dataesr/scientific-paragraphs-categorization huggingface dataset:
Confounding effects were evaluated during the modelling process by checking changes in parameter estimates. The association in the multivariable linear regression model was considered significant if p < 0. 05 and a p-value between 0. 05 and 0. 10 was considered nearly significant and relevant to describe. Normal probability tests and plots were examined to check whether the assumptions of normality and homoscedasticity of residuals were fulfilled. All statistical analyses were performed using SPSS statistics 22 (IBM). All tested variables can be found in Table  1.
Study design and limitations Only a limited number of studies have investigated the associations between production parameters, other herd characteristics and antimicrobial usage  [20]. A recent review of Aarestrup  [10]  emphasizes the need for research on effects of interventions. The current study attempts to provide a first overview of the associations between production parameters, preventive measures such as high biosecurity status and vaccination level and herd and management characteristics with the level of antimicrobial usage. Knowledge on these associations might be used as input for future intervention studies. We should however, be aware that this study is likely influenced by the fact that the participating farmers resembled the better end of the population since their selection was based on willingness to participate (except in France where random sampling was used, by selecting herds from the database of Institute for pig and pork industry including on average 53 % of herds located in North-West France with >49 sows) and interest in the topic, resulting in a selection bias.
Acknowledgements The MINAPIG consortium would like to thank all participating farmers, their employees and herd veterinarians for their cooperation. The authors would also like to thank the other MINAPIG consortium partners for their help in developing the protocol for this study. Members of the MINAPIG consortium in alphabetical order:  Margit Andreasen ,  Boehringer Ingelheim, Denmark ;  Annette Backhans ,  SLU, Sweden ;  Catherine Belloc , ONIRIS, France;  Lucie Collineau , SAFOSO, Switzerland;  Jeroen Dewulf ,  Ghent University, Belgium ;  Ulf Emanuelson ,  SLU, Sweden; Elisabeth grosse Beilage, TiHo Hannover, Germany ;  Bernd Grosse Liesner ,  Boehringer Ingelheim, Germany ;  Christian Alexander Körk ,  Boehringer Ingelheim, Germany ;  Ann Lindberg ,  SVA, Sweden ;  Svenja Lösken ,  TiHo Hannover ,  Germany ;  Merel Postma ,  Ghent University, Belgium ;  Hugo Seemer ,  Boehringer Ingelheim, Germany ;  Marie Sjölund ,  SVA  and  SLU, Sweden ;  Katharina Stärk , SAFOSO, Switzerland;  Vivianne Visschers ,  ETHZ, Switzerland.
The animal handling and other procedures were in accordance with the current European (directive 86/609/EEC) and Spanish (RD 53/2013) legislations, supervised by the Animal Welfare Committee of the University of Navarra (CEEA 045/12). Inocula were prepared in sterile 10 mM PBS (pH 6. 85), and ϳ5 ϫ 10 4 cfu in 0. 1 ml (the exact dose was assessed retrospectively by plating dilutions of the inocula) were administered intraperitoneally to each mouse. For each strain, 10 mice were inoculated, and the number of cfu in spleen was determined at 2 and 8 weeks after inoculation as described previously  (37). The individual data were normalized by logarithmic transformation. Flow Cytometry-To analyze activation and maturation, BMDCs were analyzed for surface expression of classical mat-uration markers at 24 h post-treatment with the different LPS variants.
Cells were labeled with fluorochrome-conjugated antibodies specific for mouse CD11c:PE-Cy7 (clone N418), IA-IE:PE (MHC class II clone M5/114. 15. 2) (PE), CD86:FITC (Clone GL-1), CD40:AlexaFluor 647 (clone 3/23), and CD80: PE-Cy5 (clone 16-10A1), all from BioLegend. Labeled cells were then subjected to multicolor cytometry using an LSR II UV spectrophotometer (BD Biosciences), and the data were analyzed using FlowJo software by first gating on the CD11c ϩ population (100,000 events) prior to quantifying expression of receptors. Cells were stimulated with 10 g⅐ml Ϫ1 of LPS puri- fied from the B. melitensis wild type or Bm_wadC mutant strain or with Escherichia coli LPS (O55:B5) as a positive control. Cytokine Measurement-Murine IL-6, IL-12p70, and TNF-␣ were quantified in culture supernatants of stimulated BMDC by sandwich enzyme-linked immunosorbent assays (ELISA) according to the manufacturer's protocol (eBioscience).
