dataesr/scientific-paragraphs-categorization huggingface dataset:
| Statistical analysis Additional statistical analyses were performed using R version 3. 5. 2 (Eggshell Igloo). Significance was set at α ≤. 05. To evaluate the general diuretic effect of torasemide, regardless of dose, the urine volume and urinary output pre-and postadministration of torasemide (dependent variable) were compared using a mixed model with "animal" as random effect and the factor "treatment" as fixed effect. Mixed models with "animal" as random effect and "time" as fixed effect were used to compare the baseline of several parameters with results observed at different time points after treatment. The dependent variables were blood pressure, urine output, uNa : uK ratio, USG, uAldo : C ratio, plasma K, Na, Cl, and pCr. These analyses were performed on the entire dataset to evaluate the global effect of treatment and on subsets for each treatment dose.
The aim was to analyze records by 24 h periods, starting from 24 h prepartum to 72 h postpartum. Because the farrowing process started in various time depending on the sows, full records were finally obtained for 5-4 multiparous sows per replicate and per maternity room, generating data for 6 C sows (2 per replicate) and 7 CE sows (2, 2 and 3 in replicate 1, 2 and 3) in one system, and for 12 E sows (4 per replicate) in the other system. Records were analyzed by 24 h periods, starting from 24 h prepartum to 72 h postpartum. The analysis of sow behavior focused on the mutually exclusive items of postures (standing, ventral and lateral lying, and sitting) and the mutually exclusive items of activities (nursing, resting, investigation towards the environment, and other behaviors such as ingestion and eliminative behaviors).
Milk and blood cell analyses by flow cytometry All antibodies were purchased from Clinisciences (Nanterre, France), except CD172a (Santa Cruz Biotechnology, Dallas, TX). The total numbers of polymorphonuclear cells together with macrophages, monocytes and non-monocyte mononuclear cells of milk were quantified by cytometry as previously described  [23]. Briefly, cells obtained from 10 mL fresh milk samples were divided in two samples stained with the anti-porcine CD172a antibody or with the corresponding isotypic control. Cells were suspended in 0. 5 mL PBS with 1 µL of the DNA-selective Vybrant® DyeCycle™ Ruby stain (Thermo Fisher Scientific, Waltham, MA). Total events were acquired from 100 µL cell suspension using the MACSQuant flow cytometer (Miltenyi Biotec), analyzed with the MACSQuantify software, and the absolute numbers of cells contained per mL of milk were backward calculated.
The integrity of isolated RNA was assessed using the Agilent RNA 6000 Nano kit with an Agilent 2100 Bioanalyzer (Agilent Technologies France, Massy, France). Average RNA integrity numbers were 7. 6 ± 0. 5 (mean ± SD). Altogether, 106 RNA samples (n = 13-16 per mitogen condition for the C group, n = 11 per condition for the CE group, and n = 9-10 per condition for the E group) were generated for quantitative PCR analyses. First-strand cDNA synthesis was performed with 1 μg of total RNA, by using High Capacity RNA to cDNA Kit (Applied Biosystems, Foster City, USA). Primers were designed from porcine sequences available in Ensembl or NCBI databases using Primer Express® v3. 0 software (Applied Biosystems). Amplification reactions and disassociation curves were carried out on a Step One Plus TM real-time PCR system (Applied Biosystems).
Statistical analyses Analyses were performed using SAS software (SAS Inst. Inc. , Cary, NC). Discrete variables were analyzed by Generalized Linear Models using the GENMOD procedure, with the treatment (C, CE, or E) and the replicate (1, 2, or 3) as fixed effects. A binomial law with a Logit link function was used for mortality rates, scores of lameness and reactivity to human, and the use of manipulable material at DG 101. A Poisson law with a log link function was used for numbers of piglets per litter and numbers of lactation bouts after farrowing. Other data were analyzed by linear models (ANOVA) using the MIXED procedure, with the treatment as a fixed effect and the replicate as a random effect. Normality of the distribution and equality of variance of continuous variables were checked visually, and adequate transformations were performed when needed: cortisol concentration and gene expression data were square root transformed before analysis, milk cell proportions and behavioral data expressed in ratio were transformed using the arcsinus square root transformation.
