dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Daniel González  and  Ludivine Jaravel  for help in caring for the animals,  Gianfranco Chávez Marchetta ,  Jimena Chaca Olalde  and  Coralie Piccin  for help with data collection, and  Carolina Rojas  for excellent technical and bibliographical assistance. We also thank  Patrick Gouat  and  Christophe Féron  for their support and for helpful discussions. This article has been published as part of Frontiers in Zoology Volume 12 Supplement 1, 2015: New Perspectives in Behavioural Development:  Adaptive Shaping of Behaviour  over a Lifetime?. The full contents of the supplement are available online at http://www. frontiersinzoology. com/supplements/ 12/S1.
Statistical analysis The 50% effective concentration (EC 50 -value) was calculated using PoloPlus © (version 1. 0; LeOra Software Company, Petaluma, California, USA). These EC 50 -values are reported on the basis of tannin content in the F1 and F2 fractions (see Table  1 ). To assess possible synergistic effects of the tannin/flavonoid combinations, we used the observed inhibitory effects of the individual treatments to calculate the additive inhibitory effect that would be predicted to be achieved by the mixture under the assumption of independent action, using the definition of Bliss additivity  (Bliss, 1939). The observed effect of the mixture was then compared to this predicted additive effect, with deviations from the predicted values indicating either synergy (greater effect) or antagonism (lower effect)  (Williams et al.
Acknowledgements C. Klongsiriwet  is grateful to the  Royal Thai Government  for a PhD scholarship. Hildred PYO-Farm (Goring-on-Thames, Reading, U. K. ) kindly provided leaves from black and red currant bushes and Fig. 4. Synergistic effects between condensed tannins (CT) and luteolin (60 and 30 mM) on the inhibition of Haemonchus contortus larval exsheathment after 60 min.
Phylogenetic analysis Sequences of ribosomal 16S rRNA and Rpob genes and proteins were retrieved from GenBank (S1 Table ). The DNA dataset for the 16S sequences used for phylogenetic analyses contained 26 taxa and 1389 characters. The dataset for the Rpob partial coding sequences contained 22 taxa and 303 characters. The dataset for the Rpob amino-acid sequences contained 25 taxa and 101 characters. Two Clostridium species were used as an out-group. Sequences were analyzed using MEGA version 5. 2. 2  [29]. Each set of sequences was aligned using Muscle software, and the best substitution model, that is, the one with the lowest Bayesian Information Criterion (BIC), was selected. Phylogenetic trees were constructed using the maximum likelihood (ML) algorithm and tested with the bootstrap method (500 replications).
Assay of snail survival after Paenibacillus exposure Sixty uninfected albino B. glabrata snails originating from Brazil (BgBRE) with shell diameters ranging from 7 to 11 mm were divided in two groups and placed in 3 L of Ca. Paenibacillus glabratella-free water. One group of 30 BgBRE snails was exposed to five Paenibacillus nov. sp. infected pigmented (BgVEN) snails for 3 months and a second group of 30 BgBRE snails (controls) was exposed to five uninfected BgVEN snails for 3 months. Albino snails were chosen because they facilitated tracking of the spread of bacteria and could be easily separated from infected pigmented snails. Kaplan-Meier survival analyses followed by pairwise log-rank tests were used to compare survival data  [32, 33]. Infection was determined by direct observation using a binocular stereomicroscope, and mortality was assessed every 2 or 3 days.
Molecular identification of Paenibacillus and phylogenetic analysis In order to identify the microbial pathogenic agent, we performed a PCR analysis of genomic DNA extracted from the white nodules using universal bacterial primers (27f and 1492r) and eukaryotic-specific primers (Unif-F-15 and Unif-R-1765). No amplification was obtained with eukaryotic-specific primers, whereas one ~1400-bp fragment with a single sequence was amplified using primers for the 16S rRNA gene. This sequence was compared against the NCBI database of 16S ribosomal RNA sequences using a BLASTN search. The best hits were for the 16S rRNA genes of Paenibacillus alvei (accession number NR_113577. 1) and Paenibacillus apiarius (accession number NR_040890. 1) with 95% and 94% identity, respectively. In order to confirm this identification, we partially amplified the RNA polymerase beta subunit gene (Rpob) using previously reported primers for the Rpob gene of different Paenibacillus species  [26, 27] , and compared the predicted amino acid sequences against the NCBI protein database using a BLASTP search.
