dataesr/scientific-paragraphs-categorization huggingface dataset:
NMR Spectroscopy 1D and 2D NMR spectra of B. suis biovar 1 PS (10 mg in 0. 5 mL D 2 O), biovar 2 PS (5 mg in 0. 5 mL D 2 O) and Y. enterocolitica O:9-PS (9 mg in 0. 5 mL D 2 O) were recorded at 47uC on a Bruker AVANCE III 700 MHz spectrometer equipped with a 5 mm TCI Z-Gradient CryoProbe. 1 H chemical shifts were referenced to internal sodium 3-trimethylsilyl-(2,2,3,3-2 H 4 )-propanoate (TSP, d H 0. 00) and 13 C chemical shifts were referenced to external dioxane in D 2 O (d C 67. 40). Data processing was performed using vendor-supplied software. The assignments of the 1 H and 13 C resonances of the PS were obtained by analysis of 1D 1 H and 13 C NMR spectra together with 2D NMR spectra from multiplicity-edited 1 H, 13 C-HSQC experiments  [33] , 1 H, 13 C-HMBC experiments  [24]  with a 45 ms delay for evolution of longrange couplings and 1 H, 13 C-HSQC-1 H, 1 H-TOCSY experiments  [25, 26]  with mixing times of 50, 100, 150 and 200 ms.
Sequence Analysis Homologies of Brucella O-antigen glycosyltransferases wboA, wboB, wbkA and wbkE and upstream sequences and of the predicted proteins were studied using the NCBI (National Center for Biotechnology Information) and TIGR (The Institute for Genomic Research) sequences accessible through the Bucella Bioinformatics Portal (http://www. phidias. us/bbp/data/index. php) and BLASTN and BLASTP tools. For other Brucella LPS possible glycosyltranferases, the data available at the Carbohydrate Active Enzymes database (http://www. cazy. org/GlycosylTransferases. html ) were used.
At K = 31, substructure appears in the Standardbred samples, which correlates to those individuals identified as pacers and that fall into an individual clade in the parsimony tree (Figure  S7 ). At K = 29, the Miniature and Shetland continue to be assigned to the same cluster (q-values = 0. 55 and 0. 95, respectively). The next highest proportions of assignment of the Miniature horse are to the clusters described by the New Forest Pony (q = 0. 20) and Icelandic (q = 0. 11). No value of K evaluated eliminated signals of admixture from all populations in the dataset at K = 38 (the actual number of populations sampled) or any value of K through 45 (data not shown).
F ST All pairwise F ST values calculated between the 37 populations (excluding the Florida Cracker) were significant as tested by 20,000 permutations (Figure  4 ). The lowest level of differentiation was found between the Paint and Quarter Horse populations (F ST = 0. 002), while the greatest divergence was observed between the Clydesdale and Mangalarga Paulista (F ST = 0. 254). The two Thoroughbred populations had an F ST value of 0. 004, while the two Standardbred populations had 10-fold greater divergence (F ST = 0. 020) than the minimum observed value in this dataset; this value is similar to that observed between the Lusitano and Andalusian (0. 021). An F ST value of 0. 006 was identified between the Tuva and Mongolian populations. The global F ST value was 0. 100. AMOVA computed on the set of 37 samples (excluding the outliers identified in Structure and the Florida Cracker) showed that 10.
The Clydesdale and Exmoor, in addition to having high individual estimated coefficients of inbreeding, also show the lowest within-breed diversity observed in the dataset. A lack of diversity in the Clydesdale and another British draft breed, the Shire, is likely a result of a severe population bottleneck observed in most draft breeds with the onset of industrialization and after the conclusion of World War II (WWII) as well as selection for size and color  [45, 46]. The Exmoor pony, considered to be one of the purest native breeds of Britain, has been naturally selected for survival in harsh winter conditions on the moors in southwest England  [45, 47]. Similar to the draft breeds, the Exmoor population decreased significantly after WWII to approximately 50 individuals, undoubtedly influencing the diversity observed in this study.
Finally, several rare populations are included in this dataset. The Caspian is one of the oldest breeds in the Middle East and was thought to be extinct until its recent rediscovery in 1965. The Florida Cracker, a now rare breed, was developed in the United States from feral stock of Iberian descent  [57]. The sample size of the Florida Cracker limits the conclusions that can be drawn regarding within-breed diversity. However, the Caspian shows high N e , H e , and estimates of f, given its rarity. After rediscovery of the breed, which historically was believed to represent a type of landrace population,  [58]  describes a three-year survey, which found approximately 50 individuals remaining, noting that many could not be considered ''pure. '' In addition,  [33]  were unable to show evidence of a recent bottleneck in the Caspian breed.
