dataesr/scientific-paragraphs-categorization huggingface dataset:
A Latin square design was used to balance the sample order. Before the start of the sensory analysis session, pasta brand 3 (Pasta 3) was chosen as the pivot because it was the most "central product" in terms of color, texture, and taste. Participants evaluated the samples according to three criteria (visual appearance, in-mouth texture, and flavor) with free comments (attributes) as "more" or "less" than the pivot. Negations and hedonic comments were avoided. All attributes were listed and categorized and then grouped by synonyms after discussions with panelists and a dictionary of synonyms was generated using the software TASTEL (version 2015. 2, ABT informatique, Rouvroy-sur-Marne, France) The main descriptors were selected by frequency of citation. The number of negative comments were subtracted from the number of positive comments for each descriptor and the resulting scores were adjusted to obtain only positive scores.
Data analysis Data were analysed using NVivo qualitative software version 12. Interview records were transcribed verbatim and thereafter translated into English by the primary researcher, who speaks fluent French and English. Data was coded deductively using a coding frame, and inductively, by adding codes emerging from the data. In more detail: The first author used the main components of the Policy Triangle to develop a coding frame with four core-themes: content, actors, process and context. For each theme, codes were developed for subthemes. The usefulness of these codes was tested by using this coding framework to code the first three transcripts in NVivo. Subsequently the codes were discussed with and validated by a second researcher. The codes were then applied to all transcripts, but at the same time additional emerging codes were considered.
For document reviews, data was extracted using a data extraction framework, which was developed based on the research questions. Interview data were used to complement, compare, and contrast information from the document review, which allowed for triangulation of findings. To reduce the influence of background and views of the researchers on data collection, analysis and interpretation, data collection was carried out according to a predefined protocols and pretested tools. Moreover, three researchers were involved in the analysis, which allowed critical discussions and reflexions on concepts, and on interpretation of data, thus addressing potential bias.
education (MoHE) Power over the training schools to adopt the curriculum Training schools Public training school: National Training Institute in Health Public training school. Responsible for the development of training Sciences (INFSS) programmes. Train midwives and nurses Power to act on the adoption of the curriculum Private training schools Train midwives and nurses for the health labour market Power to act on the adoption and implementation of the curriculum Employers Health structures etc. Users of graduates Power to act on recruitment of the graduates Beneficiaries of the Associations and council of midwives and nurses Responsible for the promotion and/or the regulation of the professions and training the practices Students Participation in the training International agencies Donors/ Technical partners Technical and financial support Facilitate adoption by providing technical or financial assistance to institutions and schools.
program Tomato Analyzer. The narrow-sense heritability for each trait was estimated using the midparent offspring regression method. Second, a separate set of 19 pecan genotypes were phenotyped for the same shape traits for two years and heritabilities were estimated by variance component analysis. Heritability estimates ranged from 0. 41 to 0. 83 for the mid-parent offspring regression method and from 0. 26 to 0. 78 for variance component method. Additionally, elliptical Fourier transform was performed to study the overall variation in nut shape. The first three principal components obtained from Elliptical Fourier transform explained 65. 9% of the total variation in shape attributed to the nut's roundness and angularity of proximal and distal ends. The use of the image-based high-throughput method of phenotyping and the heritability estimates obtained in this study directly benefit pecan breeding programs focusing on nut shape traits.
Image acquisition and processing Two-dimensional photographs of nuts were taken with a digital camera with the nut's suture side facing the camera. The camera was set up on the adjustable stand, and nuts samples were laid out horizontally on a light box. A U. S. quarter dollar coin (diameter = 2. 43 cm) was used as a scale for each photograph. The raw images were first adjusted for the brightness and contrast so that each nuts image would have black color in white background (BW image), using the batch function implemented in ImageJ 1. 52a  (Schneider et al. 2012). The BW images were then color inverted to make images readable to TA. The macros used to batch process all the images at once are available at (https:// github. com/ gitga urab/ pecan morph ometr ics). The processed images were then analyzed in Tomato Analyzer 4.
