dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods Sequencing data from modern horses were retrieved from ENA and NCBI repositories. Raw reads were processed and mapped against EquCab2 using PALEOMIX with default parameters  [16]. Horse IDs, breed assignments, accession codes, and the resulting average depth-of-coverage per genome are reported in Table  S1. Evolutionary conservation is known to represent an excellent predictor of fitness effects, because mutations at sites that remained highly constrained during evolution are likely to be deleterious. We thus used phyloP conservation scores to estimate the potential impact of mutations. These scores summarize the evolutionary constraint position-wise, across a genome-wide alignment of 46 vertebrate species  [17, 18] , including EquCab2 as reference assembly for the horse genome  [19].
Sites were masked otherwise. Given both phyloP scores and genotype calls, we estimated the genetic load for each horse genome as: load = i phyloP i #homozygous where i iterates over all the homozygous positions carrying a deleterious allele, and phyloP i is the phyloP score at the genomic position i. We assigned as deleterious the less frequent (or absent) allele in the 46-way alignment, provided that at most two variants were segregated. Two approaches were applied to characterize the historical periods yielding inflated mutational loads. First, we retrieved all the previously published and radiocarbon-dated ancient genomes from ENA (PRJEB31613). After excluding mules, donkeys, and specimens belonging to other non-caballine lineages (i. e. , not ancestral to modern horse breeds), we retained genomic data from 153 ancient horses (Table  S2 ).
The tree branch lengths were used as proxies for neutral substitution rates, potentially revealing past episodes of elevated drift such as demographic collapses. Pseudo-haploidized data were also used to characterize genetic purging, which involves selection against recessive mutations that are phenotypically exposed, such as those found at the homozygous state, both within and without Runs of Homozygosity (ROHs) resulting from inbreeding  [25]. We approximated the strength selection by calculating the average genetic divergence of one given horse individual to the domestic donkey at constrained (dN; phyloP > 1. 5) and neutral sites (dS; phyloP < 1. 5). Strong negative selection purges out mutations at constrained sites, reducing dN and leading to negative dN-dS values. Conversely, deleterious variants are not efficiently removed under relaxed negative selection.
One exception to this pattern pertains to one single Percheron horse present in our genome dataset. This breed consisted of large, draft horses, and is known to have been extremely influenced by Arabian bloodlines while being founded in France, before becoming extremely popular worldwide, especially in the U. S. , in the 19th century  [50]. As a result, its geographic range was exceptionally large for a draft breed, which may have helped preserve sufficient genetic diversity, until conservation programs started in the 1960s. Conservation programs often rely on closed stud practices to maintain un-admixed populations that are better adapted to native environments. For endangered breeds, this implies that foals can only be registered if descending from purebred studbook-registered parents. Similar rules govern selection programs to improve specific breeds, such as Thoroughbreds, for which a studbook was established well before Darwin formalized the concept of evolution through natural selection in 1859  [51].
Vieira et al. (2011a)  evaluated preslaughter data from 13,937 broiler flocks recorded daily during 2006 in a commercial slaughterhouse in south-eastern Brazil. The main factors that influenced daily mortality rate were mean dry bulb temperature and RH, lairage time, daily periods, density of broilers per crate, season of the year, stocking density per lorry, transport time, and distance between farm and slaughterhouse. Season was found to have significant effects (p < 0. 05) on average mortality rates. The highest risk was observed in summer (0. 42%), followed by spring (0. 39%), winter (0. 28%), and autumn (0. 23%). A decrease in preslaughter mortality of broilers during summer (p < 0. 05) was observed when the lairage time was increased, mainly after 1 h of exposure to a controlled environment.
Expansion of gases in the body cavity Definition: The decompression applied to the birds inside the chamber might lead to expansion of gases in body cavity such as in the gut, although no evidence was found in experiments performed on the LAPS. The welfare consequences of gas expansion have not been elucidated during the assessment of the LAPS system carried out by EFSA AHAW Panel (2017). It is known that rupture of the intestine due to gas expansion does not occur during LAPS (EFSA AHAW Panel, 2017), but no data are available to rule out the possibility of colic-like pain occurring in conscious poultry during the first phase of LAPS. The absence of proof in the literature should not be considered for now as a proof of absence of this specific hazard. Since LAPS is not currently in place, this hazard could be only presumed to occur, but there is a level of uncertainty that cannot be ignored and needs to be highlighted mainly if, in the future, LAPS becomes a common practice.
