dataesr/scientific-paragraphs-categorization huggingface dataset:
The rest of the spots  (1552, 1099, 985, 602, 845, 1928, and 1969)  were equally intense in both T. vivax strains. All 17 spots selected in the first screening (T. vivax-specific) were excised and analyzed by MS/MS. No spectra were recovered from one TvMT1 high-intensity spot (1909) but the spectra from the remaining 16 spots were analyzed: five more intense in TvMT1, four in TvLIEM176, and seven with similar intensity in both strains. Among these 16 spots, 29 peptides were consistent with proteins in the Uniprot database. These peptides were used to BLAST the T. vivax transcriptome database  (Greif et al. , 2013). Six proteins were identified from the spots displaying higher intensity in TvLIEM176, putatively annotated as alpha tubulin, beta tubulin, arginine kinase, glucoseregulated protein 78, paraflagellar protein 3, and T-complex protein 1 subunit theta.
A number of factors have been incriminated in the etiology of anemia and these include hemolysis as a result of immunological factors and trypanosome products, hemodilution and  impaired erythropoiesis  (Katunguka-Rwakishaya et al. , 1992). In infections with T. vivax, direct alteration of the red blood cells surface is expected to be an important mechanism in anemia pathogenesis due to the secretion of sialidases by these parasites, leading to their phagocytosis  (Guegan et al. , 2013). Overall, data collected from sheep infections demonstrated that TvLIEM176 strain had high virulence and moderate pathogenicity and TvMT1 strain had low virulence and high pathogenicity. These striking differences led to the hypothesis that these two strains also differentially express their proteome when replicating in sheep.
Differentially expressed proteins identified in the proteome of two Trypanosoma vivax isolates of different virulence and pathogenicity, indicating the expression. Tbr, Trypanosoma brucei rhodesiense; Te, Trypanosoma evansi; Tc, Trypanosoma cruzi; Tbg, Trypanosoma brucei gambiense; Tr, Trypanosoma rangeli, Tbb, Trypanosoma brucei brucei; Tv, Trypanosoma vivax. Identification of the protein in the T. vivax transcriptome database (http://www. bioinformatica. fcien. edu. uy/Tvivax/). Homology with the same protein from other trypanosomes, according to UniprotKB database (https://uniprot. org). Number of amino-acid residues, according to the T. vivax transcriptome database (http://www. bioinformatica. fcien. edu. uy/Tvivax/). Score (%). Biological function, according to UniProtKB (https://www.
Acknowledgements This research was supported by grants from  ECOS NORD  (Project Number  V09A01 ) and from the  Consejo de Desarrollo Cientifico, Humanistico y Tecnologico (CONDES) of the University of Zulia. Mass spectrometry experiments were carried out using facilities of the  Functional Proteomics Platform of Montpellier. We are very thankful to  Dr. Sara L. Zimmer  for critically reading the manuscript.
uniprot. org)and TriTrypdb (http://tritrypdb. org/tritrypdb/)databases. Molecular properties according to the literature: V, virulence; P, pathogenicity, VC, vaccine candidate; DT, drug target; DC, diagnostic candidate (antigenicity and immunogenicity). TvLIEM176 m TvMT1 m +++ + +++ + +++ + +++ + +++ + +++ + (continued on next page) Biological Prop k Reference l Function j Protein DT (Lama et al. , polymerization, 2012; GTP catabolic Nanavaty process et al. , 2016; Werbovetz, 2002; Yakovich et al. , 2006) VC (Li et al. , 2007; Plouffe and Belosevic, 2006) V ( Grébaut et al. , 2009) GTP catabolic DT (Lama et al. , process, protein 2012; polymerization Nanavaty et al. , 2016; Werbovetz, 2002; Yakovich et al. , 2006) VC (Li et al. , 2007; Plouffe and Belosevic, 2006) V ( Grébaut et al. , 2009) Metabolic process V and P (Grébaut et al.
