dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION The follow-up of fifteen bands of 102 piglets enabled the creation of a substantial database composed of daily and individual data on water, feed consumption and weight of weaned piglets to study the possibility of early diarrhea detection on post-weaning. This study showed that feed consumption in relation to weight does not show significant differences between the three weight categories (light, medium and heavy) in healthy pigs. The conclusion is similar for water consumption, except in week 5 when the heavy pig category consumed significantly more water than the light pig category. This result of week 5 should nevertheless be interpreted with caution because the average water consumption of heavy pigs was abnormally increased by the results of one piglet which was a very heavy user or waster of water.
This finding was also interesting because it allowed us to dispense with an analysis by weight category to compare the water and feed consumption of healthy and sick animals in the rest of the study. The data recorded indicates that a piglet drinks on average an amount of water equivalent to 7-11% of its bodyweight, whether healthy or sick. A previous study carried out in France confirms this data  (Massabie et al. , 2013; Massabie et al. , 2014). Of course, these data also include the waste of water, that represented around 19-20% of the total amount of water registered with nipple bowls  (Torrey et al. , 2008). But waste of water can be higher with some pigs that play or refresh themselves with water: this is probably what happened in week 5 for one pig in the healthy and heavy group. The data obtained for feed consumption included between 3 and 5% of live weight and confirmed the first results described from two batches in a previous study using the same machines and the same protocol  (Rousseliere et al.
All these results are insufficient for a commercial tool, which requires at least 85% of sensitivity and specificity. The very low sensitivities seen here may be the result of overlearning the methods with healthy animals. Indeed, sick animals only represented 15% of the training base, which may not be enough for the model to learn the specific behavior of these sick animals compared to healthy animals. Another explanation for this poor performance is the very high intra-individual variability in the feed consumption of healthy animals. The model therefore failed to distinguish a sick animal from a healthy animal, since their feed consumption was in the same range. To improve the results obtained, the next step will be to analyze variables already included in our database but not investigated up to now (such as: duration inside feed and water automata, rooms temperature, veterinary treatments,.
ACKNOWLEDGMENTS Lorena Girre ,  Delphine Loiseau ,  Romain Richard , and the caretakers ( Experimental Pig Station  of  IFIP, Romillé, France ) are acknowledged for care and management of the pigs. The authors would like to thank  Didier Concordet ,  Remi Servien , and  Malika Chassan  for their reflection and discussions on the analysis of the data.
Bacterial Strains Data regarding the strain characteristics are summarized in Table  1. The E. faecium Aus0004 wild-type (WT) reference strain was used  (Lam et al. , 2012). This vancomycin-resistant (vanBpositive) clinical isolate was recovered from a bacteremic patient and belongs to the CC17. The MIC of amoxicillin for Aus0004 strain was 256 mg/l. Three mutants were constructed to evaluate the role of Ern0160 in GIT colonization. As described by  Zhang et al. (2011) , an ern0160-deleted E. faecium Aus0004 mutant (named 0160) was constructed by allelic exchange with a truncated copy of the gene corresponding to 5 -3 positions of ern0160 using pWS3 and specific primers (Supplementary Table  1 ). The ern0160 gene with its native promoter and rho-independent terminator was cloned into the pAT29 shuttle vector using specific primers  (Trieu-Cuot et al.
Suddenness Test In winter trial, we tried another test that finally could not be implemented for technical reason; therefore, data were only collected in summer and fall. The aim of this test was to evaluate the reactivity of the cow to a sudden event. Therefore, we dropped an object in front of the target cow and noted the reaction. A white plastic bowling pin (H = 45 cm ø =10 cm) hanging on a fine string was used as a stimulus. The previous evening, the bowling pin were installed above the cows, out of their reach and field of vision. All cows were in a standing position 5 min before the test. The video recording (GoPro R , San Mateo, USA) was started 1. 5 min before the start of the test for a 2-min duration, using a camera mounted on tripod in front of the cows (cows were previously habituated to the procedure).
