dataesr/scientific-paragraphs-categorization huggingface dataset:
Horses and Study Design The study included 30 Standardbred racehorses, sampled in the eld between October and December 2020, and 32 horses referred to the hospital between February and December 2021. Inclusion criteria were respectively to be in active training ( eld), or to be referred for decreased performance and/or respiratory disease. Data regarding housing conditions (indoor/outdoor), bedding (straw, wood shavings), hay forage (dry/soaked/steamed), activity (training/resting) and duration since last training, occurrence of poor performance or exercise intolerance, as well as reported respiratory clinical signs (cough, dyspnea, nasal discharge, noise during exercise, increased respiratory effort at rest, epistaxis), were collected. Each horse was submitted to a thorough clinical examination by an ECEIM diplomate or resident.
| Method development and validation For the detection and quantification of the investigated analytes, a previously published method  29  based on LC/MS/MS was adopted for the detection of SDO and TMP. Different hydrolysis methods to cleave potential phase-II metabolites in urine samples were tested. Since 3 DMT seems to be excreted mainly conjugated as sulfate and glucuronide, hydrolysis of urine samples via Helix pomatia was carried out in order to quantify the combined fraction of conjugated and free analytes. Depending on plasma concentration, about 51% of TMP and 14%-72% of SDO are bound to plasma proteins. 16 Thus, a proteolysis of plasma samples via bovine protease was performed in order to liberate protein-bound analytes. Unambiguous identity of SDO, AcSDO, TMP, 3 DMT, OHT and Optimised ion transitions were in accordance with generated product ion scans of the precursor ions (Figures  1  and 2 ) as well as with data from the literature, where available.
F I G U R E As further evidence, non-hydrolyzed p. a. samples were analysed for the protonated precursor ions of 3 DMT-glucuronic acid (m/z 357 [M + H] + ) and 3 DMT-sulphate (m/z 453 [M + H] + ). Signals matching both analytes were detected in different p. a. samples showing typical product ions for 3 DMT (Figure  2 ). The obtained MS data clearly indicate that both glucuronic acid and sulphate conjugates of 3 DMT can be formed in the horse. These results are in accordance with the studies in humans, rats and pigs  20, 31  where the predominant glucuronidation of desmethylmetabolites has been described. Sulfatation has not been reported before, but it is very typical for the metabolism in horses and has been reported in several equine studies before. 32 A F I G U R E 9 Urine disposition of TMP and   summary of the proposed metabolism of TMP in the horse is depicted in Figure  13.
Microbial investigation The infection control team conducted the investigation. A retrospective review of the microbiology database did not find any additional cases of M. fortuitum BSI since 2014. Water samples were performed to identify the contaminating source. Two potential sources of exposure of the patient were sampled: the bathroom tap in her hospital room and the shared shower of the oncology unit where she showered daily, whereas she had a Huber needle in place protected solely by a transparent dressing. Additionally, 39 supplementary water samples (including 8 with 0. 45-μm pore-size filters) were taken from 7 different hospital buildings (including 9 samples in the oncology building) to determine the extent of the contamination of the hospital water network. Briefly, 250 mL mixed water (temperature range, 24.
Discussion We describe a case of a M. fortuitum catheter-related BSI originating from the shared shower of the oncology unit. To date, 30 cases of M. fortuitum BSI have been reported in the PubMed database. Most BSIs caused by M. fortuitum are catheter-related BSIs. 8 In this report, the patient had a favorable outcome after PAC removal associated with a combination of amikacin and imipenem, in agreement with the literature. 9 However, the source of  M. fortuitum BSI has been rarely identified and was reported to be hospital water in a single paper. 5 e found the contamination of hospital water by NTM to be frequent (26. 8%). This result is consistent with those of a previous report, which showed an even higher contamination rate of 52. 2%. 2  The presence of NTM in the water supply can be explained by their ability to produce biofilms and resist commonly used disinfectants.
MALDI MS analysis of lipid A Mass spectra were obtained on a timsTOF flex MALDI-2 instrument (Bruker, Bremen, Germany) in the negative ion mode. Instrument was calibrated before each experiment in an electrospray mode by a direct infusion of the Agilent Calibration mix (Agilent Technologies, Santa Clara, CA, USA). Tandem MS analyses were performed with the following settings: 3,000 shots/spot on average, collision energy: 110-120 eV, isolation width: m/z 4, collision RF: 1,000 Vpp, transfer time: 110 μs and prepulse storage: 11 μs. To detect product ions in the low range m/z, the collision RF and transfer time were changed to 300 Vpp and 30 μs, respectively. Data were analyzed using mMass v5. 5. 0  (Strohalm et al. , 2010)  and Compass Data Analysis v 6. 0 (Bruker). Fragmentation patterns of predicted lipid A structures were confirmed in ChemDraw v18.
