dataesr/scientific-paragraphs-categorization huggingface dataset:
Lactobacillus paracasei CNCM I-5369 isolated from a traditional Algerian dairy product produces extracellular inhibitory substances, namely, bacteriocins, which are active against a panel of pathogenic Escherichia coli strains. This activity was observed only at a narrow pH 4. 5-5, and resulted to be heat stable and sensitive to the action of proteolytic enzymes, which indicate a proteinaceous nature. This new strain has a genome of 2,752,975 bp, with a 46. 6% G + C ratio and contains at least 2664 coding sequences. The Bagel software analysis identified five open reading frames (ORFs) that are translated to new class II bacteriocin. Each ORF was cloned in frame with a Histag tail and expressed in E. coli BL21 (DE3) (pLysS) strain. Of note, each fusion protein carrying any of these ORFs at the C-or N-terminal position resulted to be active against E.
In silico Analysis of the Genome of Lb. paracasei CNCM I-5369 Strain The genome of Lb. paracasei CNCM I-5369 was sequenced using the Illumina MiSeq and HiSeq 2500 technology platforms and 2 × 250 bp paired-end reads (University of Liège, Belgium). The functional annotation of predicted genes was done with the RAST online server 2  , which predicted open reading frames (ORFs), and appropriate annotation according to the free SEED database  (Overbeek et al. , 2014). To identify putative bacteriocins, the genome of Lb. paracasei CNCM I-5369 was analyzed with the Bagel 3 online software 3  , and the amino acid sequence translation was done with the Jpred software 4.
RESULTS The bacteriocin-producing strain described here was initially identified as a Lactobacillus species. The MALDI-TOF-MS and 16 rRNA sequencing enabled its identification as Lb. paracasei with a high degree of confidence. According to the NCBI database, the strains related were Lb. paracasei M0116 with 99. 79% of identity (accession number EU780145. 1), Lb. paracasei SK04B2 (accession number KJ764645. 1), and Lb. paracasei Y132 (accession number MK774551. 1) with 99. 72% identity.
The E20 Fraction Contains Putative Bacteriocin(s) With Activity Against E. coli After each step of the purification procedure, several parameters including the antibacterial activity (AU/ml), total activity (UA), and specific activity (AU/mg) were determined. As reported in Table  3 , the E20 fraction has a total activity of 8,000 AU. The E20 fraction was adjusted to pH 4. 5 and tested against different E. coli strains. Subsequently, all these target strains appeared to be, in a strain dependent-manner, sensitive to the action of E20 fraction. Notably, control assays were done at a same pH to discard any false-positive reaction attributable to the acidity of the medium (data not shown). Therefore, strong inhibition activities were observed on E. coli target strains based on the radii of inhibition zones formed around wells (Table  4 ).
Identification of Bacteriocin-Coding ORFs Software such as Bagel or AntiSmash are routinely used to identify genes coding for bacteriocins  (van Heel et al. , 2013; Weber et al. , 2015; Collins et al. , 2018). They have algorithms with a capability to recognize specific amino acids on the bacteriocin sequence, and the Bagel software is currently the most suitable one for this application  (Oliveira et al. , 2017;  FIGURE 1 | Time course production of inhibitory compound(s) during the growth of Lb. paracasei CNCM I-5369 on MRS medium. Gray zone corresponds to OD 600nm evolution, and black bars represent antimicrobial activity in arbitrary units per milliliter (AU/ml). Collins et al. , 2018). The genome of Lb. paracasei CNCM I-5369 contains 2,752,975 bp, with a 46. 6% G + C ratio, and 2,664 coding sequences.
The in silico analysis of this genome with the Bagel 3 tool 6  enabled us to identify five ORFs (Figure  2 ) and reliably predict the likelihood that these sequences are translated to produce new class II bacteriocins with molecular sizes varying from 3. 199 to 12. 252 Da (Table  5 ). The first bacteriocin (ORF010) has a predicted molecular size of 3. 199 Da and a pI of 5. 17. Those of the ORF012 and ORF023 were 6. 300 and 6. 582 Da, with pI values of 4. 86 and 8. 25, respectively. The fourth and fifth ones (ORF030 and ORF038) have the largest molecular sizes with 10. 395 and 12. 252 Da, and pI of 8. 62 and 6, respectively. Amino acid sequences identified with Bagel 3 and Blastp online software 1 revealed some similarities between the bacteriocin predicted from ORF010 and enterocin X or Lactococcin-like family Lantibiotic  (Hu et al.