Search string used in the bibliographic database The search string was designed to retrieve relevant documents to 'animal welfare' during 'slaughter and killing' of 'poultry'. Restrictions on the different species of poultry were applied by including the genus name of different species of birds that are used for human consumption to obtain studies concerning domesticated birds used for meat production. Restrictions applied in the search string were also related to the processes characterising 'slaughter and killing' (from arrival to bleeding) of animals in containers, and the date of publication (considering only those records published after  EFSA, 2004). No language or document type restrictions were applied in the search string. The search yielded a total of 412 records that were exported to an EndNote library together with the relevant metadata (e.
Calculations and statistical analyses The chemical composition of sows was estimated from their body weight and backfat thickness using the equations proposed by  Dourmad et al. (1997)  for lipids and proteins: lipid (kg) = -26. 4 + 0. 221 EBW + 1. 331 P2, and protein (kg) = 2. 28 + 0. 178 EBW -0. 333 P2, where EBW (kg) represents the sow empty live weight estimated from body weight (EBW = 0. 905 BW 1. 013 ), and P2 (mm) the backfat thickness at the level of the last rib. At the end of gestation, sow body weight was calculated by subtracting the estimated weight of uterine contents (UCW) from body weight with UCW (kg) = 0. 3 + 1. 329 litter weight at birth (kg)  (Dourmad et al. , 1997). Statistical analyses were performed using SAS (SAS Inst. Inc. , Cary, NC). All models included the effect of treatment (C or CE) and replicate (1, 2, or 3).
The following thermocycling program was used: a 15-minute enzyme activation step at 95 ∘ C, followed by 40 cycles of 95 ∘ C for 30 s, 54 ∘ C for 30 s (VNTR4 and VNTR7)/52 ∘ C for 30s (VNTR10), and 72 ∘ C for 1 min with a final elongation step at 72 ∘ C for 10 min. PCR mix without the target DNA was included as a negative control, and PCR mix with a Leptospira strain was included as a positive control and band size control for electrophoresis gel migration. 2. 9. Vkorc1 Genotyping. Genomic DNA extractions from mice tails were performed with QIAamp5 DNA Mini Kit, Qiagen, according to manufacturer's guideline. Vkorc1 gene was amplified as previously described  [25]. The amplified products were sequenced on both strands. Obtained sequences were subsequently compared with the published Vkorc1 gene sequences for Mus musculus domesticus (Genbank n ∘ GQ905710.
Leptospiral Carriages in Trapped Mice. Based on 16S amplification, 7 mice presented positive results for Leptospira presence. Three Leptospira genospecies were identified, including a saprophytic leptospire. L. biflexa was found in 2 mice, L. kirschneri in 3 mice, and L. interrogans in 1 mouse. One mouse was found carrying the two leptospires species L. interrogans and L. kirschneri (Table  1 ). The prevalence of pathogen leptospiral renal carriage in trapped mice was 42%. From the L. interrogans DNA sample extracted, MST and VNTR profiles could not be obtained because of unspecific amplifications. From the 4 L. kirschneri DNA samples extracted, complete VNTR profiles were obtained in 2 samples and corresponded to Leptospira belonging to serogroup Icterohaemorrhagiae as shown in Table  1  according to VNTR published database  [24].
Study population This study was based on data collected through a questionnaire administered to dog breeders from 2015 to 2017. This questionnaire was completed voluntarily by breeders in France. The questionnaire included four sections in order to obtain information about the breeding kennel, dam and sire, mating and birth, and puppies of the litter. At the kennel level, the information included the number of puppies produced per year. For dam and sire, the information included the breed, the dates of birth and the parity of the dam. At the litter level, the information included the dates of the first mating and whelping, the kind of parturition (natural birth or caesarean section), the total number of puppies born alive and stillborn. For puppies, the information included the sex, the birth weight, and mortality during the first 21 days of life.
Neonatal mortality risk factors Neonatal mortality rates included the deaths of live-born puppies within the first 21 days after birth. The neonatal period was divided in two successive periods: early neonatal period (birth: Day 0 to Day 2) and late neonatal period (Day 2 to Day 21;  Chastant-Maillard et al. , 2017a , 2017b; Mila, 2015). Generalised linear mixed models were fitted using R package "lme4"  (Bates et al. , 2015) , with early neonatal mortality and late neonatal mortality as binary outcome variables. Explanatory variables were introduced in the models only if missing values represented less than 15% of the data. Thus, the fixed-effects introduced in the models were birth weight, stillbirth in the litter, sex, litter size (total number of puppies born alive), litter heterogeneity and breeding kennel size.
