dataesr/scientific-paragraphs-categorization huggingface dataset:
An initial top-down coding framework was applied based on the determinants of the BCD checklist. A second phase of coding was then conducted based on emergent themes. Coding was conducted by the first author and then theme summaries were validated by ACM, MMB, FKM and TH. Ethics and consent. Informed written consent was obtained from each participant. The research was approved by the ethics committees at the London School of Hygiene and Tropical Medicine (Submission ID: 13545) and the School of Public Health at the University of Kinshasa (Submission ID: ESP/CE/038/2017). Permission to undertake this work was given by the Departments of Health in North and South Kivu. Organisations working in the area were informed of our work and preliminary findings were shared immediately after data collection to enable utilisation within programmatic work.
Acknowledgments We would like to thank the following people for helping to facilitate the research and contributing to ongoing reflections about emergent insights:  Isiaka Hemedi ,  Justine Badhera Habamungu ,  Leon Ngwasi ,  Batian Arthur Benao ,  Marie-Paul Chirimwami ,  Karine Le Roch ,  Jean Lapegue  and representatives of the Goma WASH Cluster. We would particularly like to thank the individuals who gave of their time to participate in this research and who welcomed us into their homes and shared their personal experiences so openly. This research was undertaken as part of the Wash'Em Project which aims to improve handwashing promotion in humanitarian crises. The contents are the responsibility of the authors of the paper and do not necessarily reflect the views of our donors, USAID or the  United States Government.
Experimental design for screening starch degradability Three replicates each of the 224 test starch samples were distributed across 15 96-well plates following a randomized procedure with defined constraints. Test sample replicates were randomized to plates in a nearbalanced incomplete block design such that (i) no test sample occurred more than once on a plate and (ii) all pairs of different test samples occurred together on the same plate approximately the same number of times. The optimization of the design was performed using the 'od' package  (Butler, 2019)  running in R v3. 6. 3 (R Core Team, 2020). An example plate is shown in Fig. 1 , for each well pair, the left-hand well contained the starch sample, the right-hand well contained the corresponding blank. On a plate, each sample well was paired with a corresponding 'blank' well, for a total of 48 starch samples processed per plate, while the last plate was only partially filled with the final starch samples.
Multivariate analysis All analyses were carried out in triplicate and the results are reported as mean values ± standard deviation. Pearson correlation statistics were performed using R v3. 6. 3. Principal component analysis (PCA) was performed on the granule size distribution, amylopectin chain length distribution, amylose content and α-amylase activity to generate independent variables. The kinetic parameters and pasting properties were added as supplementary factors. A hierarchical clustering on principal components (HCPC) of the PCA was performed using the HCPC function of FactoMineR R package  (Lê et al. , 2008). Hierarchical clustering belongs to the family of unsupervised learning methods that do not "learn" from existing outputs contrary to regression modeling for instance. Starting from a configuration with one individual per cluster, the hierarchical agglomerative clustering (HAC) algorithm proceeds by merging incrementally two similar clusters together at each step until one cluster is finally formed.
Structural characterization The structural measurements for 224 samples of MAGIC starch were represented by Table  1. The entire datasets are provided in Supplementary data. Granule size distribution of MAGIC wheat starch were assessed by two methods, STD (10 μm threshold for quantifying the A-(>10 μm) and B-type (<10 μm) granules) and GMD (gaussian mixture distribution). A bimodal with STD and trimodal with GMD size distribution have been found (Fig. 2 ). Using method STD, the A-type granules represent from 13. 0% to 92. 9% of the starch volume while the B-type granules represent from 7. 1% to 87. 0% (Table  1 ), indicating a wide range of granule size distribution. Quantifying the distribution with method GMD yields lower minimum values for both A and B granules due in part to the identification of a third class, very small C-type granules, which could count up to 7.
Factors influencing native starch degradation In the present study, no correlations were found between starch structure and degradation using pairwise Pearson correlation tests (Fig. S1 ). With over 200 wheat starches being screened, it was assumed that a multivariate analysis would be able to include the interconnection among the different factors and revealed significant correlations. As a matter of fact, the HCPC enabled us to distinguish different subdivisions with defined structural properties and hydrolysis kinetics, and to establish co-occurrences of factors with "extreme" values i. e. different from the overall mean. The HCPC was applied on two datasets, where two concurrent methods (STD and GMD) were used for the quantification of the A-and B-type granules. They both revealed similar multifactorial effects among granule size distribution, amylopectin chain length distribution, amylose content and α-amylase activity on degradation kinetics of wheat starch.
