dataesr/scientific-paragraphs-categorization huggingface dataset:
Whilst the broadest possible sharing of data was endorsed by the EPAA Partners' Forum it is acknowledged that in order for data to be shared there are a number of challenges to be overcome in terms of the practical aspects, legal ownership and confidential nature of the data. In terms of the practical storing and sharing of data a number of on-line databases are available including, for regulatory purposes, ECHA's dissemination portal and to share safety data (e. g. NO(A)ELs) COSMOS DB -there are also many other databases including commercial ones. The eTOX Project  (Cases et al. , 2014; Sanz et al. , 2017)  has demonstrated the possibilities for sharing data from the pharmaceutical industry through the development of the eTOX database in the eToxSys platform (https://www. etoxsys. com/the-database.
htm). Many learnings on the extraction, curation and storage of data from legacy RDT study reports were made in the EU IMI eTOX Project  (Cases et al. , 2014; Sanz et al. , 2017). The sharing of data would be greatly assisted by the digitalisation of data and use of an appropriate electronic format -there is a clear opportunity to harmonise data storage to facilitate sharing at various levels e. g. between industry and the appropriate regulatory agency as well as with other scientists. As the data matrices become more complex with different types of data, so will the associated databases. The EU IMI eTransafe Project (http://etransafe. eu/) is attempting to create such a translational database to support human safety assessment. Also from the European perspective, the European Parliament is funding a feasibility project on the joint sharing of data across sectors.
With regard to legislation and regulations, there is an opportunity to increase harmonisation across global regions and sectors. It is appreciated that different industries will, inevitably, have different requirements for RDT studies, however, increased harmonisation in areas such as which studies are required (and any additional testing) should decrease unnecessary repetition of testing. Global harmonisation of RDT tests and mutual acceptance of data will potentially allow for a significant reduction in testing. A further opportunity is to improve knowledge on RDT for mixtures and natural products. ECHA noted that approximately two thirds of REACH dossiers were for unknown or variable composition, complex reaction products or of biological materials (UVCB) substances or mixtures. Currently there is little known about many of these, other than a small proportion of their constituents.
As an outcome of the EPAA Partners' Forum on repeated dose toxicity testing, the following conclusions were made: -Applying alternative methods when assessing systemic toxicity is a major challenge due to the complexity of interactions in the living body and for certain industries (e. g. cosmetics) this is critical due to regulatory requirements; -Although EPAA partners are committed to the 3Rs, it was recognised that up until now animal tests on systemic toxicity are pivotal for supporting many safety decisions; -Given the comprehensive data set provided by the traditional animal RDT testing, the full replacement with alternatives represents a major challenge. Breaking down the questions addressed by RDT (e. g. POD, identification of target organs) is required to make progress; -Any replacement effort requires close cooperation amongst all safety assessors (in industry, regulatory agencies, academia) at a very early stage during alternative method design and development, ideally at a global scale; -EPAA is well placed to enable cross-fertilisation, help set future research agendas and convene key players; -EPAA facilitates sharing of case studies where novel approaches to safety decision making have been used successfully.
Statistical Analysis Analyses were carried out using XLSTAT software (version 2015, Addinsoft, Paris, France). The effects of the delayed placement and EO supply on total liquid intake, water intake, total EO intakes, body and muscle weight, and the FCR ratio were analyzed by ANOVA after having checked the normality of residuals distribution and the homogeneity of variances. The fixed effect model was yij = µ + Di + EOj+ (D-EO), where Di, EOj and (D-EO)ij, were the fixed effects of the Delayed placement, EO provision, and (D-EO)ij the interaction of Delayed placement with EO provision. When there were interactions between variables, the Fisher (LSD) test was used to determine the significant differences between groups. Because the residuals were not normally distributed and variances were not homogenous between groups, data on each EO intake, behavioral tests, and gait scores were analyzed with non-parametric tests: the Kruskal-Wallis test for the EO effect and the Mann-Whitney test for the comparison between the D and C groups for each period.
