dataesr/scientific-paragraphs-categorization huggingface dataset:
The loss of the D-lactate dehydrogenase expression in B. suis in addition to less efficient denitrification suggests a better adaptation of B. microti to anaerobic or microaerophilic conditions. Interestingly, in opposition to the situation observed in B. suis with 15 pseudogenes which were acid-stress regulated in B. microti, none of the acid stress-regulated genes found in B. suis were pseudogenes in B. microti, despite the fact that a total of 79 pseudogenes are present in B. microti versus 109 pseudogenes in B. suis (NCBI Genome database;  Audic et al. , 2009). It is conceivable that the loss of certain genes during host-adaptation impairs B. suis acid stress resistance, when compared to the more ancestral and free-living species B. microti. These results therefore give additional evidence that gene mutations shape the physiology of Brucella species.
, 2009). The activation of the glycine betaine/L-proline system reflected a potential need for these osmolytes, since pH homeostasis is interconnected with osmoregulation and the redox state of the cell: H + extrusion has to be compensated to maintain osmotic and redox homeostasis. Higher expression of sulfate and taurine transporters suggested a more active sulfur metabolism in B. microti during acid stress. Taurine, an amino acid-like organosulfonate, is an osmolyte that also participates in osmoregulation. ABC transporters have been associated with diverse physiological processes from DNA-repair to gene regulation, and are related to virulence  (Jenner et al. , 2009). 10 of the identified ABC transporter-encoding genes are listed in the PHIDIAS Victors database as virulence-associated. Finally, the general induction of ATP hydrolysis-based transporters might contribute to bacterial pH homeostasis through proton expulsion by the ATPase components of these systems.
Virulence-Related, Acid-Stress Responsive Genes Several acid-responsive genes of Brucella have been previously reported as virulence-related, according to PHIDIAS Victors and VFBP databases. In our work, 69 virulence genes listed in these databases were identified as acid-stress responsive (Supplementary Table  15 ), out of 159 virulence-associated genes reported for B. suis. From the 69 genes, 28 were regulated in both species, 19 and 22 specifically in B. suis and B. microti, respectively. Altogether, 25 of the virulencerelated genes were down-regulated, while 39 were more expressed under acid conditions, and 5 showed up-or downregulation, depending on time-points or species. Speciesspecifically regulated virulence genes with increased expression under acid conditions were more numerous in B.
suis (14/19) than in B. microti (10/22), indicating specific gene expression signatures, possibly in the species' interactions with the host cell. The most represented COGs were the groups E (amino acid transport/metabolism), C (energy production/conversion) and G (carbohydrate transport/metabolism). As an example of genes listed in both databases, rfbD encodes the LPS O-antigen export system permease. It was more expressed at pH 4. 5 in B. microti, suggesting that B. microti may preserve structural integrity of the envelope by activation of specific repair mechanisms, while B. suis engages into a larger response involving chaperones, proteases and potential DNA-repairing mechanisms. Recently, Salmon-Divon et al. reported 773 B. melitensis 16M genes differentially expressed (fold change ≥ 2) between normal and low-pH conditions in TS broth at pH 4.
ACKNOWLEDGMENTS We are grateful to  A. R. Wattam  for matching Brucella locus tags between species, and  G. Jiménez-Dominguez  for participation in heatmap construction. We thank  P. Bouhours  and  M. Abrantes  for technical assistance in solution and media preparation, and  A. Roux  and  A. Trausch  in constructing plasmids in E. coli. We are grateful to  V. Jubier-Maurin ,  D. de Biase ,  A. Blanc-Potard , and  S. Gaudriault  for helpful discussions, and acknowledge the  Bio2M bioinformatics and bio-markers platform of the CHU Montpellier  ( T. Commes  and  A. Boureux ) for support in bioinformatics analysis.
MOLECULAR TYPING, EVOLUTION, PHYLOGEOGRAPHY Several papers used molecular tools to explore Brucella diversity at the sub-species level particularly focusing on the major pathogenic species Brucella melitensis (ovine/caprine), Brucella abortus (bovine), and Brucella suis (porcine) The Topic included papers that used two of these approaches, multi-locus variable number of tandem repeat analysis (MLVA) and multi-locus sequence analysis (MLSA), to examine the global population structure of the genus. Whatmore et al. reported application of an extended MLSA tool to a globally and temporally diverse collection of >500 isolates, as well as the launch of an online database allowing the community to interrogate existing data and compare new profiles. Despite relatively low resolution, over 100 sequence types were identified by MLSA and new insights into the global evolutionary history of the genus were provided with evidence of the existence of lineages with restricted host or geographical ranges.