Tanaka et al. developedan R package and web-based application granular with easy application. Dataset was uploaded to the application for defining peaks interactively and calculating the proportion of each granular fraction. 2. 2. 3. Amylopectin chain length distribution Sample preparation was adapted from O'Shea et al. (1998). Starch (10 mg) was dissolved in 400 μL of 0. 125 M NaOH and boiled for 10 min, neutralized with 16 μL glacial acetic acid, 50 μL1 M sodium acetate and.
Study Sample The 3C study is a population-based, prospective study on the relationship between vascular factors and dementia. The study started in 1999-2000 and included participants, aged 65 years or older, from three French cities: Bordeaux, Dijon, and Montpellier  [13]. The Consultative Committee for the Protection of Persons Participating in Biomedical Research at Kremlin-Bicêtre University Hospital (Paris, France) approved the 3C Study protocol, and all participants gave their written informed consent. Baseline data collection included sociodemographic, clinical, and lifestyle characteristics, medication use, neuropsychological testing, and clinical examination with blood sampling. At baseline, 1254 participants were randomly selected from the three centers, stratified by center, sex, and age, for plasma Aβ concentration measurement  [9].
Funding: The 3C Study is carried out under a partnership agreement between Inserm, the Victor Segalen-Bordeaux II University, and Sanofi-Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and first phase of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, the Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé; the Regional Governments of Aquitaine, Bourgogne and Languedoc-Roussillon, Fondation de France; and the Ministry of Research-Inserm Programme 'Cohorts and collection of biological material'. This research was funded by Union France Alzheimer, grant number RAK17019FFA.
METHODS The French NutriNet-Santé cohort was launched in 2009, aiming to investigate the links between diet and health. It is conducted according to the Declaration of Helsinki guidelines and was approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm n°0000388FWA00005831) and the "Commission Nationale de l'Informatique et des Libertés" (CNIL n°908 450/n°909 216). The study is registered at clini caltr ials. gov as NCT03335644. Electronic informed consent is obtained from each participant. Data described in the article, code book, and analytic code will be made available upon request pending application and approval. Researchers from public institutions can submit a collaboration request including information on the institution and a brief description of the project to collaboration@etude-nutrinet-sante.
Dietary Data Collection Online questionnaires (physical activity, socioeconomic status, anthropometry, and health) were regularly administered to participants, including a biannual series of 3 nonconsecutive validated web-based 24-hour dietary records (≥2 mandatory), with an estimation of portion sizes, merged with a detailed food composition database covering >3500 generic food/beverage items, with additional information on consumed commercial brands. These 24-hour records were validated against biomarkers and an interview with a trained dietitian. Dietary underreporting was identified on the basis of the method proposed by Black  12  , using the basal metabolic rate and Goldberg cut-off, and underenergy reporters were excluded.
The French official regional sanitary control of tap water (the governmental regional database SISE-Eaux) was used to estimate exposure via water consumption, by region of residence, and year of 24-hour dietary record. As regards all food additives in the NutriNet-Santé cohort, we used a double qualitative/quantitative approach: the presence of food additives was determined using 3 databases: (1) OQALI, a national database hosted by the French food safety authority (Agence Nationale de Sécurité Sanitaire et Alimentaire Nationale) and National Research Institute for Agriculture, Food and the Environment to characterize the quality of the food supply; (2) Open Food Facts, an open collaborative database of food products marketed worldwide; and (3) Mintel Global New Products Database, an online database of innovative food products in the world.
In addition to this process, which was the primary source of case identification, participants' cohort data were linked, subject to their consent, to the National Health Insurance Medical Administration Database (SNIIRAM, State Council Decree No. 2013-175). Finally, we used data from the French National Cause-Specific Death Register (CépiDC) to identify possible missing cardiovascular events in deceased participants. The International Classification of Diseases, Clinical Modification (ICD-CM, Tenth Revision) code was used to classify cardiovascular disease cases. In this study, all patients diagnosed between the date of enrollment and October 5, 2021 had coronary heart disease such as myocardial infarction (code I21), acute coronary syndrome (I21. 4), angioplasty (Z95. 8), and angina pectoris (I20.