ACKNOWLEDGMENTS We would like to thank  Sirine El Hamdaoui ,  Sarah McPherson , and  Véronique Boyer  for their assistance with the animals and with the collection of the study data. We would also acknowledge the staff of the  McGill University Macdonald Campus Dairy Complex (Sainte-Anne-de-Bellevue, QC, Canada)  for their animal husbandry and management. We would also like to acknowledge  Roger Cue  of  McGill University  for his assistance in the development of the statistical analysis. Lastly, we extend our thanks to  Athena Zambelis  for her editorial support and English reviewing.
Results: The performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores.
Artificial intelligence (AI) is described as the ability of a machine to perform "intelligent" functions, for instance, learning, decision-making, adaptation, control and perception  [9]. To execute such functions, a classification process must be triggered so that scenarios can be identified, grouped and properly treated. Machine learning (ML) is a useful AI approach when this classification process depends on a huge data analysis. ML has been used for epidemiological research  [10] , diagnosis  [11] , discriminating pathogens  [12]  and for resolving taxonomic relationships with molecular data  [13]. Thus, we propose that the complexity of Capillariidae species definition, based on egg structures, could be clarified using AI tools. A taxonomic dataset including morphological and morphometrical characteristics of parasite eggs and ecological information was constructed based on specimens from institutional helminth collections.
The process was done to clean dirt and fragments from females, so that only eggs with the chitin shell were present. The eggs' morphology and morphometry were characterized by an optical microscope (Nikon Eclipse E200) at 400× magnification using image analysis software (Image Pro Plus-Media Cybernetics, USA). Thirty eggs per specimen were evaluated, whenever available. The measures considered were: total diameter (width) and length of the eggs, mean value of the width and height of the two plugs and the thickness of the shell (Fig. 1 ). A qualification of the ornaments presented in the outer bark of capillariid eggs was also performed. The parameter of egg ornamentation was divided in four categories following the literature  [14] : (1) smooth, which has no ornaments on the shell, as described by Conboy for Trichuris trichiura eggs  [15] ; (2) punctuated, which has dots like a pitted surface, as described in Eucoleus bohemi by Conboy and Traversa et al.
Discriminant analyses and artificial intelligence/machine learning approaches A dataset of capillariid species from FIOCRUZ and MNHN collections was constructed with the morphological (eggshell ornamentation) and morphometric parameters (MM) (total length and width, base of the polar plug width and height and shell thickness) generated by specimens. In addition, ecological parameters, such as information about the host (H) and geographical location (GL) of specimens, were included. A total of 997 entries were generated (Additional file 1: Table  S1 ). Discriminant analyses were performed using Past 3. 16 software to separate species groups. First, the total length and width of eggs from all species were plotted; then, the discriminant function analysis was generated by each eggshell ornamentation: punctual, RTI and RTII.
8. 3 software  [18]  was conducted. In addition, the present research looked for new criteria to find, describe and name particular species, while keeping the top-down approach of a taxonomy rank. There are several ML/AI algorithms for classification, but only some of them provide decision trees which are similar to the taxonomic keys proposed/ used by systematics specialists to discriminate biological species. Therefore, we focused on Weka's algorithms, which returned representations of decision trees, namely: J48  [19] , Random Tree  [20] , REPTree  [21]  and Logistic Model Tree (LMT)  [22]. The ML classification algorithms produced training models that were tested using cross-validation, providing kappa values. Moreover, we implemented an additional classification using a Majority Voting algorithm  [23] , which integrates all four decision tree classifiers to combine the predictions from multiple ML algorithms and to exploit the different peculiarities of each algorithm.
Genus Aonchotheca Five species were collected from Collection de Nématodes Zooparasites of MNHN. The hosts of all the species were registered as mammals: Aonchotheca annulosa in Apodemus sylvaticus; A. baylisi in Lophuromys sikapusi; A. erinaceid in Erinaceus europaeus; A. murissylvatici in Evotomys glareolus; A. myoxinitelae in Eliomys quercinus; A. pulchra in Tadarida laticaudata and Nyctinomus brasiliensis. In general, the egg morphology was very similar, and the plug bases were mostly prominent, except in A. baylisi, which had a thickening of the eggshell in the plug base region, masking the prominence. The most common egg ornamentation was RTII with four species (Fig. 5l, n, p, r ). One punctuated type ornamentation was present (Fig. 5t ). Aonchotheca pulchra was the only species in this study that did not have ornamentation on the eggshell surface (Fig.