Challenges exist for measuring magnesium concentration  [23] , and standardized laboratory tests that accurately evaluate magnesium levels are lacking  [24]. Currently, in adults the reference interval for serum magnesium ranges between 0. 75-0. 95 mmol/L (1. 82-2. 30 mg/dL), while serum ionized magnesium ranges between 0. 50-0. 69 mmol/L  [19, 25]. These values are based on data reported in the 1970s  [26]. However, since serum magnesium respond to dietary manipulation  [23] , and magnesium content in fruits, cereals and vegetables markedly declined over the past 40 years  [24] , the distribution of serum magnesium in normal population should be updated. In addition, serum magnesium concentration is most often used to assess magnesium status yet only 1% of total body magnesium is present in blood.
CPLs map onto 35 out of 40 brown trout linkage groups indicating a polygenic basis to pigmentation patterns. They are mostly located in coding regions (43. 4%) of 223 candidate genes, and correspond to GO-terms known to be involved in pigmentation (e. g. calcium and ion-binding, cell adhesion). Annotated candidates include genes with known pigmentation effects (e. g. SOX10, PEML, SLC45A2), but also the Gap-junction Δ2 (GJD2) gene already shown differentially expressed in trout skin. Patterns of admixture were found significantly distinct when using either the full SNP data set or the set of CPLs, indicating that pigmentation patterns accessible to practitioners are not a reliable proxy of genome-wide admixture. Consequences for management are discussed.
-PHENOTYPIC DATA Acquisition of phenotypic data followed  Lascaux (1996)  and is used by numerous angler associations and management authorities in France. The set of variables considered in this protocol is listed in Table  S1  and illustrated on a picture in Fig. S1. Data were recorded from individual photographs of the 112 fish considered for genomic analyses. Photographs were taken at fishing on slightly anesthetized trout with eugenol. A camera Canon® EOS 1000D was used. After recovery, fish were released in the wild or in hatchery tanks. Quantitative variables (N =19) and semi-quantitative variables (N =11) were recorded by visual examination of photographs of the left flank of each individual fish. Quantitative variables refer to punctuation patterns of brown trout (e. g. number and diameter of spots) and are recognized important for this species  (Blanc et al.
, 1982 (Blanc et al. , , 1994)). Semi-quantitative variables refer to 'ornamental' appearance patterns (e. g. parr marks, fringes on fins, white ring around spots) that are important to practitioners or anglers. Each quantitative punctuation variable was measured independently. Ornamental variables have been coded by modalities, either present/absent  [1, 2]  or, e. g. , absent/partial/complete [1, 2, 3] (Tables  1  & S1 ). This set of variables avoided focusing only on peculiar attributes of the pigmentation patterns. Other pigmentation traits that can be also important for trout (e. g. background colour, brightness, or reflectance; Colihueque, 2010) were not considered in this study. Phenotypic data and trout pictures will be deposited on a public repository. Eleven colour patterning variables were found not significantly correlated (Table  S2 ) and retained for subsequent analysis (Table  1 ).
A cyclical coordinate descent procedure was retained for model selection  (Friedman et al. , 2010). The retained model was determined by cross-validation. Log(λ) was estimated by minimizing the mean quadratic error. The number of positive β j coefficients was estimated from log(λ), with each β j coefficient associated to a suite of SNPs considered as involved in the association. Analyses were performed with the glmnet package (https://cran. r-project. org/package=glmnet). A multi-trait GWAS was also implemented. We used the MultiPhen package  (O'Reilly et al. , 2012)  to test for the linear combination of phenotypes most associated with the genotypes at each SNP. Such one approach may potentially capture effects hidden to single phenotype GWAS. It performs a 'reversed regression', with multiple phenotype predictors and genetic variant as outcome (i.
-MAPPING AND ANNOTATION CPLs detected by RDA and GWAS have been mapped on the Atlantic salmon genome (Salmo salar;  Lien et al. 2016 ; Genbank assembly: GCA_000233375. 4) and on the high density linkage map of S. trutta  (Leitwein et al. , 2017). We searched for genes located in a 25kb window upstream or downstream of each colour-associated marker (arbitrary range). When one association with a gene was detected, markers were assigned to coding or noncoding (intron, upstream or downstream sequences) gene regions, or other specific entities (transposable elements, pseudogenes). Using S. salar annotations as inputs, gene ontologies (GO) for biological processes and molecular functions were derived for genes associated to CPLs using QuickGO (https://www. ebi. ac. uk/QuickGO/). When annotations were unavailable we further performed a literature search in order to know if genes detected within the 25kb windows were previously mentioned in former studies.
