dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Each individual was sexed when possible (small individuals cannot always be sexed -  Arsovski et al. , 2018b; Golubović et al. , 2018)  and marked using a notch-code on the marginal-scutes  (Stubbs et al. , 1984). Overall, we encountered tortoises more than 18,500 times in the field (2,798 captures plus 15,789 recaptures). Straight carapace length (SCL) and body mass (BM) were recorded, and individuals were rapidly released at the exact place of capture. To limit unnecessary handling during frequent recaptures, SCL and BM were not systematically recorded. We obtained 9,680 and 1,432 measurements of both SCL and BM from Golem Grad and Konjsko populations, respectively. During field work, we examined all dead (or dying) tortoises. In total, we found 315 dead tortoises on Golem Grad (223 males, 41 females and 51 immatures; this proportion of dead males reflects the highly biased sex-ratio in this site;  Bonnet et al.
Generalized linear mixed models were built in R Version 1. 2. 5001 (R Core Team, 2019) using the package nlme  (Pinheiro et al. , 2019). Competitive models were ranked using the Akaike information criterion (AIC,  Burnham and Anderson, 2002). The masses of certain organs were highly correlated (e. g. left versus right legs). Furthermore, for the purpose of the current study, considering morpho-functional components instead of each separate element improved the conciseness of analyses. Therefore, we assigned all dissected elements into 11 simple units (henceforth designated as components, Table  2 ) that were used in most analyses. The relationship between the total body mass and the respective mass of these components was examined with a backward stepwise regression. We first verified that the mass of these components, used as the dependent variables, was correlated (0.
Dissected versus living tortoises On average, dissected individuals were in the lower part of the range of variation of BCI exhibited by living tortoises (Figure  1 ). Dead tortoises may dehydrate rapidly in the field, especially those with large cracks in the shell. Some tortoises may have lost significant amounts of blood. Rapid loss of fluids may reduce body mass and thus body condition. One female harassed by nine males exhibited low body condition and may have died from exhaustion. On the other side of the spectrum, our sample also included one large female with a high BCI. Overall, our dissection data encompass most of the variation in adult body size, but are not fully illustrative of the whole range of body condition observed in the populations monitored (e. g. heaviest males and small individuals were lacking).
Body reserves Fat bodies were virtually absent in all dissected individuals, representing only 0. 0% to 0. 5% of the total body mass. Kopsch (2006)  did not provide information regarding fat bodies in the dissections of 92 captive individuals (3 Testudo, 2 terrapins, and Geochelone pardalis); suggesting that adipose tissues were negligible. Sporadic cases of obesity have been reported in overfed captive tortoises, with pads protruding from the openings of the shell  (Burchfield et al. , 1980; Lamberski, 2013). But this situation may not occur in natural conditions: we never found an obese individual in our large datasets (> 5,000 individuals) collected in different tortoise species  (Bonnet et al. , 2001; El Mouden et al. , 2006; Djordjević et al. , 2011; Lecq et al. , 2014). Noticeably, in the current study, dissected individuals exhibited average to low body condition (except one very large female).
Acknowledgements Our research was funded by  Ministries of Education, Sciences and Technological Development  (project No. 173043 ). Permissions were issued by the  Ministry of Environment and Spatial Planning of Macedonia and the authorities of the National Park "Galičica"  ( 11-2817/2 ,  11-4093/5 , and  03-246 ). Many thanks to our colleagues  Rastko Ajtić ,  Bogoljub Sterijovski ,  Sonja Nikolić  and  Ana Ivanović , as well as to students  Uroš Ilić  and  Stefan Avramović  who considerably helped us in dissections.
Pharmacokinetic and Statistical Analysis Pharmacokinetic modeling of the plasma concentration-time data was performed using Phoenix ® 8. 1 (Certara, Cary, NC, USA). Values below the LOQ were excluded from the dataset. The structural model for both iohexol and fluconazole was a twocompartmental model with first order elimination. A multiplicative error model was used. For both PAH, R-and Spindolol, a one-compartmental model with first order elimination and multiplicative error model was used. The estimated primary parameters were volume of distribution (Vd) and total body clearance (CL TOT ). Also the following secondary parameters were calculated: elimination half-life (T 1/ 2el ), elimination rate constant (K e ), volume of distribution at steady state (V ss ), and the area under the curve from time 0 h to infinity (AUC 0→inf ).