The effects of periods on EO intake were analyzed with the nonparametric Friedman test. The Dunn test with the Bonferonni correction was used as a post-hoc test after Kruskall-Wallis and Friedman analyses. The clinical data and quality score of the chicks were analyzed using a Chi-squared test. Differences were considered to be significant when p-values were below 0. 05, to be a tendency when p-values were between 0. 05 and 0. 1 and not significant (NS) when p-values were above 0. 1. The values are presented as means ± standard deviations or medians, quartiles, maximum, and minimum values.
ACKNOWLEDGMENTS We would like to thank the staff of the  Avian Experimental Unit (INRA, UE 1295 Pôle d'Expérimentation Avicole de Tours, 37380 Nouzilly, France)  for producing the chickens and for their assistance in experiments,  S. Crochet ,  E. Cailleau-Audouin , and  E. Baéza  from the  Avian Biology and Poultry Science Unit (UMR BOA ,  INRA ,  Université de Tours, 37380 Nouzilly, France)  and  P. Constantin  ( UMR PRC, INRA, CNRS, IFCE, Université de Tours, 37380 Nouzilly, France ) for their help in experiments, and  G. Mauboussin ,  O. Honcharova , and  A. Yildirim , the students involved in the study. We also thank  M. Derval  and  C. Ingraham  for their expertise in aromatherapy and self-medication practices with livestock. The authors are grateful to  L. Bignon  and  I. Bouvarel  ( Institut Technique de l'Aviculture, F-37380 Nouzilly, France ) for helpful discussions and  S.
| Statistical analysis All statistical tests were performed using commercial software (SAS Software, Version 9. 2, Cary, North Carolina). Reported P values are 2-tailed with P <. 05 defined as significant. Baseline data were compared between groups using analysis of variance for parametric data and Fisher's exact test for frequency data. Time to event analysis was conducted separately for all-cause treatment failure and for heart disease-related treatment failure (defined earlier). Cats lost from the study for a reason not defined in the protocol, or still alive when the study was terminated by the sponsor, were included in the analysis up until the last time point at which they remained in the study and were thereafter censored. The Kaplan-Meier method was used to estimate the median and 95% confidence interval (CI) time to event, and to generate time to event graphs.
The log rank test with right censoring was used to compare times to event between groups. Univariate Cox proportional hazards (CPH) model analysis with right censoring was performed for each variable to determine the association between that variable (at baseline) and time to event; the hazards ratio (HR) and 95% CI were calculated. The database contained a total of 53 baseline variables from 151 cats, and only 62 cats reached the all-cause failure endpoint. In order to reduce the risk of overparameterization in the multivariate analysis, variables were selected for analysis as follows: 1. Heart disease was classified as either congenital or noncongenital in origin. Noncongenital cardiomyopathy cases were originally classified as HCM, HOCM, DCM, RCM, or UCM; however, cases were finally not analyzed according to these subgroups.
For echocardiogram variables in which values were available from 2 different techniques, the variable with data from the highest number of cats was selected. The LA diameter was assessed on both the short and long axis views, and the end diastolic mean LV diameter, LV free wall, and LV septum dimensions were based on the 2-dimensional and M-mode. 16, 17  Correlations for clinical chemistry and hematology variables were investigated using Spearman rank correlation, and only 1 of the highly correlated variables was selected. Hematocrit was highly correlated with red cell count (the correlation coefficient ρ =. 79) and hemoglobin concentration (ρ =. 91), therefore hematocrit was selected. Alkaline phosphatase and alanine aminotransferase activities were highly correlated (ρ =. 70), therefore the former was selected.
Echocardiography, clinical chemistry, and hematology variables were analyzed longitudinally using repeated measures analysis of covariance, with baseline as the covariate, and treatment, visit, and treatment/time interaction as effects. Data were transformed as appropriate to give the best approximation of a normal distribution. Scores for clinical signs at each time point and frequencies of adverse events in the 2 groups after day 0 were compared with Fisher's exact test. A minimum of 100 cats was planned in the protocol, as it was calculated that 50 cats per group would provide 74% power with a 25% difference between groups for the frequency of occurrence of the primary endpoint. At least 80% power required an estimated 58 cats per group. Insufficient data were available before the study, however, to allow reliable sample size and power estimations.
| RESULTS The number of cats screened was not recorded, but 151 cats were enrolled into the study at 23 centers (median 5, range 1-27 cats per center) between October 2002 and March 2005, and treated up to January 2006. Data from all 151 cats were included in the statistical analysis, which can therefore be considered an "all-randomized animal" or "intent-to-treat" analysis data set; 77 received benazepril and 74 received placebo. No separate "per-protocol" analysis was conducted. Baseline and demographic data are shown in Table  1 , and the frequency of echocardiographic diagnoses in Table  2. A total of 93 cats (62%) had clinical signs of CHF, whereas 58 (38%) were asymptomatic. The frequency of congenital disorders (P =. 009) and mean white blood cell (WBC) counts (P =. 04) were significantly higher in the group of cats randomized to receive benazepril.