-D-* This work was supported by grants from the  Swedish Research Council , the  Knut and Alice Wallenberg Foundation , and  Ministerio de Economía y Competitividad  Grants  AGL2011-30453-C04  and  AGL2014-58795-C4-1-R ; the research that led to these results received funding from the  European Commission 's  Seventh Framework Programme   FP7/2007-2013  under Grant Agreement 215536. The authors declare that they have no conflicts of interest with the contents of this article. Author's Choice-Final version free via Creative Commons CC-BY license.
8 kb; GϩC content of 54. 9%). The genome was annotated using MAKER2  (12)  and Evidence-Modeler (13) (predictions with long introns were removed). Gene predictors SNAP and Augustus were trained using Chaetomium globosum. Protein and expressed sequence tag sequences of eight fungi (six in the order of Sordariales) were used as homology evidence. Functional annotation was done using BLASTp (  14 ) against the Swiss-Prot and TrEMBL databases  (15). Protein domains, Pfam su-perfamily IDs, and gene-ontology terms were assigned using Inter-ProScan  (16). The draft genome contains 10,707 protein-coding genes. CEGMA  (17)  and BUSCO  (18)  showed that 89% of core eukaryotic genes are completely present in the assembly, and OrthoFinder  (19)  indicated that 93% of the predicted genes have orthologs in other species.
The genomic data generated provide a potential leap forward in our comprehension of the biology and pathogenic mechanisms underlying human eumycetoma and will help our understanding of the formation of fungal grains. By comparing the genome to closely related fungi not causing mycetoma, proteins can be identified that could play a role in the development of the mycetoma grain. Furthermore, this sequence offers a rich resource for the identification of M. mycetomatis-specific proteins, which could be used to develop novel diagnostic tools or which could serve as novel drug targets. The latter is especially important, since the current treatment is often limited to azole treatment and amputation. Nucleotide sequence accession numbers. This annotated genome (version 2. 0) has been deposited at DDBJ/EMBL/GenBank under the accession number LCTW00000000, BioProject PRJNA267680.
5 µl of mRNA diluted 1/20 as negative control for the specific exon junctions amplifications. Alpha-tubulin (α-tub) was used as reference. Primer sequences are listed in Additional file 4. The following protocol was used: denaturation, 95 °C 10 min, amplification and quantification (45 cycles), 95 °C for 10 s, 60 °C for 5 s, 72 °C for 20 s; melting curve, 65-97 °C with a heating rate of 0. 11 C/s and continuous fluorescence measurement, and cooling step to 40 °C. For each reaction, the crossing point cycle threshold (Ct) was determined using the "second derivate" method of the LightCycler ® 480 Software release 1. 5. 0. Reactions were carried out in duplicate then the mean Ct was calculated. The amplification of a unique band of each locus was verified by size separation on a LabChip GX capillary electrophoretic system.
We identified 3 SNPs between SmBRE F0 and GH2 F0 for SmPoMuc 3. 1(r1r2) promoter sequence and 6 SNPs for Smp_171100 sequence using Sequencer software. Primers including SNPs for the two loci were designed, and the regions were amplified by qPCR. For the SmPoMuc 3. 1(r1r2) promoter, qPCR was applied on a 9 kb PCR product containing the 2-kb region upstream of the transcription start site (TSS)  [21] , and for Smp_171100 sequence, qPCR was applied directly on genomic DNA for SmBRE F0, SmGH2 F0, F1, F2 and F3 miracidial population. qPCR products were sequenced using Eurogentec facilities (http://www. eurogentec. com/life-science. html). We checked and aligned nucleotide sequences manually using Sequencer and BioEdit softwares.
Acknowledgements We would like to thank  Benjamin Gourbal ,  Claudine Junien  and  Richard Galinier  for fruitful discussions. We thank also  Anne Rognon  and late  Bernard Dejean  for providing valuable technical support. Special thanks go to  G. Olivieira  ( Instituto Nacional de Ciência e Tecnologia em Doenças Tropicais, Fundacao Oswaldo Cruz, Brazil ) for the generous gift BgBAR / SmLE strains (SIBIO license 42195). This work is dedicated to our friend and colleague  Jérôme Buard  who passed away during the writing of this manuscript.