The most closely related protein sequences were from Paenibacillus thiaminolyticus (accession number AY728285. 1), with 94% similarity and 87% identity (E-value = 6. 10 -62 ), and P. alvei (accession number WP 005544566. 1), with 96% similarity and 94% identity (E-value = 9. 10 -59 ). The family relationships of the infectious agent described in this work were clarified by constructing phylogenetic trees. Similar topologies were obtained using different methods and datasets. The most terminal nodes were highly supported, whereas the relationships among the different families were less consistent, with lower concordance across the two 16S and Rpob datasets (Fig. 6 ). Nevertheless, these analyses established a new pathogenic bacteria clade within the Paenibacillus genus that is most closely related to P.
This work was supported by funds from the  Centre National de la Recherche (CNRS)  and the  Universite de Perpignan Via Domitia (UPVD) , and by a grant from the  ANR   JCJC INVIMORY  number  ANR-13-JSV7-0009. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Architectural Changes Eclipse E400 Nikon microscope, with DS-FI camera driven by NIS-D element software (Nikon) was used to capture images (100× magnification) and take the measurements for architectural evaluation of the digestive mucosa. A total of about 30 well-oriented villi and crypts per loop were selected on each section. A villus was measured from the tip to the shoulder (crypt-villus junction) and a crypt was measured from the shoulder to its base (Figure  1c ). The crypt-depth to villus-height ratio was calculated to assess the intestinal architectural changes.
Proliferative and Apoptosis Indexes Proliferative and apoptotic cells were counted after immunohistochemistry in several sites on the mucosa. A minimum of 20 well-oriented villi and crypt units were assessed on scanned marked slides (Panoramic 250 Flash II-3D Histech), and analyzed with Pannoramic Viewer software (v. 1. 15. 2, 3DHISTECH Ltd, Budapest, Hungary,). Proliferative cells were counted in the upper one-third of the villi, i. e. villus tip (positive cells/total cells: lamina propria plus epithelial cells), and in the bottom two-thirds of the crypts from the basis (proliferative index of crypt enterocytes) (Figure  1c ). For apoptosis, the total cells counts, lamina propria counts and villus enterocytes counts were measured in villus tip (upper one-third). The enterocyte apoptotic index was calculated by dividing the positive enterocyte number by the total number of enterocytes (×100) in the villus tips while the proliferative index of crypt enterocytes was calculated by dividing the number of positive enterocytes by the total number of enterocytes (×100) in the crypt bases (Table  2 ).
Statistical Analysis The two experiments were designed as randomized blocks. The explants and loops data are presented as means ± SEM, expressed as percentages of the control values. Plots of fits versus residuals followed by Bartlett's test, and normal plots of the residuals by Anderson-Darling's test were carried out to confirm the assumptions of homogeneity of variances and the normal distribution of residuals, respectively. If these assumptions did not hold, the data were normalized and homogenized by log10 or square root transformation prior to being analyzed. All tests were performed using the MINITAB package software (V. 13. 0, Minitab Inc. , State College, PA, USA). The data were analyzed by applying the GLM option of ANOVA analysis, followed by pairwise comparisons, Tukey's or Bonferroni's tests.
Acknowledgments This study was supported by the  ANR DON & Co project. S Cheat  was supported by doctoral fellowships from  TECHNO I Scholar Program ,  Erasmus Mundus. J. Gerez  was supported by Grant No. 593/08  from  CAPES/COFECUB. The authors are grateful to  AM Cossalter  and the  CIRE team  for the animals care and handling; to  O. Lafaix  for helping in the loops experiments; to  P. Pinton  and  S. Desto  for helping in explants experiments; to  C. Bleuart  and  I. Pardo  for technical assistance for immunohistochemistry; to  F. Lyazhri  for statistical advice; and to  D. Warwick  for language editing. The loops surgery was conducted at  INRA Centre de recherche Val de Loire (Plate-forme CIRE Chirurgie et Imagerie pour la Recherche et l'Enseignement, UMR Physiologie de la Reproduction et des Comportements  ( INRA 0085 ,  CNRS 7247 , université  François-Rabelais de Tours , Institut français du cheval et de l'équitation), 37380  Nouzilly, France).