Materials and Methods As our research group recently published a protocol paper  [37]  describing the exact protocol of this acute, randomized, double-blinded, placebo-controlled cross-over study, only the main methodological parts are highlighted in this report. This trial was approved by the ethical committee of Ghent University Hospital (April 2018) and was registered at clinicaltrials. gov (ID: NCT03722199). Written consent was obtained from all subjects before enrollment. Recruitment was conducted by spreading out flyers in Ghent and announcements on (social) media. Volunteers came from different parts in Flanders, Belgium. Data collection was performed from 5 October 2018 until 20 March 2021. However, since the COVID-19 pandemic forced the cancellation of all measurements for approximately 6 months and substantially hindered participation afterwards, data collection was strongly impeded in the last 12 months.
After a 70-min resting period during which questionnaires (Autonomic Symptoms Profile Questionnaire, Epworth Sleepiness Scale) were filled out, a second BP measurement and FMD-test were executed. Subsequently, a dynamic handgrip exercise Participants were asked to follow specific guidelines concerning physical activity and CF-low food and drink consumption starting from 3 days prior to each test day, as described previously  [37]. Immediately following the second test day, participants wore an accelerometer (Actigraph wGT3X-BT, dominant side, hip  (39) ) and a Continuous Glucose Monitoring System (blind mode, IPro2, Medtronic, Northridge, CA, USA) for 7 consecutive days. Study data was pseudonymized, collected, and managed using REDCap. Blood samples were stored in the medical biobank of the Ghent University Hospital (Bioresource center Ghent, Gent, Belgium, ID: BE 71067049)  [39].
Acknowledgments: We thank  J. Heyman  ( CNRS, UMR6118, Rennes 1 university, France ) for providing access to his software of glycemic excursions and variability calculation,  P. Fança-Berthon  from  Naturex part of Givaudan  for providing the cocoa extract as well as theobromine and caffeine powders, and  A. Bertrand  ( Statistical Methodology and Computing Service, SMCS, UCLouvain, Belgium ) for her help with statistical analyses.
Study Design and Setting This was a retrospective longitudinal sub-study nested in the ANRS12174 randomized trial (NCT00640263) that followed 1273 HIV-1 exposed infants for up to 50 weeks in Zambia, Uganda, South Africa and Burkina Faso from November, 2009 to May, 2012. Trial protocol  (41)  and main findings (42) have been published. None of the participating mothers received any antiretroviral drugs (ARVs) during breastfeeding but their newborns received infant antiretroviral prophylaxis (AZT and 3TC before randomization on day 7 of life and either 3TC or lopinavir/ritonavir (Kaletra) after randomization) for PMTCT. The study population for this sub-study was the 563 motherinfant pairs enrolled in the Zambian trial site using a large biobank of breast milk samples collected during the trial.
Data Collection and Management Na + , K + and cytokine assay results were entered in an MS Excel worksheet and individually crosschecked for errors. The resulting clean worksheet was imported into SPSS version 25 (IBM Corporation, New York, USA) for analysis. Concurrent blood test results and sociodemographic data were extracted from the trial database and merged with the dataset of breast milk results using participants' individual identifiers and study visit as matching variables. The resulting database was checked for errors and prepared for analysis.
Based on the Na + /K + ratio, participating mothers were divided in three groups; namely: SCM negative at all visits (SCM negative); mild SCM at least at one visit but no severe SCM at any visit (mild SCM); and severe SCM at least at one visit (severe SCM). From each group we randomly selected participants to include in the retrospective cohort study. We then determined normal ranges for cytokine concentration in mothers whose samples tested negative for SCM throughout the follow up period as 2. 5 and 97. 5 percentiles after removal of outliers (mean ± 3SD). We determined the proportion of samples with high cytokines in the different groups of SCM. Generalized linear mixed models were used directly or after log 10 transformation to compare continuous variables between groups depending on whether the data was normally distributed or not.
Fixed degrees of freedom were used with robust covariance and mean estimate tests were adjusted using sequential Bonferroni procedure with a significance level of 0. 05. In a sensitivity analysis we compared the results within-breast and between breasts. Multinomial logistic regression model was used to determine the effect of recurrent severe SCM on HIV-1 shedding in breast milk. All variables associated with the dependent model (p < 0. 200) were included in the model (method: entre). CFV, CAV and SCM status were determined based on both breasts as previously described  (33). We used Statistical package for social science (SPSS) version 25 (IBM Corporation, Armonk, New Yolk to perform statistical analysis.
