reference sequences. SimPlot software (http://sray. med. som. jhmi. edu/SCRoftware/simplot/) was also used for characterizing with higher detail the genomic mosaicism of the identified recombinant prophages, as previously described for bacterial pathogens  75. The similarity estimations were performed by using the Kimura two-parameter model with sliding window and step sizes that varied according to each recombinant genome.
Acknowledgements This work was supported by the the  Fundação para a Ciência e a Tecnologia (FCT)  project grant  PTDC/EBB-EBI/119860/2010  and by the  University of Malaya-Ministry of Education (UM-MoE) High Impact Research (HIR)  Grant  UM. C/HIR/MOHE/13/5  (h- 50001-00-A000033 ). F. F. V. is recipient of a postdoctoral fellowship from  FCT  ( SFRH/BPD/95125/2013 ).
Data Availability. The genomes of the prophages are available with the accession numbers KX119174 to KX119206. The trimmed reads were submitted to the Sequence Read Archive (SRA), with the accession numbers SRP064706 to SRP064710, SRP071062, SRP071067, SRP071271, SRP071274, SRP071276 to SRP071280, SRP071282, SRP071284, SRP071289 to SRP071296, and SRP072438 to SRP072441.
Introduction The growth rate of nursing piglets is mainly determined by milk nutrient output by the dam. As a consequence, the quantity and the composition of milk produced by sows are key factors to reach a successful piglet production. Milk production appears to be highly variable and depends on many factors. It can be affected by sow characteristics (genotype, litter size, parity number, and lactation stage) and by environmental factors (feeding management, photoperiod, climatic parameters)  [1]. Under tropical conditions, we estimated that milk yield was reduced by at least 30 to 50% in comparison with data obtained in temperate countries  [2]. This result is mainly connected to the combined negative effect of high ambient temperature and relative humidity resulting in a concomitant reduction of voluntary feed consumption and milk production combined with the reduced ability of the sow to mobilize maternal body reserves.
In this later model, the sow was considered as a random effect and the repeated measurement option of the mixed procedure of SAS was used with an autoregressive covariance structure to take into account the correlations between repeated measurements on the same animal. Means comparison was performed according to the Pdiff option of SAS procedure using Tukey test for contrasts. Residual values were computed from the preceding models (without the random sow effect) and residual Pearson correlations between lactating performance and mean milk composition parameters were calculated using the CORR Procedure of SAS/STAT.
An Amplicon library was then prepared using GS Junior Titanium Kits (Roche/454 Life Sciences) and the library was pyrosequenced on a Genome Sequencer Junior (Roche/454 Life Sciences). The generated data were analyzed with PyroMIC © (protected software, IDDN FR. 001. 400018. 000. S. P. 2014. 000. 31230) which contains a cleaned fungal database created from ITS NCBI fungal sequences. Ultimately, only sequences of >100 bp, with ≥98% homology, and 0. 0 e-value were considered for species identification. Haplotypes of sequences of interest were done by means of CD-HIT software  (Li and Godzik, 2006; Ghannoum et al. , 2010; Findley et al. , 2013). A nucleotide alignment, by CLUSTAL X, was used to build a dendrogram using the Neighbor Joining, Kimura 2 parameter method, 1,000 bootstrap, with the MEGA6 software  (Tamura et al.
, 2013). As for the UDS method, 2,247 sequences (matching quality criteria) were used for analysis. For identification at the species level, the software considered 1,832 sequences with an average length of 440 bp. Overall, four different species were identified, of which B. meristosporus represented 80% (1,466/1,832) of the sequences, Malassezia globosa 7. 2% (1,32/1,832), Malassezia restricta 6. 8% (124/1,832), and Candida zeylanoides 6% (110/1,832), with an average homology of 98. 8, 98. 7, 99. 0, and 99. 0% respectively. Among the B. meristosporus sequences, four haplotypes were found and considered as representative sequences (NCBI Accession number: KM246888, KM246889, KM246890, KM246892) to construct a Neighbor Joining tree with all Basidiobolus spp. references that are available on NCBI.
