dataesr/scientific-paragraphs-categorization huggingface dataset:
The uncertainty about the year of birth of the animals whose date of birth was not known fully justifies their exclusion but reduces the sample size and potentially biases the subset used for the survival analyses. Another limit of the survival analyses is that the subset used was not fully representative of the general population of adults registered in the FSDI database, having a higher percentage of saddle horses and a lower percentage of donkey and pony categories. Moreover, in the draught horse group, mares were largely dominant. Among draught horses, many entire males are intended for slaughter, which probably explains why these animals are no longer present in a population of animals over 2 years old. This is consistent with other survival analyses, that were based on the deaths registered in the SIRE base (natural deaths, euthanasia and slaughter) and in particular with the early and rapid decrease in the survival curve during the first 3 years for a population of draught horses intended for slaughter  [15, 21].
Conclusion The FSDI database has proven to be a valuable source of data for studying mortality in the French equine population. It provided an initial approach to equine death ratios on a national scale and an original description of temporal variations in mortality based on a large population of dead equines (141,008). However, the combined use of the FSDI and SIRE databases was limited by the small proportion of equines (18,884) for which  the identification number was both registered in the FSDI database and traceable in the SIRE database. Therefore, results of the survival analyses should only be considered as a preliminary approach to the longevity of French equines. The systematic registration of identification numbers is the most crucial improvement required of the FSDI database. Indeed, such an improvement would enable comprehensive interoperability and synergistic use of the FSDI and SIRE databases.
In fact, this would provide a representative description of longevity, a detailed temporal description and modelling of equine mortality by smaller age groups, sex and smaller breed groups and near real time monitoring of equine mortality. Moreover, such an improvement in the FSDI database would enhance the estimation of the live equine population by more accurate knowledge of dead animals currently registered in the SIRE database, because owners do not systematically declare deaths to the SIRE database managers. Furthermore, cadaver disposal data were used to provide a temporal description, model deaths and objectify excess mortality, thus suggesting its potential interest for equine health surveillance as already documented for human and cattle mortality  [18, 35, 36]. With this goal in mind, useful data, in particular the type of death (natural or euthanasia), could also be collected in the future from owners on a voluntary basis when they request online removal of their dead equine.
Indeed, attitudes regarding the practice of euthanasia can vary greatly between breeds, between countries and over time and without any link to an outbreak. They do, however, depend on other factors such as ethical guidelines, economic factors and changes in regulations with regard to slaughterhouse admission, etc. It would also be useful to collect data on the probable causes of death. Slaughtering data should also be taken into account, as variations in these data could lead to misleading interpretations of certain excess mortality figures brought to light through the FSDI database. Even if not used for detection purposes, equine mortality monitoring could represent an efficient tool for evaluating the impact and evolution of identified health events. It could therefore help in decision-making and assessing the efficiency of control measures.
Acknowledgements We are grateful to the  French Ministry of Agriculture  for providing us with access to their equine mortality data, the French horse and riding institute for providing access to the SIRE data and the  Conseil Regional de Basse-Normandie  for its support. We would also like to thank the staff that manages the SIRE database, the  French  fallen stock companies for data entry and  Delphine Libby-Claybrough , professional translator and native English speaker for her review of the manuscript.
At least one additional parity was produced to evaluate the correlated responses to selection on production traits on both females and castrated males. Data comprised records from 1451 male pig candidates to selection in generations G0 to G8 and records from 1629 females and castrated males to compute the responses to selection from G1 to G9. The average inbreeding level in generation G9 was 0. 19 in the LRFI line and 0. 18 in the HRFI line. Animals born in a given farrowing batch were gathered at weaning (28 days of age) in the same post-weaning unit in one farm. They were tested from 10 weeks of age until slaughter (105 kg BW until G5 and 115 kg BW afterwards) in four pens per batch equipped with single-place electronic feeders. In all batches, 12 animals were allotted per pen. Animals were offered ad libitum a pelleted diet based on cereals and soya bean meal containing 10 MJ net energy (NE)/kg and 160 g CP/kg, with a minimum of 0.