Acknowledgments We thank  Nathalie Druesne-Pecollo, PhD  (operational coordinator);  Younes Esseddik ,  Thi Hong Van Duong ,  Régis Gatibelza ,  Jagatjit Mohinder ,  Rizvane Mougamadou , and  Aladi Timera  (computer scientists);  Fabien Szabo de Edelenyi, PhD ,  Julien Allegre ,  Nathalie Arnault ,  Laurent Bourhis ,  Nicolas Dechamp  (data-manager/statisticians);  Cédric Agaësse ,  Alexandre De Sa ,  Rebecca Lutchia  (dietitians);  Paola Yvroud, MD  (health event validator), and  Maria Gomes  ( Nutrinaute support ) for their technical contribution to the  NutriNet-Santé study. We thank all the volunteers of the NutriNet-Santé cohort.
Quantitative real-time PCR To extract total RNA, the RNeasy Plus Minikit was employed (Qiagen, Valencia, CA, USA). The cDNA was produced using Power SYBR ® Green Master Mix (Life Technologies, CA, USA) and detected with the Applied Biosystems 7500 equipment. The inducible NO synthase (iNOS) gene expression levels were adjusted to GAPDH. Table  1  lists the primer sequences and gene accession numbers.
SARS-CoV-2-Human Protein-Protein Interactions (PPIs) The experimentally determined protein-protein interactions (PPIs) between SARS-CoV-2 and human host proteins were retrieved from the publication of the respective high-throughput yeast-2-hybrid experiments in which all pairwise combinations of 28 SARS-CoV-2 proteins against ~16,000 human proteins were systematically tested  [65]  and the available literature  [41] [42] [43] 45, 66]. The experientially derived interconnectivity of human host factors was mined from the PICKLE (Protein InteraCtion KnowLedgebasE) meta-database  [67].
Because our data (Section 3. 1) suggested DDC as a potential disease severity marker in the nasopharyngeal tissue, we aimed to investigate whether the observed alterations of its expression in the blood are SARS-CoV-2 infection-specific or are due to ICU admission. For this purpose, we determined the mRNA levels of DDC in the ICU/non-CoV-2 patients, as a negative control, and compared them to the ICU/CoV-2 and Healthy groups. The mRNA levels of DDC in the ICU/non-CoV-2 cohort were significantly higher than the ICU/CoV-2 group and did not differ from the healthy subjects. This indicates that the reduction in DDC expression is specific to SARS-CoV-2 infection and is not associated to ICU admission. Taking into account the correlation of DDC and dACE2 expression that we observed in the nasopharyngeal tissues of both mild  [19]  and severe COVID-19 (present study) patients, we also evaluated dACE2 mRNA levels, in the same whole blood samples that were used for DDC expression analysis.
Correlation between the Expression of DDC with dACE2 and MAOs in Whole Blood Samples of Hospitalized COVID-19 Patients The two hospitalized SARS-CoV-2 patient cohorts (Ward/CoV-2 and ICU/CoV-2) were merged, and co-expression correlations for all studied genes were examined in their whole blood samples. A strong positive correlation between the expression of DDC and dACE2 was detected in the SARS-CoV-2 infected patients and the healthy subjects (r = 0. 804, p < 0. 0001 and r = 0. 689, p < 0. 0001, respectively) (Figures  8A  and S11A ), as determined by the Spearman's correlation coefficient. This is in agreement with our previous data from the nasopharyngeal swab samples of COVID-19 patients with mild/no  [19]  or severe symptoms (Section 3. 2). Moreover, a moderate positive correlation between the expression of DDC and MAOA (r = 0.