Acknowledgments We thank  Darryl Hardie  and  Jun Han  of the  UVic Genome BC Proteomic Centre  for their assistance with the LC-MS/MS assays. We thank  Angela Jackson  of the  UVic Genome BC Proteomic Centre  for her support and managing the laboratory's day-to-day operations. We also thank  Dr. Yuri O. Tsybin of Spectroswiss  for assistance with isotopic distribution modeling.
Acknowledgements:  S. Sourice  ( INSERM U1229-RMeS, Nantes ) for her help with histological data. F. Fieni  and  D. Tainturier  ( ONIRIS, Nantes ) and  N. Maillard  ( CH Vannes, France ) for their help with equine cell culture. In loving memory of  C. Tahier  for her invaluable help with the horses.
A gas chromatograph (HP 5890-SERIE II, Agilent, Waldbronn, Germany) coupled to a mass spectrometer (HP-MSD 5972 A, Agilent, Waldbronn, Germany) equipped with an HP INNOWAX polar column (30 m × 0. 25 mm; film thickness, 0. 25 µm) was used to analyse the chemical composition of Thymbra capitata NEO. The oven temperature was programmed to rise from 50 to 240 °C at a rate of 5 °C/min. The transfer line temperature was 250 °C. The carrier gas was helium at a flow rate of 1. 2 mL/ min; the split ratio was 60:1. Scan time and mass range were 1 s and 40-300 m/z, respectively. The NEO volatile compounds were identified by comparing their retention index (RI) values related to (C9-C18) n-alkanes with those of authentic compounds (Analytical reagents, LabScan, Ltd, Dublin, Ireland) available in the literature and by matching their mass spectrum fragmentation patterns with corresponding data stored in the mass spectrum library of the GC-MS data system (NIST) and other published mass spectra.
Amplification and library quantification were carried out as described previously  9. Bioinformatic processing and statistical analysis. After demultiplexing, paired-end reads from 16S rRNA-gene sequencing were quality-checked using the software package MultiQC  10. Subsequently, barcodes were removed before joining forward and reverse reads into single reads with the C++ package SeqPrep. Joined reads were filtered for quality based on the following parameters: (1) a maximum of three consecutive low-quality base calls (Phred < 19); (2) no "N"-labelled (unidentified) bases allowed; (3) at least 75% of consecutive bases in a read with Phred > 19. Sequences not matching filtering requirements were discarded. The remaining reads were combined in a single FASTA file and aligned against the SILVA database v.
Software. Reads from 16S rRNA-gene sequencing were processed with QIIME v. 1. 9  8  , using a pipeline adapted from Biscarini et al. 15. The ACE alpha-diversity index was estimated using a custom Python script (1) Y ij = µ + T i + C j + e ij , www. nature. com/scientificreports/ (https:// github. com/ filip pob/ Rare-OTUs-ACE), and R was employed to generate plots and figures using the ggplot2 R package  27. Additional data handling was performed in the R environment for statistical computing  28.
05) different from the Control. 18% of core rumen microbiota) was artificially composed for statistical analysis, comprehending all the genera that the database (SILVA v. 132) retrieved as "uncultured" or "uncultured bacterium" or "Other" or "uncultured organism" and similar. In conclusion, the core rumen microbiota comprised 307 recognised OTUs shared by 100% of the samples plus the ones defined as "uncultured or unknown" as described above (see Supplementary Table  1.
Acknowledgements This work was funded by  CSIC (Proyectos Intramurales Especiales ,  201940E115 ) and  MUR (Direzione Generale dell'internazionalizzazione e della comunicazione Programma cooperazione internazionale PRIMA-Decreto Ministeriale  26 luglio 2016 n. 593) in the context of the MILKQUA- H2020-PRIMA 2018-Section 2. Alba Martín  gratefully acknowledges receipt of a pre-doctoral grant ( PRE2019-089288 ) from  Ministerio de Ciencia e Innovación  ( MCIN/AEI/10. 13039/501100011033 ," El FSE invierte en tu futuro ").