Quality control and read mapping to the reference genome The quality control check on raw reads in FASTQ format were processed using FASTQC and the Q20, Q30 and GC contents of the clean data were calculated. The sequences obtained by RNA-Seq were splice-aligned, for each library, using STAR (version 2. 3. 0e with standard parameters)  [30]. The reads were mapped to the Capra hircus genome (assembly ARS1). The resulting alignment files were merged to produce a global splice alignment reference with samtools sort, merge and index (version 0. 1. 19-44428cd using standard parameters). This reference was processed using the Cufflinks program  [31]  (version v2. 1. 1 with standard parameters and Ensembl ref_CHIR_1. 0_top_level. gtf as reference) to identify expressed transcripts and genes. The reference transcript and gene model files were used to quantify the expression in each library with featureCounts (version 1.
Expression profiling Differentially expressed genes (DEG) of read counts were identified using the Bioconductor package DESeq2  [33]  run within the R software (v3. 4. 1). Four comparisons were performed: 1-mucosa tissue from infected versus non-infected animals; 2-lymph node tissue from infected versus non-infected animals; 3-mucosa tissue from infected resistant versus infected susceptible animals and 4-lymph node tissue from infected resistant versus infected susceptible animals. Low expression tags were filtered, keeping only genes that achieved at least 5 counts in each condition. To account for multiple testing, genes were filtered using a Benjamini and Hochberg false discovery rate (FDR) of < 0. 001.
Gene ontology (GO) and pathway enrichment analysis GO analysis was used as an international standardized gene functional classification system to describe properties of genes and their products. DEG are functionally grouped into the three GO domains (biological processes, cellular components, and molecular processes) looking for significantly enriched functions compared to the genomic background. GO enrichment analysis and GO annotations plotting were performed using the clusterProfiler R-package. Due to the lack of goat (Capra hircus) GO data, GO were analyzed based on human annotation. All enriched GO terms that possessed a p-value < 0. 01 were displayed. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the major public pathway database of biological systems that integrates genomic, chemical and systemic functional information  [34].
Quantitative real-time PCR (qRT-PCR) validation Gene expression of 7 genes (n = 5 for the abomasal mucosa and n = 5 for the lymph nodes; 3 genes were common to both tissues) was measured by q-PCR in order to validate the results obtained in the RNAseq analysis. Goat ACTB (actin beta) gene whose expression remained stable among the samples was used as the endogenous control for all reactions. A total of 2 μg of high quality total RNA (RIN > 7. 5) was used to synthetize the cDNA using M-MLV Reverse Transcriptase (Promega, Charbonières, France) according to the manufacturer's instructions. All qPCR reactions were carried out in 48-well plates in a Prime Pro 48 Real-Time PCR System and analyzed with the ProStudy Software v5. 2. 10 (Techne, Staffordshire, UK). Taqman predesigned gene expression assay (Table  1 ) and the universal PCR master mix were purchased from Applied Biosystems and the analyses were performed according to the manufacturer's instructions (ThermoFisher Scientific, Applied Biosystems, Courtaboeuf, France).
Statistical analysis on the phenotypic data The FEC variable was log transformed in order to normalize its variance. The statistical analyses of the phenotypic (FEC, eosinophilia, pepsinogen and PCV) differences between animals in the infected, non-infected, resistant and susceptible groups were tested by using PROC MIXED in SAS (v. 9. 4, SAS Inst. Inc. , Cary, NC, USA, 2012) and differences were considered significant when P < 0. 05. The results are presented after back-transformation. The association between parasitological (number of males, number of females, worm burden and establishment rate) and physiological (FEC, eosinophilia, pepsinogen and PCV) variables at slaughter were determined using Pearson's correlation coefficient (v. 9. 4, SAS Inst. Inc. , Cary, NC, USA, 2012).