Acknowledgements The authors thank  P Sellier ,  JP Bidanel  and  M Le Gall  for their contribution to the early stages of the studies, the personal from the  experimental units GenESI (Surgères) ,  PEGASE (Saint-Gilles)  and  PTEA (Petit-Bourg)  for animal care and data collection, technical staff in PEGASE and GenPhySE for chemical analyses, video analyses and genotyping. The work was funded by the  French National Research Agency (Agence Nationale de la Recherche ANR ,  ANR-08-GENM-038   PIG_FEED ,  ANR-11-SVSE7004  FatInteger), and by the  Animal Genetics division of INRA. PhD students and postdocs contributed to the work:  R Barea ,  PH Campos ,  T Le Naou ,  H Pastorelli ,  R Saintilan. The authors thank the region  Bretagne, INRA division PHASE and Animal Genetics and IFIP-Institut de la Filière porcine  for funding PhD students.
Using the cDNAs as the templates, the mRNA was assessed by real-time fluorescence quantitative PCR (qPCR) using a SYBR green PCR master mixture (ABI, Pleasanton, CA) in an ABI 7000 real-time PCR system (ABI). The primers used in the qPCR are listed in Table  2. In the qRT-PCR, the 16S rRNA gene of L. interrogans serovar Manilae strain L495 was used as the internal reference. The qRT-PCR data were analyzed using the ΔΔC T model and the randomization test in the REST 2005 software. For subsequent qRT-PCR analysis of wild-type, ΔebpA1, ΔebpA2, and ΔebpA/ebpA ϩ strains, RNA was generated from strains grown in EMJH medium with 1% rabbit serum until reaching late logarithmic phase as described above. cDNA was generated by using 1 g of triple-DNase-treated RNA with the iScript cDNA synthesis kit according to the manufacturer's instructions (Bio-Rad).
The viability of the spirochetes was confirmed by growth in standard EMJH medium. Cell morphology was measured in wild type, ΔebpA1, ΔebpA2, and ΔebpA/ebpA ϩ cells grown in standard EMJH medium to late logarithmic phase (10 7 /ml) as described above. The lengths of 100 to 200 cells of each strain were measured by using an Olympus Bx53 microscope and Olympus CellSens Dimension 1. 7. 1 software (Olympus, Hamburg, Germany). Fields were randomly selected for measuring leptospire cell length. The average lengths and the standard errors of the mean were calculated by using Prism software (GraphPad, San Diego, CA). Statistical analyses were completed by one-way ANOVA and Tukey's HSD test, where significance was defined as P Ͻ 0. 05. Determination of the contribution of EbpA to virulence in Mongolian gerbils.
ACKNOWLEDGMENTS This study was supported in part by grants from the  National Natural Science Foundation of China  (grant  81501713  [W. -L. H. ]), the  China Postdoctoral Science Foundation  (grants  2015M570516  and  2016T90547  [W. -L. H. ]),  Fundamental Research Funds for the Central Universities  (no. 2016QNA7016  [W. -L. H. ]), the  National Science Foundation  (grant  IIA-1159099  [C. J. P. ]), and the  Institut Pasteur (M. P. ).
The host reservoirs may be infected asymptomatically while infected humans with exactly the same serovars may develop serious illnesses  [15, 16]. Preventive measures by public health sections were typically focused on the increase of public awareness on the exposure risks when dealing with animals  [17]. However, understanding of the diversity of Leptospira serovars in animals and humans should also be taken into account in order to identify associations of animal reservoirs with human infection. We established a large-scale dataset gathered from passive surveillance over a six year period, which may represent the largest and longest study of leptospirosis among buffaloes, cattle, pigs and humans in Thailand so far. This basic knowledge of serovars and their maintenance hosts is critical to provide more understanding of the epidemiology of leptospirosis.