Calling test (Control). We built an arena by placing wooden planks around the perimeter of a rectangular 8 m × 3 m area on the natural floor of each elephant camp  22. First, the elephants were instructed by the mahout to stand and wait (held by another mahout if necessary) on one side of the arena until the mahout took position on the opposite side of the arena. Then the mahout gave vocal commands for the elephant to cross the arena lengthwise. A video camera (Sony HDR-CX405) was used to record the experiment. Videos were analysed using the free Behavioural Observation Research Interactive Software (BORIS)  28. The elephant's success at joining the mahout by crossing the arena was recorded as a binary response (0/1). Novel surface test. The goal of this experiment was to introduce the focal elephant to a novel stimulus by asking the animal to walk on a surface it had never encountered before.
Statistical analyses. Data are given in Online Resource 1. All analyses were performed with the statistical software R, version 3. 6. 3  30 and figures were created using the ggplot2 package 31. To investigate how task success (1: success/0: fail) was affected by elephant age and sex and mahout experience and relationship length, two Bayesian regression models were fit using the brm function from the brms package  32  with Bernoulli distributions. We employed a Bayesian framework since our sample size was relatively small and underlying data structure (singular fit) limited restricted maximum likelihood model convergence. We used weakly informative priors set automatically by the brm function. Since test sessions occurred on seven different days in four different locations, we included the test date as a random factor to control for variation due to the weather or location in both models.
The first two models assessed the effect of the mahout predictors (mahout experience and relationship length) on the success of elephants in the control task (model 1; N = 51) and in the novel surface task (model 2; N = 41). The sample size and the distribution of the data did not allow us to test for a two-way interaction between the mahout experience and the relationship length, but raw data are presented in Table  1. The last two models tested whether elephant's age and sex affected their success in the control task (model 3; N = 52) or in the novel surface task (model 4, N = 42). We tested the effects of sex and age as main predictors to examine whether elephants' responses to the novel surface or control tasks were different for males and females or depended on their age. Model selection was performed according to leave-one-out cross-validation  35  using the LOO function from the brms package  32.
Acknowledgements We are very grateful to the  Ministry of Natural Resources and Environmental Conservation from the Government of the Union of Myanmar , for giving us permission to work with the Myanma Timber Enterprise. We wish to thank  Khin Than Win ,  Mu Mu Thein ,  Thu Zar Thwin  and  Ohnmar Win  for helping with data collection, and to all elephant handlers and MTE staff who participated in and helped with this project. We also thank  Yoan Paillet  and  Michael Briga  for their help with the statistical analyses.
The Structure of the Bio-Economic Model The bio-economic model maximizes farmer's gross-margins over one period under constraints for two selected farms in the region of Thessaly, in Greece. We assumed that farmers are risk-averse towards the proposed management strategies  [16]. The proposed model is a linear one constructed by the use of mathematical programming methods in GAMS software (General Algebraic Modeling System). The objective function has the following form: U = Σgm c,n -Σ(ϕ * std) where gm is the gross-margin, ϕ is the risk aversion coefficient, std the standard deviation of the average income, and the index c stands for the conventional practices, while the index n stands for the adoption of alternative management strategies. The standard deviation of average income is calculated as follows.
iv. Crop rotation Here, we propose a crop rotation constraint that restricts the selection of crops by the farmer. According to this constraint, the cultivation surface of a crop is limited by its allowed precedents. In order to produce a matrix of crop precedents, we used data obtained by personal interviews. Consequently, the constraint has the following form: ∑ crop, s x(crop, s) ≤ ∑ pc,s x(pc, s) where ∑ pc, s x(pc, s) is the surface of the precedent crop in each soil type.
| Sequence analysis and phylogeny Deduced amino-acid sequences of GluCl subunits from B. malayi, C. elegans, H. contortus and P. univalens were aligned using the MUS-CLE algorithm included in the Seaview 5. 0. 4 package  (Gouy et al. , 2010). Percentage of identity between deduced amino acids of mature protein without peptide signal was obtained using the EMBOSS Needle software  (Madeira et al. , 2019). Signal peptide predictions were performed using the SignalP 3. 0 server  (Bendtsen et al. , 2004) , and membrane-spanning regions were predicted using the SMART server  (Schultz et al. , 1998). Phylogenetic analyses were performed on coding sequences predicted from genomic data available in databases or cloned cDNA sequences when available. Maximal likelihood phylogeny reconstruction was performed using PhyML V3.