The pairwise F ST values between the Thoroughbred and the Arabian do not suggest any less divergence than observed between the Thoroughbred and a majority of the other breeds. In addition, at K = 29 the Arabian assigns to the Thoroughbred cluster at only 2. 3%. If the Arabian did have significant influence on the Thoroughbred breed, there are several possible explanations for why the supposed Arabian influence is not more apparent. The first is related to SNP ascertainment and the bias of SNPs toward modern variation in Thoroughbred. It is possible that the genes derived from the Arabian are at or near fixation in the Thoroughbred, which would reduce the chance that these SNPs, and the variation described within them are present in the dataset. Another possibility is that the current Arabian sample, taken from the United States, may not reflect the Arabian lineage(s) that were influential in the founding of the Thoroughbred.
3 (after pruning for MAF. 0. 05). In samples that were obtained as a result of GWAS, ''control'' individuals were preferentially chosen for inclusion in these analyses. When necessary, DNA isolation from hair roots took place using a modification of the Puregene (Qiagen) protocol for DNA purification from tissue. Modifications include the addition of 750 ml of isopropanol rather than 300, increasing the precipitation spin time to 15 m at 4uC, and washing the pellet twice. Approximately 1 mg of DNA was used for SNP genotyping using the Illumina SNP50 Beadchip according to the manufacturer's protocol. All genotype calls were extracted from the raw intensity data using GenomeStudio (Illumina) with the minimum gencall score threshold of 0. 15. The raw intensity scores were available for all populations with the exception of the Lusitano and Maremmano.
Data Pruning SNP discovery was conducted using horses from seven breeds (Akhal Teke, Andalusian, Arabian, Icelandic, Quarter Horse, Standardbred, Thoroughbred) as well as the reference genome of a Thoroughbred mare  [41]. To eliminate ascertainment bias as much as possible, horses from the discovery breeds were removed from the dataset, which was then pruned to exclude SNPs with MAF less than 0. 05. All horses were then replaced and those SNPs removed from all analyses. In this new, complete data set, SNP markers that failed to genotype in at least 99% of the individuals and SNPs that had a MAF of 0. 05 or less across all samples were removed as well as SNPs on ECAX. SNPs that were in LD across breeds were also removed; files used for basic diversity indices were pruned for r 2 ,0. 1 in PLINK  [71]  considering 100 SNP windows and moving 25 SNPs per set (-indep-pairwise 100 25 1.
Within-breed Diversity Expected heterozygosity (H e ) and AMOVA were calculated in Arlequin3. 5  [74]  on all four data sets. AMOVA was conducted on the primary data set with breeds designated as populations and excluding the Florida Cracker due to small sample size. Analyses were also conducted grouping the two Thoroughbred and Standardbred samples together by breed. F IS was calculated and significance tested on the primary data set, with 10,000 permutations of the data in Genetix  [75]. Individual inbreeding coefficients (f) were calculated in PLINK based upon loss of heterozygosity (-het).
Among Breed Relationships Pairwise F ST values were calculated on the primary, 10,536 SNP dataset in Arlequin3. 5  [74]  using Reynolds' distance  [76]  with significance tested using 20,000 permutations. A neighbor joining (NJ) cladogram was built using breed allele frequencies calculated from the primary SNP set using the packages seqboot, gendist, neighbor, and consense and Nei's genetic distance  [38]  in PHYLIP ver3. 69  [77]. Bootstrap support from 1,000 iterations of the data was used to assess support for the resulting majority rule consensus cladogram. A parsimony cladogram was constructed using 10,066 SNP markers pruned from the original data set using the MAF and genotyping rate criteria as above and allowing for R 2 ,0. 2 (-indep-pairwise). The domestic ass was included as an outgroup for traditional and new-technology searches in TNT  [78].
Cluster Analysis Clustering of breeds into genetic groups was examined using the program Structure 2. 3. 3  [79, 80]  assuming K = 1 to 45. The Structure algorithm included the admixture model and correlated allele frequencies. Three iterations of each K value were conducted with 35,000 MCMC repetitions (15,000 burn-in). The convergence of Structure runs was evaluated by equilibrium of alpha and likelihood scores. The value of K most suitable to explain the diversity in these data was predicted by the highest mean estimated ln P(X|K) while minimizing variance and also making biological sense  [80, 81]. The replicates from each run of K were input into CLUMPP  [82]  and the average cluster membership calculated using the LargeK Greedy algorithm. Output from CLUMPP was visualized in Distruct  [83].