ACKNOWLEDGEMENTS This work benefited from the  Montpellier Bioinformatics Biodiversity (MBB)  platform from the  LabEx CeMEB , an  ANR  " Investissements d'Avenir" program  ( ANR-10-LABX-04-01 ). J. Pouzadoux and  A. Jourdan  are acknowledged for inputs. ML was partly supported by a grant from  LabEx CeMEB.
DATA ACCESSIBILITY Genomic data are from  Leitwein et al. (2018)  and available at NCBI Short Read Archive under the study accession SRP136716. The final VCF and the PLINK files for S. trutta LGs are available under the study accessions SRZ187687 and SRZ187688. Upon acceptance, trout pictures and phenotypic data will be deposited on Dryad or another repository.
RNA Sequencing (RNAseq) The RNAseq libraries were prepared with 30 ng of input total RNA using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs, #E-7765S, Évry-Courcouronnes, France) with the NEBNext rRNA Depletion Kit (New England Biolabs, #E6310L) and following the manufacturer's instructions. The libraries were then paired end sequenced (75 cyclesx2) on the NovaSeq 6000 instrument (Illumina, Paris, France) using the NovaSeq 6000 SP Reagent Kit (300 cycles; #20027465, Illumina). RNA-seq data analysis was performed using ArrayStudio (Qiagen, Courtaboeuf, France). Briefly, raw data QC is performed then a filtering step is applied to remove reads corresponding to rRNAs as well as reads having low quality score or shorter than 25 nt. Reads were further mapped to the Cyno.
WashU 2013 genome, based on the contigs assembled from a WGS project submitted by Washington University (WashU) in 2013, using OSA4 [12] (Omicsoft Sequence Aligner, version 4, Qiagen) and quantified using Ensembl. R94 model of transcriptome, paired reads were counted at gene level. Differential analysis of gene expression was performed at gene level using DESeq2  [13]. The variable multiplicity was taken into account and false discovery rate (FDR) adjusted p-values calculated with the Benjamini-Hochberg (BH) correction  [14]. Functional analysis of differentially expressed genes was performed using IPA (Qiagen)  [15]. Gene Set Enrichment analysis was performed using Ingenuity and GSEA software  [16, 17]. High-throughput sequence data are available on the Gene Expression Omnibus (GEO) under the GSE154901 accession number.
Statistical Analysis The statistical significance of differences between groups was analyzed using GraphPad Prism v7. 0 software (GraphPad Software, San Diego, CA, USA) t-test (unpaired, parametric, equal standard deviation, two-tailed), Mann-Whitney t-test (unpaired, nonparametric, two-tailed) and Friedman test (matched data, nonparametric) with post hoc Dunn's multiple comparison test. The application of these statistical methods to specific experiments is noted in the figure legends.
As an alternatrive to the present model, PharmaCo-Cell Company has commercialized a BBB model prepared from primary cultures of monkey (Macaca irus) brain capillary endothelial cells, co-cultured with rat brain pericytes and astrocytes. However, its full characterization has not yet been published, with little information on how cells are obtained, and the model established. This newly generated dataset may therefore constitute a powerful tool for better understanding the BBB biology. BECs constituting the BBB are in close contact with pericytes, encapsulated by the basement membrane, and surrounded by astrocytic endfeet processes and other cell types like microglia and neurons  [88]. Consequently, the obtention of pure BECs, and thus of an accurate gene expression data on the BBB constitutes a real challenge.
Melanophore and xanthophore counts Following previously reported protocols in adult fish  [31, 32] , melanophores and xanthophores cells were counted in 1 mm 2 regions in the dorsal area from the edge of the head to the dorsal fin. To contract melanosomes, fish were anesthetized in tricaine and immersed in 10 mg/ml epinephrine (Sigma) solution for 30 min. Images were analyzed using ImageJ software. Data are expressed as mean ± standard error of the mean (SEM) and evaluated by t-test, where a p-value<0. 05 was determined to be statistically significant. n = 2 fish per category.
Adipose Tissue Cellularity Cellularity analysis was performed in peri-ovarian adipose tissue as previously described  [21]. Approximately 40 mg of adipose tissue was digested by collagenase at 37 °C for 15 min. The sample was subsequently placed on a heated microscope slide and images of isolated adipocytes were taken with a camera-equipped microscope (Axioimager. Z1 and Axiocam MRc, Zeiss, Oberkochen, Germany). The diameter of at least 1000 cells was assessed by image analysis using ImageJ software (Bethesda, Maryland, USA) and then perimeter was calculated, allowing the calculation of the mean adipocyte weight (density of triglycerides = 0. 92). Fat cell number was estimated by dividing the entire peri-ovarian adipose tissue lipid content by the mean adipocyte weight. Approximately 100 mg of the same adipose tissue was sampled and stored at -20 °C for lipid measurement.