The fungal culture was sent to the Laboratorio Corpogen Corporation for molecular identification. This was performed by amplification and sequencing of the ITS and 5. 8S RNA of the nuclear rDNA by PCR using the primers ITS1 (5 0 -TCC GTA GGT GAA CCT GCG G-3 0 ), ITS4 (5 0 -TCC TCC GCT TAT TGA TAT GC-3 0 ), and ITS 5 (5 0 -GGAAGTAAAAGTCGTAACAAGG-3 0 ). The 768-bp sequencing product was compared with the GenBank database using BLASTN (National Center for Biotechnology Information (NCBI)) and UNITE. It showed 98. 2% homology of its full-length sequence with the species S. erythrospora from the family Saksenaeaceae (Figure  2  ). The sequence obtained in this study was submitted to GenBank under accession number KU951560.
Animals No vertebrate animals were used directly in the study. Adult female A. suum worms were collected from Marshalltown Pork These Tables list key protein targets and ligands in this article which are hyperlinked to corresponding entries in http://www. guidetopharmacology. org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY  (Southan et al. , 2016)  or the PubChem or Wormbase databases. Entries in the IUPHAR database are permanently archived in the Concise Guide to PHARMACOLOGY 2015/16  (Alexander et al. , 2015).
Sequence analysis For convenience, the sources of the AChR subunits are indicated, in this article, by three letter prefixes. Thus, Ace, Asu, Cel, Hco, Llo, Nam, Sra, Tca and Xle, refer to Ancylostoma ceylanicum, Ascaris suum, Caenorhabditis elegans, Haemonchus contortus, Loa loa, Necator americanus, Strongyloides ratti, Toxocara canis and Xenopus laevis respectively. Database searches around the sequence of Asu-ACR-16 were performed with the BLAST Network Service (NCBI), using the protein-protein BLAST (BLASTP) algorithm  (Altschul et al. , 1997). Signal peptide predictions were carried out using the SignalP 4. 0 server  (Bendtsen et al. , 2004) , and membrane-spanning regions were predicted using TMpred  (Hofmann and Stoffel, 1993). Distance trees were generated on the full-length deduced amino acid sequences aligned with the MUSCLE programme  (Edgar, 2004).
Data and statistical analysis The data and statistical analysis comply with the recommendations on experimental design and analysis in pharmacology  (Curtis et al. , 2015). The acquired data from electrophysiological recordings were analysed with Clampfit 9. 2 (Molecular Devices, Sunnyvale, CA, USA) and GraphPad Prism 5. 0 (Graphpad Software Inc. , La Jolla, CA, USA). In all recordings, the peak currents in response to applied drugs were measured, and except where otherwise indicated, peak current values obtained were normalized to 100 μM ACh and expressed as mean ± SEM. Dose-response relationships were analysed by fitting log dose-response data points with the Hill equation as previously described  (Boulin et al. , 2008) , while desensitization kinetics in response to the potent agonists were fitted using a single exponential decay fit: f t ð Þ ¼ ∑ n i¼1 A i e À t τ iþC = where n is the number of components, A the amplitude, t the time, Ʈ the time constant and C the constant y-offset for each i component.
Identification of Asu-ACR-16 sequence We used the Cel-ACR-16 sequence (Supporting Information Fig. S1 ) as a query in a BLASTP search in protein databases from nematodes which allowed the identification of a potential complete coding sequence of an ACR-16 homologue in the pig parasite Asu (ERG81952. 1). RT-PCR experiments using primers designed using this predicted sequence led to the amplification of a full coding sequence (Figure  1A ), which was submitted to GenBank under the accession number KP756901. When we used the Asu-acr-16 transcript sequence as a query, a BLASTP search resulted in the identification of highly conserved (complete or partial) homologous sequences from other parasitic nematode species belonging to Clade III (T. canis and L. loa), Clade IV (S. ratti) and Clade V (H.