Although this ornament is observed in Baruscapillaria, Capillaria, Eucoleus and Tridentocapillaria, the frequency is not too high. The genus Echinocoleus exhibited RTI and punctuated ornamentations, but this cannot be assumed to be a pattern, as the genus had only two species studied. Pearsonema plica and C. collaris presented RTI ornamentation, although the egg morphology was different; one was narrowed on the extremity (Fig. 4g ) and the other was rounder (Fig. 5c ), respectively. Regarding the statistical analysis of egg measures, no relation between genera was detected. The same was observed for discriminating species. Even though length showed more relevance than width, a large part of the measures overlapped, and it was impossible to discriminate among most of them. When discriminant analysis was employed using the dataset separated by ornamentations, only 5 species among 28 could be identified (E.
Regarding GL, first, for a more complete dataset, the parameter was defined by all the continents where the species was recorded, based on an extensive literature revision of capillariid identifications. However, species with worldwide distribution, such as C. hepaticum, presented multiple re-entrances in the dataset. The observed ML decision errors indicate that, whenever such worldwide distribution exists, it will be necessary to have additional egg features to improve the results. When using two different entrances for the same specimens, both for H and GL, the program tends to choose which one differentiates more between species. This could be erroneous because it does not consider the second entrance as a possible variable. For this reason, the GL parameter was then expressed as the site where specimens were collected, based on the FIOCRUZ and Fig.
This analysis showed the presence of different populations that were not evident using smaller datasets  [13] , corroborating the importance of a bigger dataset and the use of ML/AI in the classification of helminths. The present study has some limitations relating to the dataset. It contains 28 species and 8 genera of capillariids out of more than 300 species and 25 genera described. Therefore, it contributes with a small portion of the real scenario of the biological diversity in capillariids. Despite about 30 eggs each examined, some species are represented by one specimen, what could be a restriction in the possible intraspecific and ecological variations. In addition, multiple hosts or geographical origins in the same species could be interpreted by the system as a discrepant character and, consequently, the learning is wrongly addressed.
However, capillariid species in general are not so restricted. The solution we found was both a generalization and constriction of information on host and geographical location, respectively. The addition of new curated information from other biological helminth collections will enable the construction of a stronger, well-supported dataset and a better taxonomic definition using ML/AI. To our knowledge, this study is the first to apply artificial intelligence techniques to the taxonomic definition of biological species, opening an opportunity of application in health, biodiversity and technology research in other important taxa.
DPE 3D Printing The prepared mixtures, pellets or milled extrudates, were then added to the hopper of a M3DIMAKER™ pharmaceutical 3D printer (FabRx Ltd. , London, UK) with a direct powder extruder nozzle as previously reported  [26]. AutoCAD 2014 (Autodesk Inc. , San Rafael, CA, USA) was used to design the templates of the printlets, which were then exported as a stereolithography (. stl) file into the 3D printer software (Repetier host V 2. 1. 3, Willich, Germany). The selected 3D geometry was a cylindrical printlet (10 mm diameter × 3. 6 mm height). The printer settings in the Repetier Host software were as follows: feed 2100 steps/mm, infill 100%, high resolution with brim, without raft, speed while extruding (20 mm/s), speed while travelling (90 mm/s), number of shells (2), and layer height (0.
20 mm). The flow rate, extruder temperature, and feed rate were adjusted for each formulation (Table  1 ). The 3D printer used (FabRx Ltd. , Kent, UK) was specifically designed with a direct single-screw powder extruder and a nozzle diameter of 0. 8 mm. Its design is based on a single-screw HME with rotation speed (and hence extrusion) controlled by the 3D printer software (Repetier-Host V 2. 1. 3, Willich, Germany). Furthermore, the extruder nozzle moves in three dimensions to create the objects in a layer-by-layer fashion (Figure  1 ). After printing each formulation, the extruder was removed from the printing platform and the screw and barrel were dismounted and washed to avoid cross-contamination between different formulations.