To deal with unbalanced data, the random under sampling approach described by  Chan and Stolfo (Chan and Stolfo, 2001; Weiss, 2004)  was used and generalised linear mixed models were fitted with all selected variables. Models were fitted on 80% of each balanced sub-datasets (training sets) and their classification performances were evaluated on the remaining 20% of each balanced sub-datasets (testing sets). Results were combined across balanced sub-datasets using the median. Then, pvalues, odds ratio, and their 95%CI, were obtained for each parameter. The median area under the receiver operating characteristic curve (AUROC) was used to assess the ability of the models to differentiate puppies which die during neonatal period (0-21 days) and those that survive.
Birth weight thresholds In order to determine birth cut-off values for each breed, ROC and CART procedures were carried out on the dataset using birth weight and neonatal mortality data for each breed. Analyses were performed using "pROC"  (Robin et al. , 2011)  and "rpart" (Therneau et al. , 2017) R packages. Firstly, ROC curves were used to identify optimal cut-off values for birth weight regarding neonatal mortality for each breed. The effectiveness of the parameter to discriminate between puppies dying during neonatal period and those that survive was assessed using the AUROC. If the AUROC was greater than or equal to 0. 70, the cut-off value was determined for each breed on maximized Youden's J statistic (J = Se + Sp -1;  Greiner et al. , 2000; Hajian-Tilaki, 2013). A sensitivity greater than or equal to 75% was used in order to minimize false negative result.
An example of false negative result was a puppy dying before 21 days of age but who was classified as a survivor based on birth weight, and thus not specifically nursed whereas it should have been. Secondly, CART analysis, based on the recursive partitioning method, was used for the identification of puppies at high risk of neonatal mortality (for more information, see  Lemon et al. , 2003; Saegerman et al. , 2011). To prevent the model from over-fitting the dataset, any node that as fewer than five subjects could not split and was forced to become terminal node. The decision rule of the first root node was used to defined CART threshold for each breed.
, 1999; Xu et al. , 2010). Thus, CART thresholds could be used to define low birth weight puppies. CART thresholds for breeds with similar birth weight distributions also varied. For example, the Beagle and the English Bulldog were not statistically different in birth weight but their CART thresholds were 261 and 216 g, respectively. When comparing the German Shepherd and the Bernese Mountain dog, the average birth weights were not statistically different. However, the CART thresholds were 318 g for the German Shepherd and 387 g for the Bernese Mountain dog. The data support that the relationship between low birth weight and neonatal death being different between breeds. More studies are needed to further clarify this relationship and to describe the differences between breeds. Building a larger database through automatic data collection with varying characteristics of puppies, and follow-up until being sold (at the minimum), could lead to earlier identification of at-risk neonates.
DNA (0. 2 g/l) extracted using InstaGene matrix (Bio-Rad, Marnes-la-Coquette, France) was sequenced using MiSeq platform (Illumina, Inc. , San Diego, CA, USA) paired-end sequencing and automated cluster generation with dual-indexed 2 ϫ 250-bp reads. A 535,000/mm 2 cluster density filter and a 96. 3% cluster passing quality control filter were applied. A total of 8,690,521 reads were assembled using SPAdes version 3. 12. 0 with the option "-careful" in order to reduce the number of mismatches and short indels (6). Default parameters for k values, i. e. , k-mer values of 127, 99, 77, 55, 33, and 21, were applied. A total of 977 human DNA contigs were removed via a BLASTn script that connected remotely to the NCBI database (nr/nt) (BLASTϩ 2. 3. 0). Annotation using Prokka version 1. 13 (7) of the M.
tilburgii strain MEPHI genome sequence (mean coverage depth, 6. 5ϫ) comprises 102 contigs, with a total assembly size of 3,238,527 bp and 66. 3% GC content (N 50 value, 56,170 bp; coverage, 5. 6ϫ). One 19,595-bp contig (62. 9 GC% content), exhibited 77% nucleotide similarity with Mycobacterium sp. strain KMS plasmid pMKMS02 (GenBank accession no. CP000520) using BLASTn. The M. tilburgii strain MEPHI genome was predicted to contain 4,207 genes, including 4,154 protein-coding genes, and 53 RNAs (49 tRNAs, 3 rRNAs, and one transfer-messenger RNA [tmRNA]). Phylogenetic trees based on the 3,504-bp rpoB and the 1,523-bp 16S RNA genes confirmed phylogenetic placement into the Mycobacterium simiae complex (Fig. 1B  and C ). The genomic similarities estimated using OrthoANI version 0. 93. 1 (8) and in silico DNA-DNA hybridization estimated using the GGDC version 2.
