dataesr/scientific-paragraphs-categorization huggingface dataset:
94 between them in aggregated dataset A. Each standard deviation more laying counter/standup events predicted 0. 29 standard deviation lower mobility score. The mobility score standard deviation in this sample was 0. 8, so a standard deviation more laying counter predicted 0. 23 (0. 8*0. 29) lower mobility score. Figure  1  shows the modest differences in laying counter by mobility score from aggregated dataset A. For aggregated dataset B, neither Laying Counter nor Stand Up were associated with mobility score. In aggregated dataset B, Activity and Standing Index had a standardized coefficient to mobility score of -0. 38 and -0. 28, respectively, and were statistically significant (p < 0. 05). Both are measures of activity, the former through the whole day, the latter while the cow was standing.
Similar standardized coefficients were found in aggregated dataset A. However, these associations were not statistically significant. In this study, parity was not associated with mobility score and was not correlated with any of the 14 behavior measures in this study. mobility score. In aggregated dataset B, Activity and Standing Index had a standardized coefficient to mobility score of -0. 38 and -0. 28, respectively, and were statistically significant (p < 0. 05). Both are measures of activity, the former through the whole day, the latter while the cow was standing. Similar standardized coefficients were found in aggregated dataset A. However, these associations were not statistically significant. In this study, parity was not associated with mobility score and was not correlated with any of the 14 behavior measures in this study.
In summary, none of the assessed behaviors were consistently associated with mobility-score across the four individual trials. Stand up/laying counter had a significant association with mobility in aggregated dataset A. This is consistent with another Irish study with grazing cows where changes in behavior were indicative of lameness  [17]. This association was only statistically significant in one individual trial (1. A). Contradictory nonsignificant positive correlation coefficients were found in individual trials 1. B and 2. Activity and activity while standing (Standing Index) were the strongest indicators of mobility score in this study. They both had relatively high standardized coefficients with mobility in both aggregated datasets A and B. However, these were not statistically significant in the analysis of aggregated dataset A.
Main Findings To date, the variables most used to assess lameness using accelerometers have been differences in behavior  [34]. Contributing to mixed literature, the assessed behavior measures were not reliably associated with mobility scores. Based on the present and other results  [14, 20, [23] [24] [25] 34] , measuring lying time appears to be of limited value for lameness detection. Activity and Standing Index were indicative of mobility scores in both aggregated datasets A and B, but only statistically significant in B. These modest associations indicate these behavior variables could only play a small part in a mild and moderate lameness detection system for grazing cows.
Analysis of 16S Sequences The Find Rapidly OTU with Galaxy Solution pipeline implemented on a galaxy instance 1  was used for data processing  (Escudié et al. , 2017). In brief, paired reads were merged using FLASH  (Magoč and Salzberg, 2011). After denoising and primers/adapters removal with CUTADAPT  (Martin, 2011) , de novo clustering was performed using SWARM with a local clustering threshold  (Mahé et al. , 2014) , with aggregation distance d = 3 after denoising. Chimeras were removed using VSEARCH  (Rognes et al. , 2016). We filtered the dataset for singletons and performed affiliation using Blast + against the Silva database (release 128, September 2016) for 16S rRNA gene amplicons. Finally, operational taxonomic unit (out) tables were produced in a standard BIOM format for subsequent analyses.
We then used the packages phyloseq 1. 24. 2  (Mcmurdie and Holmes, 2013)  and vegan 2. 5-4  (Oksanen et al. , 2019)  with RStudio (R Core Team, 2017). Sample B. gla BRE _JC_7 had too low coverage (155 reads) and was thus discarded from subsequent analyses. Non-bacterial sequences as well as singletons remaining after all the secondary filtering steps were discarded from the dataset. We rarefied the data according to the sample with fewer sequence numbers (18,299 reads for the Individual Experiment and 15,969 reads for the Common Garden) to normalize for sequencing coverage. We characterized the beta-diversity dissimilarities using principal coordinates analyses (PCoA) and hierarchical clustering on Bray-Curtis (BC) distance matrix (ranging from 0 for identical communities to 1 for completely different communities).