Statistical Analysis When possible, all data, except for the CE-SSCP Profiles (see below), were expressed as the mean ± Standard Error (SE) of results obtained from 12 animals up to D2 (data for the first sacrifice), and then for 8 animals, computed with SAS software  (SAS, 2004). These data were analyzed by the ANOVA test for repeated and correlated series, after checking for homogeneity of the residual variance (Hartley's test). When only two groups of data were compared, the t-test was used. Variable Differences were considered significant at p < 0. 05 and trends were discussed at p < 0. 1. CE-SSCP Profile analyses were performed with GeneMapper™ (Applied Biosystems, Foster City, USA) and Bionumerics (Applied Maths, Sint-Martens-Latem, Belgium) software. The GeneMapper™ software was used to align the profiles obtained based on the internal migration standard.
Husbandry characteristics To obtain and provide a short summary of the known phenotypic changes affecting the health and general fitness -and therefore, putatively required to apply for project approval by animal ethical committees and to adequately maintain the strains -is of the utmost relevance. Information on fertility of both genders, and any special considerations concerning the breeding strategies to be applied, should be monitored and conveyed. Similarly, information on the viability and fertility of both heterozygous and homozygous mutants should be recorded and provided for appropriate breeding strategy, housing, handling and maintenance of the mouse line. Data on the history of the microbiological status of the rodent strain are of key importance for a smooth colony transfer between laboratories, and to recognize any health problems that might not be linked to the mutation itself.
Known strong positive and negative bovine serum samples (based on previous ELISAs) were added to each plate allowing calculation of the percent positivity (PP) for each sample  [36]. For experimental infection sera, combined weighted estimates of sensitivity from sequential sera of nine infected animals and the weighted standard errors with 95% confidence limits were calculated according to Eisler et al. ,  [13]. The significance of the differences observed between the TvGM6 and TvWTL ELISA was compared using the McNemar test using GraphPad Software (GraphPad Software Inc. ). For field sera, cut-offs were established using a minimum of 10 PCR negative bovine sera from areas non-endemic to tsetse (peri-urban Maputo, Mozambique). Cut-offs were calculated as the mean PP added to two standard deviations.
Primary antibodies used were either mouse anti-TvGM6 sera (1/2000) or mouse anti-TcoGM6 sera (1/2000), and rabbit anti-paraflagellar rod (1/50). Slide wells were washed using blocking solution (3x50 µl). Secondary antibody diluted 1/100 in blocking buffer (20 µl) was added to slide wells and incubated for 60 min. Secondary antibodies used were Alexa Fluo 488 conjugated goat antimouse IgG and Texas Red® conjugated goat anti-mouse IgG (Invitrogen, Carlsbad, CA, USA). DAPI (20 µl/ slide well) diluted in PBS (final 0. 5 µg/ml) was added to each slide well. Slides were viewed using a Zeiss Axio Imager Z1 fluorescent microscope and images captured using the MetaMorph® software (Molecular Devices, CA, USA) at a total magnification of 100x.
TvGM6 : Cross-reactions with heterologous T. congolense infection The TvGM6 ELISA was also tested with sera from T. congolense experimental infections, and produced a pattern similar to the TcoGM6 indirect ELISA (data not shown), with a peak of antibody response 25 DPI and a rapid decrease below the threshold post-treatment. However, the TvGM6 ELISA was consistently weaker than the TcoGM6 ELISA when detecting heterologous infection. Due to this initial finding of a crossreaction, it was decided to test the TvGM6 indirect ELISA with T. congolense-infected field sera to determine the sensitivity of this test for the heterologous infection and the results are shown in Table  3. The sensitivity of TvGM6 ELISA with T. congolense field infections gave a mean sensitivity of 85% (95% CI: 76. 8 to 94.