Acknowledgements The authors thank  Dr. De Scheerder ,  Dr. Temmerman ,  Dr. Buylaert ,  Dr Walter ,  Dr. Verfaillie , and  Dr. Fretin  for their support. Gratitude is also due to  R. Bakinahe ,  P. Vannoorenberghe ,  D. Desqueper ,  F. Zinini ,  N. Armatys ,  G. Rey  and the laboratory staff of the Bacterial diseases in livestock unit of the  CODA-CERVA, Brussels , and the  National Reference Center for Leptospirosis, Paris , for their valuable technical support. Authors are also grateful to the  French Ministry of Agriculture  for the trace-back investigation of the rodents. This work was possible thanks to the collaborative attitude of patients.
Farm and riding school sampling Our study aimed to evaluate drug efficacy for three drug classes and if possible, to have a control group. Therefore, we aimed to build four groups of at least five horses with a minimal faecal egg count (FEC) of 150 eggs per gram as recommended by the World Association for the Advancement of Veterinary Parasitology, WAAVP guidelines  (Coles et al. , 1992). To reach this number of infected individuals, bigger stud farms, i. e. with at least 20 producing mares, were pre-selected from the French Horse and Riding Institute (IFCE) database, in Normandy, Loire Valley, Aquitaine and Burgundy. Two additional criteria were defined to increase the chance of finding horses with sufficient faecal egg excretion load to undertake FEC reduction test. First, premises with less than 40 horses were discarded.
In that case of method 1, the time-matched control group is used to account for variation in FEC between the two sampling timepoints independent of treatment. Therefore, blocks of individual FEC before and after treatment were sampled with replacement from horses belonging to the treated or the control group within a farm. In both cases, computation was performed 10,000 times to yield the empirical distribution of the FECR from which 2. 5 and 97. 5% percentiles were sampled to derive the 95% confidence interval. Pearson's correlations between FECR of the two methods were estimated using observations from the 24 premises where a control group was available using the rcorr() function of the Hmisc package v4. 0-3  (Harrell and Dupont, 2017).
Dimensionality reduction by multiple correspondance analysis (MCA) While the discarding of strongly correlated variables addressed the most problematic collinear relationships, significant correlations were still present between the 18 remaining variables (Supplementary data 2). Remaining variables were related to the use of anthelmintics, the use of pasture, or the general constraints applying to the premise (work load measured by the number of horses per employee, and annual veterinary expenses per horse). For each of these three categories, a multiple correspondence analysis was performed on the dedicated set of variables with the FactoMineR v. 136 package  (Lê et al. , 2008). Premise coordinates on the first two components of the MCA were subsequently used to define three-level (premise constraints variable) or four-level variables (anthelmintics and pasture usage).
Marginal modelling of drug efficacy A marginal modelling approach of individual horse faecal egg count reduction rate (FECR) was applied as outlined elsewhere  (Walker et al. , 2014)  and implemented in R (R Core Team, 2016) with the geepack package v1. 2-1  (Højsgaard et al. , 2006). In that framework, individual egg counts measured at a given time, before or after treatment, are assumed to be Poisson distributed and thus modelled with a log-linear regression model. This model includes environmental variables (premise type, region, and the three variables built from the MCA) interacting with a binary variable coding for the treatment, i. e. taking value of 1 after treatment or 0 before treatment. This variable accounts for the treatment-mediated change in egg count reduction, hence estimating FECRs, while the fitted interactions estimate the contribution of considered environmental conditions to FECRs  (Crellen et al.
