dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of the sampling assumptions and analytical methods There was no significant difference between the observed efficacy from the 80 trials as calculated from the grouped and unscreened datasets, although the additional pre-treatment data reduced the uncertainty in the true reduction. This was reflected in smaller 95% confidence intervals and therefore fewer datasets classified as inconclusive for the grouped data compared to the unscreened data (one fewer for Markov chain Monte Carlo and five fewer for the bootstrap method). However, the screened datasets significantly over-estimated the efficacy (compared to the grouped datasets) by an average of 2. 4% over the 80 trials, and for the bootstrap method in particular, were also associated with a tendency to classify trials as efficacious or inconclusive rather than inconclusive or confirmed anthelmintic resistance.
Each of the 25 trials with 100% observed reductions were classified as inconclusive by the Markov chain Monte Carlo method using screened, unscreened and grouped datasets, indicating that the non-parametric bootstrap classification of efficacious produced by the nonparametric bootstrap is not reliable for these datasets. Based on the grouped data from the remaining 55 trials with observed reductions of less than 100%, 19 were classified as confirmed anthelmintic resistance by both Markov chain Monte Carlo and bootstrap, 26 as inconclusive by both methods, 3 as inconclusive by bootstrap but confirmed anthelmintic resistance by Markov chain Monte Carlo, and 7 as efficacious by bootstrap but inconclusive by Markov chain Monte Carlo. Full results of the comparison between the analytical methods are given in the Supplementary data file.
The sample group sizes are in line with previous studies in Europe  (Demeler et al. , 2009; Areskog et al. , 2013) , and with the WAAVP guidance on anthelmintic resistance evaluation  (Coles et al. , 1992). However, larger group sizes are known to increase the accuracy of the efficacy evaluation, which has been noted by several authors using more sophisticated methods of analysis than those recommended in the current WAAVP guidelines  (Gill et al. , 1986; Denwood et al. , 2010; Levecke et al. , 2012). Moxidectin was found to be efficacious on 20 and ivermectin on 16 out of 40 farms (including the datasets with 100% reduction). In all countries except Italy, a high number of farms with decreased efficacy were observed (ivermectin; n ¼ 24 and moxidectin; n ¼ 20). Some of these cases with decreased efficacy were listed as inconclusive, as it is not possible to classify them as fully effective or as confirmed anthelmintic resistance based on the available faecal egg count data.
3 35. 0 95. 0 INC FR05 MOX 8 168 15 465 0 0 0 0 100 100. 0 100. 0 EFF 0 100 ND IVM 7 216 15 735 2 9 0 45 95. 2 82. 5 100. 0 INC ND FR06 MOX 9 110 15 315 6 47 0 135 52. 6 À12. 1 86. 1 CAR 6 94 0 100 IVM 9 138 15 600 3 37 0 210 68. 5 2. 5 98. 1 INC 0 100 FR08 MOX 9 137 30 420 0 0 0 0 100 100. 0 100. 0 EFF 12 88 ND IVM 9 170 30 465 0 0 0 0 100 100. 0 100. 0 EFF ND FR09 MOX 10 74 15 165 6 21 0 45 70. 1 43. 9 87. 7 CAR NL 0 100 IVM 10 176 15 1125 1 2 0 15 98. 8 94. 3 100. 0 EFF 6 94 FR11 MOX 9 75 15 210 2 5 0 30 92. 7 78. 9 100. 0 INC 5 95 NL IVM 8 84 15 240 0 0 0 0 100 100. 0 100. 0 EFF FR12 MOX 10 167 60 345 8 50 0 195 68. 8 39. 6 88. 7 CAR 0 100 0 100 IVM 10 161 75 345 5 36 0 120 77. 3 57. 6 94. 1 CAR 0 100 ND ¼ Cooperia spp Ostertagia spp Farm IVM MOX IVM MOX GE01 63% 61% 99% 98% GE02 95% 96% GE03 69% 100% GE04 100% 93% 100% 100% GE05 95% 98% 100% 100% GE06 95% 74% 99% 95% GE07 93% 90% 30% 85% GE08 À284% 92% GE09 92% 99% GE10 96% 99% GE11 93% 84% 99% 92% GE12 36% 94% 71% 98% UK02 60% 86% 100% 83% UK07 98% 93% 100% 100% UK08 46% 83% 98% 98% UK10 99% 97% 100% 100% UK11 90% 98% 84% 96% UK12 73% 89% 95% 99% UK13 85% 96% 74% 97% UK14 12% 87% 100% 92% FR06 71% 55% 100% 100% FR12 66% 70% 100% 100% <95% 14/19 13/19 5/20 5/20.