cruzi: 85. 2% T. b. gambiense: 80. 3% T. vivax Y486: 100% T. b. gambiense: 99. 4% T. equiperdum: 99. 4% T. vivax Y486: 94. 8% T. congolense: 87. 6% T. b. brucei: 87. 6% T. vivax Y486: 43. 8% T. b. gambiense: Identification in T. vivax transcriptome e TvMiraNov_c43 TvMiraNov_rep_c5263 TvMiraNov_c1046 TvMiraNov_c10506 TvMiraNov_rep_c12168 TvMiraNov_c274 Peptides/ Unique peptides 9/9 15/3 4/4 2/2 2/1 6/5 Species or Taxonomy d Tbr Te Tc Tbg Tbg Tc Accession Number or ID c P04106 A1XXJ2 O96507 D0A7U5 D0A3R6 O18658 SN b 1919 1979 1986 1919 1979 1986 1919 1986 1986 559 Protein a Alpha tubulin Beta tubulin Arginine kinase Glucose-regulated protein 78 T-complex protein 1 subunit theta Par3 c Accession number of the protein according to UniProtKB database (https://uniprot. org). d Species: e f g Theoretical Mr and pI, not determined for fragments of proteins.
For each trial, the total number of visited cups, the number of visited baited cups (the chicken placed its head above the cup and ate the mealworm) and non-baited cups, the latency to visit all baited cups, the number of revisited baited cups, the total number of revisited cups (baited and not baited) were recorded. Through these data it is possible to calculate an index relating to reference spatial memory: Index to assess spatial reference memory: This reference memory index measures the chicken's ability to discriminate between baited cups and non-baited cups  (Nordquist et al. , 2011; Tahamtani et al. , 2015). The index results are between 0 and 1, with 1 being the best possible score.
RANGING BEHAVIOR The stability of the ranging behavior (number of range visits) between 4 days of observations was analyzed by Spearman correlation, due to non-normality of the data. In order to avoid multiple correlations, we combined the data for days 44 and 49 and for days 57 and 64 for each chicken (both sexes), then the correlation between the two periods (days 44 + 49 and days 57 + 64) for the number of visits were tested. The variable "number of range visits" and "distance index" were then calculated by adding all visits on the outdoor range for each chicken at the four ages of observations in order to characterize the ranging behavior level of each chicken. The sex effect for these variables was analyzed on the pooled data of four observation ages using a Mann-Whitney test. Of the 120 individuals with a poncho, 113 could be studied throughout the study (n = 62 females, and n = 51 males).
At the end of the spatial test period, four chickens (one low ranger and three high rangers) were excluded because they did not visit more than 2 cups (with or without mealworms). The number of chickens included in the analyses was n = 24. All statistical analyses were performed using IBM SPSS Statistics 21 and using R version 3. 5. 3 with the 'R commander' package. Statistical significance was accepted at p ≤ 0. 05, while tendency was considered for 0. 05 < p < 0. 09. Data are presented as means or estimated marginal means ± SD.
Statistical Analyses The MedCalc R statistical software (Ostend, Belgium) was used. Continuous variables are expressed as median, range, mean ± standard deviation. Correlations between categorical variables were analyzed using the Pearson Chi 2 test. For univariate survival analyses (Kaplan-Meier curves and logrank tests) and multivariate survival analyses (Cox proportional hazards models), reported results include the Hazard Ratio (HR), its confidence interval (95% CI), and the p-value of each covariate. For all statistical tests, a p-value < 0. 05 was considered significant.
ACKNOWLEDGMENTS The authors deeply acknowledge  Dr. Elie Dagher  and  Dr. Jérôme Abadie  (both  Dipl. ECVP, Oniris, Nantes, France ) who participated in the histopathological and immunohistochemical evaluation of the feline mammary carcinomas included in this study;  Prof. Mario Campone  ( Integrated Center for Oncology Nantes/Angers, France ) for his input in comparative oncology and funding acquisition;  Dr. Floriane Morio  and  Dr. Clotilde de Brito , who helped in collecting the clinical and follow-up data;  Dr. Jean-Loïc Le Net ,  Dr. Virginie Théau ,  Dr. Pierre Lagourette ,  Dr. Olivier Albaric  and  Dr. Sophie Labrut , the veterinary pathologists who performed the initial diagnoses of feline mammary carcinomas, as well as  Mr. Bernard Fernandez ,  Mrs. Florence Lezin , and  Mrs. Catherine Guéreaud , the technicians in histopathology who made the slides.