Snail Phylogeny Phylogenetic analysis was performed using 28S rRNA gene sequences from the National Center for Biotechnology Information database (B. glabrata, AF435694. 1; B. pfeifferi, MG461588. 1; P. metidjensis, AF435671. 1; and B. truncatus, AF435659. 1). The 28S rRNA gene sequence of a Physidae species, Physa sp. (Egypt) (sister family of the Planorbidae) was used as an outgroup (AF435654. 1). The sequences were aligned using  MUSCLE (v3. 8. 31) , and the tree was reconstructed using the maximum likelihood method implemented in the PhyML program (v3. 1/3. 0 aLRT) with 500 bootstraps on 2   (Dereeper et al. , 2008 (Dereeper et al. , , 2010)).
DISCUSSION To understand the host effect in shaping microbiota in Planorbidae schistosomiasis vector snails, we characterized the individual bacterial communities associated with several strains of B. glabrata, B. pfeifferi, P. metidjensis, and B. truncatus snails. Working on individuals reared in lab conditions favored the control of most of the parameters that can influence microbiota composition. In the present study, the whole microbiota was characterized using 16S amplicon sequencing. We identified 31,207 OTUs among the seven different snail strains. Most of OTUs were not assigned to the species level, and 63% were assigned to the genus level. This corresponds to the limitation of the 16S V3V4 marker resolution. In addition, the Blast + -based pipeline we use for taxonomic affiliation avoids false affiliation when a sequence matches with several sequences in the database.
If several Blast results have identical scores for a given OTU, and these taxonomies differ across hits, the OTU is set to "multi affiliation"  (Escudié et al. , 2017). A few studies have characterized the cultivable flora of B. glabrata and have identified Aeromonadaceae, Enterobacteriaceae, Moraxellaceae, and Pseudomonadaceae as being the most prevalent bacterial families in this species  (Ducklow et al. , 1979 (Ducklow et al. , , 1981;; Silva et al. , 2013). The dominant families described in the previous studies were also represented in our dataset. However, the relative composition of microbiota at the phylum level revealed that Proteobacteria were dominant for most of the different Biomphalaria strains, represented by three main families: Rhodobacteraceae (Alphaproteobacteria), Comamonadaceae (Betaproteobacteria), and Xanthomonadaceae (Gammaproteobacteria).
ACKNOWLEDGMENTS We thank  Nathalie Arancibia  and  Jean-François Allienne  for technical assistance. We are grateful to the  genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group  for providing help and computing resources thanks to Galaxy instance http://bioinfo. genotoul. fr. This study is set within the framework of the  "Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and  CeMEB  ( ANR-10-LABX-04-01 ).
Acknowledgements This review paper is based upon work from  COST Action CA15134 -Synergy  for preventing damaging behaviour in group-housed pigs and chickens (GroupHouseNet), supported by  COST (European Cooperation in Science and Technology ; www. cost. eu). The text represents the authors' views and does not necessarily represent a position of the Commission, which will not be liable for the use made of such information. A. Prunier   0000-0003-3070-6613.
25 μL of fixable viability dye efluor780 (eBioscience) was added for 5 min on ice. Cells were fixed by addition of 50 μL of 2% V/V para-formaldehyde for 10 min on ice, washed by addition of 100 μL of cytometry buffer and centrifuged before resuspension in buffer and storage at 4˚C for a maximum period of 24 h before acquisition. Acquisition was done on CytoFLEX (Beckman Coulter) after calibration; 10. 000 to 30. 000 events were recorded for each condition. Analysis was performed using the FlowJo V10 software. The mean fluorescence intensity (MFI) for mTLR4 corresponds to live cells.
Biomechanical testing The two types of suture techniques were tested separately but with the same mechanical test as in the study by Morton and colleagues. Eight static tensile tests were carried out using a traction system (AGS-X Shimadzu, Japan) with a pre-test of 20 mm/min traction until the load reached 30 N, to straighten the system. The tensile test consisted in a 25 mm/min traction until failure and the sampling rate for data acquisition was set at 100 Hz. In total, 8 experimental set-ups were investigated in a random order: 4 testing the mechanical resistance to tearing of the 8 polypropylene sutures (S1D&G, S2D&G) and 4 others to the tearing of the overlock sutures made with UHMWPE thread (SF3D&G, SF4D&G).
