dataesr/scientific-paragraphs-categorization huggingface dataset:
Design, sample size and sampling This study, cross sectional and descriptive, involved elderly (age ! 60 years) living in community, resident for more than 6 months in the locality, not hospitalized and without hearing impairment/disability. Due to the lack of data in Cameroon, the prevalence of undernutrition and obesity was considered unknown for the calculation of the sample size. The estimated prevalence of altered nutritional status was 50%, and the margin of error was chosen at 4%. Using Schwartz's formula  [26] , the minimum sample size was 597. A twostage sampling procedure (i. e. , stratified for areas and simple random for neighborhoods and villages) was used. Urban and rural areas were defined according to the country's 2010 general population and housing census  [4]. The ten regional capitals were considered urban, and all other localities were considered rural.
Data collection An electronic questionnaire was used by trained staff through a suit and free open-source software (KoBoToolbox, Cambridge, USA). The socio-demographic data were age, gender, living area, marital status and regime, religious belief, level of education, household size, position in the household, lifestyle, housing, activity, and income, chronic diseases were cancer, diabetes, hyperlipidemia, hypertension, tuberculosis, viral infections, chronic pain, incontinence, stroke, arthritis or osteoarthritis, eye disorders and malaria) and blood pressure. The "lifestyle" referred to whether the elderly lived alone or with one or more relatives, "housing" designated whether the person was renting or not and "Living place" was defined as whether the person lived in a personal or third party's home.
Statistical analyses Data analysis was carried out using SPSS. 26 (SPSS, Chicago, USA). Quantitative data were expressed as mean ± standard deviation (SD) and qualitative in number and percentage. The dependent variable was reduced to three modalities (undernutrition, normal/overweight, obesity) because of the relevance in clinical practice of undernutrition and overweight as the two majors at risk situations among the elderly. The t-tests, ManneWhitney, ANOVA, KruskaleWallis tests were used to compare quantitative variables. The Chi-2 test was used for qualitative comparisons and the Fisher exact test when the theoretical frequencies were <5. Cohen's kappa coefficient of agreement (very strong>0. 8) was calculated between the two diagnostic methods for undernutrition  [31]. Univariate and multinomial logistic regression were carried out to identify associations between nutritional status by BMI and associated factors.
Acknowledgements We would like to thank the  ALAIR-Limousin Association  for its financial support and the following members of the  Groupe de Recherche sur le Vieillissement au Cameroon (GRVC)  for facilitating and/or contributing to the data collection: Dr (Mrs) V eronique  Priscille Nguiamba ,  Mrs Aur  elie  Michelle Mimba Mengue, Nad ege Mayan Matouki, Edwige Laure Nzi e, C ecile Nad ege Kingu e Ekam e ,  Didja Wassah ,  Annie Ga€ elle Dekembaye ,  Brigitte Floriane Ombous ,  Anne Marie Ngo Manyim Ma Mayada , and  Mr François Etotogo ,  Thierry Roland Njille Ehawa ,  Samaki Charles Yaya ,  Maurice Menzo ,  Lionel Wilfried Mbono Mbono ;  Dr Julien Magne  for statistic guidance, as well as the elderly and their legal representatives.
Study setting The study, named WADANUT, was conducted in five pediatric HIV clinics in three West African countries (National University Hospital Center [CNHU] Cotonou, Benin; CHU Tokoin, Lomé, Togo; CHU Cocody, Centre de Prise en charge et de Formation  [CePreF]  and CHU Yopougon, Abidjan, Côte d'Ivoire). These clinics are part of the IeDEA pWADA (pediatric West African Database on AIDS) collaboration, which is an international research consortium established in 2006 to collect data on HIV and AIDS  [21].
Data collection Weight and height were collected at each visit by trained health care professionals, following standard procedures  [22] , using a SECA mechanical scale, a recumbent length scale for children under two years of age and a vertical height scale for children over two years of age. Anthropometric indicators expressed in Z-scores were calculated according to WHO growth standards  [23, 24] , using reference tables. There were recalculated during the analysis with the WHO Anthro and WHO Anthroplus softwares. In addition to wasting and underweight, stunting, defined as Height-for-Age Z-score (HAZ) was also documented. At inclusion, data on socio-demographic characteristics (age, sex, primary caregiver, orphanhood) were collected. Other data were collected at inclusion and during follow-up, such as clinical and immunological status (CD4 count, viral load and WHO stage at last measurement, opportunistic infections in the last six months/ since last visit), and treatment (ART regimen and cotrimoxazole prophylaxis).