Acknowledgements We would like to express our gratitude to  Debra J Woods ,  Zoetis Animal Health, Kalamazoo, MI, USA , for the generous supply of derquantel. We would also like to thank  Professor Shu Hua Xiao ,  National Institute of Parasitic Diseases, Shanghai, Peoples' Republic of China , for the gift of tribendimidine. Research funding was by The  Hatch Act, State of Iowa ;  NIH  grant  R01 AI047194   National Institute of Allergy and Infectious Diseases  to R. J. M. ;  NIH  grant  R21AI092185   National Institute of Allergy and Infectious Diseases  to A. P. R. ; and the  Schlumberger "Faculty for the Future"  programme to M. A. and A. P. R. The funding agencies had no role in the design, execution or publication of this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Allergy and Infectious Diseases.
All data files were compiled and maintained using an open-source office suite (LibreOf-fice5). Because transmission electron microscopy (TEM) is considered the gold standard for the identification of CNP, TEM was performed on the in vitro synthesized CNP stock solutions as described above. TEM studies were also performed to identify CNP formed in vivo in the urine of healthy, colony cats. In each case 5 microliters of samples were applied to a coated copper mesh and excess fluid wicked away. After complete air-drying, each prepared mesh was inspected with electron microscopy at 80KV.
Methods and Findings Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson's correlation between observed and estimated prevalence at validation locations was 0. 693; the mean prediction error (average difference between observed and estimated values) was 0.
Onchocerciasis Prevalence Data Relevant survey data were extracted from the OCP epidemiological database, which contains 5,816 surveys conducted in 2,581 (geo-referenced) villages across all operational phases of the OCP in the 11 participating countries in West Africa (see Fig A within S1 File for locations of all village sites in the OCP epidemiological database). There were 9 operational phases (summarised in Table  1  and depicted in Fig 1B ). A total of 737 villages had parasitological surveys that met the inclusion (pre-control) criteria (see S1 File Text A 'Data description and pre-control data selection criteria  ' and Fig B therein). Prevalence ranged between 0 and 94. 8% with a mean of 51. 7% and a median of 56. 4%. With the exception of one village, numbers examined per village ranged from 23 to 828 with a median of 171.
Climatic and Environmental Data An ensemble of explanatory environmental and climatic covariates which were deemed useful in the estimation of microfilarial prevalence was identified building on the methodology presented in a previous mapping study for another filarial infection  [36]. Initial inclusion was broad and partly based on knowledge of parasite and vector ecology (e. g. distance of villages to fast flowing rivers), as well as variables considered to affect their distribution and life cycle (e. g. climatic data). Climatic data were extracted from WorldClim Global Climate Data, http:// www. worldclim. org [43]. Hydrologically-derived variables were extracted and calculated from the Hydro1K database  [44, 45] , and an alternative set of hydrologically-derived variables was extracted from the Rivers of Africa dataset (derived from HydroSheds)  [46] , which includes alternative metadata about the stream lines.
Normalized Difference Vegetation Index (NDVI) data were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset for the years 1981 to 1986  [47] [48] [49]  (the earliest data available in this dataset). Data layers, generated from the MODIS instrument on board the Terra satellites, were also used. These layers included land surface temperature (day and night), land cover classification, NDVI and enhanced vegetation index (EVI)  [49] [50] [51] [52] [53]. A layer of vegetation type based on physiognomy and floristic composition from White's Vegetation Map of Africa was also extracted and used  [54]. We initially tested layers for altitude derived from digital elevation models (DEMs) from different mapping projects, which were extracted from the Global 30 Arc-Second Elevation (GTOPO30) DEM  [55, 56]  and the Global Land One-km Base Elevation Project (GLOBE)  [57].
Temporal Trends in the Data Although the baseline survey data used in this study were collected over a period  of 16 years (1974-1990) , exploratory inspection of pre-control prevalence boxplots by year showed that there was no significant temporal trend. It was, therefore, deemed unnecessary to include the effect of time as these data were assumed to be representative of the pre-control endemic equilibrium situation at the beginning of each OCP phase and commencement of intervention by river basin. Time-varying changes in the environmental covariates were also treated as negligible. The WorldClim dataset uses interpolated data averaged over 50 years, whilst the MODIS data, representing 10-day means, were averaged by ecological quarter (1st: Dec-Feb, 2nd: Mar-May, 3rd: Jun-Aug, 4th: Sep-Nov) across the entire duration of available data at the time of analysis (February 2000-July 2013).
