dataesr/scientific-paragraphs-categorization huggingface dataset:
WadC Brucella LPS activates human mo-DCs We then determined whether Brucella LPSs would elicit activation of DCs from human origin in vitro. Human monocytes DCs (mo-DCs) were derived from PBMC from healthy blood donors in the presence of GM-CSF and IL-4 (GM/IL-4 mo-DCs). After 7 days of culture, these mo-DCs were stimulated for 72 h with Bm-wt or Bm-wadC LPS, both at 20 ng/mL. Cell culture supernatants were used for inflammatory cytokine measurement by ELISA. We found that Bm-wt LPS in human GM/IL-4 mo-DCs did not trigger any secretion of IL-6, IL-12p70, TNFa or IL-10. In contrast, Bm-wadC LPS prompted strong secretion of IL-12p70 and TNFa while IL-6 and IL-10 secreted levels reached higher values, comparable to those obtained with E. coli (Fig. 2 ). These findings reminiscent of those observed with the GM-DC model reveal that our murine GM-DC data can be translated to human GM/IL-4 mo-DCs.
Proliferation of T cells was traced by measuring CFSE dilution with flow cytometry. BMDCs incubated with LPS alone without OVA did not induce T cell proliferation (data not shown). Data from one representative experiment are shown in Fig. 4A  and statistical analyses from pooled data of at least 3 independent experiments are presented in Fig. 4B. Bm-wt LPS generated a slight but non-significant increase of CD4 C or CD8 C T cell proliferation in FL-DCs only, ranging from 11. 9% in mock-treated cells to 16. 5% and 20. 8% to 35. 2%, respectively (Fig. 4 ). The two DC models treated with Bm-wadC LPS were potent activators of both CD4 C and CD8 C T cell proliferation, as was E. coli LPS. These results indicated that wt Brucella LPS despite FL-DC activation capacity was unable to trigger full T cell activation in vitro, whereas the Bm-wadC LPS, was responsible for a strong T cell activation in both GM-and FL-DCs.
Statistical analysis All values are expressed as mean § standard deviation. All experiments were performed at least three times in triplicate otherwise indicated. Statistical analyses were done using with the GraphPad Prism software. Non-parametric one-way ANOVA test followed by variance analysis with the Benjamin and Hochberg test were performed, otherwise indicated in the figure legend. Differences between values were considered significant at P < 0. 05 ( Ã , P < 0. 05; ÃÃ , P < 0. 001; ÃÃÃ , P < 0. 0001; ÃÃÃÃ , P < 0. 00001).
(2012). The experimental details for the amplification of the 40 assay genes were previously published  (Thépault et al. , 2016). The CGF fingerprints were then visualized using a standard gel electrophoresis containing 2% of agarose colored with GelRed R (Interchim, Montluçon, France) in accordance with supplier recommendations. The reference strain NCTC11168, was used as positive control in all assays, since the strain is known to contain the 40 assay genes. PCR results were converted into binary data corresponding to the absence (0) or the presence (1) of the genetic marker in bacterial genomes and were stored into BioNumerics R software (v 6. 5, Applied Maths, Belgium). A dendrogram was built using the simple matching distance coefficient and unweightedpair group method using average linkages (UPGMA) of clustering in BioNumerics R , as previously described  (Taboada et al.
Multilocus Sequence Typing (MLST) Alleles of the seven housekeeping genes for MLST (aspA, glnA, gltA, glyA, pgm, tkt, and uncA) were determined within clinical isolates from 2009 in a previous study  (Thépault et al. , under review). In this study, alleles, sequence types (ST) and clonal complexes (CC) of isolates from cattle and clinical cases from 2015 were determined by Whole Genome Sequencing (WGS), using Ion Torrent technology available at the Anses sequencing platform, and by comparison of the sequences to the publically available PubMLST database (http://pubmlst. org/campylobacter) on BIGSdb  (Jolley and Maiden, 2010). MLST characterization through WGS was performed on selected subsets of 77 and 79 isolates from cattle and clinical cases from 2015, respectively. Isolates selection was made to keep the same genetic structure in the subsets than in original populations, by selecting equal proportions of each CGF40 genotypes within cattle and clinical isolates from 2015, than proportions of these clusters observed in each original population.