Dietary diversity and nutritional habits Among children under two years of age at inclusion, 24/27 had dietary diversity data at the first visit, and 14 at the last visit. Of the seven food groups asked for infants, the cereals group was cited by all, and the dairy products, flesh foods and other fruits and vegetables groups by more than half. The mean scores at inclusion and at the last visit were respectively 3. 7 (SD 1. 3) and 4. 1 (SD 0. 9) (p-value signed rank=0. 019). Overall, 11/24 (46%) reached the MDDS on the first visit, and 10/14 (71%) on the last visit. The score at the first visit was lower in Benin (mean 3 [SD 0. 6], 18% having a MDDS) than in Côte d'Ivoire and Togo (overall: mean 4. 3 [sd 1. 4], 69% having a MDDS, p-value analysis of variance 0. 034). Among children over 2 years of age at inclusion, 256 completed the dietary diversity questionnaire at the first visit, and 166 of them also at the last visit.
At inclusion, 100% of children in Côte d'Ivoire and Togo reported having breakfast in the morning and 83% in Benin. Overall, in Benin, Côte d'Ivoire and Togo respectively, 79%, 96% and 79% of children took three meals, and 78%, 79% and 81% took at least two snacks per day. Results were similar at the end of the study (data not shown). Frequencies of consumption at the first visit, for all sixteen groups, are displayed in Fig. 2. Trends were similar on the last visit. All children reported cereals, and more than half reported oils and fats (92%), spices, condiments and beverages (91%), fish and seafood (75%) and other vegetables (84%) categories. Sweets were also reported by more than 60% of the population. The least reported food groups were organ meat (5%), vitamin A rich fruits (11%), vitamin A rich vegetables and tubers (14%) and others fruits (14%).
The distribution of the products and the nutritional assessment in the clinics may not have been entirely appropriate. Also, the nutritional products chosen for this protocol may have been inadequate for this study population. Although RUTF have been shown to be effective for malnourished children and adults  [36, 37] , few studies have used these products in children living with HIV older than five years of age  [20]. Information about adherence to the nutritional products were not collected systematically during the intervention and relied on selfreporting from the caregivers, which may not have been the most appropriate way to measure adherence, with a risk of desirability bias. Furthermore, information on food insecurity and biological data could have been useful for further analysis, but were not recorded due to time and funding restraints.
Funding Research reported in this publication was supported by the US National Institutes of Health (NIAID, NICHD, NCI and NIMH) under Award Number U01AI069919 (PI: Dabis). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. JJ was funded by Sidaction. Funders had no role in the design of the study and collection, analysis, interpretation of data and in writing the manuscript.
Acknowledgements The authors are grateful to  Marc Lahaye  and  Kevin Vidot  for fruitful discussions. The authors acknowledge the financial support from SOLEIL for VV, CD and FC regarding Proposal 20171187. The authors also thank the  French national research Network GDR 3710 INRAE/CNRS SYMBIOSE -Synthons et matériaux biosourcés  for its support and the  ANS μReshape ' funding support received from the  TRANSFORM department of INRAE. This research was also funded by the  INTERREG VA FCE Program ,  FLOWER  project, Grant Number  23.
Ratio of gases was determined through an indirect open circuit calorimeter  [33] , which monitors O 2 and CO 2 concentration at the inlet ports of a tide cage through which a ow of air (0. 4 L/min) is ventilated and compared regularly to a reference empty cage. Whole EE was calculated according to the Weir equation  [34]. The ow was calibrated with a O 2 and CO 2 mixture of known concentration (Air Liquide, S. A. France). Mice were individually housed in metabolic cages 2 days before experimental measurements, at 22 ± 1°C room temperature with food and water available ad libitum. Mice were daily IP injected with saline to minimize handling stress. On the experimental day, mice were IP injected with saline or ghrelin and monitored for 4 h. Data collection was recorded every 15 min during the whole experiments and data extracted was raw value of VO 2 consumed, VCO 2 produced (mL/h) and energy expended (kcal/h).