Data sources A total of 7,218 livestock serum samples derived from 432 buffaloes, 3,648 cattle, and 3,138 pigs were submitted to National Institute of Animal Health, the Department of Livestock Development, Ministry of Agriculture, Thailand from January 2010 to December 2015 under the passive surveillance program of leptospiral seropositivity, which is a part of the passive surveillance program. Most of samples in this study were collected from rural areas. This study was not basically designed for research proposal; an animal ethic protocol was not required. The samples were sent by different reasons, e. g. , routine diagnosis, health check, and leptospirosis investigation. Most animals were not vaccinated against leptospirosis (6,934, of 7,218 samples, 96. 07%) and were collected from rural areas.
The 1,990 human serum samples under suspicion of leptospirosis, which have some clinical symptoms of leptospirosis such as high fever, headache, muscle aches, Jaundice, and diarrhea, were sent to National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Thailand during January 2010 to December 2015. Data collection was performed as a part of routine clinical examination procedures for which results were previously transmitted to patient and consent was thus not required by the Ethics Committee. Data contained in the patient's records, without any patient information, except location and time, was de-identified prior to an anonymous analysis.
Logistic regression We fitted data from all serum samples using the logistic regression model to investigate seropositivity with regard to species and regions. The selection of the best model used a forward stepwise approach based on the Akaike Information Criterion. The best logistic regression model was the univariate model for the serovars Bratislava, Ranarum, and Sejroe (Table  2 ) and the multivariate model for the serovars Tarassovi and Shermani, and MAT appearance (Table  3 ). The effect of the region was analyzed in reference to the central region and the effect of the species in reference to humans. The central region was chosen as it is the region for which has the highest number of sample. Thus it is the most powerful disposition to study contrast between regions in reference to the central.
Indeed, the present study was based on data derived from passive surveillance system. One must interpret the results with caution. Thus, an active surveillance with well sample collection design is also suggested to confirm our passive surveillance to perform to better understand the disease transmission in the field. The size of samples in each species and region could not be controlled and depended on farmers decision making and public health campaigning. The samples in this study were not exclusively collected for leptospiral surveillance. However, this sample size was large enough to illustrate the whole picture of leptospiral infection in livestock in Thailand. Additionally, the samples were collected from different scales of livestock operations. The risk of leptospiral infection was, therefore, different.
Acknowledgments We thank the staff of the  Regional Veterinary Research and Development Center and National Institute of Animal Health , the  Department of Livestock Development, Ministry of Agriculture and Cooperatives of Thailand  for providing data of animal leptospirosis. We also thank the staff of  National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Thailand  for providing human data. We thank also the  Ministries of Foreign Affairs, France (MAEDI) ,  Higher Education and Research, France (MESR) , the  Office of the Higher Education 481 Commission, Thailand (OHEC) ,  Mahidol University , and  Naresuan University  for their support in the  Siam-PHC program. We thank  Prof. Dr. Angeli Kodjo  and  Prof. Dr. Serge Morand  for helpful discussions.
Study design and patients Data were collected prospectively mainly by two dermatologists (PC and RP) at the dermatology department of Cayenne Hospital between Jan 1, 1969, and Dec 31, 2013. We defined a confirmed case of M ulcerans infection as a clinically compatible cutaneous or bone lesion with the presence of M ulcerans detected in the smear or by histological examination (both with Ziehl-Neelsen staining), in culture, or with a positive M ulcerans PCR targeting IS2404. We defined a suspected case as a clinically and histologically compatible (dermal necrosis or coagulation) cutaneous or bone lesion that healed with treatment.
Procedures We collected data for the following variables for all confirmed and suspected cases of M ulcerans: age, date of diagnosis, sex, residence, location of the lesion, type of lesion, associated symptoms, and diagnostic method (smear, culture, PCR, or histology). We obtained population data (1967, 1974, 1982, and 1990-2013), stratified by age and geographical location, from Insee. We pooled the 22 districts of French Guiana into five areas according to their proximity and the existence of connecting roads: Cayenne (Cayenne, Remire, Matoury, Macouria, and Montsinery), Sinnamary (Sinnamary and Iracoubo), Mana (Mana, Javouhey, and Awala Yalimapo), Kourou, and rural areas (all other districts).