Data Collection Behavior Behavior was recorded in real time over each experimental repetition using ceiling-mounted cameras (Panasonic WV-CP254H) attached to a digital video recording system (Geovision GV-1480). The behaviors defined in the ethogram (Table  2 ) were then extracted using continuous sampling by one trained individual using Observer XT 11 software (Noldus Information Technology, Leesburg, VA). Each Period (1-3) was sampled entirely. The behaviors were split into two categories, the postures: stand, sit, and lie (sternally or laterally); the fundamental needs: drink, eat and the nursing activity. Postures were mutually exclusive behaviors, whereas fundamental needs were coded as "start-stop" behaviors. At all times, each sow was necessarily in one of the three listed postures.
For each behavior listed, the total number of occurrences as well as the duration were measured. To standardize traits between FIGURE 2 | Protocol followed for each repetition of the trial. The protocol for the 10 sows was repeated 8 times (2 times/day for 4 days). The water flow rates assigned for each sow were calibrated before the trial. Behavior and heart rate were recorded continuously over the 3 periods. Two sows of the 10 had missing data for the drinker pressing/drinking behavior: one for all the repetitions and the other one for the first five repetitions, due to partial obstruction of the camera's view. For three of the sows most distant from the camera, lying behavior was not defined as "sternally" or "laterally" because of the camera's brightness and contrast quality limits. Drinking and eating bouts were determined using a bimodal Gaussian density curve to fit the behaviors' log-transformed intervals  (35).
Heart Rate and Heart Rate Variability Heart rate monitors (Polar S810i, Polar Electro Öy, Kempele, Finland) were attached to six sows [see methodology in  (36) ], and set to record and store successive interbeat intervals (IBI) during each experimental repetition. The IBI data were then downloaded onto a computer via a Polar interface IR transfer (Polar Electro Öy, Kempele, Finland) and stored for later processing. Preliminary processing of the IBI data involved the visual and mathematical comparisons of individual beats with their neighboring beats to identify any anomalous or ectopic intervals. Spurious beats were assigned an error classification and carefully edited according to the recommendations for editing anomalous data published elsewhere  (37). Where possible, three sections of 512 beats (one per phase and approximately 4 min long) were extracted from each treatment for time and frequency domain analysis, together with nonlinear (including geometric) analysis.
Only sections with less than 0. 5% error were used. Kubios HRV 2. 1 software  (38)  was used to obtain heart rate variability (HRV) variables. The following 3-time domain variables were examined: (1) IBI mean (RR mean; RR is the interval between successive R peaks of the QRS complex of the Electrocardiogram wave). Interbeat interval mean provides general variability information (39); (2) root mean square of successive RR differences (RMSSD), which reflects the integrity of vagus nerve-mediated autonomic control of the heart; and (3) the standard deviation of all RR intervals of the dataset (SDNN), which is a good predictor of overall variability present at the time of recording. Frequency domain analysis was done using a Fast Fourier Transformation (FFT) obtaining high (HF), and low frequency (LF) bands, expressed in normalized units (n.
Statistical Analysis Statistical analyses were performed with the software R  (41). Because no difference between flow rates was obtained regarding all the behavioral and heart rate traits, the three flow rates groups were merged together to improve statistical power, and analyses were completed testing the effect of a cooling flow rate between 0. 25 and 0. 85 (COOLED) or no cooling flow rate (CONTROL). Effects of treatment on variables were estimated using repeated measures models with the function lmer from the R package "lme4. " For Period 1, the model included the treatment as fixed effect, and both the sow and the repetition being included as random effects. For Period 2, the Period 1 measurement was included in the previous model as a linear covariate. For Period 3, both Period 1 and Period 2 measurements were included as covariates.
DNA Manipulations and Analyses Sequence data were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG 2  ). Searches for DNA and protein homologies between Brucella species and other α-proteobacteria such as Ochrobactrum, Rhizobium, or Agrobacterium were carried out using KEGG, Basic Local Alignment Sequence Tool (BLAST 3  ), and Clustal Omega 4  from the European Molecular Biology Laboratory -European Bioinformatics Institute (EMBL-EBI 5  ). New glycosyltransferase identification, using B. abortus 2308 was supported by Carbohydrate-Active enZymes database (CAZy 6  ). Primers were designed using Primer 3 input 7  and synthesized by Sigma-Aldrich. Plasmid DNA was extracted with Qiaprep spin Miniprep (Qiagen GmbH). When needed, DNA was purified from agarose gels using Qiack Gel extraction kit (Qiagen) and sequenced by the Servicio de Secuenciación of CIMA.