The AUC 0→48h was calculated by noncompartmental analysis (NCA) using the linear up-log down trapezoidal method. The nonrenal clearance (CL NR ) was obtained by subtracting the CL R from CL TOT. The CL TOT of iohexol is a measure for the GFR. The CL R of PAH is used to estimate the ERPF. The filtration fraction (FF) was calculated by FF % ð Þ = GFR ERPF Ã 100 The filtration clearance of the unbound marker (CL fil ) was calculated by f u × GFR, where f u is the unbound fraction of the compound in plasma. Net tubular anion and cation secretions were calculated as CL R -CL fil. CL fil -CL R of fluconazole was used to calculate the net tubular reabsorption of fluconazole. To investigate if it was possible to reduce the time span of urine and blood collection the PK calculations of fluconazole were repeated using the collected data up to 24 h.
The urinary recovery, which is the fraction of the administered dose recovered in the urine was calculated as A e /D. Results are presented as mean ± standard deviation  (SD). Differences between the PK parameters for the pindolol isomers were investigated using a Wilcoxon signed rank test [SPSS 25. 0 (IBM, Chicago, IL, United States)]. The same approach was used to assess the agreement between the CL R fluconazole after calculation using the data up to 24 and 48 h. The significance level was set at p = 0. 05.
Ongoing trials (phase 1 and phase 2 against Trichuris trichiura) will enable us to evaluate its possible toxicity  [25]. This point is key because the development of another benzimidazole to treat filariases, flubendazole, was interrupted in 2017 because its toxicity associated with effective doses was considered unacceptable  [30]. The second macrofilaricidal candidate is emodepside, whose efficacy was demonstrated in pre-clinical trials  [29] , and which was evaluated in two phase 1 trials, one using single ascending doses  [24] , and the other using multiple ascending doses (https://clinicaltrials. gov/ct2/show/NCT03383614). The last candidate is a drug called TylAMac (Tylosin Analogue Macrofilaricide, ABBV-4083), which is a macrolide antibiotic effective against Wolbachia  [41, 43] , and which was also evaluated in a phase 1 trial terminated in 2018 (https://www.
5 mL/min; injection volume: 10 lL; autosampler temperature: 4 °C; column temperature: 40 °C; and flush port: methanol. The detection consisted in multiple reaction monitoring (MRM) in positive mode. IVM was detected for m/z 892. 400 > 307. 200. The lower limit of quantification (LLOQ) for the method used was 0. 1 ng of IVM per mL. The analysis of the pharmacokinetic parameters (mean C max , T max , C last , and AUC 0-tlast ) was undertaken with the help of WinNonlin, Phoenix 64, version 8. 0 software.
Acknowledgements. This study was co-funded by the  Institut de Recherche pour le Développement  ( IRD, Marseille, France), MedinCell (Jacou, France ) and the  Research Foundation for Tropical Diseases and the Environment (REFOTDE, Buea, Cameroon). We thank the REFOTDE personnel for their help in the field, as well as  Dr. Elizabeth Hene , veterinarian, and  Lucy Enow  and  Emilia Ayompe , veterinary assistants, for having performed the skin snips and the nodulectomies of the animals. We thank the personnel of the  Echevarne Laboratory, Barcelona, Spain , for having performed the pharmacokinetic analyses.
Isolate collection The study was conducted with the European Union Reference Laboratory for equine diseases other than African horse sickness (ANSES, Laboratory for Animal Health, France), the National Reference Laboratories for CEM from Belgium, Czech Republic, France, Germany, Poland, Spain and Switzerland and two laboratories outside Europe: the Central Veterinary Research Laboratory in Dubai, United Arab Emirates, and the National Veterinary Services Laboratories in Ames, United States. A total of 367 T. equigenitalis strains isolated from horses, two ponies and two donkeys, were included in this study: 119 strains were obtained and characterised using MLST; the MLST sequence data for 242 strains were downloaded from the online Taylorella MLST database (https://pubmlst. org/taylorella/), and the MLST sequence data for six strains were obtained from Hicks et al.
