dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We determined that five different strains of M. ulcerans could use 28 different substrates as sources of carbon. These results were authenticated by the negativity of the negative controls introduced in every plate and the reproduction of data over two replicates. Moreover, stringent criteria were used to ensure the predictive value of the positive results. However, only seven of these 28 substrates were found to be specifically used by M. ulcerans and not by the phylogenetically closest species M. marinum. Three of these seven carbon sources indeed contain indispensable amino-acids. The carbon sources here determined for M. ulcerans may by incorporated in culture media in the perspective of enhancing the isolation and culture of this pathogen. Indeed, M. ulcerans is a slow-growing mycobacterium and the availability of an improved method for its culture would improve the diagnosis of Buruli ulcer patients and the quest for environmental reservoirs  [32].
Sampling location Snails were sampled in two pastures belonging to a private farm called ''La Fontana'' located in Ecuador, near Machachi (Province of Pichincha, in the county of Meija). The first sampled site (site 1) (0°26 0 37. 59 00 S; 78°32 0 24. 12 00 W, 2794 m above sea level (a. s. l. )) and the second sampled site (site 2) (0°26 0 30. 66 00 S; 78°32 0 38. 21 00 W, 2781 m a. s. l. ) were wet pastures regularly grazed by cattle belonging to the farm. Data concerning general information about the farm, cattle management, and fasciolosis history were also collected. A total of 184 Holstein Jersey cattle producing milk and 13 horses belong to this private farm, with 1200 ha of pastures. Pasture rotation took place every 2 months. Both sites were approximately separated by 1000 m and were crossed with small brooks (''acequias'') with slow flow (without connection between sites) and the snails were sampled all along the mud edge.
1 (3730 DNA analyzer; Applied Biosystems) by GIGA Genomics Facility (Liège University, Belgium). Both strands of each sample were compared and used to reconstruct a consensus sequence. This was done in triplicate for each sample. Consensus sequences were made according to the results of sequencing of the PCR products and were aligned using  BioEdit 7. 1. 10 [26]  and analyzed using BLASTN 2. 2. 26 searches in GenBank (http//www. ncbi. nlm. nih. gov/ BLAST). The species identity of sequences (144) obtained from PCR products was determined according to the highest BLAST match (with a threshold of 99-100% similarity).
Cycle sequencing reactions were performed (in duplicate and in both directions) by BigDye terminator v3. 1 (3730 DNA analyzer; Applied Biosystems) by GIGA Genomics Facility (Liège University, Belgium). Consensus sequences were made according to the results of sequencing of the PCR products and were aligned using BioEdit 7. 1. 10 [26]  and analyzed using BLASTN 2. 2. 26 searches in GenBank (http//www. ncbi. nlm. nih. gov/BLAST). The species identity of sequences obtained from PCR products was determined according to the highest BLAST match (with a threshold of 99-100% similarity).
Sequencing reaction and alignment In order to accurately identify the collected lymnaeid species, ITS-2 sequences of 24 snails were examined. All the sequences (144) were identical. This sequence [GenBank: KJ590135] was found to be 100% identical to L. schirazensis [GenBank: JF272602]. Figure  1  shows the sequence alignments of the consensus sequence and the 10 first ITS-2 sequences retrieved from the BLASTN analysis of Lymnaeidae found in South America.
Molecular parasite identification To assess the fluke species involved, 10. 4% (9) of the 86 snails containing DNA of Fasciola sp. were randomly selected at site 1 and three snails containing DNA of Fasciola sp. at site 2 were added. The region of COX-1 of the 12 samples was amplified and sequenced in duplicate and in both directions (48 sequences). All the sequences were identical but one. This sequence was found to be 99% identical to F. hepatica [GenBank: GQ121276] and 94% identical to F. gigantica [GenBank: GU112458].
Statistical analysis Fisher's exact test was used to compare microscopy and PCR results for the site considered. Concerning site 1, the frequencies by date were not homogeneously distributed (p < 0. 001) in function of the result obtained by crushing, whereas results obtained by PCR were homogeneously distributed (p = 0. 10). The difference between the data obtained by crushing in function of the time (date) was tested by a v 2 test for site 1; prevalences for sampling dates A and B were significantly higher than for the other dates (v 2 ddl 1 = 9. 35; p = 0. 002). The distribution of size of snails in relation to the data obtained through microscopy or PCR at site 1 was assessed using a two-sample Wilcoxon rank-sum test. The size of the snails recorded positive for F. hepatica through crushing was significantly higher (p < 0.