Adipose Tissue Cellularity Cellularity analysis was performed in peri-ovarian adipose tissue as previously described  [21]. Approximately 40 mg of adipose tissue was digested by collagenase at 37 • C for 15 min. The sample was subsequently placed on a heated microscope slide and images of isolated adipocytes were taken with a camera-equipped microscope (Axioimager. Z1 and Axiocam MRc, Zeiss, Oberkochen, Germany). The diameter of at least 1000 cells was assessed by image analysis using ImageJ software (Bethesda, Maryland, USA) and then perimeter was calculated, allowing the calculation of the mean adipocyte weight (density of triglycerides = 0. 92). Fat cell number was estimated by dividing the entire peri-ovarian adipose tissue lipid content by the mean adipocyte weight. Approximately 100 mg of the same adipose tissue was sampled and stored at -20 • C for lipid measurement.
Acknowledgments: We thank  Gwennhael Autret  and the  Plateforme Imagerie du Vivant of Paris Descartes  for the acquisition of the MRI images, and  Nachiket Nadkarni  for his help with the MRI image acquisition and analyses. We thank  Bénédicte Duchamp ,  Jinghang Li ,  Sophie Pelluet ,  Julien Piedcoq ,  Martin Roosen  and  Vanessa Vilmara  for their contribution in conducting the experiment.
Acknowledgments This work was supported by  ThermoScientific , an  ERC Starting Grant  (HabitablePlanet;  306655 ), and two  NERC Consortium Grants ("Deep Volatiles" ,  NE/M0003/1  and " Tellurium and Selenium Cycling and Supply (TeASe) "  NE/M010848/1 ) awarded to HW. HW and MLP also acknowledge salary support from a  NERC Advanced Fellowship  ( NE/F014295/2 ) and a  MSCA Individual Fellowship  (SPECADIS,  794825 ), respectively. We thank  Philip Pogge von Strandmann  for helpful discussions and tips to set up the measurements,  Kathrin Schilling  and  Tom Johnson  for their help and advice at throughout the project.
Statistical Analysis Differences between samples were calculated using one way ANOVA and the post hoc Tukey test (p < 0. 05) XL-STAT (version 2009, Addinsoft, Paris, France), and data were expressed as a mean ± standard error calculated from at least three independent experiments. The resulting data were analyzed by principal component analysis (PCA) using FactoMineR software. The principal component analysis was performed using version R 3. 5. 2 (www. r-project. org, R foundation for statistical computing).
Regarding L. monocytogenes, cells exhibited an altered cell wall, separation of membrane layers and leakage of intracellular contents (Figure  3C (2)). Foods 2020, 9, x FOR PEER REVIEW 9 of 20 TEM was used to observe the ultra-structural modifications, if any, of pathogens cells treated with CFS from Lactobacillus strains. The data gathered showed the disruption of the cell wall structure and the condensation of ribosomes. Therefore, bacterial death resulted from the wall's mechanical strength and osmotic lysis. The cytoplasm content of treated E. coli cells was agglutinated and appeared as globules of variable size. Nevertheless, the membrane was easily observed as being devoid of any break or clear cut (Figure  3A (2)). Notably, the treated S. aureus cells did not show any particular intracellular damage.
This study set out to (i) determine whether dietary EPA is cardioprotective in male rats made septic by cecal ligation and puncture (CLP), (ii) estimate the influence of dietary ALA in septic male rats, (iii) and evaluate the physiological and molecular mechanisms underlying the effects of these PUFAs. For this purpose, three groups of male Wistar rats were fed a diet each differing in their PUFA composition: the first one mimicking the Western diet was rich in n-6 PUFAs and deficient in n-3 PUFAs, the second one was fortified with EPA and the third one was supplemented with ALA. After 6 weeks of feeding, sepsis was induced by CLP and, exactly 24 h later, cardiac function was evaluated in in vivo and ex vivo. The data were enhanced by the measurement of several of the parameters involved in the inflammation process and production of oxidative stress as well as those describing the fatty acid profile of circulating and membrane lipids.