Acknowledgements We are grateful to the following people:  Z. Benglay ,  L. Lomille ,  H. Vitouley ,  Y. Memel ,  H. Sakande  (CIRDES) and  M. Diouf  ( GALVmed ) for facilitating the provision of sera from  Burkina Faso ;  C. Cordell  and  H. Erasmus  from  ClinVet  for providing sera from cattle infected with T. congolense and T. vivax as part of other studies conducted at their sites;  P. Macucule  for providing characterised T. congolense sera from Mozambique;  R. Miambo  and  N. Matsinhe  for technical assistance (Mozambique) and to  L. Riviere  for critical reading of this manuscript. We would also like to thank  J. De-Foucauld  (CEVA),  B. Dungu  and  T. Rowan  ( GALVmed ) for their support and advice.
This research was supported by the GALVmed with funding from the  UK Government's Department for International Development (DFID)  as part of GALVmed's  Animal African Trypanosomosis Programme (DFID Programme: Controlling African Animal Trypanosomosis (AAT) (Aries code   202040-101 ), and by  CEVA santé animale (Libourne, France). This work was also supported by the  CNRS , the  Ministère de l'Éducation Nationale de la Recherche et de la Technologie , the  Conseil Régional d'Aquitaine and the Laboratoire d'Excellence (LabEx) ParaFrap (French Parasitology Alliance for Health Care)   ANR-11-LABX-0024. Regassa Fikru  received a PhD grant from the  Belgian Directorate General of Development Cooperation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
At each TS, the PEBV were estimated independently for the two traits, using a standard BLUP-Animal Model procedure  [9]  and the BLUPF90 software (http://nce. ads. uga. edu/~ignacy/newpro grams. html), considering all phenotypes and pedigrees recorded since the creation of the herds. At each TS, the best 10 of the 81 female candidates from each herd and the 9 best of the 360 male candidates from across the five herds were selected based on PEBV gl to partially replace the pool of male and female reproducers.
Technical efficiency criteria One hundred independent replicates of five years of selection under the BL_ref scenario, followed by 10 years of selection under each of the 10 alternative scenarios, were simulated for the W1:1 and W1:3 breeding goals. For each replicate of each breeding scenario x breeding goal combination, the following summary statistics were calculated: the mean of the true breeding values of the piglets born in each TS for traits 1 and 2 and for the breeding goal, in order to measure the realized genetic gain; the mean of the inbreeding coefficients of the piglets born in each TS based on pedigree information using PEDIG software  [11] , to measure the evolution of inbreeding over time; the correlation between the TBV and the PEBV or GEBV of the selection candidates for each TS, in order to estimate accuracies of the selection for traits 1 and 2.
Acknowledgements The authors would like to thank  A. Legarra  for supplying the program used for GBLUP evaluations, and  I. Misztal  for supplying the BLUPF90 software. The authors would also like to thank  S. Lemarié  for his valuable advice,  V. Hawken  for the linguistic review of the manuscript, and two anonymous reviewers whose comments contributed to improve the manuscript. The authors also wish to thank the  ANR  for its financial support ( ANR_10_GENOM_BTV_015 ).
A subset of studied specimens was deposited in the Museu de Ciencies Naturals de Barcelona, Catalonia, Spain, with Accession Numbers MZB 2013-0019 and MZB 2013-0020 (two females), and MZB 2013-0021 and MZB 2013-0022 (two males). Murid hosts were identified following the nomenclature and classification in Chaval  [1].
Acknowledgements. This study was supported by  French   ANR 11 CPEL 002 , project  BiodivHealthSEA, Local impacts and perceptions of global changes: Biodiversity, health and zoonoses in Southeast Asia  (www. biodivhealthsea. org)and the  French ANR Biodiversity   ANR 07 BDIV 012 , project  CERoPath, Community Ecology of Rodents  and their Pathogens in a changing environment (www. ceropath. org). A. Ribas  and  J. Miquel  were partially supported by  2009-SGR-403  from the  Catalan Government (Spain). We are especially grateful to  Nuria Cortadellas  and  Almudena Garcia  from the  Unitat de Microscopia, Facultat de Medicina, Centres Cientı ´fics i Tecnologics de la Universitat de Barcelona (CCiTUB)  for their help with the preparation of the samples.
Posture and stepping behaviour The standing posture of sows was recorded for a 24-h period according to a previously validated method  (Ringgenberg et al. , 2010) , using one accelerometer (Pendant G Acceleration Data Logger, Onset Computer Corporation, Pocasset, MA, USA) fixed on a rear leg. Posture was recorded every 5 s and the percentage of time spent standing over 24 h was estimated. Two accelerometers fixed on both rear legs of the sows were also subsequently used to record vertical acceleration 10 times/s during 1 h, following the morning feeding  (Ringgenberg et al. , 2010). Acceleration data were thereafter converted to detect steps and standing posture, and then number of steps per minute while standing, and latency to lie down after feeding were calculated.