MLST The seven MLST loci (gltA, gyrB, fh, shmt, tyrB, adk and txn) were amplified and sequenced according to previously published experimental conditions  (Duquesne et al. , 2013). The Taylorella MLST database (https://pubmlst. org/taylorella/) was used to assign allele numbers and STs. Novel alleles and STs were allocated by the database curator. All the sequence data for the 119 strains typed in this study are available from the Taylorella MLST database.
Data analysis The sequence data obtained from the online Taylorella MLST database and from the literature  (Delerue et al. , 2019; Hicks et al. , 2018)  were incorporated into the dataset. The sequences of every individual locus were trimmed to equivalent lengths, like in the Taylorella MLST database. Sequences were aligned and a phylogenetic tree drawn up using MEGA software version 7. 0  (Kumar et al. , 2016). For each unique ST, sequences of the seven MLST loci were concatenated, giving an inframe sequence of 3521 bp not including nucleotide insertions/deletions. The phylogenetic tree was based on the concatenated sequences using the maximum likelihood method based on the Tamura-Nei model  Tamura and Nei, 1993. Support for internal nodes was estimated using the nonparametric bootstrap method with 1000 replications.
The tree was rooted with the ST28-concatenated sequences of the Taylorella asinigenitalis CIP 107673 T reference strain as the outgroup, extracted from the Taylorella MLST database. Relatedness between STs was analysed based on allelic profiles using eBURST software version 3  (Feil et al. , 2004; Spratt et al. , 2004). The BURST algorithm, based on a set of hierarchical rules relating to the number of single-locus variants (SLV) and double-locus variants (DLV) involved, identifies mutually exclusive groups named clonal complexes (CCs) of related STs in the population, and attempts to identify the primary and subgroup founding ST for each CC. A CC was defined as major when a primary founding ST was predicted. All others were considered minor. Global optimal goeBURST  (Francisco et al. , 2009)  diagrams were drawn up using PHYLOViZ software version 2  (Francisco et al.
A hundred and nineteen strains were characterised using MLST, and the MLST sequence data of 248 additional strains were accessed from the online Taylorella MLST database and the literature  (Delerue et al. , 2019; Hicks et al. , 2018). Despite the large selection of strains in terms of geographical origin, year of isolation and equine breed, one area of concern is the amount of emphasis put on the number of strains within the same ST. In this study, a T. equigenitalis strain was usually isolated from a CEM-positive animal reported in a single year. However, several strains with the same ST may be represented multiple times, subjected to repeated sampling (perhaps in relation to a treatment protocol) or due to animals CEM-positive over a period of several years (Additional file 1); nevertheless, considering the known data on strains, this situation concerned only a limited number of strains and has not been considered a significant bias.
Compared to previous MLST results, the present study confirms the circulation of the founding complex, CC1, in France over a long period of time  and confirms its distribution in  Spain (2015 Spain ( -2016) )  and Germany too, but not in the six other European countries investigated, i. e. Austria, Belgium, the Czech Republic, the Netherlands, Poland and Switzerland. In the 1970s-80 s, and perhaps still today, CC1 was circulating worldwide yet it does not seem to have spread throughout Europe according to the dataset analysed. This observation should nevertheless be confirmed by a retrospective analysis of strains isolated from the first reported outbreaks in these European countries, if they are available. Apart from the epidemiological relationships described through CC1, few epidemiological relationships between non-European and European countries were revealed in the present study.
Within Europe, ST16 included in CC2 has emerged as the dominant genotype with a cross-border circulation; it is composed of strains from  Austria (2004 ), Belgium (2008 -2013 ), France (2004 -2012 ), Germany (1989 -2009 ), Poland and Switzerland (2005 -2008). ST46, the primary founder of CC8, seems to have the same profile as ST16 without any Polish strains, but it was found to be mainly composed of German strains plus the strain isolated in 2018 from the vaginal discharge of a French mare infected by cryopreserved stallion semen collected and processed in Germany in 2012  (Delerue et al. , 2019). This Germanic origin could be extended to CC8, which is mainly composed of German strains first isolated in 1981. Interestingly, the genetic heterogeneity of German strains was much higher (0. 60 STs/strain) than that of the entire dataset despite representing only 5.