All these traits should be monitored through several generations of pigs, strictly assessed and excluding those that are subjected to any undesirable deviations. Furthermore, any potential interactions between genotypes and the environment should be considered, as well as the feature variables of the disease. Health status can be monitored through the assessment of physiological parameter such as body temperature, oxygen, pH, osmolarity, or glucose concentrations  (17). These physiological parameters are not specific but they can be used as indicators of DT when compared to data relative to pathogen load. However, in animal breeding, those traits are not easy to measure and quantify. Some routinely collected data that are used as indirect measure, enabling the modeling and the quantification of resistance and tolerance, and allowing the assessment of the benefits of selection, and identifying which traits (DRs, DT, or both) should be object of selection  (23).
Cortisol accumulation in pig bristles is sometimes used and reflects cumulative HPA axis activity  (57). SAM activation can be evaluated through plasma E and NE concentrations, but it is also subjected to diurnal variations. Measuring salivary αamylase activity is another approach to assess SAM activation  (58). Also, salivary chromogranine A (CgA) level is a reliable marker of SAM activation in pigs  (59) , since it is released in saliva in response to catecholamine while being not affected by age, gender, or circadian rhythm. Finally, few studies evaluated the potential use of plasma β-endorphins level to monitor individual stress response  (60). Until now, research programs had mainly focused on the description of the deleterious impact of stress mediators on immune responses. However, recent data suggest that stress mediators could also promote tissue repair and increase DT, especially through their action on tissue-resident macrophages  (61, 62).
Other measures, such as back fat thickness can be easier to perform on the farm, since animals do not need to be moved to fixed facilities. Reproductive performance measures of sows are easier to assess, since no specific equipment is needed. However, with the advent of Precision Livestock Farming (PLF) technology, many measures should become easier and faster to monitor on farm at the individual level and this will provide new opportunities to assess DT. For example, data related to feed consumption and feed efficacy could be analyzed in real time and each deviation highlighted for attention. However, standard growth and reproductive performance parameters have to be known for each genetic line in order to appreciate any deviation, even if narrow.
Visual and Acoustic Stimuli Horses were shown two animated pictures simultaneously, one on their left and one on their right, while a speaker (Ultimate Ears Megaboum 3, Newark, California, CA, USA) played a human non-verbal vocalization at 67 dB, 2 m away from the horse. The horses were tested with the same two animated pictures that came from the K-DEF database (F01-NE-HA and F01-NE-AN  [37] ). These pictures were validated by 168 people with 98% and 95% accuracy in identifying the emotion  [37]. They consisted of videos (1 s) of a woman's face either expressing joy or anger: the face was neutral in the beginning of the video and showed one of these emotions at the end (the timing of the emotional transition was identical for both animated pictures). The projected faces were approximately 2.
5 times larger than a real person's face. Moreover, we used two vocalizations that came from another validated database (M_6 and T_11  [38] ). They were validated by 20 people with 100% and 95% accuracy in identifying the emotion  [38]. They consisted of a woman's voice pronouncing the (a) phonetic sound with different emotions. We chose vocalizations with the same duration as the animated pictures, one expressing joy and the other expressing anger.
Coding and Statistical Analyses During all sessions including familiarization, the horses were equipped with a heart monitor system (Polar Equine RS800CX Science, Polar Oy, Finland), and their behavior was filmed by 3 cameras (two at the front to code the horse behavior in response to the vocalizations and one at the back to code their looking duration towards the pictures). The following variables were later analyzed with BORIS software (v. 6. 0. 6  [39] ), using the frame-by-frame mode (with 25 frames per second) for more precision. All the videos were analyzed by a same coder without sound, so that the coder was blind to the vocalization that was played. Moreover, a second coder analyzed 20% of the videos and the interobserver reliability was assessed for each behavioral measure by an Interclass Correlation Coefficient (ICC;  [40] ).
3. Heart rate in response to the valence of the vocalization: the mean heart rate and the maximal value across the duration of the whole trial, and the difference in the mean heart rate between the first and last 5 s of the trial. As the trials followed each other closely and the heart rate might not have been reset to a basal level between the trials, we analyzed these variables during the first trial only. Due to technical issues with the heart monitor system, data were missing for several individuals (30 individuals were used to analyze the mean and maximal value of the heart rate, and 22 were used to analyze the difference in the heart rate between the first and last 5 s). The excluded individuals were average in their behavior (mean ± SEM of excluded individuals for the relaxed posture: 0.