RNA-seq mapping and quantification After trimming the adaptors (cutadapt, v. 2. 4)  (Martin, 2011) , high-quality reads averaging 28. 4 million reads per sample were obtained. The resulting count matrix of raw data and the experimental design are available at:10. 5281/zenodo. 7548947. Sequencing quality was assessed for each sample (before and after mapping) using MultiQC (v. 1. 6)  (Ewels et al. , 2016). Reads were aligned on the B. abortus genome (Brucella abortus strain Wisconsin)  (Suárez-Esquivel et al. , 2016)  from GenBank  (Clark et al. , 2016)  using bowtie2 (v. 2. 3. 4. 1)  (Langmead and Salzberg, 2012). The reference genome annotation was complemented with NCBI Reference Sequence GenBank: LT671512. 1 (Chormosome 1) and NZ_LT671513. 1 (Chromosome 2). Read summarization and annotations were done with the featureCounts (v 1.
RNA-seq statistical analysis Gene expression profiles were analyzed using the R software (R: A Language and Environment for Statistical Computing, 2017) and several Bioconductor  (Gentleman et al. , 2004)  packages, including DESeq2  (Love et al. , 2014 ) (v 1. 26. 0) and SARTools (v 1. 7. 0) (Varet et al. , 2016). The statistical analysis included (i) data description, (ii) data normalization and exploration (iii) testing for differential expression for each gene/pseudogene between the strains. -Dataset description: The dataset included 24 samples from 5 different strains, each having the number of replicates indicated in parenthesis Ba R-R (5), Ba 2308 , (5), Ba R-RD58A (5), Ba R-RD58E (5), and Ba R-(4). For this dataset, 3,301 genes/pseudogenes were identified for each condition. -Data normalization and exploration: Data normalization was performed according to the DESeq2 model and package.
Normalized read counts were obtained by dividing raw read counts by the scaling factor associated with the sample they belong to (locfunc = "median"). The data variability was explored by performing hierarchical clustering and principal component analysis (PCA) of the whole sample set after the counts were transformed using a variance stabilizing transformation. Frontiers in Microbiology 05 frontiersin. org Hierarchical clustering was calculated using Euclidian distance and the Ward criterion for agglomeration. PCA was performed using DESeq2  (Love et al. , 2014 ) (v 1. 26. 0). -Differential analysis was performed to identify genes/ pseudogenes having a significantly different expression between each pair of the different strains Ba 2308 , Ba R-, Ba R-RD58E, Ba R-R, and Ba R-RD58A using the R software (v.
1. 2. 5033), R packages DESeq2 (v 1. 26. 0) and SARTools (v 1. 7. 0) (Varet et al. , 2016). The strategy was to fit one linear model per gene/pseudogene using a design of one factor (strain) to estimate the coefficients (log 2 FC) and corresponding value of p. Raw p-values were corrected for multiple testing using Benjamini and Hochberg's method  (Benjamini and Hochberg, 1995). Genes /pseudogenes having an adjusted p value <0. 05 were considered differentially expressed for the given pairwise comparison. Heatmaps were generated with R (package pheatmap v 1. 0. 12), using genes with differential expression (|log2FC| > = 3. 5) for at least one pairwise comparison.
(B) HeLa cells were infected using MOI of 500 with the indicated B. abortus strains as in (A). At 48 h post-infection, cells were extensively washed, fixed, permeabilized, and intracellular bacteria detected with a mouse-anti Brucella antibody and a goat anti-mouse IgG Alexa Fluor Plus 488 conjugate (green). The ER was stained using rabbit anti-calnexin antibodies and a goat anti-Rabbit IgG Alexa Fluor Plus 594 (red). Cells were visualized by confocal microscopy. Scale bars, 10 μm. Data are representative of three independent experiments. and Ba R-(Figure  6 ). This group included genes previously shown to be positively regulated by BvrRS such as omp25 and exoR  (Manterola et al. , 2007; Viadas et al. , 2010; Rivas-Solano et al. , 2022). There is also presence of genes encoding for membrane proteins such as ABC-transporters and lipoproteins confirming the relevance of BvrRS in the homeostasis of the membrane.