Bacterial growth inhibition The bacterial strains in suspension were exposed to serial dilutions of the pomegranate extract. At the highest concentration (0. 116% w/v), the growth of all strains (except N. shayeganii, for which the data were inconclusive) was partially or completely inhibited (Figure  -2 ). At the concentration found in Veggiedent FR3SH chews and Aquadent FR3SH solution (0. 035% w/v, vertical dotted line in Figure  -2 ), the growth of N. canis remained completely inhibited and the growth of the other strains was partially inhibited (Figure  -2 ). Based on the inhibition curves, the MIC of the pomegranate extract for N. canis was determined to be 0. 00048% (w/v). The non-inhibitory trations of pomegranate extract were 0. 00009% and 0. 0061% for N. canis and P. gulae, respectively.
Human milk sampling, pasteurization and in vitro dynamic digestion Milk samples were collected, pasteurized and digested, by means of DIDGI®, as previously described  (Nebbia et al. , 2020). Briefly, mature HM (1-3 months after delivery) from 5 healthy donors was collected and stored (frozen at -20 • C for < 4 months) by the HM bank at the Regina Margherita Children's Hospital in Turin (Italy). The HM was thawed, pooled, and either stored without further processing (RHM, at -20 • C) or pasteurized beforehand by means of HOP (62. 5 • C for 30 min) or HTST (72 • C for 15 s). HOP was performed using the donor HM bank equipment (Metalarredinox, Verdellino, BG, Italy). HTST was performed using a patented proprietary small-scale device  (Giribaldi et al. , 2016). Each milk donor involved in the research signed a written consent form, which stated that protection of the mothers' and infants' data was ensured.
, 2017)  against an HM protein database  (Molinari et al. , 2012) , to which the common Repository of Adventitious Protein (http://thegpm. org/crap) had been added. The possible posttranslational modifications were serine or threonine phosphorylation, methionine oxidation, lysine or arginine lactosylation, and cyclisation of glutamine or glutamic acid into pyroglutamic acid. Any potential protein modifications as a result of the use of AEBSF (Pefabloc) as a serine protease inhibitor were also considered: aminoethylbenzenesulfonylation of histidine, lysine, tyrosine and serine. Peptides identified with an e-value < 0. 01 were automatically validated, and this resulted in a false discovery rate of the peptides of<0. 4%.
Quantification of the peptides Each identified peptide was quantified, by means of label-free MS, using the MassChroQ software  (Valot et al. , 2011). A m/z width of ppm was used to obtain extracted ion chromatograms of the peptides in time-aligned chromatograms, and the area under the curve was then quantified. When a peptide was measured with several charge states, all the ion intensities were summed.
Biochemical characteristics of the peptides Several biochemical characteristics were determined for each peptide: the amino acids at the cleavage sites in positions P1 (C-term end) and P1 ′ (N-term end), the number of essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine and valine), the isoelectric point, the molecular weight and the Grand Average of Hydropathy Value (GRAVY), as defined by the ExPASy "ProtParam tool"  (Gasteiger et al. , 2005). A positive GRAVY corresponds to a hydrophobic peptide and a negative one to a hydrophilic peptide  (Kyte & Doolittle, 1982). Bioactive peptides were searched within the BIOPEP  (Minkiewicz et al. , 2008)  and MBPDB  (Nielsen et al. , 2017)  databases, both of which were accessed in June 2020. Only an exact matching between sequences was considered.
Statistical analyses Data analyses were performed using PAST software, version 3  (Hammer et al. , 2001). Before the analysis, the MS/MS abundances of each peptide were corrected for the digestion time-specific dilution factors, resulting from the dilution caused by the progressive gastric/ intestinal juice release and emptying within the DIDGI® system. The peptide abundances were then transformed by means of log-scaling [log10(abundance)], and any missing data were given an arbitrary value of 1 when necessary (multivariate and univariate statistics). A Venn diagram distribution of the peptides was built for each digestion time. Sporadic peptides, detected in only one sample replicate, were not considered. An overall multivariate analysis of variance (Principal Component Analysis -PCA) was conducted separately on the peptides from gastric digestion (n = 395), and on the intestinal peptides (n = 856), by considering each peptide [log 10 (abundance)] as a variable and the sampling time of each digestion replicate and each meal as individual.