Parasitological data Once gastrointestinal tracts were defrosted, the abomasum, and small and large intestine were longitudinally opened (n = 84), the mucosa scrapped and the content washed and filtered through three sieves of 6. 3, 3. 2 and 0. 3 mm, respectively. The content was diluted in 1000 ml of tap water in a sedimentation cup and three aliquots of 100 ml (10%) each were examined to collect parasites. Male nematodes were cleared in lactophenol and Cestodes stained in Semichon's carmine and later identified.
Acknowledgements E. Serrano was supported by the  postdoctoral program  ( SFRH/BPD/96637/2013 ) of the Fundação para a Ciência ea Tecnologia, Portugal. This study was partly funded by the  scientific program  " Approche Intégrée de la Démographie des Populations d'Isard " sponsored by the  ONCFS  and the  SEFaS. Author details 1 Departamento de Sanidad Animal ,  Facultad de Veterinaria ,  Universidad de Murcia, Murcia, Spain. 2 Servei d'Ecopatologia de Fauna Salvatge (SEFaS) ,  Wildlife Health Service -Departament de Medicina i Cirugia Animal ,  Universitat Autònoma de Barcelona, Bellaterra, Spain. 3  Departament de Sanitat i d'Anatomia Animals, Bellaterra ,  Universitat Autònoma de Barcelona (UAB) ,  Bellaterra, Spain. 4  Centre National d'Études et de Recherche Appliquée Faune de Montagne, Office National de la Chasse et de la Faune Sauvage (ONCFS) ,  Juvignac, France.
To our knowledge, less it is known on the development of the neonatal pig. Data on milk composi-2 sows used to farrow in outside huts, in this study farrowing took place in indoor farrowing houses due to experimental purposes (piglets weighing, colostrum collection, piglet bleeding). All farrowing rooms had an ambient temperature between 20ºC and 23ºC. Piglets were provided with local heating (175 W infrared lamps) in the creep area. Details on gestation and lactation diets and also creep feeding of the piglets are given by  Charneca et al. (2012). Diets were the usual at the farms and were not modified for experimental purposes. Briefly, LL sows were fed twice a day with commercial gestation (14-16% CP; 13-13. 4 MJ DE) and lactation (17-18% CP; 13. 8 MJ DE) diets based on cereals and soybean meal.
Farrowing surveillance, and data and samples collection Gestation lengths were only determined at the E site and at the private LL farm. Three LL sows from the private farm had abnormally long farrowing (> 9h). They were excluded from the analyses. All farrowings were attended but none was induced. Soon after birth, piglets had their umbilical cord cut at about 10-12cm from the navel after which, they were identified, roughly dried, weighed to the nearest 1g (birth weight, BW0), using an electronic balance equipped with an integration system, and returned to the sow at birth place. The birth time was recorded. These operations were performed very quickly, usually within 2-3 min of birth. All piglets were re-weighed at 24h, 21d, and 28d (the private LL farm excepted) after birth. Piglet weight and approximate time of death were also recorded.
Statistical analysis Statistical analyses were made using the IBM SPSS Statistics software  (v. 21, 2012). All analyses were first made within-genotype, comparing production sites. When traits residues were not normally distributed the comparison of means was made by the Mann-Whitney U non-parametric test. Comparison between genotypes of performance of sows and piglets was analysed by analyses of variance (ANOVA). For piglets weight at birth and 21d analyses of covariance (ANCOVA) were performed using litter size of born alive and litter size at 21d, respectively, as covariates. To compare litters of different sizes and to determine whether birth order affected within-litter birth weight of the piglets or CI, the order of the piglets in the birth sequence was expressed as relative piglet birth order (RBO) and were calculated as follows RBO=(Birth order-1)/(Total piglets born-1), with RBO ranging from 0 to 1.
308; p<0. 001) and in LL genotype b CI(g) =0. 22 (±0. 02, SE) BW0 (g) (R 2 =0. 187; p<0. 001) (Fig. 2 ). The relationship between CI and RBO was not different between genotypes (p=0. 462). slopes among genotypes, analyses of covariance (AN-COVA) were performed using the regression independent factor as covariate and evaluating the significance of the interaction between the covariate and genotype. Data on colostrum production of the sows were adjusted by covariance to litter size (LS) and to litter weight (sucking piglets, LWB) using genotype as fixed effect. To determine the effects of parity, gilts and sows were grouped as follow: group 1, parities 1 and 2 (AL=15 sows, LL=20 sows); group 2, parities 3, 4 and 5 (AL=17 sows, LL=19 sows); and group 3, parities higher than 5 (AL=13 sows, LL=6 sows).