0 online tool (9) were 87. 17% and 33. 8% with M. simiae (GenBank assembly accession no. GCA_000455305), 87. 15% and 33. 6% with Mycobacterium sherrisii (GenBank assembly accession no. GCA_001722325), 82. 88% and 26. 8% with Mycobacterium triplex (GenBank assembly accession no. GCA_000689255), 82. 76% and 26. 6% with Mycobacterium florentinum (NCBI RefSeq accession no. NZ_LQOV00000000), 82. 62% and 26. 1% with Mycobacterium genavense.
(NCBI RefSeq accession no. NZ_JAGZ00000000), 82. 42% and 26. 3% with Mycobacterium ahvazicum (NCBI RefSeq accession no. NZ_FXEG00000000), and 82. 35% and 26% with Mycobacterium lentiflavum (NCBI RefSeq accession no. NZ_CTEE00000000), respectively. Roary pangenome analysis (10) of the M. simiae complex including M. tilburgii strain MEPHI yielded a total of 37,895 genes distributed as 1,077 core genome genes, 403 soft-core genes, and 5,547 shell genes (Fig. 1D ). PathogenFinder 1. 1  (11)  predicted M. tilburgii strain MEPHI to be a human pathogen (probability, 0. 736) with detection of 14 pathogenicity-associated proteins. Analysis using the Resistance Gene Identifier version 5. 0. 5 database  (12)  indicated that the M. tilburgii strain MEPHI genome harbored rpbA, murA (C117D), mtrA, katG (I335T), and pncA (H82R) mutations conferring resistance to rifampin, fosfomycin, macrolides, penam, isoniazid, and pyrazinamide, respectively.
ACKNOWLEDGMENTS This work was supported by the French Government under the "Investissements d'avenir" (Investments for the Future) program managed by the Agence Nationale de la Recherche (ANR, National Agency for Research; reference Méditerranée Infection 10-IAHU-03). This work was supported by Région Sud (Provence Alpes Côte d'Azur) and   (13, 14). Phylogenetic inferences were obtained using the maximum likelihood method based on the Tamura and Nei model (1,000 bootstrap replicates); bootstrap values of Ն90% are given at the nodes. (D, Left) Newick tree of Mycobacterium tilburgii strain MEPHI and other members of the Mycobacterium simiae complex, generated by Roary from binary gene presence/absence in the accessory genome. (D, Right) Plot of Roary gene presence/absence analysis results (blue, gene presence; white, gene absence).
Clinical inputs The analysis incorporated a second-line treatment course with L-AMB for patients discontinuing first-line treatment. The percentage of patients receiving second-line treatment was calculated using data from the SECURE and VITAL trials by considering the number of patients that discontinued first-line treatment due to no or insufficient response to treatment, adverse events or intercurrent illness. The figure from the isavuconazole arm of the SECURE trial was applied to both isavuconazole and voriconazole cohorts as the difference observed in the SECURE trial was not statistically significant. It was also assumed that all patients with mucormycosis receiving voriconazole would switch to second-line treatment once pathogen information became available. The proportion of patients moving to second line treatment are summarized in Table  1.
Results from a 2018 UK budget impact analysis, whereby isavuconazole was estimated to reduce the total budget in patients with renal impairment, provide an example of how inclusion of additional clinical benefits with the use of isavuconazole instead of voriconazole may lead to economic advantages  70. Although the perspective of the model was the UK NHS, clinical inputs in the model were largely populated from clinical trial data (i. e. the SECURE and VITAL studies) which included patients from multiple countries across multiple continents. Clinical outcomes may have differed if a solely UK-based population had been included, which may have, in turn, impacted costs, resource use, and the overall ICER. It should also be noted that patients participating in clinical trials have to meet pre-specified inclusion criteria which may not reflect the diverse range of baseline characteristics observed in real-world practice.