Hypothalamic sulci were used as lateral limits and a 1. 5 mm-thick slice was taken parallel to the base of the hypothalamus. Hypothalamus were collected in TRIzol Reagent (Invitrogen Inc), and total RNA was isolated according to the manufacturer's protocol. To remove residual DNA contamination, the RNA samples were treated with RNAse-free DNAse (Qiagen). 2. 8 Statistical analyses. All statistical analyses were performed using GraphPad Prism 6. 0 and differences were considered signi cant when P < 0. 05. The rapid and delayed time periods in each experiment were determined as the timepoints in which a signi cant difference was obtained from control groups plus the timepoints immediately before and after. No signi cant differences (P > 0. 09) were observed between the number of c-Fos + cells in the ARH of vehicle-treated mice that were perfused 1 (n = 2), 2 (n = 7), 3 (n = 1), 4 (n = 2) or 5 (n = 1) h after treatment and thus their data were pooled and termed vehicle-treated group.
Ghrelin treatment induced slights increments of locomotor activity at 0. 5 and 2. 5 h post-treatment. The capability to detect signi cant effects of ghrelin on locomotor activity depended on the way data was processed (compare 2c vs 2d) suggesting that this effect of ghrelin is less robust than ghrelin-induced food intake. Such mild effect of ghrelin on locomotor activity detected at speci c time points posttreatment together to the fact that locomotor activity can be assessed using different experimental strategies (e. g. beam breaks, video tracking, etc. ) may explain previous inconsistencies in terms of the reported ghrelin's effects on locomotor activity  [17] [18] [19] [20] [21]. Due to the sampling rate of food intake and locomotor activity, we could not determine if locomotor activity occurred in anticipation to food intake.
Acknowledgments: We would like to thank to  Dr. Marcelo Vatta  from the  University of Buenos Aires  for providing CCK-8S. Authors' information:  María Paula Cornejo ,  Guadalupe Romero ,  Gimena Fernández ,  Mirta Reynaldo  and  Mario Perello  work at  Laboratory of Neurophysiology of the Multidisciplinary Institute of Cell Biology [IMBICE, Argentine Research Council (CONICET) and Scienti c Research Commission, Province of Buenos Aires (CIC-PBA) ,  National University of La Plata ], 1900  La Plata ,  Buenos Aires ,  Argentina. Raphaël G. P. Denis  and  Serge Luquet  work at  Université de Paris ,  Unité de Biologie Fonctionnelle et Adaptative (BFA) ,  UMR 8251 , CNRS, F-75013 Paris, France.
Primers sequences for NPY were sense: 5′- GCCAGATACTACTCCGCTCTG-3′, antisense: 5′-GATCTCTTGCCATATCTCTGTCTG-3′ [GenBank Accession No. NM_023456. 3], product size 68 bp. Primers sequences for AgRP were sense: 5′- TTGGCAGAGGTGCTAGATCCA-3′, antisense: 5′-AGGACTCGTGCAGCCTTACAC-3′ [GenBank Accession No. NM_033650. 1], product size 108 bp. Primers sequences for L19 were sense: 5′- AGCCTGTGACTGTCCATTCC-3′, antisense: 5′-TGGCAGTACCCTTCCTCTTC-3′ [GenBank Accession No. NM_009078. 2], product size 99 bp. The concentration of RNA samples was ascertained by measuring optical density at 260 nm. The RNA integrity was con rmed by.
After incubation in blocking buffer (10 mM TriseHCl pH 8. 0, 150 mM NaCl, 0. 05% Tween 20, 5% nonfat skimmed milk powder), the membranes were incubated overnight at 4 C with the phosphorylated form of 4E-BP1 on serine 65 (Cell Signaling Technology, Ozyme, France). After washing, the primary antibody was removed and a 1 hr-incubation was done with horseradish peroxidaseconjugated secondary antibodies (1:10 000 dilution; Jackson ImmunoResearch Laboratories, Baltimore, Maryland, United States). Proteins were then visualized using enhanced chemiluminescence (ECL Select™ Western Blotting Detection Reagents; GE Healthcare) on ImageQuant Las 4000 system (GE-Healthcare) using a CCD camera. Band density was quantified using ImageJ software. For normalization, blots were stripped using antibody stripping buffer (Gene Bio-Application, Paris, France) and then reprobed for total 4E-BP1 proteins (Cell Signaling Technology) to verify the relative amount analyzed.