Evaluation of Body Composition The animals body weight along with the body composition were determined by nuclear magnetic resonance imaging with an adequate spectrometer (Echo MRI LL, Houston, TX, USA) just before the surgical procedure and at the end of the 24 h postoperative period. The data thus enabled the measurement of the lean body mass, fat mass and total body water in the animals. After the postoperative period, the weights of abdominal (perirenal plus visceral adipose tissues) and epididymal fats were measured.
The perfusion pressure was also evaluated with a pressure probe inserted just before the aortic cannula. In conditions of perfusion at fixed coronary flow, the perfusion pressure corresponds to the coronary pressure. Changes in the coronary pressure reveal modifications of coronary volume (coronary dilatation, constriction and/or obliteration of coronary vessels). All the parameters of cardiac function were measured at the 30th minute of perfusion and the data were recorded and analyzed with HSE software (Hugo Sachs Elektronik, March-Hugstetten, Germany). Just before the end of the recording, samples of arterial and venous perfusion fluids were collected anaerobically for determination of oxygen partial pressures with a blood gas analyzer (Radiometer, Neuilly-Plaisance, France). Cardiac oxygen consumption was calculated by subtracting the venous oxygen partial pressure to the arterial one and by multiplying the difference by the coronary flow.
Statistical Analysis Results are presented as mean ± SEM. The data were subjected to a 2-way analysis of variance describing the effects of the diet, those of sepsis and the cross-interaction between these two factors. When it was necessary, the means were compared using Duncan's test. A probability lower than 0. 05 was considered significant. All the calculations were performed using the NCSS (Number Cruncher Statistical System, 2010) software (NCSS, LLC, Kaysville, UT).
In Vivo Cardiac Function When the cardiac function was determined in the in vivo situation, early sepsis led to a stimulation of the heart rate and contractility in the DEF and EPA groups, but not in the ALA group where the pathology triggered a significant decrease in the LVDP and contractility and a status quo for the heart rate. These data contrast with the results of the isolated hearts which displayed a strong sepsis-induced negative inotropic effect only in the DEF group. The major difference between the in vivo and ex vivo conditions was the coronary flow. In the ex vivo situation, the coronary flow was fixed and similar for all of the hearts. This was not the case in vivo, since the coronary flow depends on both the systolic aortic pressure and dilatation status of the coronary arterioles.
After CLP, the lowest systolic aortic pressure was observed in the ALA group. On the other hand, the strongest coronary constriction occurred in the same group, as was indicated by the high ex vivo coronary pressure. Thus, it can be said that the in vivo coronary flow was the lowest in the ALA-CLP group. In a parallel study, we determined, ex vivo, the LVDP under conditions of increasing coronary flows, and we demonstrated a positive linear relation between the two factors: doubling the coronary flow multiplied the LVDP by a factor 2 (data not shown). Since we suspected that the in vivo coronary flow in the ALA group was the lowest among the groups of CLP-subjected animals, this would explain why the LVDP and contraction, in the ALA group, were also the most subdued. This could explain the low contractility observed, in vivo, in this group despite the fact that no cellular damage was noticed ex vivo.
Limitations of the Study In the present study, we performed our measurements 24 h after sepsis induction to satisfy the requirements of the local committee of ethics for the animal experimentation and animal welfare structure of our laboratory in terms of animal comfort. Thus, we cannot certify that the long-term effects of EPA would be beneficial. However, data from the literature support this hypothesis. Indeed, numerous animal studies  [15] [16] [17]  report the long-term beneficial effects of dietary EPA-rich fish oils on the survival rate during sepsis. Additionally, the parameters that we determined do not include measurements of apoptosis and fibrosis. It would have been interesting to obtain data describing these cellular phenomena to better characterize the beneficial effects of EPA.
Methods Study design: we conducted a descriptive cross-sectional study of cholera cases at Kakuma refugee camp and Kalobeyei integrated settlement. Data were extracted from the medical records and line lists at the cholera treatment centres between May 2017 to May 2018. Study setting and population: the outbreak occurred in two refugee camps situated in the arid and semi-arid lands of Turkana County, northern Kenya (Figure  1 )  [28]. Kakuma is a stable refugee camp established in 1992; following an influx of refugees in 2014 a separate settlement, Kalobeyei integrated settlement was established 25km away from Kakuma. Kakuma is divided into four camps, namely Kakuma I, Kakuma II, Kakuma III and Kakuma IV. Kalobeyei is administratively divided into village 1, 2 and 3  [1]. They host approximately 185,543 refugees and asylum seekers, mainly from neighbouring Eastern African countries  [29].