| Electrophysiological recording in X. laevis oocytes Distinct batches of defolliculated X. laevis oocytes were purchased from Ecocyte Bioscience (Dortmund, Germany) and micro-injected using the Drummond nanoject II microinjector with 72 ng of cRNA when subunits were expressed singly or with 50 ng for each subunit when expressed in combination (1:1 ratio). Two-electrode voltageclamp recordings were performed at a holding potential of À80 mV to assess the expression of the GluCl channels as previously described  (Lamassiaude et al. , 2021). Currents were recorded and analysed using the pCLAMP 10. 4 package (Molecular Devices, San Jose, CA, USA). All recordings were performed at room temperature (20 ± 1 C). Dose-responses relationships for glutamate were carried out by challenging oocytes with 5-10 s of application of increasing concentration of glutamate (from 1 μM up to 30 mM, depending on the receptor) with 2-min washing steps with Ringer solution between each application.
| Data and statistical analysis The data and statistical analysis comply with the recommendations of the British Journal of Pharmacology on experimental design and analysis in pharmacology  (Curtis et al. , 2018). Each experiment corresponds to current recordings made on at least five different oocytes (n) from at least five distinct defolliculated egg batches. Only one drug was tested on each oocyte. In order to minimize variation, current amplitudes were normalized to the maximal effect obtained with a saturating glutamate concentration. None of the experiments were blinded or randomized as pharmacological characterization of the GluCls were performed with a set of selected drugs. The group sizes varied with the type of experiment performed. In order to investigate putative functional recombinant homomeric or heteromeric GluCls, responsive or not to 1-mM glutamate, at least eight recordings were performed on independent oocytes for each putative receptor.
For the heteromeric GluCls selected for further pharmacological characterizations (i. e. , AVR-14B/GLC-2 and GLC-2/GLC-3), expression robustness (variability between oocytes) was evaluated with at least 17 recordings performed on independent oocytes. For each GluCl subtype, glutamate dose-response and agonist/antagonist experiments were performed on at least five independent oocytes. Statistical analysis was undertaken only for studies where each group size was at least n = 5. All statistical analyses were performed using the software R (v3. 6. 1). All data were tested for normality with a Shapiro-Wilk test, and outliers were included in data analysis and presentation. When data followed a normal distribution, statistical significance was calculated using one-way ANOVA with post hoc Tukey's HSD test using the Bonferroni correction for comparisons among more than two groups (Figure  4 ).
vector pTB-207, and RACE-PCR product were cloned into pGEM-T (Promega, Madison, WI, USA). Eurofins Genomics (Luxembourg) sequenced all constructs. Sequences of Cel-AVR-14B (CAA04170), Cel-GLC-2 (AAA50786), Cel-GLC-3 (CAB51708) and Cel-GLC-4 (NP_495489. 2) from C. elegans were available on GenBank as well as Pun-AVR-14B (ABK20343) subunit coding sequence from P. univalens. Using the GluCl sequences of C. elegans and Pun-AVR-14B as queries, tBLASTn searches in WormbaseParasite databank (https://parasite. wormbase. org/Tools/Blast?db=core) allowed the identification of full-length coding sequence of GluCls from B. malayi (Bma-GLC-2: XM_001893073. 1from P. univalens (Pun-GLC-2: NODE_2545302; Pun-GLC-3: NODE_2129897 and NODE_2250308; Pun-GLC-4: NODE_1817943, NODE_2402242 and NODE_2418647). Primer sequences designed for RACE PCR and amplification of the full-length coding sequences of each subunit are provided in Table.