For parasite dissection, whole mussels were deep frozen (-80°C ) and stored until dissection. After thawing the mussels (n = 15), mantle tissue was dissected out and placed between 2 glass plates, which were used to apply pressure on the tissue in order to locate the parasites under the stereomicroscope (Nikon SMZ800). Once located, metacercariae and pearls (pearls were retrieved from the mussels before using the glass plates in order to avoid breaking them) were carefully isolated with the aid of fine forceps and brushes. Parasites for molecular identification were deep frozen again, and the metacercariae for morphological identification (n = 13) were measured at the microscope (Olympus BX61) with attached camera (Olympus DP74) using the Olympus CellSens Entry 2. 2 software. After removing the remaining soft tissues of the hosts, pearls were air dried and stored in Eppendorf tubes at room temperature until processing.
bursicola, and consequently they would present underdeveloped genital systems important for accurate identification. Morphological studies need to be coupled with molecular tools to safely develop quick and precise diagnostics. The approach used herein has allowed morphological discrimination of a parasite species, and confirming its presence based on molecular evidence. Although no G. bursicola sequence was available in NCBI, consequently, the morphological identification could not be molecularly linked to an existing sequence. The infection prevalence reported in the present study is coherent with data reported by  Galaktionov et al. (2015) , which ranged from 46. 7% to 51. 6% in Tromsø, but went up to 83. 9% in Iceland. The prevalence data reported by  Benito et al. (2022)  in histological samples of mussels collected in the same sampling sites as in the present study were lower (15.
Methods Following  Jardat and Lansade (2021) , our literature search was carried out in the Web of Science database (https://www. webofscience. com) from September 2022 to February 2023. The search was based on two concepts: a) 'Animal domestication' and related terms, and b) 'cognitive abilities' and related terms (Table  1 ). As researchers on animal domestication may disagree on what species can be considered as domesticated or not  (Chansigaud, 2020) , we included the main researched domesticated species and their wild relatives (such as dogs/wolves, wild boar/domestic pig, red junglefowl/chicken), but also keep our search as open as possible, using terms such as 'animal domestication' and 'domesticated animal'. The first part of the search query was enclosed in parentheses and consisted of two nested sub-queries: The first sub-query was enclosed searched for the terms "domestic" and "wild", along with a list of common researched domesticated animal species.
| Data analysis The effect of diet on changes in BW and BCS was assessed using mixed-effect models with a normal distribution of errors in a MIXED procedure (v9. 3, SAS Institute Inc. , Cary, NC). Day, Group, and the interaction Day Â Group were included as fixed effects, and Day was included as a repeated measure per horse. Gastric glandular scores were categorized into physiological (0-1; "nonulcerated") or clinically relevant (2-4; "ulcerated") groups. First, we assessed the relationship between diet fed before the beginning of the trial and GGD at D0. For that purpose, we compared dietary intake at D0 for the nonulcerated and ulcerated groups using the LSMEANS option in a MIXED procedure with a Tukey adjustment (SAS v9. 3). Then, the second outcome was to estimate the success in preventing GGD incidence at D21 or D42, defined as remaining in the nonulcerated group for the horses that were not ulcerated at D0.
Finally, the clinical success in the ulcerated group at D0 was evaluated. This curative success was defined as transitioning from the ulcerated group at D0 to the nonulcerated group at D21 or D42. The difference in the proportions of success and failure according to the diet was compared at D21 and D42 with a Fisher exact test. The odds ratio and 95% confidence intervals (CI) were calculated (SAS v9. 3). The level of significance was set at P <. 05. T A B L E 1 Estimated daily composition of the ration and nutrient intakes for the intensively exercised trotters that completed the trial in the 4 training centers (means ± SD).
Funding provided by CASDAR (French Ministry of Agriculture and Food) in the research program PULSE involving La Coopération Agricole, Désialis and Lab To Field. The responsibility of the Ministry cannot be held liable for any result of this study. We acknowledge the veterinary team of CHVE Livet and the trainers for their contribution to the organization, and Pauline Grimm, Agathe Martin, Amandine Martin, and Laetitia Pivry for their assistance with data collection.
The data were also corroborated by an exponential predicted model able to calculate the reduction in the force of infection in the monitored ibex population from 2012 to 2018  [18]. The possible use of an extended vaccination program for brucellosis eradication in the ibex population was also explored. However, the implementation of Rev. 1 conjunctival vaccine in natura has not been recommended, as the amplification and shedding capacity of Rev. 1 was much higher in ibex compared to goats within 90 days following vaccination  [19]. The ruminant brucellosis definitive diagnosis needs bacteriological, molecular and serological methods, the latter being routinely used in control, eradication and surveillance programs  [20, 21]. Indirect immunological tests were developed in order to detect antibodies against O-polysaccharide of the smooth Brucella lipopolysaccharide (S-LPS).