This calculation allowed a mean M ulcerans infection incidence to be obtained, which we presented as the number of cases per 100 000 person-years. We calculated age-specific incidence with the number of cases for an age class divided by the sum of the yearly exposed population of that age class for a time period. We classified patients younger than 15 years old as children. We compared qualitative data using a χ² test. We evaluated temporal changes of mean incidence and age using univariate linear regression. We tested the slope of the regression line (ß) for significance. We used a Pearson's goodness of fit test. The significance threshold for the p value was 0•05. This study was done at a single centre and resulted in anonymised patient records (the database did not include names or any variable that could allow precise identification of patients) as authorised by the French regulatory authorities.
90; working distance = 0. 9 mm). The lateral resolution is an (x, y) interval of 0. 129 μm, with a vertical numerical step of 1 nm. The analyses were performed using the Scale-Sensitive Fractal Analysis using Toothfrax and Sfrax software (Surfract, www. surfract. com) following  Scott et al. (2006). Complexity (Asfc or Area-scale fractal complexity) is a measure of the roughness at a given scale. Anisotropy (epLsar or exact proportion of length-scale anisotropy of relief) measures the orientation concentration of surface roughness (Table  1 ; Appendix 1). Before each sets of statistical tests in this study, data were rank-transformed in order to ensure the conditions for parametric tests were met  (Conover and Iman, 1981). Data were then analyzed using two-way repeated measures ANOVAs and corresponding post-hoc tests (Pairwise Student test, performed with the Bonferroni adjustment method to take into account non-independent observations).
Complexity (Asfc) and Anisotropy (epLsar) were calculated for each scan. This results in a dataset with 60 observations (UM1-3 and lm1-3) for a 10ewe sample. The ewes considered in this study have different ages, and so some individuals do not have a fully erupted third molar. However, they are considered in the datasets so the observation they represent is associated with no values for the two textural parameters. In order to simulate samples with isolated cheekteeth belonging to possibly the same individuals, three sets of 10, 20, or 30 observations are drawn randomly from each 60-observation sample (grass, clover, and clover/barley; Figure  1. 5). This process is repeated a thousand times to generate a significant amount out of possible cases. As stud-ies on fossil specimens have to primarily contend with isolated teeth with no indication as to whether or not they belong to the same individual, no precaution is taken here to avoid sampling multiple observations from the same individual (although the random sampling is without replacement).
The result is a dataset with three groups (grass, clover, and clover/barley) containing 10, 20, or 30 observations each. For each of the thousand iterations, data are rank transformed to ensure the conditions to carry out parametric statistical tests are met. As we are considering datasets as we would in the fossil sample, single classification ANOVAs (and not repeated measures) are then performed to highlight any significant differences between clover-, grass-, and clover/barley-fed ewes. Results are reported as the frequency of significant p-values over the 1000 iterations.
We overcome this issue by identifying dental facets from upper and lower molars that indeed share the same functions during the lingual shearing phase I and thus similar textures on enamel surfaces. Finally, the ewe dataset is used to simulate the characteristics of a fossil sample. We show that the common practices of combining isolated determined and undetermined upper and lower molars (especially using inappropriate dental facets) resulted in a loss of ecological discrimination at a population scale, but also at the level of species and communities through space and time. To track environmental changes in the fossil record requires us to indeed strengthen the protocol rather than the sample size.
ACKNOWLEDGMENTS The authors thank the staff from the  Mourier Farm Station, E. Pottier (IDELE) ,  B. Grillon ,  J. -M. Grolleau  (Limovin),  F. Vannier  ( President of the CIIRPO ),  J. Merceron  (Director of the IDELE), as well as G. Reynaud (iPHEP). The authors thank  N. Brunetière  and  A. Francisco  ( P prime Institute, CNRS, ENSMA, and University of Poitiers ), two members of the  TRIDENT  project team. We are also grateful to editors of PE and anonymous reviewers that improved the manuscript by all of their comments. This study was financed by the Project  ANR   TRIDENT  ( ANR-13-JSV7-0008-01 , PI:  G. Merceron ).