Screening for Putative LPS Core Glycosyltransferases A bioinformatic search in the Carbohydrate-Active Enzymes database CAZy 8  revealed 23 ORFs in the genome of B. abortus 2308 (Supplementary Table  S1 ) that could code for glycosyltransferases. We excluded from further analysis BAB1_0108-cgs, which is involved in cyclic glucan synthesis  (Briones et al. , 2001) , BAB1_1786-mtgA and BAB1_1450-murG, both related to peptidoglycan synthesis, BAB1_1171-lpxB, probably implicated in lipid A formation  (Iriarte et al. , 2004) , and BAB1_0553-wbkA, BAB1_0563-wbkE; BAB1_1000-wboA and BAB1_1000-wboB, four genes that belong to the O-PS synthesis route  (McQuiston et al. , 1999; Godfroid et al. , 2000; González et al. , 2008). Similarly, four ORFs correspond to those glycosyltransferases already known to be involved in the synthesis of the LPS core: BAB1_0639-wadA  (Monreal et al.
FUNDING This research was supported by the  Institute for Tropical Health funders (Obra Social la CAIXA , " Fundaciones Caja Navarra " and  "Roviralta ,  PROFAND, Ubesol, ACUNSA, " and "Artai ") and grants  MINECO  ( AGL2014-58795-C4-1-R ). MS-B is the recipient of a Ph. D. Fellowship funded by the  "Asociación de Amigos de la Universidad de Navarra. "Work at  CITA-Spain  was also sustained by Grants from  MINECO  ( AGL2008-04514-C03-03/GAN  and  AGL2014-58795-C4-3-R ).
4)  [4]. The widespread emergence of antimicrobial resistance has raised concerns in recent years about the safety of anticoccidials and their potential impact on human health, animal health, and the environment  [5, 6]. Furthermore, the Federation of Veterinarians of Europe has recently released a position paper advocating increased veterinary oversight of anticoccidials in the European Union  [7]. To investigate the current status of coccidiosis in commercial poultry production, and the benefits, safety, and alternative options to anticoccidial use, a comprehensive review of the literature was conducted based on a guide for methodologies for systematic reviews in Agri-Food Public Health  [8]. The search was performed in the following databases: CAB International, AGRICOLA, and ISI Web of Knowledge to access peer-reviewed literature.
, 2007)  which is implemented in the symbolic language REDUCE, GenSSI (Generating Series for testing Structural Identifiability)  (Chis et al. , 2011a)  implemented in Matlab ® , and the IdentifiabilityAnalysis application  (Karlsson et al. , 2012)  implemented in Mathematica. All of these three toolbox are freely available. The identifiability methods used by DAISY and GenSSI are explicitly referred in their acronyms. The IdentifiabilityAnalysis application uses the exact arithmetic rank approach. While DAISY and GenSSI perform global identifiability analysis, IdentifiabilityAnalysis performs local identifiability analysis, but has the advantage of allowing the analysis of complex models. Overall, the outcome of these toolboxes is a qualitative report that displays the parameters that are identifiable.
The calculation of the sensitivities can be performed by symbolic manipulation of the model equations using dedicated software such as the Matlab ® Toolbox IDEAS  (Muñoz-Tamayo et al. , 2009) , which is freely available at http://genome. jouy. inra. fr/logiciels/IDEAS. Under a number of technical assumptions that include theoretical identifiability, the covariance matrix • of the ML estimator satisfies • ≥ FIM v * (4) This equation is known as the Cramér-Rao inequality. An approximate • ' of the covariance matrix of the parameters can be computed at the Cramér-Rao lower bond evaluated at i • ' = FIM v i (5) It must be kept in mind that this approximation is only valid asymptotically, when the number of data points tends to infinity, the statistical hypotheses on the noise are satisfied, and that i is close to *.
Acknowledgements We would like to thank the developers of the identifiability toolboxes DAISY (Bellu et al. , 2007),  GenSSI  (Chis et al. , 2011a) and IdentifiabilityAnalysis (Karlsson et al. , 2012) for rendering their software freely available and favouring Open Science. Many thanks to  Dr. Maria Pia Saccomani  ( University of Padova, Italy ) for helpful insights on the use of DAISY. We thank  Nicolas Friggens  ( INRA-AgroParisTech, France ) for his critical comments on the manuscript. Rafael Muñoz-Tamayo  is indebted to  Éric Walter  for his rigorous and provocative explanations on the fascinating subject of parameter identification.