Spatial Dependence in the Data and Model-Based Geostatistical Analysis A geostatistical model (used to develop a continuous map of pre-control microfilarial prevalence across the OCP area) is simply a Generalised Linear Mixed Model (GLMM) with environmental covariates to account for the fixed effects, a spatial term that accounts for the spatial correlation in the data, and a random effect term that accounts for the non-spatial variation in the data, yielding a Generalised Linear Spatial Model (GLSM). If we assume that the number of individuals found positive for O. volvulus microfilariae at location x i is Y i out of the total number of people examined by skin snip at such location, n i , then Y i is a binomial random variable, Y i * Bin(n i , p i ) where p i is the proportion of individuals positive for skin microfilariae at location i.
An exploratory tool widely used in geostatistics is the semivariogram  [58]. Under the assumption of spatial dependence, there is a relationship between the variance of the difference Environmental covariate layers for Log 10 distance to river and distance to river were derived from these vector shapefiles. 2 Multiple digital elevation models from these sources were tested and the best performing variables were selected for further testing. See S1 File for details. 3 This dataset is currently unavailable from the Global Land Cover Facility website. Second, we used these in the GLSM to fit a model of linear predictors (the environmental variables as fixed effects), conditional on some stochastic process S(Á), i. e. , the unobserved, underlying spatial process (which enters the model as a location-specific random offset in the linear predictor).
We denote the vector of j = 1,. , p regression parameters as β and the parameters of the correlation function as θ. We can use this model specification and the data to make predictions about locations of interest which may not have been sampled, but for which values of the regression coefficients are available (environmental covariates). Continuing with the notation of  [27] , we may specify a vector of prediction at target locations, T = T(S Ã ), where (See  [27]  for further details and extensions to this framework. ) The modelling framework was implemented using the geostatistical modelling software from the package geoRglm (Version 0. 9-2)  [59]  of the R statistical environment for computing (Version 3. 0. 1) [60]. SÃ ¼ Sðx Ã 1 Þ;. ; Sðx Ã m Þ.
Initial Selection of Environmental/Climatic Covariates A systematic Bayesian step-wise procedure was used to select the best set of uncorrelated predictor covariates for use in the geostatistical model. This method builds upon that of Austin and Tu  [61]  by fitting univariate Bayesian GLSM models for both linear and quadratic functional forms of each of the initial predictor covariates. We tested model fit by means of a Kfolds cross validation, with K = 1,. , 10. The data were randomly partitioned into a training set, consisting of approximately 90% of the data (N T K = 663), whilst the remaining 10% (N V K = 74) were held-out and used in the validation exercises. In each K-fold a different set of input data points was held out, such that each of the 737 data points appeared in one and only one of the K-fold validation data set.
For this reason, N V K¼10 comprised 71 data points and N T K¼10 666 training data points (see Text E 'K-folds model validation' and Fig G in S1 File for locations of data points in each K-fold). Each model was then independently fitted against the 10 training datasets. The Deviance Information Criterion (DIC), which is a Bayesian measure of model fit  [62] , was used to compare how well each covariate was able to estimate prevalence at a set of held-out validation locations. Ten datasets were chosen as a compromise between number of bootstraps and computational time. For both linear and quadratic functional forms of all of the input covariates, using each bootstrap dataset in turn we calculated the DIC for that model. For each covariate, the functional form yielding the lowest DIC was used to select either linear or quadratic forms for further evaluation.
The upper and lower limits of support were set to ensure exploration of a wide prior parameter space. The nugget variance, τ 2 -not a parameter estimated by the model-was specified as a relative nugget (i. e. as the proportion of the total variance of the spatial process that is due to non-spatial effects). The relative nugget was computed using the parameter estimates from the theoretical variogram model fitting procedure, giving a value of 0. 279. The variance Var[S(x)] = σ 2 was assumed to follow a chisquared distribution, composed of the sum of squares of random normal deviates (from the mean of this distribution, set to 0), drawn at each data location. Although initial testing indicated that adequate mixing of most MCMC chains was achieved with a burn-in of less than 1. 5×10 5 iterations, some bootstrap datasets did not converge, so the burn-in length was increased to 1.