The effect of SARS-CoV-2 Infection on Catecholamine Pathway-Related Gene Expression in Cultured Epithelial Cells We have previously shown that SARS-CoV-2 infection significantly reduces DDC expression in Vero E6 infected cells at 24 and 48 h post-infection (h. p. i. )  [19]. This was also observed in A549 cells, as indicated by data derived from the RNA-sequencing Skyline database (http://rstats. immgen. org/Skyline_COVID-19/skyline. html (accessed on 28 August 2022))  [73]. To support our aforementioned in vivo results on the effect of SARS-CoV-2 infection on the expression of catecholamine-related genes with cell culture data, the expression of MAOA, MAOB, DBH, and VMAT2 in Vero E6 cells infected for 24 and 48 h was studied. As shown in Figure  9 , SARS-CoV-2 infection significantly increased the expression of MAOA (up to 2-fold) at both time points.
A moderate increase in MAOB expression at 24 h post-infection, followed by a substantial increase (up to 2. 5-fold) at 48 h post-infection was detected. Slightly enhanced mRNA levels of DBH were detected at both time points compared to the mock-infected control (Figure  9 ). Moreover, the expression of VMAT2 was significantly enhanced especially at 24 h post-infection. To complement these results with data from human epithelial cells, we studied the impact of SARS-CoV-2 on the expression of catecholamine-related genes in A549 cells, by processing data from the Skyline database. Higher mRNA levels of MAOA, MAOB and VMAT2 were observed upon infection in these cells also, while DBH and COMT expression was not significantly altered (Figure  S13 ). To conclude, SARS-CoV-2 negatively altered the mRNA levels of DDC, whereas MAOA and MAOB positively, possibly to reduce the intracellular dopamine levels.
p. i. )  [19]. This was also observed in A549 cells, as indicated by data derived from the RNA-sequencing Skyline database (http://rstats. immgen. org/Skyline_COVID-19/skyline. html)  [73]. To support our aforementioned in vivo results on the effect of SARS-CoV-2 infection on the expression of catecholamine-related genes with cell culture data, the expression of MAOA, MAOB, DBH, and VMAT2 in Vero E6 cells infected for 24 and 48 h was studied. As shown in Figure  9 , SARS-CoV-2 infection significantly increased the expression of MAOA (up to 2fold) at both time points. A moderate increase in MAOB expression at 24 h post-infection, followed by a substantial increase (up to 2. 5-fold) at 48 h post-infection was detected. Slightly enhanced mRNA levels of DBH were detected at both time points compared to the mock-infected control (Figure  9 ).
Moreover, the expression of VMAT2 was significantly enhanced especially at 24 h post-infection. To complement these results with data from human epithelial cells, we studied the impact of SARS-CoV-2 on the expression of catecholamine-related genes in A549 cells, by processing data from the Skyline database. Higher mRNA levels of MAOA, MAOB and VMAT2 were observed upon infection in these cells also, while DBH and COMT expression was not significantly altered (Figure  S13 ). To conclude, SARS-CoV-2 negatively altered the mRNA levels of DDC, whereas MAOA and MAOB positively, possibly to reduce the intracellular dopamine levels. In turn, non-cytotoxic concentrations of dopamine or L-Dopa (Figure  S14 ) in SARS-CoV-2 infected Vero E6 cells decreased the virus-related cytopathic effect, by 55% and 59%, respectively (Figure  10 ).
The detection of the cytokine release was performed using biotinylated IFNγ-, IL-4-, or IL-17A-specific antibodies in the presence of HRP conjugated to avidin. Soon after, the colorigenic substrate, 3-amino-9-ethylcarbazole (AEC Substrate Set, BD Biosciences) was added. The color development was stopped by tap water, and air-dried plates were analyzed with a computer-assisted ELISPOT image analyzer (Series 1 ImmunoSpot Analyzer, ImmunoSpot Version 4. 0 Software Academic, Cellular Technology Limited, Shaker Heights, OH, USA).
Phagocytosis Assay by Flow Cytometry The moDCs and mCherry-expressing bacteria were coincubated at a ratio of 1:4 for 3 or 24 h at 37 • C in a 5% CO 2 atmosphere or on ice as a control. After the incubation period, moDCs were stained with allophycocyanin (APC)-labeled anti-CD1a mAb (BioLegend, San Diego, CA, USA). Cell analysis was performed with an ACEA NovoCyte 3000 RYB flow cytometer (Agilent). Data analyses were performed with the FlowJo vX. 0. 7 software (Tree Star Inc. , Ashland, OR, USA). The moDC population was gated according to the forward/side scatter parameters by excluding the noninternalized, free bacteria. The phagocytosis ratio was determined as a percentage of mCherry-positive cells or according to the median values of mCherry fluorescence intensity (MFI). Similarly, the frequency of phagocytic cells was determined in the CD1a-positive and the CD1a-negative moDC subpopulations as a percentage of mCherry-positive cells.