These insights allow inferences regarding the patterns of parasite transmission and recruitment within the environment  [9]. Moreover, by using molecular tools, studying the population genetic structure will enable to determine whether changes in gene frequencies provide insight into the effectiveness of treatment, understand the impacts of treatment on the gene pool and population structure of Schistosoma parasites, and establish whether movement of humans from refugia or non-treated areas introduces new parasites into local populations  [10, 11]. A meta-analysis of eight Schistosoma mansoni (two published and six unpublished) microsatellite datasets collected from individual schistosome-infected school-children showed that S. mansoni populations were more diverse in East than West African schools, but heterozygosity levels did not vary significantly with geography  [12].
Data analysis In this study, miracidia from all the patients within a single study site were treated as a population while all miracidia in a single host are treated as infrapopulation. The allele frequencies were calculated using the program MICROSATELLITE TOOLKIT (software available upon request). Both the expected and observed heterozygosities were also calculated using MICROSAT-ELLITE TOOLKIT and their statistical significance tested using the chi-square test at α =0. 05. FSTAT was used to test for deviations from Hardy-Weinberg equilibrium using exact tests, testing the hypothesis that observed diploid genotypes are the product of random union of gametes. An exact test for linkage disequilibrium between pairs of loci was performed using the FSTAT. Mean estimates of F IS (inbreeding coefficient) for each population and pairwise F ST (between all population pairs) were also calculated following the method of Weir and Cockerham  [21].
Deviation of F IS and F ST values from zero was tested using a permutation test. All F statistics were carried out using FSTAT 2. 9. 3. 2  [22]. Isolation By Distance (IBD) was tested correlating genetic distance (Fst/(1-Fst)) and geographic distance in kilometer. The paired t-test is used to compare two sample means where there is a oneto-one correspondence (or pairing) between the samples while Friedman's test was used for ordinal data or an interval-scale variable that is not normally distributed  [23]. Genetic structuration was assessed using both Principal Component Analysis (PCA) using Genetix software  [24]  and bayesian approach using Structure software  [25]. Full-sib analyses was assessed by estimating mean number of genetically unique adult worm pairs and its standard deviation for each patient using Colony software  [26].
1 %, Sille-Elgo and Ziway is 8. 99 %, Wondo Genet and Ziway is 8. 21 % and, Kemissie and Wondo Genet is 3. 8 % (Table  6 ). No link between genetic distance and geographic distance (IBD) is observed (Spearman correlation n = 6, r = 0. 6, p = 0. 24). Spatial genetic structuration or distribution of Schistosoma mansoni isolates in this study was determined by both Principal Component Analyses (PCA) using Genetix software  [24]  and Bayesian approach using Structure software  [25]. The first two axes of the PCA (46. 9 and 34. 9 % of the total variation for principal component 1 and 2, respectively) split miracidia into four groups of points corresponding to the four villages (Fig. 1a ). Only some miracidia from Kemissie are in the Wondo-Genet or Ziway group of points. Each patient could be affected to its own village (Fig.
Study design A cross-sectional study to investigate the awareness and perceptions of zoonoses amongst cattle owners, commodity chain and health workers (both veterinary and medical) was conducted between June 2012 and May 2013. Commodity chain respondents included processors and retailers of meat and milk that were selected from butchery, supermarket and abattoir operators. Since the study was designed to address issues related to awareness perceptions and practices, it was believed (by us) that the inclusion of commodity chain and health workers was vital to study perceptions and practices that may help to mitigate the risk of zoonoses in rural communities. Questionnaires were developed to assess awareness, perceptions, and practices towards zoonoses. To ease data processing, minimize variation, and improve precision of responses, the questionnaires comprised mainly of closed ended (categorical) questions.
To improve quality of data collected, owners or management were interviewed as they were accountable for all activities at their respective outlets. They also made management decisions as to where and how their commodities are distributed in the communities, some information which may not be obtained from a random sample of abattoir or retail shop employees. In the case of medical health workers, especially in the low and medium interface areas, workers were selected randomly from those available. In the high interface areas, due to low numbers, all workers that were available when the rural health centre was visited were interviewed. Preference was given to health centres serving the cattle owners who were interviewed. A total of 32 medical health workers were interviewed. All the study areas had a total of twenty six  (26)  veterinary health workers in post and because they could be easily accessed all these were interviewed, giving a total of 58 health workers recruited for the study.