This study was funded by the  Project MALIN (La Region Guadeloupe and Fonds Europeens FEDER). H. M. A. was supported by a  doctoral fellowship  from the project  European Graduate School in Animal Breeding and Genetics  ( EGS-ABG, Erasmus Mundus)  and the division of animal genetics of  INRA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Mutation Analysis of the Resistance Genes To detect specific mutations in the frxA, rdxA, gyrA, gyrB, and 23S rRNA genes, a PCR-based sequencing approach was carried out for a selected number of H. pylori isolates, including the susceptible and resistant strains. The oligonucleotide primers are shown in Table  1. The PCR products were sequenced on both strands by the Sanger sequencing method using an automated sequencer (Macrogen, Seoul, Korea). All complete and partial DNA sequences were edited by Chromas Lite version 2. 5. 1 (Technelysium Pty Ltd, Australia). Comparative sequence analysis between resistant and sensitive strains was carried out using BioEdit software version 7. 2. 5  [36]. The DNA and deduced amino acid sequences were aligned and coordinated to H. pylori 26695 (GenBank: CP003904.
Nucleotide Sequence Accession Numbers The sequences obtained from this study were submitted to NCBI under the following GenBank accession numbers: domain V 23S rRNA, MH040926-MH040949; gyrA, MH054262-MH054292; gyrB, MH054293-MH054319; frxA, MH054320-MH054346; rdxA, MH054347-MH054374.
Analysis of lymphocyte subpopulations Lymphocyte subpopulations were identified from EDTAtreated blood samples by two types of staining using monoclonal antibodies: (1) FITC-conjugated anti-pig CD4 (Acrys Antibodies, Herford, Germany), PE-conjugated anti-pig CD8α (BD Biosciences) and PerCP-conjugated anti-pig CD3 (Bio-Techne, Lille, France) antibodies, or (2) PE-conjugated anti-pig CD21 (Acrys Antibodies) and PerCP-conjugated anti-pig CD3 in the dark for 15 min at room temperature. Erythrocytes were removed using a lysis solution (BD Biosciences, Le Pont de Claix, France). The DNA marker DRAQ5™ (1 μM final; Biostatus, Leicestershire, UK) was added to identify nucleated cells, then samples were analysed with a FACS Aria flow cytometer (BD Biosciences) and data computed using the FlowJo software (FlowJo, Ashland, Oregon).
Kinetic response to LPS Cortisol, glucose and free fatty acid plasma levels were assessed after LPS administration as previously described  [23]. Plasma cytokine levels in response to LPS injection were quantified in heparin-treated samples by ELISA (porcine IL-6 and TNFα, Bio-Techne). In order to investigate divergent selection effects on gene expression response to LPS, we tested expression of 34 genes modulated by LPS. These genes were provided by the literature and from our own data (  [23]  and unpublished data). Gene expression analysis was performed as previously described  [23]. Total RNA extraction was done using the Nucleospin RNA Blood kit (Macherey-Nagel, Hoerdt, France) followed by DNase treatment. The quality of each RNA preparation was verified through the Bioanalyser Agilent 2100 (Agilent Technologies, Massy, France) and low-quality RNA samples were discarded (RIN < 8).
One μg of total RNA was reverse-transcribed. The transferrin receptor and glutamyl-prolyl-tRNA synthetase genes were used as housekeeping genes, according to previous data. Pre-amplified samples were analysed with a 96 × 96 Dynamic Array™ IFC (Fluidigm) following the protocol defined by Spurgeon et al. [24]. Each gene was tested twice for each sample. Four dilution points containing a pool of all samples were used to determine PCR efficiency. Data were analysed using BioMark Gene Expression Data Analysis software (Fluidigm) to obtain Ct values. The Pfaffl method was applied to compute the relative expression of each gene  [25]. A linear model was performed to reveal the genes differentially expressed according to the population (high, low) and the time point (0, 1, 4, 24 h): expr i ¼ β 0 þ β 1 t þ β 2 population i þ β 3 t Ã population i þ ε i.