Parasitemia values were log 10 -transformed, and the control group (non-infected) was not included in this particular analysis since control sheep had no parasitemia during the study. REML was used as the estimation method, and Kenward-Roger method was used to calculate the degrees of freedom. Adjusted means are reported with 95% confidence intervals (95% CI). Pairwise comparisons at each day (corrected for multiple comparisons using Sidak's method) as well as overall treatment effect (F test including both simple effects and interactions) were performed. Significance level was set a priori at 0. 05. All statistical analyses were performed with Stata 15. 1 software (StataCorp, USA).
Image analysis and statistics The gels were scanned and visualized using a Typhoon 9400 (GE Healthcare) using appropriate wavelengths and filters for Cy2: 490 nm, Cy3: 550 nm, and Cy5: 650 nm. Spot detection and quantification were performed with Progenesis SameSpots v3. 0 software (Nonlinear Dynamics Ltd. ). Spot quantitation was normalized using a Cy2-labelled internal standard. The volume of each spot was calculated as the product of spot area and spot intensity. Differential expression analysis was performed by comparing matched spots volumes between the T. vivax isolates. Protein expression of T. evansi was used as an exclusion filter. To select relevant spots, the one-way ANOVA test (P-value ≤0. 05 and a fold change ≥2) provided in the SameSpots software (Table  S2 ) was used.
Fragment ions generated by CID were detected at the linear trap. Normalized collision energy of 35 eV and activation time of 30 ms were used for CID. All spectra were recorded under positive ion mode using the Xcalibur 2. 0. 7 software (Thermo Fisher Scientific) with the instrument operating in the information-dependent acquisition mode throughout the HPLC gradient. The mass scanning range was m/z 400-2000 and standard mass spectrometric conditions for all experiments were: spray voltage, 2. 2 kV; no sheath and auxiliary gas flow; heated capillary temperature, 200 °C; capillary voltage, 40 V and tube lens, 120 V. For all full scan measurements with the Orbitrap detector a lock-mass ion from ambient air (m/z 445. 120024) was used as an internal calibrant as described  (Olsen et al. , 2005).
All MS/MS spectra were searched against the Trypanosoma entries of either SwissProt or TrEMBL databases (http://www. uniprot. org/; v 2012_07) by using the Proteome Discover software v 1. 3 (Thermo Fisher Scientific) and Mascot v 2. 3 algorithm (http://www. matrixscience. com/) with trypsin enzyme specificity and one trypsin missed cleavage. Carbamidomethylation was set as fixed cysteine modification and oxidation was set as variable methionine modification for searches. A peptide mass tolerance of 5 ppm and a fragment mass tolerance of 0. 5 Da were allowed for identification. Management and validation of mass spectrometry data were carried out using Proteome Discoverer software v 1. 3 (P < 0. 01 for 2 peptides or more/protein). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al.
A persistent decrease to around 29-30 breaths/ min occurred from 30 dpi (Fig. 3C ). Complete vital signs data are presented in the Table  S3  and Fig. S3. The clinical outcome of the disease was characterized by a variety of clinical signs. Initially, reduced feed intake and lethargy were observed. Later, facial edema, enlarged superficial lymph nodes, unilateral and bilateral lacrimation, pale or icteric membrane mucous, and weight loss were also observed. More severe clinical signs (icteric membrane mucous and harsh emaciation) were observed in the three TvMT1-infected animals.
T. vivax strains with different virulence and pathogenicity patterns exhibit differential expression of some proteins After separating sample-specific labeled proteins by DIGE as described in Materials and Methods Sections 2. 6 and 2. 7, 754 spots were detected by SameSpots software in the DIGE gel images from T. vivax TvLIEM176 and TvMT1 strains. Spot intensity levels were quantitated and normalized using a Cy2-labeled internal standard. Seventeen protein spots which had at least a 2-fold higher intensity (ANOVA P < 0. 05, Power > 0. 9) in T. vivax, compared with T. evansi, were initially identified. These 17 T. vivax-specific spots were then analyzed to identify those with at least a 2-fold difference in intensity (ANOVA P < 0. 05, Power > 0. 9) between the T. vivax strains (Fig. 4 ). Six spots exhibited higher intensity in TvMT1  (758, 793, 1912, 1909, 1903, and 1906) , and four in TvLIEM176  (1919, 1979, 1986, and 559).