Data collection A general clinical examination was performed before exercise. The gait of the horse was assessed by trotting in hand in a straight line over 30 m to exclude any lameness. Before starting the exercise, the two different devices for heart rate monitoring were placed on the horse and the registrations were started at the same time. These were a telemetric ECG (Televet 100 ® , Jørgen Kruuse, Denmark) and an HRM (Polar S810, Polar Electro Öy, Finland). The electrodes of the ECG were placed according to the positions proposed by Zucca et al. (33)  (Figure  1 ), and linked with a wire to a registering box in the saddle pad. For the HRM, one electrode was placed under the saddle just behind the withers on the left side and the other one was placed on the left side over the heart under the belt.
Data analysis After the exercise test, the recordings were transferred to a Windows ® -computer by Bluetooth ®. The first step of analyze was performed offline with the reading software belonging to each device. For the HRM, the Polar Pro Trainer 5 ® software was used to correct the data with the set up "very high" filter power and a minimal protection zone of "1 beat per minute (bpm). " This setting was chosen to have a minimum of corrections by the Polar software, in order not to correct and flatten the recording twice and, therefore, minimize the loss of variability in the HR. After visual assessment of the data, each HRM recording (with an average duration of 90 min) was then exported as RR intervals in milliseconds to a text file. If the corrected curve was too erratic and didn't allow to see the different parts of the exercise test, the recording was removed from analysis.
For the ECG, the Televet 100 5. 0. 0 ® software was used, and the raw data were visually assessed. Parts of the registration with severe artifact due to loss of electrodes for example were excluded from the analyses. For the remaining ECG data, RR intervals were calculated by the software by setting the RR deviation recognition at 30% as suggested by the manufacturer. The RR intervals were subsequently exported in milliseconds as a text file. For comparison of the recordings of the two devices, further processing of the data was necessary (Figure  2 ). For this, the exported RR intervals from both devices were corrected by a home-made Excel ® algorithm. This algorithm was programmed to recognize the five types of registration errors that have been defined in veterinary medicine  (21, 35). This includes errors as erratic values (I); RR intervals that are abnormally long (II); or short (III) directly followed by a short or long interval, RR-intervals corresponding to the double or triple of the precedent RR-interval (IV); or extremely short successive intervals (V).
As the aim was to compare the devices, no effort was made to classify the identified errors as physiological or not. The recognition limit was set as a variation superior to 30% with the previous or following RR interval. After automatized correction of the entire datasets with the Excel ® algorithm, the RR text file was imported into the Kubios HRV ® software (University of Eastern Finland) for analysis of the HRV. Two paired ECG-HRM datasets of 5 min were chosen, one from the warm-up period and one from the canter phase. The choice was made visually on a stable HR period, starting from a common spike on both recordings (ECG and HRM) (Figure  3 ). Trend components were removed from every dataset using the "Smoothn priors" method with a flattening parameter (lambda) of 500 ms as recommended by Tarvainen et al.
Pearson's coefficient assesses then the correlation between two datasets, while Lin's coefficient really assesses the agreement between two datasets. The Bland-Altman analysis allows to compare data in absence of a definite gold standard and to quantify agreement and to assess agreement even if there are systematic errors in the compared data sets and, therefore, a correlation might still be present even if one method over-or underestimates the true value  (39, 42). Furthermore, it allows to have a visual representation of the agreement and an idea of the way the data are-or not-agreeing. For the Bland-Altman analysis, satisfactory agreements were set at 5% of the mean value of the measure of HRV for the first agreement measure (i. e. , accepted mean difference) and 20% of the mean value of the measure of HRV for the second measure (i.
| Covariates Data relating to age, sex, education level, smoking status, parental history of allergic disease, place of upbringing (farm with livestock, farm without livestock, village in rural area, small town, suburb of city, inner city) and pet ownership (cats and dogs in childhood and current pet keeping) were retrieved from interviews performed during the clinical examinations of the ECRHS and RHINESSA participants, the same day as the blood samples were taken. Smoking (in adults) was categorized into never smokers, previous smokers and current smokers. The study subject's level of education was categorized as primary school, secondary/technical education and college/ university, whereas the adolescents were categorized as being students/still in school.