G. schirazensis role in fasciolosis in Ecuador Of the different markers used hitherto in lymnaeids, ribosomal DNA ITS-2 and secondarily ITS-1 are the most useful for studies at species level  [8]. Within mitochondrial DNA (mtDNA), recent knowledge indicates that these markers should be used with great caution when dealing with lymnaeid species belonging to different genera and even those well separated within the same genus. Of particular concern is the saturation of nucleotide positions, the fact that evolutionary hot spots may be missed, and additionally that there is extensive evidence for mtDNA introgression  [53]. The ITS-2 sequence alignments undoubtedly ascribed the collected lymnaeid snails to G. schirazensis. The GenBank sequence [JF272602] came from a voucher deposited in two collections and identified by a specialist  [8].
Acknowledgements. This study was performed with the financial support of the  Huynen Foundation  ( University of Liège, Belgium ). The authors would like to thank the  Interdisciplinary Group of Applied Genoproteomics (GIGA) Genomic Facility  (http://www. giga. ulg. ac. be) for sequencing support and the team at the  International Center for Zoonosis (Ecuador). Finally, the authors are deeply indebted to the owner  Marcelo Riviera  and all the staff of the private farm '' La Fontana '' for their kind help.
Bioinformatic and Modeling Studies The bioinformatic tools available at the ExPASy website 2  were used to predict the secondary structure and the possible occurrence of transmembrane domains and of a signal peptide sequence in BMI_II339. The results reported herein were obtained using the TMHMM software (available at www. cbs. dtu. dk/services/TMHMM/). Protein sequences alignments were generated with Clustal Omega (version 1. 2. 4). The glutaminases model structures from Brucella were obtained by comparative homology modeling using Modeller v9. 18  (Sali and Blundell, 1993)  and using several glutaminases as template structures (PDB codes: 3AGD, 4BQM, 3IF5, 3IH8, 1MKI, 2OSU, 2PBY, 3SS3, 1U60, 3UO9, 3VOY).
Statistical Analysis Data from AR assays were analyzed via "two-way ANOVA" using the Bonferroni test (as available in the GraphPad Prism software suite, version v5. 0a). Data were expressed as means of three independent experiments with standard deviations. In order to perform the ANOVA analysis, all data were converted into percentage of survival for each time-point (30, 60, and 120 min) (n = 3 per group) with respect to time 0 (set as 100%). Differences were considered statistically significant when P < 0. 05.
0). No viable bacteria were detected at the same time-points in the absence of Glu or Gln (data not shown). The data represent the mean (SD) of three independent experiments. of each or both of these systems among Brucella strains. In order to validate this hypothesis, a comparative analysis focused on the putative protein sequences of GadB, GadC, and GlsA (available from NCBI, Broad Institute, and PATRIC databases) was performed. Based on this analysis, these three proteins were predicted to be functional in new and atypical species/strains including B. inopinata BO1, Brucella sp. 83/13 isolated from Australian rodents, Brucella sp. BO2, Brucella sp. 09RB8471 and 09RB8910 isolated from frogs and marine mammal strains, i. e. , B. ceti M644/93/1 and B. pinnipedialis M163/99/10 (Figure  5  and Supplementary Table  S1 ).
Depending on the source database (Patric or NCBI) the sequence of BMI_II339 is predicted to be either 83 or 54 amino acids long. Despite the difference in length of the N-terminal portion of BMI_339 and its homologs in other Brucella species (Supplementary Figure  S2A ), the secondary structure predictions suggest that this protein possesses two transmembrane domains separated by four to five amino acids. Notably, the first transmembrane segment of BMI_II339 shares 42. 8/61. 9% identity/similarity with the single transmembrane segment in the "connector" SafA (Supplementary Figure  S2 ) involved in the modulation of the acid stress response in E. coli  (Mitrophanov and Groisman, 2008). This may suggest that BMI_II339 could also act as connector affecting the activity of an acid sensing apparatus that in Brucella has not yet been discovered, in contrast to E.