The severity scale for lesions was defined as 1: no lesion; 2: lesion smaller than 1 cm; and 3: deep and severe lesion. The severity scale for claw size was defined as 1: difference between medial and lateral claw sizes <25%; 2: difference between medial and lateral claw sizes. 25%; and 3: excessive length of one or both claws. Data were analysed after transformation in binary variables (presence v. absence of lesion (i. e. scores 2 and 3 v. 1), presence v. absence of severe lesion (i. e. scores 3 v. 1 and 2)) for each type of lesion. Prevalence of a type of lesion or abnormality was calculated for each claw as the percentage of sows presenting the lesion considered. Finally, a sole-heel lesion score for each foot, ranging from 8 to 24, was calculated as the sum of heel, white line, sole and heel-sole junction scores on the two claws.
Statistical analyses A Principal Component Analysis (PCA) was performed with the PRINCOMP procedure of SAS (SAS, 2002) on all continuous data available for the four different limbs (i. e. kinematics, footprint analysis, global foot and sole-heel lesion scores) to analyse relationships between variables and between-limb characteristics. Effect of the position of the limb (front v. rear) on the same variables was analysed using the MIXED procedure of SAS with the limb as an experimental unit. Variables that were affected by the position of the limbs were thereafter analysed separately for front and rear limbs. Length of the body was highly correlated with all other anatomical characteristics (r. 0. 55; P , 0. 0001) and was used as a covariate in further analyses of distances between footprints and stride length measured with kinematics to take into account the size of the sow.
Prevalence of foot lesions was analysed for the effect of the position (front v. rear) and the side (left v. right) of the limb and the position of the claw (medial v. lateral) for each type of lesion using the GLIMMIX procedure of SAS with a binary distribution, a logit link function and the claw as the experimental unit. For each type of lesion, the effect of the presence of severe lesions (score 3) was analysed for each continuous variable available for different limbs (i. e. kinematics, footprint analysis and stepping), using the MIXED procedure of SAS with the limb as the experimental unit. Data obtained from footprints, kinematics, postures, stepping behaviour and global foot and sole-heel lesion scores were compared between sows with different gait visual score, using the MIXED procedure of SAS with the sow as the experimental unit.
Only three sows were scored 2 or 3 for the lying-to-standing transition. Therefore, sows were categorised between sows that did or did not stand up, and data were analysed using x 2 -tests between the three categories of lameness. Effects of limbs and back conformation on degree of lameness were analysed using Cochran-Mantel-Haenszel x 2 -tests. To analyse further relationships between the various measurements collected, partial least squares analysis (PLS procedure) was used to relate lameness visual score (response variable) to other continuous variables at the sow level. The PLS analysis works by extracting successive linear combinations of the predictors, called factors, thereby optimally explaining both the response variation (lameness visual score) and the predictor variation (covariates).
Results Data set description Sows weighed on average 223 6 24 kg measured 151. 1 6 33. 5 cm long, and their backfat thickness was on average 21. 2 6 3. 9 mm (means 6 s. d. ). Their limb conformation score ranged between 0 and 18, with a mean of 13. 6 6 2. 0 (mean 6 s. d. ). The PCA performed with all continuous variables available for the four limbs of each sow (Figure  3 ) revealed a clear division between the front and the rear limbs and between variables that were influenced by the position of the leg and the others that were not. In line with the PCA, results were significantly different between front and rear legs for diagonal and ipsilateral distances between footprints, swing time, mean and amplitude of the carpal and tarsal joint angles, foot height and global foot lesion score (Table  2 ).
Acknowledgements The authors would like to thank  Claire Corriveau ,  Marjolaine St-Louis ,  Anna Kerivel ,  Maud Ge  `te and  Jonathan De  ´om for their invaluable technical assistance,  Steve Me  ´thot for help in statistical analyses and all the staff at the  Swine Complex  for care of the animals. This project was funded by the  Federation des Producteurs de Porcs du Quebec and the Agriculture and Agri-Food Canada Matching Investment Initiative. The authors also thank  MIC (Motion Imaging Corporation ,  Simi Valley , CA) and  Intergrys (Mississauga, ON)  for their support with the kinematic equipment and software.