Behavior of parturient and lactating sows The behavior during parturition and lactation was recorded for a sub-group of 6 C sows, 7 CE sows and 12 E sows. The farrowing duration (4. 8 ± 2. 2 h) and the time spent in the different postures during farrowing did not differ between treatments (P > 0. 10). During the 24 h preceding and the 72 h following farrowing, the gestational environment had almost no influence on the total time spent in the recorded postures and activities (P > 0. 5, data not shown). However, the analysis of the repartition of these behaviors during the 24 h period revealed differences between groups of sows (Table  4 ). The duration and the frequency of standing bouts (P = 0. 036 and P = 0. 052) and of lateral lying bouts (P < 0. 01 and P = 0. 016) were influenced by treatment.
Acknowledgments The authors thank  C. Calvar ,  B. Le Bris  and the staff of  Crécom (Chambre d'Agriculture de Bretagne, France) ,  Raphael Comte ,  Sophie Daré ,  Carole Guerin ,  Frédérique Mayeur ,  Sandrine Tacher ,  Christine Tréfeu  and  Françoise Thomas  from the technical staff of PEGASE (INRA), and collaborators from  Artemis and Deltavit CCPA group.
Selection of mecC-MRSA by T. erinacei Four culture broths of T. erinacei IMI 101051 were screened for antibiotic activity against a collection of S. aureus control strains. All of the culture broths produced large inhibition zones against two penicillin-susceptible S. aureus strains-ATCC 9144 (Oxford S. aureus) and ATCC 25923-but much smaller zones against the penicillinase-producing S. aureus strain ATCC 29213 and the mecA-positive S. aureus strain ATCC 43300 (Fig. 2  and Extended Data Fig. 4 ). The role of mecC and blaZ LGA251 was assessed by screening the culture broths for antibiotic activity against two mecC-MRSA wild-type strains belonging to CC130 (02. 5099. D) and CC425 (LGA251) and their isogenic mutants. The mutants with deleted mecC (ΔmecC), blaZ LGA251 (ΔblaZ LGA251 ), and mecC and blaZ LGA251 (ΔmecC-blaZ LGA251 ) produced significantly larger inhibition zones compared with the corresponding wild-type strains, although the zones of the ΔblaZ LGA251 and ΔmecC-blaZ LGA251 mutants were larger compared with the zones of the ΔmecC mutants (Fig.
The Danish collection of human isolates comprised 334 mecC-MRSA and 2 MSSA isolates that were collected from colonized or infected individuals between 1975 and 2016 as part of the national MRSA and S. aureus bacteraemia surveillance programmes. The remaining CC130, CC425 and CC1943 isolates were selected to represent the known geographical distribution (mainly western and central Europe) and host repertoire (mainly humans, cattle, sheep, goats and wild animals) of each clone. Whole-genome sequencing of the 1,157 isolates was performed on different Illumina platforms at the Wellcome Sanger Institute or at Statens Serum Institut. Short-read sequence data are available in the European Nucleotide Archive/NCBI Sequence Read Archive under BioProject IDs PRJEB15105, PRJEB21015, PRJEB2655, PRJEB2755, PRJEB2756, PRJEB28206, PRJEB3174, PRJEB32898, PRJNA596428 and PRJEB43456 and the genome accession numbers are provided in Supplementary Table  1.
Sequence analyses Draft genomes were de novo assembled using SPAdes (v. 3. 15) 46. Multilocus sequence typing (MLST) was performed by comparing the draft genomes with the S. aureus MLST database 47. We used the scn gene (which encodes staphylococcal complement inhibitor-A and an indicator of the IEC-1 element 48 ) in S. aureus strain Newman (GenBank: NC_009641) and the vwb SaPI gene in SaPIbov4 (GenBank: HM211303) as queries in BLASTN searches against the draft genomes, setting length match to 0. 9 and similarity match to 0. 7. These parameters were chosen to account for allelic diversity of the vwb genes located on SaPIs (sharing 76-100% nucleotide identities with each other)  49  while effectively excluding scn homologues located outside the IEC-1 element (sharing 48-61% nucleotide identities with scn)  50.
Phylogenetic analyses of S. aureus CC130, CC425 and CC1943 All 991 mecC-MRSA and 136 MSSA isolates belonging to CC130, CC425 and CC1943 were included. Mapping of sequence reads and SNP calling were performed using NASP (v. 1. 0) 54 as follows: (1) sequence reads were mapped against the reference genome of mecC-MRSA CC425 isolate LGA251 (GenBank: NC_017349) with the Burrows-Wheeler Alignment tool  55  ; (2) SNP calling was achieved using the GATK Unified Genotyper  56, 57  , setting depth of coverage and unambiguously base calls to ≥10× and ≥90%, respectively, and ignoring insertions and deletions; and (3) SNPs contained in repeats were excluded using NUCmer  58, 59. Unrooted maximum-likelihood phylogenetic trees were built from core-genome SNP alignments with PhyML (v. 3. 0)  60, 61 under the HKY85 substitution model after applying NNI moves to improve the BIONJ starting tree.