GenBank under the following accession numbers: B. malayi (Bma): AVR-14B (MW196269), GLC-2 (MW196266), GLC-3 (MW196267), GLC-4 (MW196268); C. elegans (Cel): AVR-14B (MW196270), AVR-15A (CAA04171), AVR-15B (CAA04170), GLC-1 (AAA50785), GLC-2 (AAA50786), GLC-3 (CAB51708), GLC-4 (NP_495489. 2), UNC-49B (AAD42383); H. contortus (Hco): AVR-14B (CAA74623), GLC-2 (CAA70929); P. univalens (Pun): AVR-14B (MW187941), GLC-2 (MW187938), GLC-3 (MW187939), GLC-4 (MW187940).
in B. malayi and P. univalens genomic/transcriptomic databases allowed the identification of four independent sequences corresponding to putative homologues of avr-14, glc-2, glc-3 and glc-4 from both species. In contrast, homologues could not be found in B. malayi and P. univalens for glc-1 or avr-15. The full-length cDNAs of avr-14, glc-2, glc-3 and glc-4 from the three nematode species were cloned, sequenced and submitted to GenBank under the accession numbers provided in Section 2. All GluCl subunits identified including AVR-14B, GLC-2, GLC-3 and GLC-4 from C. elegans, B. malayi and P. univalens present the typical characteristics of a Cys-loop receptor subunit. This includes a predicted signal peptide in the extracellular Nterminal part (with the exception of Bma-GLC-2), the first Cys-loop domain specific for ligand-gated ion channels (LGIC) composed of two cysteines that are 13 amino-acid residues apart, the second Cysloop domain found in GluCls, and four transmembrane domains (TM1-4) (FiguresS2 and S3).
ACKNOWLEDGEMENTS We thank  Prof. Adrian Wolstenholme  from the  Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA , for providing the Brugia malayi worms and the careful and critical reading of the manuscript. We thank the group of  UEPAO (Experimental Unit of Orfrasière Animal Physiology, INRAE Centre Val de Loire, Nouzilly 37380, France)  for providing Parascaris univalens eggs and adults. N. L. is the grateful recipient of a PhD grant from the  Animal Health Division of INRAE  and from the  Region Centre-Val de Loire, France. N. L. was supported by a grant from the  RTR Fédération de Recherche en Infectiologie  (https://www. infectiologie-regioncentre. fr/) of the  Région Centre-Val de Loire. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
on GenBank under the following accession numbers: B. malayi (Bma): AVR-14B (MW196269), GLC-2 (MW196266), GLC-3 (MW196267), GLC-4 (MW196268); C. elegans (Cel): AVR-14B (MW196270), AVR-15A (CAA04171), AVR-15B (CAA04170), GLC-1 (AAA50785), GLC-2 (AAA50786), GLC-3 (CAB51708), GLC-4 (NP_495489. 2), UNC-49B (AAD42383); H. contortus (Hco): AVR-14B (CAA74623), GLC-2 (CAA70929); P. univalens (Pun): AVR-14B (MW187941), GLC-2 (MW187938), GLC-3 (MW187939), GLC-4 (MW187940).
Acknowledgements This paper is dedicated to  Elke Zimmermann , a pioneer in the study of communication in nocturnal primates. We are very grateful to  Ute Radespiel  and  Marina Scheumann  for the invitation to contribute to the  Festschrift for Elke Zimmermann. Financial support was obtained from  UMR 7179 CNRS MNHN , and a PEPS 2009 grant attributed to P. Y. H. by  INEE-CNRS. E. H. was funded by a  Deutsches Forschungsgemeinschaft (DFG)  research grant (no HU  1820/1-1 ). Publication ISEM n°2021-267.
Using additional information  (Chauvin, 2010) , the percentage of sows and growing pigs treated was also estimated  (Meheust et al. , 2017). Following on from the consensus, the specific monitoring tool for the prescribing and dispensing stage set up by veterinary practitioners recorded annual cephalosporin deliveries over the 2009-2012 period. Lastly, cephalosporin usage at farm level was documented by individual farm surveys initiated by INAPORC on a 3-year basis  (Hemonic et al. , 2018). The first and third of these tools were also able to detect any change in other antimicrobial classes. To document the impact of this ASP on antimicrobial resistance, data from two complementary tools already operational in France.
Selection of Cases and Data Collection This study was conducted from January 2013 to December 2019, including all cattle over 2 years of age submitted for necropsy to the PULA of Oniris, Nantes, France. For each necropsy, the age in years ((2-3), (4-5), (>5)), cattle breed type (beef or dairy), year of death  ((2013-2015) , (2016-2017), (2018-2019)) and season of death (autumn/winter, spring/summer, corresponding respectively to the periods of October to March and April to September) were recorded.