080 ± 0. 023; global mean: 0. 081 ± 0. 008; mean of excluded individuals for the vigilant posture: 0. 593 ± 0. 075; global mean: 0. 572 ± 0. 020) and came from the two conditions (anger vocalization and joy vocalization). All horses were familiarized with the heart monitor system before the beginning of the experiment during the familiarization. All statistical analyses were performed using R 3. 0. 2 (R Core Team, 2013). The significance threshold was fixed at 0. 05. The preference index was analyzed with a generalized linear mixed model with a binomial distribution (as it is a percentage) using the "lmer" function in the lmerTest package  [44]. The congruency and the valence of the picture were looked at, as well as the valence of the vocalization, and the interaction effect of the congruence of the picture by the valence of the vocalization were added as a fixed effect.
Furthermore, as the pictures and the vocalizations came from different databases, there was no correlation between the timing of the facial movements observed on the animated pictures and the vocalization heard. Hence horses could not base their looking preference on that cue. However, as we only used one set of pictures and one set of vocalizations, further study would be necessary to test whether these results can be generalized to different pictures and vocalizations. Finally, it can be noticed that the percentages of time spent looking at each stimulus were relatively low (below 25%). This can be explained by the restrictive criterion we used to define this behavior (muzzle oriented within 45 • towards the picture). Indeed, similarly low values are common in other studies using this criterion (e.
2. 3. 0) of the Genotoul bioinformatics platform (http://sigenae-workbench. toulouse. inra. fr) to be used in the FROGS (Find Rapidly OTU with Galaxy Solution) pipeline  [27]. During the FROGS pre-process, sequences were depleted of barcode and the sequences with a non-appropriate length or containing ambiguous bases were removed. Next, reads were clustered into de novo operational taxonomic units (OTUs) using SWARM algorithm [28] with, at first, a denoising step to build very fine cluster using the minimal distance equal to 1 and, secondly, with an aggregation distance equal to 3. Chimeras were then detected and removed with VSEARCH  [29]. Additionally, filters were applied to the OTUs in order to remove singletons  [30, 31]. The OTUs selected were taxonomically assigned using the Silva release 132 reference database  [32].
Statistical Analysis of Sequencing Data Statistical analysis was processed using the RStudio software version 1. 0 (with R statistical package version 3. 5. 1, R Development Core Team, http://www. R-project. org). OTU structure and composition analyses were performed using the phyloseq R package  [33]. Visualization of data was performed using the ggplot2 R package. Alpha diversity indices were calculated using a paired non-parametric t-test with the following indices: Inverse Simpson index, Chao 1 index, number of observed OTU phylogenetic diversity (PD) and Shannon index. Prior to beta diversity calculations, rarefaction using the transform counts methods was applied to the dataset. Statistical differences in Bray Curtis distance between GI locations were tested using a multi-analysis of variance (MANOVA) performed with ADONIS using the vegan R package  [34]  with 9999 permutations and represented by principal coordinate analysis (PCoA) plots.
Function Prediction with Tax4Fun Tax4Fun, an online tool hosted into Galaxy (v. 2. 3. 0) of the Genotoul bioinformatics platform (http://sigenae-workbench. toulouse. inra. fr), was used to predict functional genes of prokaryotic microorganisms across GIT sections  [23]. Tax4Fun transforms OTUs picked up against the Silva 123 database into a taxonomic profile of KEGG (Kyoto Encyclopedia of Genes and Genomes) organisms and normalize these predictions by the 16S rDNA copy number. The most important functions of each intestinal segment were graphically represented using the Rstudio software version 1. 0 (with R statistical package version 3. 5. 1, R Development Core Team, http://www. R-project. org).
Physiological Parameters of the GIT of Piglets The weight of organs and pH data collected while sampling is represented in Figure  S1. As expected, the pH measured in the stomach was lower than that of the other anatomical regions of the GIT. Individual variability was remarkably reduced for the pH data of the cecum and proximal colon contents (Figure  S1A ). Segments from the foregut displayed higher weight but also greater inter-individual variability compared to hindgut anatomical regions (Figure  S1B ).