Optical Microscopy Images were obtained using a Leica EZ4 HD ® microscope (Leica, Wetzlar, Germany) with an integrated high-definition digital camera, set to 8× magnification. The images were then edited with the Leica LAS EZ software program. For calibration purposes, an image (obtained under the same conditions) of a standard slide containing a straight 1 cm segment, with 100 divisions, was employed. Scanning Electronic Microscopy (SEM) SEM printlet images were obtained using a scanning electron microscope HITACHI TM3030 Plus (Hitachi, Tokyo, Japan) with an acceleration voltage of 15 kV. Samples were fixed on a support using a double-sided adhesive and covered with platinum using a high-resolution SEM coated spray Polaron SC7640 (Quorom Technologies, Lewes, UK).
Determination of Drug Loading One printlet of each formulation was placed in a volumetric flask with 60:40 acetonitrile: water mixture (50 mL) under ultrasound for 5 min until complete dissolution (n = 2). Samples of the solutions were then filtered through a 0. 22 µm PTFE filter (Millipore Ltd. , Dublin, Ireland) and the concentration of drug was determined by external standardisation. Quantification was carried out using a fresh standard stock solution prepared each time before starting the analysis. The standard solution of PZQ was prepared by dissolving 9 mg of PZQ in mobile phase to obtain a final concentration of 0. 18 mg/mL. Each sample solution was prepared and analysed in duplicate. The results were expressed as a % of PZQ recovery. PZQ was determined in the printlets by a high-performance liquid chromatography (HPLC) system (Shimadzu Scientific Instruments, Kyoto, Japan) comprising a diode array UV detector (SPD-10A VP), a pump (LC-10AD VP), an autosampler (SIL-20A VP), and an interface (SCL-10A VP) for the acquisition of data through a software (Ez Start).
In Vitro Drug Release Studies The drug release profiles of the printlets were monitored using a USP-II paddle apparatus (DT 60) (ERWEKA, Heusenstamm, Germany). Paddle stirrers at a speed of 50 rpm and temperature of 37 ± 0. 5 • C were used in each test. The printlets were placed at the bottom of a 900 mL vessel of 0. 1M HCl media (without surfactant). During the dissolution test, 5 mL samples were taken and filtered through 0. 22 µm PTFE filters, and the drug concentration was determined by HPLC (method described in Section 2. 2. 3 (Determination of Drug Loading)). Tests were conducted in triplicate. Data are reported throughout as mean ± standard deviation.
22 um membrane, and PZQ analysed by HPLC. Tests were conducted in triplicate. Data are reported throughout as mean ± standard deviation using Microsoft Excel (Microsoft Corp. , Redmond, WA, USA) software (version 2016 MSO). The PZQ taste-concentration profile was previously determined with the rat briefaccess taste aversion (BATA) model  [48]. It was found that the half maximal inhibitory concentration (IC50) was 0. 06 mg/mL, and the taste threshold was 0. 03 mg/mL. The classification proposed by Mohamed-Ahmed et al. [48]  was used to classify levels of PZQ released at different time points as fully tolerated, well tolerated, tolerated, aversive/untolerated, or highly aversive/highly untolerated, and predict taste masking efficiency of the different formulations.
PARR analysis. For dogs diagnosed with 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 the immunoglobulin heavy chain genes in B lymphocytes and the T-cell receptor γ in T lymphocytes (Table  2 )  18, 53, 54. Amplification was performed using the Type-it Multiplex PCR Master Mix (QIAGEN, Hilden, Germany), as described previously  55  using the following conditions: denaturation at 92 °C for 5 min, 35 cycles of 95 °C for 8 s, 49 °C for 10 s, and 72 °C for 15 s (for IgH major, IgH minor, and TCR Burnett), or using 30 cycles of 95 °C for 45 s, 48 °C for 30 s, and 72 °C for 15 s (for IgH Tamura, T Yagihara VA, and Vb)  55. The PCR amplicons were sequenced using the 3130 ABI sequencer (Applied Biosystems), and clonal profiles were determined using the Genemapper Software.
Acknowledgements The authors acknowledge 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://dog-genet ics. genou est. org) that provided the samples,  Stéphane Dréano  for the sequencing facilities, and  Yann Le Cunff  for help in statistics. The authors acknowledge Laurent Griscom (IGDR, Rennes) who assisted them in proofreading the manuscript.