For intestinal peptides, PCA, excluding the peptide abundances at 30 min of digestion from all samples, was also performed (n = 822). Sporadic peptides, present in only 1 sample replicate, were not included in the analysis. PCA was conducted by giving a different color code to each processing treatment, and by setting each digestion time point as a group. Data were automatically auto-scaled before analysis by means of PAST software. Two types of clustering analysis were conducted, taking into consideration only peptides that were detected in at least two gastric times and three intestinal digestion ones. The abundance of each peptide was expressed for each time point as the relative ratio with respect to the maximum value of the same peptide during digestion, and was averaged over the three digestion replicates at each time point for each processing treatment.
Relative abundance data were used for an ascending hierarchical clustering, on the basis of Ward's agglomeration of the minimum within-cluster variance in PAST. The number of clusters was determined from the bar heights at one of the most marked jumps. A kmeans non-hierarchical clustering was also performed by setting the number of target clusters according to Ward's clustering results. The two clustering methods were compared in order to obtain the best overlap between the two methods. The data included in the clustering procedure were visualized by means of PCA, whereby the peptides were set as samples, the cluster numbers as groups and the relative abundance as variables. The association of the qualitative and quantitative characteristics with each cluster was checked with Fisher's test and the Kruskal-Wallis test, respectively.
Univariate statistical analyses were conducted separately on the peptides present in the undigested milk, during gastric digestion, or during intestinal digestion. An analysis of variance (ANOVA) was conducted on the [log 10 (abundance)] of the peptides present in at least one replicate in all the treatments (n = 70 for undigested milk, n = 117 for gastric peptides, n = 194 for intestinal peptides), and in at least 2 gastric times or 3 intestinal digestion ones. One-way ANOVA (for undigested milk, with processing treatment as the factor) and two-way ANOVA (for gastric or intestinal peptides, with processing treatment, time and their interaction as factors) were fitted to the log10 abundances using PAST software. When statistical significance was reached for a treatment (pvalue < 0. 05), post-hoc tests were run (Tukey's or Dunn's test for oneway ANOVA, depending on the normality of the residuals; Tukey's test for two-way ANOVA).
Quartz crystal Microbalance with Dissipation (QCM-D) Preparation of gold sensor The adsorption process of different compounds was monitored using a commercial QCM-D device (Q-Sense AB, Sweden), which was equipped with Q-Sense software (Q-Soft, Q-Sense) and a temperaturecontrolled measuring chamber along with a peristaltic pump. Before measurement, the gold sensor was placed in a UV/ozone chamber for 10 min and immersed in a solution which consists of ammonia (NH 4 OH, 25 %), hydrogen peroxide (H 2 O 2 , 30 %) and ultrapure water mixed at a ratio of 1:1:5 at 75 ℃ for 10 min. Then, the dried gold sensors were placed in the UV/ozone chamber again for another 10 min.
QCM-D experiments SPI, 7S, 11S globulin, DG, MC, DG-MC (GM) complex was prepared as a series of 2 mg/mL solution, and all samples were ultrasonicated for 20 min to remove bubbles. The cleaned gold chip was installed in the QCM-D module and excited at a fundamental frequency. The experiments were started by pumping Milli-Q water into the QCM-D chamber until a stable baseline was obtained. Subsequently, the protein samples were pumped into the chamber until equilibrium was reached (△F3 < 1 Hz within 5 min). After the layer of protein was rinsed, DG, MC and GM complex were introduced. This was then followed by rinsing gold sensor with Milli-Q water to remove the loosely adsorbed molecules. The Voigt viscoelastic model in Q-Tools software was used to fit and calculate the changes in adsorption mass and thickness.