Statistics Data are expressed as means ± SEM. The effect of citrulline on body weight, food intake and tumor growth following chemotherapy was tested using 2-way repeated-measures analysis of variance (ANOVA) (dietary treatment x time) followed by Holm Sidak posthoc tests. Treatment differences on tumor, oxidative stress and anthropometric data were analysed using the t test for independent samples, or ManneWhitney rank sum test when necessary. Plasma parameters were analyzed using KruskaleWallis one way ANOVA on ranks (SigmaPlot). p < 0. 05 was considered significant. To establish the effects of plasma citrulline concentration, food intake, plasma haptoglobin concentration, relative tumor volume and relative protein content of both jejunum and colon on relative body weight, multiple linear regression analysis was carried out.
rs691536 was genotyped for all individuals using Illumina HumanOmni2. 5-8 (Omni2. 5) BeadChip and analyses were performed using PLINK 1. 9 and R. KING software used to check family relationship by estimating kinship coefficients (https://people. virginia. edu/~wc9c/KING/ manual. html, accessed on 1 July 2021).
GWAS Analysis (Genome-Wide Association Study) We perform a GWAS analysis for ßI perception in the Malagasy cohort following the recommendation presented in Marees, et al. [30]. Based on a dataset file comprising 2314914 SNPs for 206 individuals, we performed a quality-control procedure, removing sites with missing proportions >0. 2 and a Hardy-Weinberg equilibrium filter at 1 × 10 -6. We continued through the following steps using 2,262,562 sites. Using Plink 1. 07  [31] , we performed association analyses and visualization was done using R (package "qqman"). From the HORDE database (https://genome. weizmann. ac. il/horde/, accessed on 1 July 2021  [32] ), we listed all SNPs deletions and insertions altering the amino-acid sequence of olfactory receptors known to be activated by the beta-ionone molecule in in vitro studies (i.
Estimating Local Ancestry: Inference of Ancestry for Each Allele Local ancestry assignment across the genome was performed based on HumanOmni2. 5-8 data using the ELAI algorithm  [33]  and unphased data from the 1000 Genomes Project  [27] , with a pool of all African individuals as the  [26]  source and a pool of all Asian individuals as the Asian source. Succinctly, ELAI uses a two-layer hidden Markov model to detect the structure of haplotypes allowing to model two scales of linkage disequilibrium (one within a group of haplotypes and one between groups), and thereby infer local ancestry of admixed individuals. Based on the genotyping dataset of the Illumina HumanOmni2. 5-8 covering the whole chromosome 11, this allows identifying the ancestral origin of a distinct chromosomal segment within each individual genome.
The global distribution of the rs6591536 polymorphism can therefore be considered a good proxy for predicting the diversity of ßI perception of current world populations; based on the available genomic dataset, a large variation in the proportion of ßI-insensitive individuals worldwide can be expected  [19]. βI is present in many plants, fruits, and by-products (tomatoes, white-fleshed nectarines, violet flowers, grapes, and therefore wine), and is also widely used as a flavor enhancer in the food industry  [40] [41] [42] [43]. Therefore, genetic predisposition to βI anosmia/hyposmia could substantially influence the diversity of appraisals and product choice  [18, 34]  and food culture across the world. Future cross-population studies will provide a better understanding of the influence of genetic polymorphisms on dietary practices and culture  [22].
Statistical Analysis All analyses were carried out in triplicate and obtained results were expressed as mean values ± standard deviation. The difference among the mean values was statistically evaluated by using one-way analysis of variance (ANOVA) through LSD test (p ≤ 0. 05) using XLSTAT Statistical software version 2007 (Addinsoft, Paris, France). Principal Component Analysis (PCA) was performed on physicochemical and functional data by MATLAB R2012b software (The MathWork, Natick, MA, USA) coupled with the PLS Toolbox v. 7. 5 (Eigenvector Research, Manson, WA, USA).