Data used in the present study were collected on Large White purebred sows from March 1998 until January 2012. Until 2008, the batch system was based on a 6-weekly and 3-weekly batch farrowing (four batches of 12 sows). From 2009, the batch farrowing has switched to a 3-week batch farrowing (seven batches of 10 sows). The full pedigree was used for all analyses and contained 416 ancestors for sows having records. Sow and litter performances (n = 329 and 931, respectively) were available for sows issued from 53 sires and 162 dams. During pregnancy, sows were restrictively fed a standard commercial diet containing 13 MJ/kg of digestible energy (DE) and a minimum of 140 g/kg of CP. In order to standardize body condition at farrowing, pregnancy feed allowance was calculated for each sow from BW and backfat thickness at estrus according to the model proposed by  Dourmad et al.
Climatic records and calculations Records of climatic parameters were available outside the experimental farm from a closed Campbell weather station within 50 m of the experimental unit (from the AGROCLIM INRA web interface). A thermal humidity index (THI) was calculated for each day based on the following formula proposed by the National Oceanic and Atmospheric Administration (NOAA) (1976; cited by  Zumbach et al. , 2008) : THI ð CÞ = T Àð0:55À0:0055 RHÞ ðTÀ14:5Þ where T is the average daily ambient temperature (°C) and RH the average daily relative humidity. In a preliminary study, different daily climatic parameters for ambient temperature and THI (maximum, average, range, at 0700 h, at 1200 h) were compared using partial correlations analysis (R package ppcor), to identify the climatic parameter that better explains the variation in performance (ADFI, LBWg, BWc, RT, CT and RR).
Statistical analyses Because of the low number of sows per batch, contemporary groups were formed by the combination of consecutives batches within the same season and the same year (n = 29 groups of 32 ± 13 sows). Six parity groups were constructed,  namely 1, 2, 3, 4, 5  and parity 6  and above (n = 225, 180,  155, 149, 104  and 118 , respectively, for ADFI records). First, the effects of parity (1 to 6), THI (1 to 5), contemporary group (1 to 29) and the linear covariates of lactation length, number of piglets to be nursed, and the random effect of the sow were tested on the sow lactation performance and their thermoregulation parameters through an ANOVA using the MIXED Procedure of SAS/STAT (version 9; SAS Institute, Inc. , Cary, NC, USA, 1999). Only significant effects (P < 0. 05) were kept in the final models.
Second, animal mixed models were run to estimate variance components for all the traits. However, the unbalanced and incomplete data added computational difficulties, and preliminary single-trait analyses showed that the heritability of ΔRT, ΔCT, ΔRR and TCI were closed to 0 with standard error higher than the corresponding estimated heritabilities. These traits were not considered in further genetic analyses. The final explanatory variables for genetic analyses included six traits (ADFI, LBWg, BWc, RT, CT and RR) for data averaged by lactation and seven traits for repeated data (daily feed intake, RT, CT and RR at 0700 and 1200 h). Variances were estimated using the REML procedure fitting an animal model with ASREML version 3. 0  (Gilmour et al. , 2009). For the estimation of variance components of repeated measures data during lactation, random regression models (RRM) were performed.
Descriptive statistics Under HS, sow's feed intake is decreased, reducing milk production and thus piglet BW, with possible subsequent negative consequences on reproductive sow performance (Black et al. , 1993). The descriptive statistics for the main variables in the database are reported in Table  1. Phenotypic characteristics of sow performances were in accordance with the values reported in the literature on lactating performance under HS conditions  (Martins et al. , 2008; Lewis and Bunter, 2011). The average daily outside ambient temperature and relative humidity were 24. 7 ± 1. 3°C and 89. 3 ± 5. 6%, respectively. The average THI during lactation was 24. 1°C, ranging between 21. 1°C and 26. 4°C. The THI allows combining the effect of ambient temperature and humidity on the studied traits.
Acknowledgments The financial support of the  Guadeloupe Region  and the  European Union  social funds is gratefully acknowledged. The authors thank  Hélène Gilbert  for stimulating discussions and helpful comments on the manuscript. The authors also wish to thank  C. Anaïs ,  K. Benony ,  D, Beramice ,  B. Bocage ,  M. Bructer  and  M. Giorgi  for their technical assistance.