Molecular modeling The docking study was employed to investigate the molecular interaction of SPI, DG and MC. Previously, the crystal structures of soybean 7S protein (PDB:1UIK) and soybean 11S protein (PDB:1FXZ) were downloaded from Protein Data Bank (https://www. rcsb. org/). Then, the 3D structure of small molecule was downloaded from Pub-Chem database according to the CAS number. Pymol2. 3. 0 was used to remove the protein crystal water and the original ligand, and the optimized structures of protein, polysaccharide and polyphenol were imported into AutoDockTools-1. 5. 6 for hydrogenation, charge calculation, charge distribution, atomic type designation and storage in "PDBQT" format. AutoDockVina1. 1. 2 was used to perform the molecular docking simulations. The relevant parameters of 11S target were set as center_x = 7.
Until recently, the σ S response was believed to be predominantly transcriptional. However, using a MS-based proteomics approach to unravel the impact of σ S on global protein production, we identified a large number of genes down-regulated at the protein level, but not at the transcript level, suggesting that post-transcriptional regulation plays a larger role in σ S gene regulation than previously recognized  [7]. Small RNAs (sRNAs) might be mediators in some of these post-transcriptional effects. Indeed, we have identified several sRNAs genes under positive control by σ S in ATCC14028  [5]. σ S -dependent expression of most of these sRNAs has been subsequently observed by  Colgan et al. in another strain of S. Typhimurium  [8]. Analysis of gene expression in 18 mutants in regulatory genes, including rpoS, was reported in the Salmonella Gene Expression Compendium (SalCom) database  [8] , and we noticed that several σ S -activated sRNA genes were up-regulated in a Δfur mutant (S1 Fig)  deficient  for the ironbinding global ferric uptake regulator Fur.
To limit interferences, analysis was performed using a collision cell technology (CCT), which introduces a supplementary gas mixture of H 2 (7%) and He (93%) for the determination of V, Cr, Mn, Fe, As, Se, Ag, and Sb concentrations. A certified river water sample (SRM 1640a, NIST, Gaithersburg, USA) was repeatedly analyzed to check for data quality for all elements, except for Ti for which no certified values are provided. The SRM 1640a was 10-fold and 100-fold diluted to fit sample concentration range. The measured concentrations fall with 5% of the certified values for all elements, except Al (6. 5%), As (7%) and Sb (8%).
Effects of σ S on the Salmonella ionome in stationary phase The observed control by σ S of genes involved in iron and manganese metabolism prompted us to assess the impact of σ S on the Salmonella ionome. Inductively coupled plasma mass spectrometry (ICP-MS) was used to compare levels of cell-associated metals in the wild-type and ΔrpoS strains grown to stationary phase in LB (Fig 5A  and S1  Dataset experiment 1). The ΔrpoS mutation had no significant effect (p>0. 05) on the cellular concentration of Se, Ni, Cu, Sr, Ba and Ti and modest effects on the cellular concentration of Na, V, Zn, Fe, As, Mo, Cd and Tl (fold change < 2, p<0. 05, S1 Dataset experiment 1). In contrast, a marked effect of the ΔrpoS mutation (fold change > 2, p<0. 001) was observed on the cell-associated concentration To validate these effects of σ S on the ionome, a complementation experiment was conducted (Fig 5B and S1 Dataset experiment 2).
The cloned rpoS gene on pSTK4 was able to restore wild-type levels of Mn, Mg and Co in the ΔrpoS strain, thus confirming the role of σ S in the control of the cell-associated amount of these cations. The potassium contents of the wildtype strain and ΔrpoS mutant harboring pSTK4 were similar and significantly higher (fold change >2, p<0. 001) than that of wild-type and ΔrpoS strains harboring the empty vector (Fig 5B and S1 Dataset experiment 2). These data were consistent with a positive effect of σ S on the cell-associated concentration of potassium. However, the potassium amount was not significantly different (fold change 0. 96, p> 0. 1) between the ΔrpoS mutant and the wild-type strain harboring pACYC184, and was even lower than that in the absence of plasmid (Fig 5A  and 5B ). The potassium concentration was similar in the batches of LB used in experiments 1 and 2 (S1 Dataset).