Discussion This study, conducted on a large data set of adult cattle necropsies, both described causes of death and investigated the macroscopic and histological patterns and pathogens associated with BRD. This study highlights that BRD is the second leading cause of death after digestive disorders and that FHN bronchopneumonia associated with M. haemolytica is a frequent macroscopic and histological diagnosis in adult cattle. We described data from adult cattle that were submitted to the PULA for necropsy from January 2013 to December 2019. The sample was large, but only cattle from West France were included in the study, so we do not expect to provide a generalizable assessment of causes of death at the national level. Furthermore, the sample, consisting of both cattle that were euthanized and cattle that died unassisted, was biased toward a certain profile of diseased animals and causes of death.
A detailed summary of the data compilation is available online on the NICEATM webpage dedicated to the collaborative modeling project (NTP 2020). To obtain chemical structure information, CASRNs served as identifiers to search the U. S. EPA's DSSTox database hosted in the CompTox Chemicals Dashboard  (Grulke et al. 2019; Richard and Williams 2002 ; U. S. EPA-NCCT 2014a;  Williams et al. 2017)  as well as other cross-checked online databases: ChemIDPlus (NIH 2016), PubChem  (Bolton et al. 2008)  and ChemSpider (Royal Society of Chemistry 2015). The collected structures were then processed using a standardization workflow developed for the purpose of generating QSAR-ready structures compatible with most modeling approaches  (Mansouri et al. 2016a; McEachran et al. 2018). In fact, this workflow was first developed in KNIME  (Berthold et al.
2008)  for the CERAPP project and was also employed for CoMPARA  (Mansouri et al. 2016a (Mansouri et al. , 2018 (Mansouri et al. , 2020)). The workflow is a multistep process that includes: • A filter to remove inaccurate chemical representations, inorganics/metallo-organics, mixtures, and general representations that are not specific (Markush structures, repeating monomers, connection points) • A standardization step for ring representations, isomers/ mesomers, and other tautomeric forms • A step to identify salts/solvents, counterions, and duplicate structures. The workflow can be downloaded from GitHub or KNIME hub as KNIME workflow or used in command line within a Docker container (https://github. com/NIEHS/QSAR-ready, https://kni. me/w/_ iyTwvXi6U3XTFW1, https://hub. docker. com/r/kamelmansouri).
Additional Evaluation of the Consensus Models As a further evaluation step for the consensus predictions, highly curated experimental in vivo acute oral toxicity data were used to assess concordance between predictions and experimental outcomes. Briefly, a data set was tandemly generated for the study of variability in acute oral toxicity animal data  (Nelms et al. 2020; Ly Pham et al. 2020). This data set was limited to chemicals associated with multiple LD 50 values (including discrete values, limit tests, and ranges/CIs) as well as additional entries pulled from the European Chemicals Agency (ECHA) database for risk assessment (ECHA 2020). The chemicals in this data set were not used in the original CATMoS modeling project. After an initial consistency analysis between the two data sets, a thorough manual curation resulted in a total of 916 chemicals with at least two discrete LD 50 values to be used for the evaluation of the predicted LD 50 values, and a data set of 1,323 chemicals with at least two LD 50 entries including discrete LD 50 values, limit tests, ranges, and CIs to be used for the evaluation of the predicted binary and multiclass categories.
The global AD is a Boolean index based on the leverage approach for the whole training set, whereas the local AD is a continuous index in the 0-1 range based on the most similar chemical structures from the training set  (Mansouri et al. 2018; Sahigara et al. 2012). The extended, similarity-based predictive approach as well as the WoE consensus are implemented as the CATMoS consensus model in the OPERA application (version 2. 5)  (Mansouri et al. 2018). OPERA can be downloaded from the National Institute of Environmental Health Sciences GitHub repository (https:// github. com/NIEHS/OPERA) and used locally via a commandline interface or user-friendly graphical interface. The use of this standalone application facilitates the generation of CATMoS predictions by providing different user input options.
The simplest way for the user to input chemicals would be via a text file with chemical identifiers such as CASRN, DTXSID (U. S. EPA's DSSTox database public identifier) or InChIKey. In fact, OPERA contains an internal database with the complete list of ∼ 800,000 DSSTox chemical structures (periodically updated) stored in QSAR-ready format and ready to use for prediction with any of its models. In addition, users can provide their own chemical structures in SMILES or SDF formats as described in  Mansouri et al. (2018). Since version 2. 6, OPERA has been equipped with an embedded version of the above-mentioned structure standardization workflow and can generate QSAR-ready structures prior to prediction. The local nearest neighbors-based and the global leverage-based AD approaches implemented in OPERA help the user determine whether their chemicals are within the model's interpolation space, where it is safe to generate predictions.