parasitizing endemic cyprinids of the Balkan Peninsula. First, we analyzed the degree of endemism in Dactylogyrus species parasitizing these cyprinids. Next, we focused on the phylogenetic relationships between endemic Dactylogyrus and commonly distributed Dactylogyrus (species shared between central European and endemic Balkan cyprinid species) in order to infer potential scenarios of historical contact between different cyprinids. Concerning Dactylogyrus species with a wide host range, we also searched for genetic structuration by analyzing the level of genetic diversity and its correlation with the geographical distances between their hosts. Finally, we assessed the species status of generalist Dactylogyrus on the basis of molecular data in order to test whether the degree of genetic variability was in concordance with the current species status based on a classical taxonomical approach.
The PCR for partial 28S was performed using the same conditions as described in Šimková et al. 82. The PCR products were checked on 1% agarose gel and purified using ExoSAP-IT kit (Ecoli, Bratislava, SK) following the standard protocol. Purified products were directly sequenced using the PCR primers and BigDye Terminator Cycle Sequencing kit (Applied Biosystems, Foster City, CA). Sequencing was performed on an ABI 3130 Genetic Analyzer (Applied Biosystems). New sequences were deposited in GenBank (their accession numbers are shown with asterisks in Table  5 ). Phylogenetic analyses. DNA sequences were aligned using fast Fourier transform in MAFFT  83. The sequences were trimmed to concur with Dactylogyrus sequences obtained from GenBank. The sequences for 14 Dactylogyrus species from central European cyprinids were obtained by sequencing in this study or acquired from GenBank (see Table  5  for accession numbers).
Acknowledgements We are grateful to  Jaroslav Červenka ,  Milan Gelnar ,  Maria Lujza Červenka Kičinja ,  Kristýna Koukalová ,  Tomáš Pakosta ,  Eva Řehulková ,  Kateřina Vyčítalová  and  Petra Zahradníčková  for their help with fish dissection and parasite collection and to  Ivan Bogut ,  Dario Marić ,  Spase Shumka ,  Denik Ulqini ,  Ivana Buj ,  Zoran Marčić , and  Stamatis Zogaris  for help with host fish specimen collection. We kindly thank  Matthew Nicholls  for English revision of the final draft and  Timo K. Pajunen  from the  Finnish Museum of Natural History  for his curatorial services. This study was funded by the  Czech Science Foundation  (project number  15-19382S ).
Data Availability All new sequences of Dactylogyrus obtained during this study were deposited in NCBI GenBank under accession numbers MG792838-MG793066. Appropriate accession numbers according to Dactylogyrus species and specific rDNA regions are presented in Tables  1 2 3. Since whole fish specimens were completely processed during parasitological dissection, additional specimens of each analysed host species were collected from the same locality and fish vouchers were deposited in the ichthyological collection of the National Museum in Prague (Czech Republic). Voucher specimens of the sequenced Dactylogyrus species (excluding undescribed species) are deposited in the Finnish Museum of Natural History in Helsinki (available under the accession numbers MZH KN10850-989).
goreni. org/). Sources of material included histopathology databases from government, academia, and industrial laboratories throughout the world. Content includes spontaneous and aging lesions as well as lesions induced by exposure to test materials. A widely accepted and utilized international harmonization of nomenclature for endocrine lesions in laboratory animals will decrease confusion among regulatory and scientific research organizations in different countries and provide a common language to increase and enrich international exchanges of information among toxicologists and pathologists. (.
Acknowledgements Photographs were kindly provided by the team members and  Drs. Jonathan Carter  ( Covance, Harrogate, UK ),  Christopher Gray  ( Covance, Harrogate, UK ),  Michael Elwell  ( Covance, Chantilly, VA, USA ),  Kathrine Heider  ( EPC, Switzerland ),  Ron Herbert  ( National Institute of Environmental Health and Safety, Raleigh, NC, USA ),  Hijiri Iwata  ( LunaPath LLC, Hamamatsu, Japan ),  Virginie Piccicuto  ( Covance, Harrogate, UK), Graham Ince (Covance, Harrogate, UK ),  Claudio Petterino  ( Charles River ,  Edinburgh, UK) , and  Jerrold Ward  ( Global VetPathology, Montgomery Village, MD, USA ).