Statistical analysis Data recording and edits were done using Microsoft Excel®. Statistical analysis was carried out using Stata SE version 11 for windows (Stata Corp. , Texas, USA) to generate descriptive statistics (frequencies/proportions) related to awareness on zoonoses. The data on awareness, perceptions and practices were analyzed with respect to the different groups of the respondents and their areas of origin (0 = high interface; 1 = medium interface; 2 = low interface). For cattle owners, their level of education received (0 = received no high school certificate of education; 1 = received high school certificate of education) was perused according to the areas and the type of religion (0 = traditional; 1 = Christian; 2 = non-believers). Their perceptions (their understanding) on the how the different zooneses were transmitted and the risk factors for their transmission were tabulated.
Fisher's exact Chi-square (χ 2 ) test was used to measure associations between categories and values of P < 0. 05 were considered as significant. Further evaluation of awareness to zoonoses was performed using the principal component analysis of the FactoMineR package  [22] , a package for multivariate data analysis with R  [23]. The variables were projected on planes constructed with axes. Each axis was a linear combination of the variables that accounted for part of the variability of the whole set of data. The relative importance of each component was expressed by variance (eigenvalue) of its projection or by the proportion of the variance expressed. Close proximity or superimposition in the same quadrant of the variable factors (related to awareness, perceptions or practices) and the groups of individual respondents suggested that the individuals and the variables were positively correlated while position on opposite quadrants of the plane suggested a negative correlation.
Acknowledgements This work was conducted within the framework of the  Research Platform "Production and Conservation in Partnership (RP-PCP ). The project was funded by the  French Ministry of Foreign Affairs , through the  French Embassy in Harare, Zimbabwe  ( RP-PCP grants ) for which the principal investigator (B. M. Gadaga) was a recipient. The authors are indebted to the  Department of Livestock and Veterinary Services  and the  Ministry of Health and Child Care  for endorsing the study, the  Chiredzi medical  and veterinary staff for facilitating field work and the respondents for their consent and utmost cooperation.
Experimental population and phenotyping In this study, we used phenotype data collected during a large-scale Eimeria maxima challenge study on Cobb500 broilers  [19]. The challenge study was performed using 2024 Cobb500 broilers randomly distributed in 44 (challenge) and two (control) litter pens (3 m × 1 m) each containing 44 birds. For the GWAS, we used only data collected from the challenged animals, with control animals excluded from the analysis. Traits were measured at two levels: "global phenotyping", which was performed on all animals and "detailed phenotyping", which was performed on a subset of 176 animals. The experimental layout of the challenge is presented in Fig. 1. BWG, HEMA, BT and PC were measured on all animals. BWG was calculated as BWG = (BW at day 22-BW at day 15)/BW at day 15.
Genotype data DNA was extracted from blood samples obtained from 1972 animal samples at day 16 of the experiment. Genotyping was performed using a 580 K Affymetrix ® Axiom ® HD genotyping array (Affymetrix, Santa Clara, USA)  [20]  at a commercial laboratory (GeneSeek, Lincoln, USA). Quality control of the genotype data was performed using PLINK 1. 9  [21] [22] [23]  and included the sample call rate (>98 %), SNP call rate (>98 %), minor allele frequency (>2 %) and removing SNPs with extreme F-statistic values. A total of 138,568 SNPs out of 580,961 were removed during the quality control (See Additional file 1: Table  S1 ). SNP thresholds and sample call rates were set at 98 % and all SNPs and individuals with a call rate less than 98 % were excluded from the analysis. We did not perform imputation of the residual missing genotypes.
The F-statistics distribution for the SNPs that remained after the quality control steps was assessed, and all SNPs outside the adjusted interquartile range were excluded from the analysis. The interquartile range for skewed distribution of F-statistics was calculated as suggested by Hubert and Vandervieren  [24]. Sex was assessed using PLINK 1. 9 by analyzing SNPs on the Z and W chromosomes. Detected females were included in the analysis with sex considered as a covariate (See Additional file 1: Table  S1 ). Finally, after quality control, we obtained a dataset that included 443,587 SNPs distributed along 28 autosomal chromosomes, two linkage groups (LGE22C19W28_E50C23 and LGE64) and chromosome Z. Mean, median and standard deviation for base pair distances between neighboring SNPs across chromosomes are in Table  S2  (See Additional file 2: Table  S2 ).