PCR for Antigen Receptor Rearrangements (PARR analysis) For dogs with a diagnosis of high-grade multicentric lymphoma, paired samples (tumor DNA and plasma DNA) were analyzed by PCR for Antigen Receptor Rearrangement (PARR). M13-tailed primers were used to target immunoglobulin heavy chain genes for B lymphocytes and the T-cell receptor γ for T lymphocytes (Table1)  18, 37, 38. Amplification was achieved with the Type-it Multiplex PCR Master Mix (Qiagen), as previously described  39  , and with the following conditions: denaturation at 92°C for 5 min, 35 cycles of 95 °C for 8 sec, 49 °C for 10 sec, and 72 °C for 15 sec (for IgH major, IgH minor and TCR Burnett), or with 30 cycles of 95°C for 45 sec, 48°C for 30 sec and 72 °C for 15 sec (for IgH Tamura, T Yagihara VA and Vb)  39. The PCR amplicons were separated on a 370 ABI sequencer (Applied Biosystems) and Clonal profiles were determined with the Genemapper Software.
According to input quantity, only reactions containing at least one, two, three and four positive droplets for ]0-7ng], ]7-15ng], ]15-100ng] and >100 ng input quantity respectively were classified as positive in the ddPCR analysis. Concerning MDM2 and TRPM7 in canine OMM, copy number was determined using QuantaSoft analysis software (version 1. 7. 4, Bio-Rad) that calculated the ratio of the target molecule concentration A (copies/μl) to the reference molecule concentration B (copies/μl) multiplied by the number of reference copies in the canine genome (copy number = (A/B)*2). Those genes were considered amplified when the copy number was ≥ 3 and ratio's confidence interval was > 2. The same method was used to quantify MRD in the plasma of lymphoma-affected dogs (A: lymphoma-specific antigen receptor rearrangement and B: CFA 26 region, control, and ctDNA% = (A/B)*100).
Acknowledgments The authors acknowledge all the technicians, pathologists, veterinarians and nurses that contributed to the project (particularly  Drs A. Garand ,  F. Allais  and  A-S Guillory, IGDR, Rennes) , the  Cani-DNA Biological Resource Center (Biosit , Rennes: http://doggenetics. genouest. org)that provided the samples,  Stéphane Dréano  for sequencing facilities,  Yann Le Cunff  for its help in statistics. The authors acknowledge Laurent Griscom (IGDR, Rennes) who assisted them for proofreading of the manuscript.
Source of support: AP is funded by a PhD fellowship from the  FHU CAMIn University-Hospital Federation Cancer, Microenvironment & Inovation  ( 2018-2020 ) of the  University of Rennes 1, France. This study is partly funded by the  American Kennel Club Health foundation CHF  grant  2446. The  Cani-DNA BRC  (http://dog-genetics. genouest. org), is funded through a PIA1 funding ( 2012-2022 ): the  CRB-Anim infrastructure ,  ANR-11-INBS-0003.
Then, a regression model is fit independently to each range of speed. A support vector machine (SVM)  [16]  is used for the classification part. This method refines the regression model's accuracy for the slow speed regime. SVM is a machine learning approach that can be used for both classification and regression. The concept is simple: one has to provide a dataset, called the training dataset, with a known variable of interest value (for example, IMU signals matched to their associated speed) that will be used to build a model. The model will then be able to predict the value of the variable of interest for new data. The model has to be trained with cases that can be encountered in its future application, without which it will perform poorly. The objective of this work is to develop a model that can be included in a smart device that can provide the rider with the movement parameters of his/her horse, in daily routines such as during a training session, as well as during competition events, using only one IMU fixed in the pommel of the saddle.
Data The database used for model development was made of 3221 canter strides from 58 ridden jumping horses of different breeds, size (129-176 cm), age (5-18 years old), and different levels of competition (amateur or professional). One IMU (LSM6DSL, STMicroelectronics, Geneva, Switzerland) placed in the saddle pommel close to the horse's withers was used to measure tri-axial acceleration (range ±8 g) and tri-axial rotation rate (range ±2000 dps) at a sampling frequency of 100 Hz. It was fixed directly to the saddle tree, before its assembly. Data collected by the IMU were sent via a Bluetooth R antenna to a smartphone (iPhone X, Apple Inc. , Cupertino, CA, USA) and then stored on an online server. Two different protocols were used to collect data: the first one was speed measurement for a straight path, and the second one was speed measurement for a curved path.