Nearly 50% of the horses participating in endurance events are eliminated at a veterinary examination (a vet gate). Detecting unfit horses before a health problem occurs and treatment is required is a challenge for veterinarians but is essential for improving equine welfare. We hypothesized that it would be possible to detect unfit horses earlier in the event by measuring heart rate recovery variables. Hence, the objective of the present study was to compute logistic regressions of heart rate, cardiac recovery time and average speed data recorded at the previous vet gate (n-1) and thus predict the probability of elimination during successive phases (n and following) in endurance events. Speed and heart rate data were extracted from an electronic database of endurance events (80-160 km in length) organized in four countries.
In total, the database included 4070 different horses and 7032 starts in high-level national and international endurance events over distances of 80 to 160 km (rated as 1-, 2-or 3-star events) (S1 Table ). A total of 1622 horses participated in at least two events; the proportion of multi-starters did not differ significantly as a function of distance, country or the reason for elimination (S2 Table ). The events took place in four countries: France (FRA, accounting for 78. 7% of the event), the United Arab Emirates (UAE: 4. 7%), and Spain+Portugal (SPA+POR, pooled data: 6. 6%). Missing data for some horses (age, phase and reason for elimination) were retrieved from the FEI's web database  [1]. Two categorical variables were used to characterize the events in which HR and speed data were collected: -the type of event, according to the distance ridden in one day: 1-star events (80 to 119 km: 27.
2%), 2-star events (120 to 139 km: 54. 9%) and 3-star events (140 to 160 km: 17. 9%). -the country in which the event was organized: FRA, SPA+POR and UAE. Climate data (temperature and humidity) for these zones were extracted a posteriori from a meteorological database and are given in S3 Table  [20]. For the logistic regressions, data from SPA and POR were pooled because of the low number of events and the similar weather conditions in the two countries. At each vet gate, the HR is measured by a veterinarian using a stethoscope. The CRT was calculated as the difference between the arrival time (at the end of the phase) and the time of veterinary inspection (referred to as the "time in" by the FEI endurance rules  [2] ). When two HR values were available for the same inspection, only the first value was considered for further analysis.
Lastly, in order to check the validity of the predictive logistic regressions, we computed the probability of elimination for an independent dataset of 80 horses having competed in 1, 2 and 3-star endurance events organized in 2014. The 80 horses were randomly selected so that the dataset contained 10 qualified and 10 eliminated horses for each phase (i. e. phases 2, 3, 4 and 5).
Statistical analysis We used predictive logistic regression analysis to estimate probability of elimination from vet gates 2 to 5 and at the finish. The input variables were AS, CRT and HR measured at the previous vet gate (n-1). Given the absence of pre-ride data (CRT and HR at rest), horses eliminated at vet gate 1 (i. e. after the first phase of the event) were not included in the study. For each vet gate, an equal number of Q and EL horses were selected at random from the total set of available horses. Logistic regression consisted in computing the probability of be classified as qualified (Q) or eliminated (EL), using three explanatory variables (AS, CRT and HR) and two categorical variables (distance and country) as input variables. Briefly, the odds ratio was log-transformed (using the Newton-Raphson algorithm) in order to iteratively optimize the best model of the categorical and quantitative explanatory variables (using NCSS-2007 software  [21, 22] ).
In the present study, R 2 values were between 0. 12 and 0. 37; however these values did not accurately reflect the logistic regressions' predictive power. Using the same dataset, we also computed logistic regressions to predict the reason for elimination (i. e. LA, ME, RET and OR) at each vet gate (S4 Table ). The correct classification rate at each vet gate ranged from 51. 18% to 73. 5%; these values were lower than for Q vs. EL predictions.