Expression (2) was used to calculate K where x i is the ith column representing genotypes of the ith SNP in the X matrix, x i is the sample mean, and 1 n is an n × 1 vector of 1s. Residual effects are presented as an ε vector of length n following the multivariate normal distribution (MVN n ) (ε ∼ MVN n (0, τ -1 I n )) where I n is an n × n identity matrix. To correct for multiple hypothesis testing, the false-discovery rate (FDR) was calculated for each SNP from the distribution of p-values: SNPs with an FDR less than 0. 1 were considered significant  [27]. The results are shown in Manhattan plots constructed by the qqman R package  [28].
Assigning SNPs to genes and biological pathways The SNPs used in the GWAS were mapped to the ICGSC Gallus_gallus-4. 0 chicken genome assembly (GCA_000002315. 2) using the NCBI2R R package  [29]. A list of annotated genes corresponding to the SNPs used in the genome-wide analysis was retrieved. For this study, the publicly available biological KEGG pathways were downloaded using the Bioconductor KEGGREST (2) K = 1 p p t=1 (x i -1 n x i )(x i -1 n x i ) T. package  [30] , and the curated genes were assigned to specific pathways. The biological KEGG pathways represent a collection of biological pathway maps that integrate many units, including genes, proteins, RNAs, chemical compounds, and chemical reactions, as well as disease genes and drug targets, which are stored as individual entries in other KEGG databases.
Network-based analysis The network modeling was performed using the ingenuity pathway analysis (IPA) tool as a complementary approach to the KEGG biological pathway analysis. The ingenuity pathways database is the manually curated database of previously published relationships on human and mouse biology  [34]. The gene input list included genes that contained SNPs with p values below the inferred genome-wide threshold (P < 10 -4 ) for all traits on which GWAS was performed (See Additional file 4: Table  S4 ). In this manner, we restricted the list of putative candidate genes and exploited their documented interactions in biological pathways related to the study. The lists of SNPs (P < 10 -4 ) were assigned to their respective genes using the biomaRt R package  [35] , producing the gene list for each trait used in the GWAS.
Experimental population and phenotyping In this study, we analyzed the data collected during the large-scale challenge study on Cobb500 broilers with E. maxima as the infective agent  [19]. The experimental scheme of the large-scale challenge study is illustrated in Fig. 1. The traits were measured at two levels: "global phenotyping" performed on all 1936 challenged animals and "detailed phenotyping" performed on a subset of 176 animals (Fig. 1 ). To form the subset of 176 animals, two animals among those with the lowest and two animals among those with the highest body weight gain (BWG) were selected from each pen containing challenged animals. The traits measured on all challenged animals included BWG, plasma coloration (PC) measured as optical density (OD) of blood plasma in the 380-600 nm range, body temperature (BT) and hematocrit level (HEMA).
Biological pathway analysis To conduct the biological pathway analysis, we used all SNPs that were included in the GWAS. These SNPs were mapped to their corresponding genes using the latest genome assembly. This list of SNPs and their corresponding genes was combined with the publicly available biological KEGG pathway database. Finally, we obtained a list containing 52,204 SNPs that were included in the GWAS; these were assigned to 162 of the biological pathways associated with 4342 genes in the chicken genome KEGG PATHWAY repository (see "Methods"). The list of KEGG pathways which were significantly (P < 0. 05) enriched with genes in genomic regions associated with the measured traits is in Table  S7  (See Additional file 10: Table  S7 ). The distributions of the most frequent significant biological pathways differed considerably according to whether all measured traits or all measured traits except PC measurements were considered (See Additional file 11: Figure  S4 ).
However, we have to be aware that the biological pathway analysis should still be primarily viewed as an exploratory technique because the current statistical methodologies used for gene set/pathway analysis need further development  [61]. Moreover, the available biological pathway databases necessary for this kind of analysis are not completely annotated and do not contain all the genes present in the chicken genome. In this study, we used a statistical modeling methodology that assesses the cumulative effect of sets of SNPs on the measured traits as presented by Jensen et al. [31]  and Buitenhuis et al. [62]. For this purpose, we succeeded in assigning 52,204 SNPs from GWAS to 4342 annotated genes in the chicken genome. For PC, the top four most frequently affected pathways across all wavelengths include phenylalanine, tyrosine and tryptophan biosynthesis, endocytosis, purine metabolism, and glycosphingolipid biosynthesis-lacto and neolacto series (Fig.