Research in context Evidence before this study We searched PubMed from database inception until Dec 31, 2011, without language restrictions for clinical trials and randomised clinical trials using the search string: ("Buruli" OR "Mycobacterium" AND "ulcerans") AND ("antimycobact*" OR "antimicrob*" OR "antibiotic*") AND "treatment". The standard treatment for Buruli ulcer is combination antibiotic therapy comprising intramuscular streptomycin and oral rifampicin daily for 8 weeks. Streptomycin injections are painful and can cause ototoxicity. A systematic review found case reports and observational cohort studies that showed the potential effect of fully oral antibiotic combinations for the treatment of Buruli ulcer. No previous trials in humans have tested the efficacy of a fully oral antibiotic regimen.
To evaluate whether this unmasked assessment of the primary endpoint (ie, healing) could be biased, we invited an independent technical expert panel masked to treatment assignment to review a sample of individual image sets. A study monitor (NFS) assessed data entries. Diagnostic uncertainties and adverse events were discussed with the (co)principal investigators (TSvdW, ROP, JR) and the sponsor, using an electronic webbased platform. Annual auditing trips to the study sites were made. All data were entered on paper and uploaded into OpenClinica. The electronic data files were collected and managed by the Drugs for Neglected Diseases initiative, Africa Regional Office, Nairobi, Kenya.
Statistical analysis Assuming an efficacy of 96% for standard RS8 treat ment,  15  the sample size needed to detect inferiority of the experimental RC8 treatment by a margin of 12% for oral treatment was calculated to be 332 study participants with PCRconfirmed Buruli ulcer. Baseline comparisons of clinical and demographic characteristics according to study group allocation were done using Student's t test for parametric continuous data or the MannWhitney Utest for nonparametric data. Categorical data were compared using χ² tests. The analysis was by intention to treat using a onesided t test for the primary outcome. Time to healing was analysed by a Cox proportional hazards model to assess the cumulative probability of healing. Hazards ratios were reported with 95% CIs. We used Stata version 15 for analyses.
These included a focus on wound care, rehabilitation, and pain management during dressing changes. However, despite these measures, the median time to wound healing was similar to that reported in previous studies. 14, 15, 19 he strengths of the study were the large sample size, the diagnostic confirmation by validated PCR technology, extended monitoring and auditing, the high healing rate at week 52, the very low dropout rate, and minimal loss to followup. We used internetbased data storage and data collection, and we used a webbased platform for intensive interactions and consultations within the study team. Very few study participants needed any form of surgery. No debridement surgery was done in any of the study participants, and only four patients had skin grafts. This is consistent with findings from a study in Benin addressing the role of surgery in Buruli ulcer disease.
Acknowledgments WHO sponsored the study with additional support in cash or kind provided by  MAP International, American Leprosy Missions ,  Fondation Raoul Follereau France ,  Buruli ulcer Groningen Foundation ,  Sanofi Pasteur France , and  BuruliVac  ( EU FP7241500 ). SanofiPasteur supplied one of the three study drugs. Buruli ulcer Foundation is a legally recognised foundation situated in the Netherlands, supporting Buruli ulcer research, with volunteers contributing to support individual African researchers, and contributing in cash for supplies and travel expenses. We thank the members of the data safety monitoring board:  David OforiAdjei ,  Sarah Eyangoh ,  Alan Knell , and  William Faber. We thank  Femke M Homan  and  Jose C Duipmans  for their contribution in scoring and assessing digital images;  Sandor Klis ,  Kristien Velding , and  Till F Omansen  for their assistance;  Ohene Adjei  and  Joseph Akpaloo ,  Komfo Anokye Teaching Hospital , for their support;  Collins Asare  for safe driving; and all study participants for their patience and endurance.