By contrast, 3619 probes-corresponding to 2771 genes-were differentially expressed following exposure to DON, and 32 signaling pathways were identified from the IPA software functional analysis of the set of DE genes. When the intestinal explants were treated with S. cerevisiae boulardii prior to DON exposure, the number of DE genes decreased by half (1718 probes corresponding to 1384 genes). Prototypical inflammation signaling pathways triggered by DON, including NF-κB and p38 MAPK, were reversed, although the yeast demonstrated limited efficacy toward some other pathways. S. cerevisiae boulardii also restored the lipid metabolism signaling pathway, and reversed the down-regulation of the antioxidant action of vitamin C signaling pathway. The latter effect could reduce the burden of DON-induced oxidative stress.
cerevisiae boulardii prior to DON exposure, were very similar, and inflammation remained the hallmark of the intestinal transcriptome. It is worthy of note that, except for growth arrest and DNA damage-inducible protein gamma gene (GADD45G), the 15 other genes expressed in both experimental conditions were slightly less expressed in the DON + yeast condition (Table  1 ). Microarray data validation was performed by quantitative real-time polymerase chain reaction (qPCR) on a set of 12 DE genes in DON and DON + yeast conditions. A significant Spearman correlation coefficient (r = 0. 896) was observed between the two techniques (Supplementary Figure  S1 ), confirming the reliability of the transcriptome analyses.
Functional Analysis of the Intestinal Transcriptome Modulation by DON and/or S. cerevisiae boulardii To further investigate the protective effect of S. cerevisiae boulardii, a comparative functional analysis of DEGs in DON and DON + yeast conditions was performed using the core analysis comparative function of Ingenuity Pathway Analysis (IPA) software. Figure  3  shows that 32 canonical pathways were significantly affected in the jejunal tissue exposed to DON (p-value < 0. 05, -2 > IPA Z-score > +2). As previously described, the foremost up-regulated signaling pathways in the DON condition, including HMGB1 signaling, IL-6, IL1, TNFR1, p38 MAPK, and TREM1 signaling, were related to immunity/inflammation  [21]. The B-cell activating factor, B-cell receptor signaling, and the NF-κB signaling pathway, which not only regulates inflammation and immunity, but also cell survival and cell proliferation, were also up-regulated by DON.
Immediately after washing, the slides were scanned on an Agilent G2505C Microarray Scanner using Agilent Scan Control A. 8. 5. 1 software, and the fluorescence signals were extracted using the Agilent Feature Extraction software v10. 10. 1. 1 with the default parameters. The microarray data were analyzed using R (www. r-project. org, R v. 3. 1. 2) and the Bioconductor packages (www. bioconductor. org, v 3. 0), as described in GEO entry GSE97821, which also contains all the details of the experiments. Network analysis and functional analysis of the DE genes were performed using the Ingenuity Pathway Analysis tool (IPA, http://www. ingenuity. com) to identify gene networks and signaling pathways affected by each treatment. The IPA output included statistical assessment of the significance of gene networks and signaling pathways based on Fisher's exact test; only networks and pathways that presented a p value < 0.
Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) Analysis The reverse transcription and real-time qPCR steps were performed on total RNA samples (n = 6 per treatment group), as previously described  [47]. The primers for selected differentially expressed genes on the microarray (Supplementary Table  S3 ) were designed using Primer Express Software, and purchased from Invitrogen (Invitrogen, Life Technologies Corporation, Paisley, UK). Non-reverse transcribed RNA was used as the non-template control for verification of the genomic DNA amplification signal. The specificity of the qPCR products was assessed at the end of the reactions by analyzing the dissociation curves. The expression stability of five candidate reference genes, (Cyclophylin A (CycloA), β-actin, β2-microglobulin, Ribosomal Protein L32 (RPL32), and Hypoxanthine Phosphoribosyl transferase 1 (HPRT-1)) across different experimental samples has been investigated; it was demonstrated that RPL32 showed the highest stability (SD = 0.
Statistical Analysis The flock was considered as the experimental unit. All statistical analyses were carried with R statistical software (R Core Team, 2017) using the nlme package  (Pinheiro et al. , 2017)  and the function lme to build mixed linear regression models with the restricted maximum likelihood approach for coefficients estimation. Coefficients with a P-value ≤ 0. 05 were considered significant. Two different models were built separately for the outcomes AGE PEAK and OPG PEAK. To improve model fit, OPG PEAK was log-transformed in base 10. For performances analysis, five different models were built with the outcomes: bodyweight at slaughter, average daily gain, feed conversion ratio, percentage of livability, percentage of condemnations. For all models, treatment was included as a fixed effect and the farm was considered as a random effect.