Putative recombinogenic regions were detected and a recombination-corrected phylogeny was built with ClonalFrameML (v. 1. 12)  62. Time-resolved phylogenies, in which the date of each node is estimated, were constructed with BactDating (v. 1. 0)  63 using an additive uncorrelated relaxed clock model  64. Convergence and mixing of the Markov Chain Monte Carlo chains were determined using the R package coda  65. The time-resolved phylogenies were rooted as inferred with BactDating (v. 1. 0)  63 to maximize the posterior probability of the tree. Treedater (v. 0. 5)  66  was also applied for the same purpose and found to give similar estimates for the dating of nodes (Supplementary Figs. 6-11  and Source Data of Fig. 3 ). The phylogenetic relationships between the type XI SCCmec elements were investigated in a separate analysis.
Genome sequencing and analysis of the T. erinacei type strain IMI 101051 DNA extracted from the T. erinacei type strain IMI 101051 was used to prepare a DNA library in accordance with the Nextera XT DNA Library Prep Guide (Illumina) and sequenced on a MiSeq platform (Illumina) with 2 × 251 bp using a MiSeq Reagent Kit v2 (Illumina). Short-read sequencing data were submitted to the European Nucleotide Archive under BioProject PRJEB43453. A draft genome was de novo assembled using SPAdes (v. 3. 15) 46. We used the translated protein sequences of the P. chrysogenum pcbAB, pcbC and penDE genes and the A. chrysogenum cefD1, cefD2, cefEF and cefG genes (Extended Data Table  1 ) as queries in TBLASTN searches against the draft genome. Hits were used as queries in BLASTP searches against the UniProtKB/Swiss-Prot protein database.
Ultraviolet data were recorded between 210 nm and 550 nm. For comparative purposes, a pure standard of penicillin G (Sigma-Aldrich) was prepared at a final concentration of 9. 4 µg ml -1 and analysed using LC-MS. Mass spectra were obtained in the positive and negative ionization modes using the full scan and data-dependent MS 2 and MS 3 acquisition modes. Full scan total ion current chromatograms were obtained over the range of 125-1,800 m/z using a spray voltage of +3. 5 kV and -2. 5 kV for the positive and negative ionization modes, respectively. Three different scan events were recorded for the data-dependent acquisition modes as follows: (1) MS 2 of the most intense ion in the full scan acquisition mode; (2) MS 3 of the most intense ion in the MS 2 spectra; and (3) MS 3 of the second-most intense ion in the MS 2 spectra.
Molecular networking analysis of LC-MS data A molecular network was created using the Global Natural Products Social Molecular Networking (GNPS) workflow  67. Chromatographic raw data in the positive ionization mode were transformed to the mzXML format using the ProteoWizard command-line utility msConvert  68. The data were then filtered by removing all MS 2 fragment ions within ±17 Da of the precursor m/z value. The precursor ion mass and MS 2 fragment ion tolerances were set to 2. 0 Da and 0. 5 Da, respectively, to enable more comprehensive comparisons with the GNPS database. MS 2 spectra were filtered by choosing the top six fragment ions in the ±50-Da window throughout the spectrum. A network was then created considering a cosine score above 0. 7 and more than six matched peaks to link different nodes through edges.
LC-MS data in mzXML format and molecular networking results are available at the MassIVE repository under identifier MSV000087335. S. aureus short-read sequence data have been deposited in the European Nucleotide Archive/NCBI Sequence Read Archive under BioProject IDs PRJEB15105, PRJEB21015, PRJEB2655, PRJEB2755, PRJEB2756, PRJEB28206, PRJEB3174, PRJEB32898, PRJNA596428 and PRJEB43456 and the genome accession numbers are provided in Supplementary T. erinacei type strain IMI 101051 short-read sequence data have been deposited in the European Nucleotide Archive under BioProject PRJEB43453. Tree and genealogy files in Newick format are provided in Supplementary Data 1-10. Source data are provided with this paper. 46. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its application to single-cell sequencing.