MiSeq Sequencing Data A total number of 48 digestive contents and mucosal samples were collected during June to July 2017. The Illumina MiSeq run generated a total of 18,358,526 high quality sequences, lowered to 17,121,898 after removal of PhiX control reads. Primer sorted metrics total numbers of reads were equal to 5,364,263 and 1,882,030 respectively for V3-V4 16S primer set and the archaea primer set. Chimera removal, quality filtering and deletion of singletons led to a total of 12,970 identified taxa. The initial mean (± standard deviation) number of sequences per sample was 19,744 (± 6755) for the V3-V4 run and 15,467 (± 3086) for the cecum, proximal, and distal colon and rectum samples of the archaea run. After removal of chimeras and singletons, the mean (± standard deviation) number of sequences per sample was finally 12,361 (± 6374) for the V3-V4 run and 13,895 (± 2756) for the cecum, proximal, and distal colon and rectum samples of the archaea run.
fully effective for optimal identification, because they correspond to only one strain, as discussed by Mendoza et al. Mendoza et al. have recently published a large work involving another in-house database from nine isolates  (Mani et al. , 2019). They tested this with 43 strains of different origins and showed that there was no cross-reaction with other Pythium species. When comparing the reference spectra to one another, seven strains cross-reacted correctly with other strains but two did not, which suggests that if these two MSP had been the only ones in the database, the other strains would not have been identified properly. We agree with the statement that the higher the number of strains in a database, the better the correct identification of the microorganism achieved. However, we believe that in microbiology laboratories, even one strain (24 spectra) is better than nothing; microbiologists are well aware of the risk of false-negative results when a database is poorly implemented and they deal with this limitation every day.
Even the Bruker Filamentous Fungi database and the National Institutes of Health (NIH) database propose some species identification with the spectra of only one reference strain. For example, there is only the spectra from one strain in the NIH database for Fonsecaea pedrosoi, the agent of chromoblastomycosis. A sentence was added to the article to clarify this point for the users of MSI: "Of note, because the 24 added spectra correspond to one strain only, and because of geographical strain diversity, the users of the MSI platform must be aware that it will not provide a 100% sensitivity for P. insidiosum identification. " Also, if no identification is given with the spectra of another Pythium strain on MSI, this negative result does not stop or delay the process for identifying the agent, because a negative result on MALDI-TOF MS usually leads to sequencing.
So we truly think that although not optimal, providing free reference spectra on the MSI platform (achieved with the same protocol as used by Bruker for MSP creation), can be of help in some cases, even if not in all cases. We added a sentence suggesting the limitation of the reference spectra (see above). Of note, none of the spectra generated by  Krajaejun et al. and Mendoza et al. have been made freely available for any laboratory throughout the world, but we are open to internal spectra exchange between our laboratories to test our respective databases. Finally, this case report aimed to inform readers of pythiosis, in particular its atypical clinical and microbiological presentations, and to highlight new treatments and the approach to identification; the focus was not on MALDI-TOF MS technology alone.
Data Collection Participants involved in the present study were recruited in accordance with the latest version of the Declaration of Helsinki for Ethical Principles for Medical Research Involving Human Subjects. Ethical approval was obtained from the local institutional review board Clinical Research Ethics Committee at the Lebanese University. Protocols for genetic studies were approved by the local ethics committees for the protection of subjects involved in biomedical research (2182/28). We obtained written informed consent from all the participants. Complete medical examinations were carried out for all individuals, with data collected including demographic, anthropometric, clinical and measurements. A BMI was considered normal when a value of < 25 kg/m 2 was observed. Biochemical measurements including glycemia, lipid profile, and VitD were measured with commercial kits (Roche Diagnostics, Basel, Switzerland).
TLR4 Expression in White Blood Cells Total RNA was isolated from WBCs by an automated isolation procedure using an Aurum™ Total RNA Mini Kit (Life Science Research, Bio-Rad, Singapore, Singapore). RNA quality and stability were carefully tested on 1% agarose gel and reverse-transcribed using an iScript™ cDNA Synthesis Kit (Life Science Research, Bio-Rad). We used designed specific primers for TLR4 quantification using Primer3 software, version 0. 4. 0 (data available upon request). We carried out cDNA amplification in a reaction volume of 20 uL consisting of 70 ng of cDNA, Solis BioDyne 1× HOT FIREPol Blend Master Mix (Tartu, Estonia) and both primers. Quantitative real-time PCR (qRT-PCR) was performed using the CFX Connect™ Real-Time PCR Detection System (Bio-Rad) with an iTaq Universal SYBR Green Supermix Kit (Bio-Rad) for TLR4 transcripts.