5 (5, 6) before manual finishing. SSPACE version 2 (7) and Opera version 2 (8) were used to combine the contigs, and GapFiller version 1. 10 (9) was used to close gaps in the scaffolds. This yielded a draft genome comprising 14 scaffolds composed of 14 contigs, for a total of 6,506,908 bp with a 66. 77% GϩC content. Noncoding genes and miscellaneous features were predicted using RNAmmer (10), ARAGORN (11), Rfam (12), Pfam (13), and Infernal (  14 ). Coding DNA sequences were predicted using Prodigal (15), and functional annotation was achieved using BLASTp against the GenBank database (  16 ) and the Clusters of Orthologous Groups (COGs) database  (17, 18). The genome was shown to encode 59 predicted RNAs, including 3 complete ribosomal operons (5S rRNA, 16S rRNA, and 23S rRNA genes) and 50 tRNAs.
A total of 4,828 genes (76. 89%) were assigned a putative function, 116 genes (1. 85%) were identified as ORFans (open reading frames with no matches in the databases), and the remaining 1. 035 genes (16. 48%) were annotated as hypothetical proteins. The M. boenickei CIP 107829 genome was further incorporated into in silico DNA-DNA hybridization (DDH) (19) with reference genomes selected based on 16S rRNA gene proximity; DDH values were estimated using the GGDC version 2. 0 online tool (20). This analysis yielded 36. 35% Ϯ 3. 46 with M. septicum DSM 44393, 31. 55% Ϯ 3. 46 with M. fortuitum strain CT6, 21. 55% Ϯ 3. 32 with M. cosmeticum strain DSM 44829, 20. 95% Ϯ 3. 32 with M. neoaurum VKM, 20% Ϯ 3. 25 with M. abscessus.
subsp. bolletii strain MA 1948, and 19. 9% Ϯ 3. 25 with M. chelonae CCUG 47445, confirming at the genome level the taxonomic assignment of M. boenickei to the M. fortuitum complex. Accession number(s). The M. boenickei CIP 107829 genome sequence has been deposited at EMBL under the accession number FUWC00000000.
Therefore, it is important to demonstrate freedom from disease and achieve timely detection in this species because of the economic importance of CSF and a need to optimize the use of surveillance resources. The last outbreak of CSF in wild boar in Germany was recorded in 2009 in the federal state of Rhineland-Palatinate. Immunization with an attenuated live vaccine was used to control the disease in wild boar. The vaccination campaigns ended in 2012. Intensive surveillance during and after the vaccination campaigns, provided finally no longer evidence for the presence of CSF in the wild boar population. In June 2012, Germany therefore was declared as officially free from CSF. Thus, CSF is a suitable example to assess surveillance options to demonstrate freedom from disease with data of the German CSF surveillance system.
Materials and Methods Study areas and data. Data on wild boar and CSF surveillance were used from three federal states of Germany, namely Rhineland-Palatinate (RP), Mecklenburg-Western Pomerania (MV) and Lower Saxony (LS). These three federal states were chosen due to data suitability and availability. For the calculation of sensitivity and timeliness of the surveillance strategies, the districts of RP were used as the study area. Investigations employing participatory methods were conducted in RP and MV. Furthermore, data from all three federal states were used to parametrize the simulation model.
Risk factor analysis. To identify risk factors for infection of wild boar with CSF virus or for detecting the virus in wild boar, a literature search was conducted and potential risk factors were analysed for potential associations with virological or serological test results regarding CSF. Literature analysis. The databases Web of Science TM Core Collection, CABI: CAB Abstracts ® and Global Health ® were searched. Only publications that appeared in English or German from 1990 through to 2014 were taken into account. The following search term was used: ((risk* OR incidence* OR prevalence*) AND ("wild boar" OR "wild pigs" OR "feral pigs") AND (" classical swine fever")). Parts of the identified factors were included in a statistical analysis. Statistical analysis. Statistical analyses were done using the statistical software R (http://www.
r-project. org). Potential risk factors identified by the systematic literature search were tested for statistically significant associations with serological and virological examination results for CSF using Pearson's Chi-squared test  20. This was done on the basis of infection data of MV. A p-value of < 0. 05 was considered as statistically significant. The data of MV had been collected between 1993 and 2000, when CSF was endemic in the wild boar population in this federal state. A total of 172,906 data records were available and information on the variables date, location, age and serological and virological results were used. The data set is described in detail by Schulz et al. 21. Only the risk factors age and season were tested statistically. The risk factors population density and samples originating from passive surveillance, identified through the literature search could not be tested due to a lack of suitable data.