For both protocols, reference speed was measured by video cameras or a chronometer and matched to each stride signal to build the machine learning model. In practice, a stride is defined as the period between two successive hoof falls of the same leg. For the present work and each protocol, "strides" are defined from the maximum peak on the Z-axis, which corresponds to the dorso-ventral axis, (cf. Figure  1 ) of the raw acceleration data to the next 100 samples, in order to have the same number of points for each individual regardless of the speed, a necessary condition to use machine learning methods. Therefore, depending on the horse's speed, this data segmentation may include in one "stride" more than one real stride. The authors chose not to re-sample a cycle in order to keep the information on the duration of the stride to estimate speed.
Straight Path To get reference stride speed, IMU data were synchronized to a 4 camera 2D tracking system (Imaging Development System, GmbH, Obersulm, Germany), which had a measuring field of 26 m. Horses were equipped with 10 2D reflecting markers on anatomical landmarks (Figure  2 ), and their speed in the camera's field was derived from the markers' 2D trajectories using a custom software written with MATLAB R (R2014b). The accuracy of this system was 1. 4% of the measured distance  [18] , which would correspond to ±2. 8 cm for a 2 m measured distance, for example. Data were gathered for different speeds chosen by the rider (normal, slow, and fast), with and without jumps, before and after the camera's field, and with or without ground bars, spaced from 2. 5 to 4. 5 m, in the field of measurement.
In order to mimic real-life conditions of a jumping course, the whole database was composed of 2906 strides collected in a straight path and 315 strides in a curved path. An example of signals for one stride is available in Figure  4. The red curve corresponds to a running speed of 8. 6 m/s, and the black one corresponds to a running speed of 5. 0 m/s. The signal length for one stride was correlated to the speed.
Machine Learning Method Several methods can be tested in order to predict the speed from the IMU signals, such as Ridge, Lasso, Partial Least Squares (PLS), Principal Component Regression (PCR) and elastic net regressions  [21] , random forests, neural networks, SVM  [16] , and non-parametric  [22]  and parametric functional regression  [23]. All those methods have been tested on the current database, so we will only present here the one that gave the best results. In the present work, the SVM method was the one that gave the best results. SVM is a supervised learning algorithm that can be used for both classification and regression  [16]. Its goal is to find a function f (x) that has at most deviation from the actually obtained target y i for the training data (here, the speed per "stride") and at the same time is as flat as possible (in the present case, the smoothness parameter was fixed to 4).
Thus, the model did not consider errors as long as they were less than , but would not accept a deviation larger than this. The proportion of the training set used to create support vectors was also a tuning parameter of the algorithm, and it was limited to 75% in the present work, then was automatically calculated by the algorithm  [24]. To sum up, the six signals collected with the accelerometer and the gyroscope for each "stride" were matched with the measured reference speed for the stride. This was used as input data to train the SVM model in order to obtain the best speed estimation for new data in the future. All this process is illustrated in Figure  5. The model was developed with R software and the svm function of e1071 package  [25].
Methods Comparison To compare the accuracy of both the ODBA and SVM models, the database was cut into 2 parts: a training dataset, which was composed of a random sampling of 80% of the database, and the remaining 20% forms the test data set. This sampling prevented over-fitting because the same stride could not be in both sets. The 2 models were built on the training dataset, and their accuracy was evaluated on the test dataset. Comparison between models was done with the calculation of the percentage error in the estimated speed above 0. 6 m/s. This threshold was the minimum satisfactory for this parameter to make sense for the professionals. Percentage error above the target value of 0. 6 m/s was computed as: % error above target value = 100 × ∑ i |Measured speed at stride i -Predicted speed at stride i| > 0.
6 Total number of strides , with i corresponding to each stride of the test dataset. Models accuracy was also compared with: RMSE = ∑ i (Measured speed at stride i -Predicted speed at stride i) 2 Total number of strides and Bland and Altman plots and its 95% limits of agreement  [26] , which allowed the evaluation of differences between 2 methods used on the same individuals (here, strides). In this work, the average differences between each method and reference values obtained with 2D tracking system for straight path and chronometer for curve path were examined. Bland and Altman analysis and graphs were built with the bland. altman. plot function from the BlandAltmanLeh R package  [27].