Funding AP is funded by a PhD fellowship from the  FHU CAMIn University-Hospital Federation Cancer, Microenvironment & Innovation  ( 2018-2020 ) of the  University of Rennes 1, France. This study was partly funded by the  American Kennel Club Health Foundation CHF  grant  2446. The  Cani-DNA BRC  (http://dog-genet ics. genou est. org), is funded through PIA1 funding ( 2012-2022 ): the  CRB-Anim infrastructure ,  ANR-11-INBS-0003.
Funding: Work is supported by institutional funding from the  Centre National de la Recherche Scientifique (CNRS)  and the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and by the  Fondation pour la Recherche Médicale (FRM)  grant  DEQ20170336745 , " Laboratories of excellence" programme (Labex INFORM ,  ANR-11-LABX-0054 ), the  Excellence Initiative of Aix-Marseille Université (AMU)  (A*MIDEX), a  French "Investissements d'Avenir" programme. GGE is a recipient of o fellowship from the  Fondation de Coopération Scientifique  " Méditerranée Infection " and from  Campus France. Acknowledgments: Authors thank all members of the  JPG lab  for their support.
The demultiplexed paired end Illumina sequence reads in the FastQ format were uploaded into the Galaxy instance (v. 2. 3. 0) of the Genotoul bioinformatics platform 1  to be used in the FROGS (Find Rapidly OTU with Galaxy Solution) pipeline  (Escudié et al. , 2018). During the FROGS pre-process, sequences were depleted of barcode and the sequences with a non-appropriate length or containing ambiguous bases were removed. Next, reads were clustered into de novo operational taxonomic units (OTUs) using SWARM algorithm  (Mahé et al. , 2014)  with, at first, a denoising step to build very fine cluster using the minimal distance equal to 1 and, secondly, with an aggregation distance equal to 3. Chimeras were then detected and removed with VSEARCH  (Rognes et al. , 2016). Additionally, filters were applied to the OTUs in order to remove singletons  (Bokulich et al.
Statistical Analysis of Sequencing Data The Illumina MiSeq run generated a total of 8,107,484 and 1,467,731 high quality sequences, respectively, for the V3-V4 and archaeal sets of primers. Removal of PhiX control reads, removal of chimeras and filtering of singletons lead to a number of 51,703 ± 13,520 sequences for V3-V4 primers and 5,377 ± 4,645 sequences for archaeal primers per sample. To avoid any bias, samples containing less than 500 sequences after abundance filtering were removed from the dataset. Statistical analysis was processed using the RStudio software version 1. 0 (with R software version 3. 5. 1, R Development Core Team) 2. OTU structure and composition analyses were performed using the phyloseq R package version 1. 30. 0  (McMurdie and Holmes, 2013). Visualization of data was performed using the ggplot2 R package version 3.
2. 1. Prior to alpha and beta diversity calculations, rarefaction using the transform count methods was applied to the dataset. The following alpha diversity indices were calculated: number of observed OTU phylogenic diversity and Shannon index. Statistical differences in Bray Curtis distance between the mucin beads, bead medium and the bioreactor medium and between the pre and post-weaning diet were tested using a multi-analysis of variance (MANOVA) performed with ADONIS using the vegan R package with 9999 permutations and represented by principal coordinate analysis (PCoA) plots. The relative abundances of bacterial groups were log transformed prior to univariate statistical analyses. All univariate statistical analyses were performed using linear mixed-models (lme4 package version 1. 1.
21) with time point (days of fermentation) and ETEC treatment as fixed effects and fermentation experiment as a random effect. Analysis of variance tables was calculated with the car package (version 3. 0. 6). The means of each group were compared pairwise with the lsmeans package (version 2. 30-0) with the Tukey correction (Supplementary Table  4 ). Statistical comparisons of samples from the recovery phase of the fermentation containing or not the ETEC Ec105 strain were also performed using the Wald test of the DESeq2 R package version 1. 26. 0 at the genus level. In all statistical analyses, only P-values below 0. 05 were considered as significant.