Validation of the predictive logistic regressions with an independent dataset The ROC curves enabled us to determine the false positive rate and false negative rate with regard to the probability of elimination. When considering the horse's health, it is better to have false positives than false negatives. A probability of 50% would have generated too many false negatives. Hence, we chose a probability of elimination of 70% as a threshold for classifying the predictions based on an independent dataset from 2014. Using this threshold, 75% of the 80 horses were classified correctly (Table  4 ). The error rate of 25% corresponded to a false negative rate of 8. 7% and a false positive rate of 16. 3%.
Discussion By analyzing a large dataset from equine endurance events (from 80 to 160 km in distance) organized in four different countries, we were able to compute logistic regressions and estimate the probability of elimination at each vet gate. On average, between 64. 8 to 86. 6% of all eliminations at vet gates 2 to 5 could be predicted from data collected at the previous vet gate. Furthermore, the predictive models' robustness was confirmed with a new, independent dataset; 75% of the eliminations were correctly predicted. These results confirm the relationship between CRT, HR and AS measured at phase n-1 and elimination at phase n. Changes in CRT, HR and AS during the event are clearly associated with an increased probability of elimination at the next vet gate. The higher the speed, the higher the probability of elimination.
This result is in line with previous studies  [3, 8]. The AS in phases 2 and 3 appears to be strongly associated with the probability of elimination at vetgates 3 and 4. Most metabolic changes occur during the first half of an event  [4, 23] ; when combined with high speed, the effect of these changes  increases the probability of elimination later in the event. Elevated values of CRT and HR were also associated with a greater probability of elimination  [24]. The data collected in the present study corresponded to a quarter of the FEI-registered endurance events organized between 2007 and 2011. These data were more representative of the major events, which have a high enough budget to finance electronic time-keeping. Hence, our results might have been biased by the high-level events. Most of the events in the database took place in three European countries (SPA-PORT and FRA), with few events in the UAE.
Other countries with major endurance events (the USA, New Zealand, Australia, South America countries, the UK, South Africa, Italy, etc. ) were not included in the database. Strictly, the results presented here can be applied in FRA, ESP, PORT and UAE-the countries on which our regression models were based-and thus should be evaluated with caution in other countries (particularly those with a different climate and/or different course profiles. Twenty-three percent of the horses were represented twice or more in the database because they started in more than one event. However, participation in an event can be considered as being partially independent of the other events, since an event's outcome depends on (among other things) the horse's fitness and the prevailing environmental conditions (other competitors, the course profile, weather conditions, riding style and tactics, etc.
Furthermore, our predictive logistic Table  4. Validation of the predictive logistic regressions by applying an independent dataset (including 80 horses having competed in endurance events in 2014). By applying a 70% probability of elimination in the regression calculations, the correct prediction rate was 75%. There were relatively few false negative predictions (8. 70%), whereas the false positive rate was higher (16. 30%). regressions took into account the main effects of the event level (distance) and the country in which the event took place (which includes weather conditions and other specific factors). Eliminations for LA or MET are intended to prevent tired horses from continuing the event and then developing conditions associated with exhaustion and illness  [4, 25]. Despite the application of stringent rules and thorough veterinary examinations, it has been reported that 12% of the horses starting an endurance events require emergency treatment after wards  [12].
DISCUSSION The management of individuals with CF infected with NTM is extremely challenging. The limited amounts of published research and clinical trial data provide inadequate evidence to base management decisions on how best to screen, diagnose, detect and treat NTM-PD. As a response to this urgent clinical need, the CF Foundation and ECFS formed a committee of clinicians, scientists and infectious disease experts to develop recommendations to guide and assist clinicians in the management of NTM-PD in individuals with CF. The committee believe these recommendations should serve as a benchmark for current medical care while providing a framework to inform the development of clinical, translation and basic research studies to generate robust evidence to base future iterations of these management guidelines leading to better outcomes for individuals with CF infected with NTM.