Sensitivity and Timeliness. A simulation model was used to determine the sensitivity and timeliness of different surveillance strategies for CSF in wild boar. To simulate the different surveillance strategies and to calculate their performance, real surveillance data was used to prepare the input files for the model. The evaluation attributes sensitivity and timeliness were calculated as output variables. The structure of this model is described in detail by Schulz, et al. 21. In the present study, different data for the input files were used. In the following text only the differences to the initial model description of Schulz et al. 21 are outlined. Figure  1  is adapted from Schulz et al. 21 and illustrates the model structure in detail. All analyses were done in R (www. r-project. org).
An example of the source code of the R script is provided as Supplementary Information and the complete R script can be obtained from Jana Schulz. First, a wild boar population was generated (input file "population") and an appropriate number of wild boar was allocated to each district ("Landkreis") of the federal state of RP (Fig. 1 ). The variables age, gender and cause of death (hunted, shot sick, found dead, road traffic accident (RTA)) were assigned to each animal in the population generated in the model, depending on the appropriate proportion in a real data set of RP (Fig. 1 ), (input file "population structure", Table  1 ). To simulate a seroprevalence of 5%, a proportion of 5% of the animals were marked as serologically positive in the start month. The detailed age and sex distribution of the animals marked as infected is described in Schulz et al.
A more detailed description of the calculation of the two evaluation attributes can be found in Schulz et al. 21. Data background. Input file "population". Data obtained from studies of the Research Institute for Forest Ecology and Forestry of the federal state of Rhineland-Palatinate (RP), Trippstadt, Germany from 2012 were used. The data on the wild boar population was determined using feces and tissue samples  22. The data set contained the estimated number of wild boar for all 36 districts of RP. However, data from 12 urban districts were excluded as the number of wild boar in these districts was negligible. Input file "population structure". Surveillance data from three different areas of RP with information on the variables age, sex and cause of death were used. The data originated from the European CSF databank of the Friedrich-Loeffler-Institut (CSF-Database; http://public.
csf-wildboar. eu/Default. aspx). In total, 120,134 data sets were available. After removing all data records that did not contain information on age, sex or cause of death, 105,439 data records were available, which covered the time period from 15 March 2003 through to 22 July 2014. Input file "Infection". Data originating from MV were used to estimate the seroprevalence and the virological CSF prevalence in the simulated infected area. Data and processing have been described by Schulz et al. 21. Input file "Hunt". Hunting data provided by the Supreme Hunting Authority of the Ministry of Environment, Forestry and Consumer protection of RP were used to generate the input file "hunt". A detailed description of the data can be found in Schulz et al. 21  was used. The dataset consisted of 12,784 data records, originating from 37 districts and included data from 2011 through to 2014.
It contained information about the location and the date of sampling, the age of the sampled animal and on the surveillance type (i. e. active or passive surveillance). After excluding records with incomplete information on these variables, 12,620 data records were available for the model. The data of the 37 districts were summarized and averaged on a monthly basis. Depending on the age and origin of the sampled animals, the percentage of sampled wild boar per month was calculated. Sensitivity analysis. A sensitivity analysis was performed as described in Schulz et al. 21.
Acknowledgements This study was performed within the framework of the  RISKSUR  project that received funding from the  Eropean Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 310806. The authors wish to thank all participating hunters and staff from the lower hunting authorities of the federal states of  Rhineland-Palatinate and Mecklenburg-Western Pomerania  for the successful co-operation.
Fluidigm Biomark RT-PCR For validation of array data by Fluidigm technology 22 animals (88 samples) were kept to fit the technical constraints of this technique. Total RNA (1 μg) used in microarray experiments was reverse-transcribed as previously described  [15]. Primer sequences for genes were designed using Primer3plus software (http://primer3plus. com) and are given in Additional file 1. The TFRC gene (transferrin receptor) and EPRS gene (glutamyl-prolyl-tRNA synthetase) were used as internal controls. Pre-amplified samples were analyzed with a 96×96 Dynamic Array™ IFC (Fluidigm) following the protocol defined by  [16] , with some modifications. All measurements were performed on the same plate. Each gene was tested twice for each sample. Four dilution points containing a pool of all samples were used to determine PCR efficiency.