Statistical Analysis Analyses were performed using Microsoft Excel and GraphPad Prism v8. 0 (GraphPad Software Inc. , San Diego, CA, USA). Comparison between two groups was performed using a paired, two-tailed Student's t-test. One-way ANOVA followed by Tukey's post hoc test was used for the comparisons of more than 2 groups. Two-way ANOVA followed by Tukey's post hoc test was applied for the comparisons of two independent variables. The results are shown as mean ± standard deviation (SD). Differences were considered to be statistically significant at p < 0. 05. Significance was indicated as * p < 0. 05, ** p < 0. 01, *** p < 0. 001, and **** p < 0. 0001; or as # p < 0. 05, ## p < 0. 01, ### p < 0. 001, or #### p < 0. 0001.
8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1,000 m/z. Several controls were analyzed along with each experimental sample. Samples were spiked with a cocktail of known chemical standards before UPLC-MS/MS. A pooled matrix of samples containing an equal volume of each experimental samples were analyzed to control for sample drift across successive runs, and extracted water samples were used as process blanks. Compounds were identified based on an internal Metabolon library containing over 3,300 commercially available chemical standards. Metabolite annotations were based on matches with the retention time/index, where a positive match was defined as having an m/z within 10 ppm to a database standard, and by matching the overall mass spectral profile with database standards using both forward and reverse spectral scores.
Statistical Analysis Metabolite raw abundances were normalized by dividing the median raw abundance of that metabolite across the entire dataset for each matrix, resulting in median scaled abundances. For samples lacking a metabolite, the minimum median scaled abundance of that metabolite across the dataset was imputed as a minimum value before downstream statistical analysis. Metabolite fold differences were calculated for each metabolite by dividing the average median scaled abundance of the metabolite in one treatment group by that of a second treatment group. All samples were run in technical triplicates to approximate a Gaussian distribution. Prior to principal components analysis (PCA), all metabolites from this experiment were median-scaled for the metabolite value across the dataset for normalization.
This method of normalization allows all metabolites in the dataset to be compared as equivalent terms to each other. Analysis by two-way ANOVA identified biochemicals exhibiting significant differences for experimental parameters. Interactions and main effects for experimental parameters of probiotic bacteria and probiotic yeast, where significance was defined as P < 0. 05. To account for false discovery rate errors, a q-value was calculated for each metabolite and metabolites with a value of q < 0. 1 were excluded from downstream analysis.
DISCUSSION The fermentation of rice bran with the different probiotic microorganisms resulted in a dramatic modification of the rice bran metabolome in one-step as well as in two-step fermentations. Some metabolites of importance to nutrition (e. g. , vitamins and essential amino acids) and gut health (e. g. , prebiotics) were produced, and some anti-nutritional factors (e. g. , phytates) were reduced. Rice bran is rich in different vitamins including thiamin, niacin, and pyridoxine and we estimated to which extent it can be used to fight micronutrient deficiency. Based on a daily consumption of 5 g per day (nutrient composition as provided by USDA national nutrient database) and recommended nutrient intakes (RNI) for infants (6-12 months), we calculated that even if a contribution is possible, none of the vitamin identified by metabolomics could completely cover the needs  (19).
28 mL/min flow. Optimized settings were as follows: source temperature 150 • C, desolvation temperature 350 • C, desolvation gas flow rate 600 L/h, capillary potential 3. 00 kV, collision gas flow 0. 1 mL/ min. Cone potential and collision energy were individually optimized for each transition using the solution of the investigated compounds (Table  A1 ). For ITC the solutions after derivatization to thiourea derivatives were used. Nitrogen was used for both -nebulizing and drying gas. Argon was used as collision gas. Traces of analyzed compounds were analyzed using MRM (Multiple Reaction Monitoring) method. All analytical data were processed using MassLynx V4. 1 software (Waters Corporation, Milford, MA, USA). All analyses were performed in triplicate and the mean value was expressed in mg/100 g dry weight (dw).