Data The data consist of two dimensional position and speed sampled at 10 Hz for horses racing in Chantilly (France), at the end of the 2019 Thoroughbred horse racing season. The races were all run on a PSF track in a standard surface condition. The data are provided by France Galop the French operational body for flat horse racing , and are from roughly ten races. The tracking system is developed by Mc Lloyd. It is a miniaturized device which does not bring any discomfort to the horse or the jockey. It weighs 90g and is put by the jockey under the saddle. Reliable data is obtained thanks to a patented positioning technology and robust mobile network data transmission, even during crowded events. The accuracy of the system was validated on horses by France Galop by comparing 1ms-accurate photo finish data on more than one hundred races with gap data on the finish line obtained after processing latitude and longitude data: the device mean accuracy was confirmed to be 25cm or 2 hundredth of a second.
The optimal control problem for horse performance is solved using Bocop, an open licence software developed by Inria-Saclay France  [19]. The discretisation used to solve the system of coupled ODE's is set to one point every two meters which ensure the same accuracy whatever the race. The solution of the optimal control problem yields, for each race and each horse, the optimal effort, regulation of velocity and the V O2 profile depending on the length and topography of the race. Our aim is therefore to identify the physiological parameters f M , τ , u + , u -, e 0 , σ(e) from the available data.
Identification process The identification process is made through a bi-level optimization procedure looking for minimizing errors between the response of a Bocop simulation and the data through the following objective min p |t simu (d) -t data (d)| + 1 N N i=1 (v simu (s i ) -v data (s i )) 2 , where the subscript simu (resp. data) refers to the variable extracted from the simulation (resp. data). The distance s 1 = 0 is the beginning of the race and s N = d is the length of the race, while s i are intermediate distances. The parameter p refers to the vector containing all the variables to identify: p = (τ, e 0 , f M , d 1 , d 2 , σ M , σ f , u -, u + ), where σ M is the maximal value of σ and σ f its final value. The objective is made up of two parts: first the difference in final time at the end of the race d, and then the mean square error over the speed measured at N points.
For our identification process, N is taken equal to one thousand which ensures a good accuracy for the objective calculation (since it is one point every 2 meters for the longest race as the Bocop calculation) while keeping a relative low time cost. The points are evenly distributed between s 1 and s N. The algorithm used here is a particle swarm optimization method  [20]  available in the pyswarm library in Python 3, part of the family of the heuristic optimization methods  [21]. The main advantage of such a method is its good ability to explore the design space and its ease of use and implementation. A swarm of designs {p i } is tested, that is the optimal control problem is solved for these parameters using Bocop, and its objective value is calculated. The performance influences the speed and the January 6, 2021 4/17 direction of the particles inside the design space for the next iteration.
intracellularis subtypes  [13, 14]. Biological characterization of L. intracellularis has been severely hampered by its fastidious in vitro growth requirements  [15, 16] , and the molecular basis of L. intracellularis pathogenesis remains undetermined. To date, only six L. intracellularis genome sequences have been deposited in the National Center for Biotechnology Information (NCBI) database, three of which were obtained from cell-cultured samples, an approach that requires extensive passaging in immortalized cell lines. However, such a method is labour-intensive, slow and may risk the introduction of mutations during passaging. The first complete genome for a L. intracellularis strain was sequenced using the Sangerbased method in 2006, revealing a 1. 4 Mb chromosome and three plasmids of 27, 39 and 194 kbp.
Comparative analysis of two additional pathogenic porcine isolates has been carried out, revealing limited genetic differences  [17] , but the population structure and phylogeny of this pathogen remains unknown. In the current study, we utilized culture-independent metagenomic sequencing of 21 clinical samples from field outbreaks of porcine and equine PE from 7 countries to assemble metagenome-derived L. intracellularis whole genomes. In addition, 3 cell-passaged samples were also sequenced, generating a total of 24 genome assemblies. Comparative genomic and phylogenetic analysis uncovered the population structure of L. intracellularis for the first time, revealing a genetically monomorphic clone responsible for infections in pigs and distinct subtypes associated with equine infections. These data have provided novel insights into the genetic diversity of the bacterium, enhancing our understanding of its biology.