Acknowledgements We thank  M. Ganoti ,  F. Attila-Zoltán ,  M. Dugar ,  Z. Pokorná ,  D. Madsen ,  S. M. Gamborg ,  R. Molina-López ,  C. Rodrigues ,  T. Vieira ,  J. Viricel ,  A. Fingar ,  K. South ,  G. Prince ,  H. Gasser ,  S. Sebright ,  N. Ennew ,  C. Catchpole ,  E. Acton ,  N. Acton ,  K. Horrey  and  P. Loague  for providing hedgehog samples;  A. Medina ,  L. R. H. Kildevang ,  P. T. Hansen , and  S. M. Johansson  for technical assistance during the analysis of hedgehog samples;  A. E. Henius  for help with Figs. 1, 4 and Extended Data Fig. 8; and  V. Bortolaia  for reading the manuscript. This work is dedicated to V. R. Simpson , who passed away during the study. B. Č. B. was supported by a grant from the  Czech University of Life Sciences Prague  (no. IGA 20213106 ). X. D. was funded by a grant from the  National Institute for Health Research (NIHR) Health Protection Research Unit in Genomics and Enabling Data  (no.
Material and Methods The review is presented as a structured review, which aimed to identify scientific and grey literature across the scope of this topic. The search was conducted in Scopus, Web of Science, Science Direct, and PubMed databases. The identification of relevant articles was performed using the following keywords: "dogfighting", "dog fight", "dogfighting legal aspects", "canine welfare", "illegal sports", "animal fighting", "bite injuries", "canine pain", and "aggression". Included studies were articles related to the description of dog fight practice and methodology in several countries, laws and legal aspects, emotions in dogs, programs for dog rehabilitation, injuries caused during organized or spontaneous dog fights, and the typology of fighters. There was no settled date of publication.
Starting from acquired signals from riders and non-riders during an exercise protocol on the mechanical horse, we perform the following steps: elbow, trunk and head oscillating cycles are extracted from their corresponding signals. To better understand the behavior of the subjects, we need to organize the extracted cycles in distinct clusters with similar observations within each cluster. Cluster analysis, primitive exploration with little or no prior knowledge, is used here to perform data exploration  [20]. The outcomes of cluster analysis in this research work allow to draw conclusions about the impact of horse back riding in the behavior change of neophyte and expert practitioners. Moreover, our empirical evidences show that the investigated methods can automatically identify different riding behaviours without a priori information on rider level.
In our task, cycles have the same length. In practice we use the soft-DTW, a differentiable version of DTW proposed in Ref. [34]. As a result, the center of a cluster is computed as the Fréchet Mean  [35, 36] , instead of the classical mean, of time series. Specifically for cluster  k its center C k is obtained by solving  [34] min C 1 n k ∑ i∈𝒞 k Soft -DTW(x i , C ) using classical gradient descent optimization tools  [37]. Notice that each clustering assignment is done according to the Soft-DTW distance. The cycle clustering was performed thanks to Python (version 3. 5. 0) software  [38] , and especially the scikit-learn (version 2. 4. 8) library  [39]. Both algorithms are sensitive to parameters initialization and require to select the number of clusters.
K drug was considered additive to the natural cell death, with a potentiation factor (F2) for the highly susceptible subpopulation S1. F2 increases K drug by only increasing E max because in a preliminary analysis, it was shown there was no difference in EC 50 s for the two S1 and S2 subpopulations. A model script is given at https://rvc-repository. worktribe. com/output/1567805/supplementary-data-quantitative-pharmacodynamic-characterization-of-resistance-vs-heteroresistance-of -colistin-in-e-coli-using-a-semi-mechanistic-modelling-of-killing-curves.
Pharmacodynamic data analyses were conducted using Phoenix NLME v8. 3. 0. 5005 (Pharsight Corporation, St. Louis, MO, USA). Determination of the PD parameters from time-kill experiments of all 7 strains at starting target inoculums of 5 Â 10 2 , 5 Â 10 4 , 5 Â 10 5 , and 5 Â 10 6 CFU/mL (see https://rvc-repository. worktribe. com/output/1567805/supplementary-data -quantitative-pharmacodynamic-characterization-of-resistance-vs-heteroresistance-of-colistin-in-e-coli -using-a-semi-mechanistic-modelling-of-killing-curves) were analyzed simultaneously using a semimechanistic PD model adapted from Nielsen and Friberg  (18). The full model approach is described in (https://rvc-repository. worktribe. com/output/1567805/supplementary-data-quantitative -pharmacodynamic-characterization-of-resistance-vs-heteroresistance-of-colistin-in-e-coli-using-a-semi -mechanistic-modelling-of-killing-curves) and outlined below.