Statistical analysis A PCA was performed on raw data obtained on 6-week-old piglets using the FactoMineR package in the R software (version 3. 2. 1, R Core Team, 2016)  [26]. Redundant parameters were excluded from the analysis, i. e. MCV, MCHC, % Ret, WBC and lymphocyte counts. The first two components explained more than 40% of the total inertia and each contributed to more than 15% of the total variance. Univariate statistical analyses for experiment 1 were made using Graph Pad Prism (version 6. 0). For each parameter measured, 16 HPA lo and 16 HPA hi pigs were compared using non-parametric Mann-Whitney tests; statistical significance appears as stars on charts (*: p < 0. 05; **: p < 0. 01; ***: p < 0. 001); each point represents a single animal and lines denote medians. For experiment 2, cortisol, TNFα, IL-6, white blood cells and platelets data were transformed into their logarithmic scores for normalisation.
Antibodies were used as follows: As primary antibody, monoclonal rat anti-alphatubulin (Serotec, YOL1/34) at 1:500 dilution; as secondary antibody, Alexa Fluor 594 anti-rat dye (Life Technologies) at 1:1000 dilution. DAPI was added at 1:1000 for nuclear labelling. Cells were examined in Eclipse TE2000U microscope (Nikon, Tokyo, Japan) and digital images were acquired with an Orca C4742-95-12ER charge-coupleddevice camera (Hamamatsu Photonics, Hamamatsu City, Japan) and processed by HCImage and ImageJ software.
ACKNOWLEDGEMENTS This work was funded by the  FINOVI foundation  under a  Young Researcher Starting Grant , the  Cystic Fibrosis French Foundation Vaincre la Mucovicidose  grant  RF20130500897  and the  ANR  (grant  n°ANR-15-CE15-0011 ) to SS, and by grants  BIO2016-75030-P  from  Ministerio de Economía y Competitividad (Spain)  and  S2017/BMD-3691  ( InGEMICS-CM ) from  Comunidad de Madrid and European Structural and Investment Funds  to VJC and MM. SS is supported by an  INSERM  staff scientist contract. J. M. C-S is supported by a predoctoral contract from  UCM. We also wish to acknowledge the  Genomics and Mass Spectrometry Units at Universidad Complutense de Madrid (UCM)  for sequencing and NAD + analyses, respectively.
Materials and Methods This project was approved by the Research Institute in Semiochemistry and Applied Ethology (IRSEA) Ethics Committee (C2EA125) and the French Ministry of Research (APAFIS Process number 11949). General and specific information about the patients and the horses was collected through a questionnaire survey. Patients' medical conditions and expectations were collected with the agreement of the patients and/or their families. Nevertheless, to respect patient confidentiality, the diagnoses and expectations of individual patients were not reported; instead, data were classified into different categories. This study was conducted in Avignon, France, between March and July 2018.
Study Design and Data Collection This investigation included a resting phase and two phases of EATs (preparation and working phases). The resting phase consisted of the collection of HRV data in the normal living and husbandry conditions of the horses. The preparation phase consisted of controlled interactions between the patient and the horse, such as petting, brushing, saddling, cleaning the horses' feet, and feeding or watering the horses. These interactions differed according to the patients' needs and abilities, and were selected by the therapists. Preparative sessions were performed near the paddocks. The working phase followed the preparation phase. The patients could either ride or perform in-hand training with the horses. In-hand training included the following: the approach and the initial contact with the horse, making the horse move forward or backward, leading the horse to cross obstacles or pass parallel bars on the ground, and lunging the horse in a round pen.
The therapists made the decision of whether the patient should ride or perform in-hand training. Working sessions were performed in riding arenas of variable size. Each phase lasted between 20-30 minutes, and took place between 09:00-13:00 and 14:00-16:00 during the day. Behavior data were collected for both the preparation and working phases by one observer through direct observation. The considered behaviors included "ears pinned back" (EPB), "head lateral movement" (HLM), "snort", and "defecation" (see Table  2 )  [14, 31]. These parameters were assessed in terms of frequency. HRV data were collected during all the phases with Polar V800 Equine ® equipment (Kemple, Finland). Then, the collected data were transformed with Kubios ® software (Kuopio, Finland), and only normal R-R intervals were considered for the HRV analysis.