DnA enrichment All samples sequenced in this study are listed in Table  S1  (available in the online version of this article). Data from six additional strains (GenBank GCA_000055945. 1, GCA_000331715. 1, GCA_001975945. 1, GCA_003312265. 1, GCA_003312285. 1 and GCA_003312305. 1) were retrieved from the NCBI. All samples in this study were stored at -80 °C prior to extraction. DNA was extracted from faecal samples using the DNeasy PowerSoil kit (Qiagen), using PowerBead tubes and FastPrep homogenizer (MP Biomedicals) to homogenize and mechanically lyse bacterial cells. DNA from tissue samples were extracted using DNeasy Blood and Tissue kits (Qiagen), with the exception of sample 4242, which was extracted using phenol/chloroform and precipitated by ethanol, as previously described  [18]. The NEBNext Microbiome DNA Enrichment kit (New England Biolabs) was used to deplete host DNA from tissue DNA samples.
Sequence processing The quality of sequence read FASTQ files was assessed using FastQC (Babraham Bioscience Technologies, Cambridge, UK). All raw reads were adapter-and qualitytrimmed with Trimmomatic (v 0. 36) on paired-end mode  [19]. Kraken taxonomic sequence classifier (v 1. 0) was used to remove host reads from the filtered reads. A custom Kraken database was constructed containing Sus scrofa (GenBank GCA_ 000003025. 6), Rattus norvegicus (GenBank GCA_000001895. 4), Mus musculus (GenBank GCA_000001635. 8) and Equus caballus (GenBank GCA_002863925. 1) genomes  [20]. Host reads that mapped.
Impact Statement Proliferative enteropathy is a common enteric disease in pigs caused by Lawsonia intracellularis, highly prevalent across major pig-farming countries worldwide. Infection caused by the pathogen has a significant impact on animal health, welfare and production, imposing a huge economic burden in the pork production industry. Due to the difficulty in culturing the pathogen in vitro, little is known regarding its pathogenesis and the genomic diversity of the population remains unknown. The current work explores metagenomic sequencing to obtain L. intracellularis genome sequences, providing novel insights into the genetic diversity and evolution of the bacterium, enhancing our understanding of its biology. to the custom database were removed from the FASTQ files and unclassified reads were used for downstream analyses.
Genome assembly and annotation Draft genomes of L. intracellularis were assembled de novo for each isolate using megahit with the meta-large pre-set parameter  [21]. BWA MEM  [22]  was used to map reads back to the draft assemblies, and the contig coverage within each metagenome assembly was calculated using the script jgi_summarize_bam_contig_depths from the MetaBAT2 package  [23]. The assembled contigs within each sample were binned using MetaBAT2. Assessment of genome bins on the level of completeness and contamination was performed using CheckM  [24]. In addition, draft genomes were assembled using a reference-guided de novo approach using Kraken to extract L. intracellularis FASTQ reads prior to assembly. A custom Kraken database containing E40504, N343 and PHE/MN1-00 reference genome sequences was constructed and used to classify L.
Detection of recombination within alignments was performed using Gubbins  [33]  and visualized using Phandango  [34]. Maximum-likelihood (ML) phylogenetic inference was performed on non-recombinant core genome SNPs using IQ-TREE (v 1. 6. 3) [35]  with 1000 bootstrap replicates. ModelFinder implemented in the IQ-TREE software package was used to find the nucleotide substitution model that best fit the dataset  [36].
1) (Fig. S2 ). Multiple libraries were pooled and multiplexed on a single flow cell lane, generating between 55 and 227 million reads per sample (Table  S1 ). Samples with L. intracellularis copy numbers of ≥9×10 4 ng -1 genomic DNA achieved genome coverage of >98 % across the reference genome. A reference-guided de novo assembly approach was adapted for recovery of L. intracellularis genomes from each metagenome dataset. Reads were mapped against three L. intracellularis reference genomes -PHE/ MN1-00 (GCA_000055945. 1), N343 (GCA_000331715. 1) and E40504 (GCA_001975945. 1) -before assembly with metaSPAde to construct a consensus sequence  [37]. In this manner, 24 L. intracellularis draft whole-genome sequences with >98 % genome coverage were obtained, of which 21 were obtained directly from clinical samples and 3 were obtained from isolates propagated in McCoy cells (Table  S3 ).