ACKNOWLEDGMENTS We thank  Dopharma, Virbac , and  VMD/Inovet  for their support in this research and  Ana-Rita Rebelo  and  Rene Hendriksen  ( Research Group for Genomic Epidemiology, Technical University of Denmark ) for the provision of the mcr-3-positive E. coli isolate used in this study. The funding for this study was provided through TransPharm on behalf of Dopharma, VMD Livestock pharma/Inovet, and  Virbac. A. M. joined the  London Interdisciplinary Biosciences Consortium cohort (Doctoral Training Partnership)  as an RVC contribution and was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC). P. R. (TransPharm) was involved in the study design and revision of the submitted article. The funders were not involved in the collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
| Medical record review Data collected included signalment (breed, age, body weight, neutering status); laboratory biochemical findings; imaging and functional testing (urethral pressure profilometry (UPP), Solar Blue, Medical Measurement System, Enschede, Netherlands; anesthesia with Xylazine, ROMPUN, Elanco, Cuxhaven, Germany 1. 1 mg/kg IM); EU features (unilateral or bilateral EU, iEU or eEU, localization of terminal ureteral opening) determined by cystoscopic (Mini Multi-Purpose Rigid Telescope 30 , 9. 5 Fr, 14 cm, Karl Storz Endoskope, Tuttlingen, Germany or URF-P5 Flexible ureteroscope, Olympus Medical Systems, Rungis, France) or intraoperative findings; associated conformational abnormalities; type of treatment; additional procedure; complications; duration of hospitalization stay; preoperative and postoperative urine culture on urine collected through cystocentesis (considered positive when bacterial growth was equal to or above 3 log); and preoperative and postoperative continence status.
| Data analysis Categorical variables are expressed as frequency and percentage and numerical variables are presented as the median and interquartile range (IQR). At 1 month follow up, the association between categorical data was assessed by Fisher's exact test. Quantitative data were assessed by the Mann-Whitney U-test. At follow up >1 month, only in dogs that achieved continence after surgery, the proportions of incontinence recurrence after CLA and neoureterostomy were analyzed using an exact test comparing the difference between 2 binomial proportions. The duration of continence (median) was estimated using Kaplan-Meier survival analyses with patients censored at the time of the last follow up if no incontinence occurred. The statistical analyses were performed using commercially available software (GraphPad Software Inc.
2017; Blackburn et al. 2020; Ellison et al. 2019 Ellison et al. , 2020;; Rogiers et al. 2020; SCCS 2021c). Given the same external exposure, differences in absorption, distribution, metabolism, and excretion (ADME) parameters, such as clearance and oral absorption, lead to differences in plasma concentrations. Therefore, comparing internal concentrations accounts for the ADME differences, providing a more accurate assessment of exposure. The Cosmetics Europe Long Range Science Strategy program is leading a multi-stakeholder collaboration Abdulkarim Najjar and Corie A. Ellison have contributed equally to this work. project that aims to develop robust iTTC values that can be used for the human safety assessment  (Ellison et al. 2019 (Ellison et al. , 2020)). Physiologically based pharmacokinetic (PBPK) modeling is being used to convert the distribution of oral NOAELs from the TTC database to a distribution of internal exposures.
(2019)  explored a hypothetical example where the iTTC could be used to refine a TTC-based assessment for dermal exposure to a consumer product. In their example, a hypothetical chemical is used in a facial moisturizer and is non-mutagenic and non-genotoxic, but lacks systemic toxicity data. The hypothetical chemical has sufficient ADME data available to make predictions of internal exposure which would then be compared to an iTTC. Here, we present a case study that examines the possible use of iTTC as a tool that can be used to refine a TTC-based assessment for dermal exposures to consumer products. We expand on this assessment by utilizing a set of case study chemicals which have robust human dermal pharmacokinetic data that were collected in well-designed clinical studies. The availability of such human PK data is limited for consumer product; thus, this dataset represents a unique opportunity to evaluate the utility of iTTC.
The availability of these human PK data represents a unique opportunity to evaluate the utility of iTTC for a dataset, where external and internal exposure are carefully monitored. Additionally, there is an opportunity to directly compare the human PK data to the interim iTTC limit of 1 μM without any additionally modeling, such as PBPK modeling, which could introduce uncertainty into the evaluation. Given that this is the first case study that compares the [interim] iTTC to true human exposures, we wanted to minimize additional sources of uncertainty.