, 2016). Exponentiated estimates therefore provide the relative risk of increased (relative risk above one)/decreased (relative risk smaller than one) FECRs associated with a given environmental variable. Any variable whose relative risk confidence interval does not include one is declared as significantly impacting on the FECRs. A forward-backward procedure was implemented with the stepAIC() function of the R MASS package v7. 3-47 to select for the model minimizing the Akaike Information Criterion. The model aimed to quantify universal risk factors, i. e. considered environmental effects across drug class. Treatment group was thus added into the model. Drug class-specific analyses based on individual treatment group data taken separately did not provide reliable results and were not reported.
Results of anthelmintic efficacy tests This design provided enough resolution to detect prevalence as low as 1%, with precision of 0. 05 and assuming FEC sensitivity of 70% and specificity of 90%. A total of 688 horses from 39 premises were sampled at least once during this experiment. Out of these, 601 horses excreting more than 150 epg before treatment were enrolled for the anthelmintic resistance test (Table  1 ). Control groups were available in 24 out of the 39 retained farms (Table  1 ). Average FEC before treatment was 912 ± 762 epg (Supplementary data 3). Estimated Faecal Egg Count Reduction (FECR) and associated variation have been reported in Table  1  while premise-level estimated FECR have been attached as Supplementary data 4. FECRs measured in the 24 sites where control group was available showed highly consistent results between the two implemented calculation methods for ivermectin and fenbendazole (Pearson's correlation coefficient of 100 and 82%, respectively).
The effect of manure spreading in the context of drug resistance remains uncertain. It could simply correlate with the available grazing area and mirror the stocking density but this correlation was not significant in this dataset (r = -0. 28, p = 0. 09). Interestingly, drug resistance was significantly reduced in sites implementing a combination of FEC-based drenching programs, determination of drug dosage on an individual basis and a high level of biosecurity, i. e. little horse movement combined with a qurarantine and drenching upon horse introduction. Evidence-based drenching and individual drug dosage have long been advocated for in ruminant and equine systems as a sustainable parasite management practices, as the former is thought to reduce selection pressure  (Kenyon et al. , 2009)  and the latter is thought to prevent under dosing whose impact on drug resistance development relies on many parameters  (Smith et al.
Acknowledgements Authors are greatly indebted to the premise managers who took part to this study and gave us access to their stud or riding schools. We would like to acknowledge  Pr. C. Chartier  for kindly granting access to his parasitology laboratory,  Dr. C. Laugier  for discussing the questionnaire and  Dr. L. Crespin  for critical comments on this manuscript. We also would like to acknowledge the technical support of  Dr. C. Charvet ,  C. Koch  and  C. Musset  during this survey, and  J. Noonan ,  A. Tracey  and  P. Driguez  for their review of the English language. This work has been funded by the  French Institute for Horse and Horse Riding (IFCE)  and the  Fonds Eperon  fund for horse racing ( BIOREQUI  project, Appel à projets de la filière équine 2015), as well as the  Merial and Zoetis companies.
Statistical analyses Data from parasite tests were recorded on Excel 2010 (Microsoft corporation) and transformed to log (x + 1) before being subjected to a variance analysis with SAS software 2010, Version 6. 20. 4. IC 50 and LC 50 were calculated by probit analysis. IC 50 and LC 50 were considered significantly different when the 95% LC fails to overlap  [60]. Comparison of averages (expressed as mean ± standard error of mean) was performed using the Kruskal-Wallis test at 0. 05.
g. TMV  (Werkgroep 1991) , NRC (2012) and InraPorc  (Dourmad et al. , 2008; van Milgen et al. , 2008) ). For example, the InraPorc software summarizes the phenotypic potential of a growing pig (i. e. growth potential and ad libitum feed intake) by five model parameters. These parameters can be estimated through a statistical routine using on-farm recorded data of body weight and feed intake. Tools such as InraPorc can be used to estimate nutrient and energy requirements and utilization, evaluate the consequence of different feeding strategies, and identify feeding strategies that allow one to improve performance traits such as feed efficiency. An example of the change in nutrient and energy utilization during growth obtained from InraPorc is given in Fig. 1  for the utilization of digestible energy (Fig.