Acknowledgments: The authors want to thank the technicians and researchers from the  Zoological Park of Haute Touche, INRAE (ISP + PFIE  (https://doi. org/10. 15454/1. 5572352821559333E12,accessed on 3 July 2023),  ANSES ,  Inovalys  ( LDAV 37 ), the technicians and scientists of LDAV 73 and the  French Office for Biodiversity (OFB)  for their contribution to this study.
Animals and study design All African savanna elephants living in French zoological institutions and listed in the Zoological Information Management System (ZIMS, Species360, Bloomington, MN 55425, United States) were included (n = 21), representing seven institutions. All elephants (8 males, 13 females) had access to indoor and outdoor enclosures. Two elephants were solitary and the remaining 19 were living in groups of two or more. Table  1  shows the breakdown of the subjects by zoo. Husbandry factors were specific to each institution (diet, zootechny, enclosure size, reproduction strategies, training). Gathered data included institution (zoological park of residence), sex, age, birthplace (in captivity or in the wild), daily activity (approx. Kilometers per day), diet (including provision of branches or not), and social status (in group or solitary).
External data set from wild African savanna elephants The 16 s rRNA sequence data from free-ranging African elephants was already decribed by a previous study  (Budd et al. , 2020) , and is available online at NCBI-SRA (Sequence Read Archive, accession: PRJNA587772). Briefly, the data set included 48 female and male African elephants, 35 savanna elephants (Loxodonta africana) from the Transmara and Narok districts in southwestern Kenya and 13 forest elephants (Loxodonta cyclotis) from Lope National Park in Gabon. However in this study we only used the samples from the savanna animals, that comprised 18 and 17 female and male elephants respectively, from the age groups juvenile (n = 4; two females and two males), subadult (n = 13; seven males and six males) and adult (n = 18; nine individuals from each sex).
16S rRNA sequences data processing All sequence processing in this study was conducted using the QIIME 2 pipelines  (Bolyen et al. , 2019). The sequences were denoised using the DADA2 approach, which resolves each amplicon sequence variant (ASV)  (Callahan et al. , 2016) , and the taxonomy assignment was based on the 16S rRNA SILVA database v. 138  (Quast et al. , 2013). However, different bioinformatic pipelines (steps) were used for each dataset, as the two sets were produced by different sequencing technologies. Specifically, 16S amplicon sequencing from zoo elephants was produced with Ion Torrent technology, while the wild elephants samples were sequenced using Illumina technology. This required distinct processing approaches to accommodate the unique characteristics of each technology.
The zoo sequencing data (zoo data), which included short-read amplicon sequences from multiple regions (V2, V3, V4, V6-7, V8, and V9), were processed individually and then combined in the reconstruction of a near full-length 16S marker gene using the Short Multiple Reads Framework (SMURF) algorithm  (Fuks et al. , 2018) , wich was used as implemented QIIME2 plugin q2-sidle  (Debelius et al. , 2021). Briefly, the raw sequences were separated into sets for each V region, using the q2-cutadapt plugin. Only forward sequences were used due to the verified overlap of forward and reverse sequences for each targeted amplicon region. Each V region (sequences package) was denoised using q2-dada2 plugin. Then, the Silva database, previously curated using RESCRIPt  (Robeson et al. , 2021) , was used. The exact sequence fragments (by regions) were extracted from SIlVA sequences using qiime2 feature-classifier (extract-reads) plugin.
Using q2-sidle, each set of ASVs and the respective SIlVA fragments sequences were aligned forming kmers, then the kmer were mapped with full-length SILVA reference sequences to reconstruct the taxonomic profile. The wild sequencing data (wild data) was dowloaded for the SRA using q2-fondue pluging in QIIME2. Demultiplexed paired-end sequences were analyzed using via q2-dada2 for denoising and merging paired reads. The ASVs were aligned using the MAFFT  (Katoh et al. , 2002)  via q2-alignment plugin, and the alignments were then used to construct the phylogeny following the FastTree2 method  (Price et al. , 2010)  as implemented in q2-phylogeny. The taxonomic classification of the ASVs was performed using the Classify-Sklearn Naive Bayes method via the q2-feature-classifier plugin, based on SILVA database v.