3 f isolates grow well on blood agar plates, the routine identification of Nocardia strains using conventional phenotypical methods is a fastidious and time-consuming process that is now restricted to reference centers. 2  In recent years, 16S rDNA gene sequence analysis has become accessible to clinical laboratories for definitive species identification. 4 In our case, the strain of N. veterana was confirmed at the species level by almost complete 16S rDNA gene sequencing (1315 nt) by using SQ1 (5 -AGAGTTGATCMTGGCTCAG-3 ) and SQ6 (5 -CGGTGTGTACAAGGCCC-3 ) primers, according to previous published data. 5 The sequences obtained presented a 99. 9% sequence similarity (one difference out of 1315 nt, excluding the primers) to the type strain N. veterana DSM 44445 T. The MALDI-TOF MS identification systems are based on the comparison of the tested isolate mass spectrum with reference databases.
Â ðpreÀtreatment countÞÀðpostÀtreatment countÞ preÀtreatment count Where possible, the percentage reduction per nematode species (Ostertagia and Cooperia) was calculated. A bootstrap analysis approach based on  Vidyashankar et al. (2007)  was used to estimate the arithmetic mean faecal egg count reduction per treatment and the 95% confidence intervals. For each dataset, new values for the pre-and post-treatment counts were sampled with replacement from the observed counts, and the bootstrapped efficacy was calculated as described above. This was repeated 1000 times and the 95% confidence intervals taken as the 2. 5 and 97. 5 percentiles of the resulting distribution of bootstrapped efficacies. All statistical analyses were performed using R (R Core Team, 2015).
Comparison of the sampling assumptions and analytical methods In order to explore the statistical consequences and implications of the standard protocol followed for this study, data from the 80 trials were re-analysed under three sampling assumptions including the screened data as described above. To generate the second group of datasets, the entire pre-treatment data were considered. This included data from animals that were not enrolled in the treatment groups due to a low or negative faecal egg count from this sample (unscreened data). The motivation was to correct for regression to the mean, induced by using the same faecal egg count sample as both the screening and pre-treatment sample, which would be expected to introduce a bias in the estimated efficacy calculation. The third procedure involved extending the first procedure to utilise the pretreatment data from both treatment groups in order to reduce the uncertainty in the estimated efficacy (grouped data).
All three datasets from each of the 80 trials were analysed using two statistical methods: a modified non-parametric bootstrap method, and an Markov chain Monte Carlo method. The latter was modified from  Denwood et al. (2010)  to allow an individual animal efficacy fitted to those animals for which a post-treatment faecal egg count was observed. This approach explicitly controls for several levels of within individual extra-Poisson variance, as described by  Denwood et al. (2012). These statistical analyses were done in R (R Core Team, 2015), with Markov chain Monte Carlo results obtained using the bayescount package  (Denwood, 2015). Full details of these methods are given in the supplementary data file.
United Kingdom The results in the UK are summarised in Table  4. The study was conducted on five dairy farms (UK06-10) in Northumberland from July to September 2011, and five dairy farms (UK02 and UK11-14) in the region of West Sussex from August 2011 to October 2012. The baseline faecal egg count in the UK was medium to high, with an arithmetic mean faecal egg count between 95 and 722 eggs per gram of faeces, and an individual faecal egg count ranging from 37. 5 to 3075 eggs per gram of faeces. It is interesting to note that on farms UK02 and UK14, all treated animals were excreting eggs by 14 days after ivermectin and moxidectin treatment. Moxidectin was considered to be efficacious on four farms (UK09, UK10, UK11 and UK13), of which one farm (UK09) was associated with a 100% observed reduction.
Anthelmintic resistance was confirmed on one farm (UK14) and five cases were found to be inconclusive. In 4 of these cases, the species-specific efficacy calculation (Table  6 ) indicated that the efficacy was below 95% for Cooperia and Ostertagia (UK02) or for Cooperia only (UK07, UK08, UK12). Ivermectin was considered to be efficacious on three farms (UK07, UK09 and UK10), of which 1 farm (UK09) was associated with a 100% observed reduction. Confirmed anthelmintic resistance was identified on three farms (UK02, UK12 and UK14), and the results on four farms were inconclusive. In 3 of these cases, the species-specific efficacy calculation (Table  6 ) indicated that the efficacy was below 95% for Cooperia and Ostertagia (UK11, UK13) or for Cooperia only (UK08). Before treatment, coprocultures were successful for nine out of ten farms and both Cooperia spp.