Principal Components Analysis Before performing PCA, the datasets obtained from physicochemical, functional and sensory analysis were concatenated in one matrix. This joint analysis was performed in order to go further in the interpretation of the relationship between the measured cheese parameters and to obtain a global overview of the effects of the incorporation of walnut in cheese formulation. Figure  2A ,B presented the PCA plots; Figure  2A  presented the similarities between cheese samples; Figure  1B  exhibited the loading map. The first two principal components accounted for 87. 36% of the total variance with a predominance of the principal component 1 (PC 1 ) (79. 05% of the variance). PC 1 separated the control cheeses from cheeses containing added walnuts. The control cheeses presented negative scores in the PC 1 while the other cheese samples presented positive scores.
, 2014)  and taxonomy assigned according to Silva v128. Archaeal sequences were analysed following standard QIIME pipeline  (Caporaso et al. , 2010)  and taxonomy assigned with RIM DB  (Seedorf et al. , 2014). OTU tables were analysed in R using the package "vegan"  (Oksanen et al. , 2016). Diversity indices (Shannon, Simpson, Richness and Evenness) were computed using implemented functions and statistical differences were tested using the non-parametric Kruskal-Wallis test to evaluate the effect of plant at each incubation time. For β-diversity analysis, OTU tables were rarefied to an even depth: 3460 reads for bacteria, 4103 for Archaea and 1000 for protozoa. Bray-Curtis method was used for computing dissimilarity indices with the vegdist function. Principal component analysis (PCoA) was performed on dissimilarity matrices with prcomp function.
Permutational multivariate analysis of variance was performed using Adonis function. Correlations were computed using vegan's function "corr" and "Hmisc" and "corrplot" packages were used for plotting correlation matrices. Differential abundance analysis to evaluate the effect of plant at each incubation time were done with MicrobiomeAnalyst  (Chong et al. , 2020)  using default data filtering (4 minimum count, 20% prevalence, low variance filter: 10% inter-quantile range), relative log expression (RLE) for normalisation and the metagenomeSeq package  (Paulson et al. , 2013).
Degradation and fermentation data: calculations and statistical analyses Rumen degradability (D) of DM and N were calculated using an exponential model with lag time  (Denham et al. , 1989) : D (t) = a + b (1 -e-c(t-L)) where D is the degradation after t hours of rumen incubation; t = hours of rumen incubation (0, 3, 6, 12, 24, 48 and 96 h); a = rapidly degradable fraction (%); b = slowly degradable fraction (%); c = degradation rate constant of the b fraction (h -1 ); L= lag time before the beginning of degradation of the b fraction (h). The nonlinear procedure (PROC NLIN) of SAS v9. 4 (SAS Inst. Inc. , Cary, NC, USA) was used to fit degradation data to the model. This model was chosen after a preliminary comparison of models with or without lag time, because lag time was higher than 3 h for 4 of the 6 feeds, and because the model with lag time globally led to lower sum of squares than the model without lag time.
5 acetate + 0. 5 propionate + butyrate + valerate) where FOM is expressed in mg and VFA in mmol. Data of both experiments were submitted to the same mixed model using the MIXED procedure of SAS including feed (n = 6 for in situ experiment and n = 7 for in vitro experiment) as fixed effect and animal (n = 3 for in situ experiment and n = 4 for in vitro experiment) as random effect. Differences between feeds were analysed using the Tukey t-test. Effects were declared significant when P < 0. 05. Principal component analyses were performed using Minitab® version 17 software (Minitab Inc. , State College, PA). Average values for each of the 6 tannin-rich plants were included for 19 variables: 7 from chemical composition, 5 from experiment 1 and 7 from experiment 2.
Data, script and code availability Sequencing data are available in the Sequence Read Archive (SRA) under accession ID PRJNA554299. Scripts and other data can be found at https://doi. org/10. 15454/8J2O5C Supplementary Table  2. Proportional abundance of bacterial families colonising tropical tannin-rich plants incubated in the rumen of cows (n= 3) for 3 h   (Paulson et al. , 2013)  Supplementary Table  8. Proportional abundance of protozoa associated to tropical tannin-rich plants incubated in the rumen of cows (n= 3) for 3 h.
Acknowledgements The authors thank the staff of  UE 1414 Herbipôle  for their technical collaboration, animal care and management,  A. Torrent  and  L. Genestoux  for their contribution in the experimental setup and sample analysis, and  A. Roche  (intern student at  INRAE ) for her contribution in the analysis of condensed tannins. We are grateful to the  INRAE MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources and  A. Bernard  for initial bioinformatic analysis. Version 3 of this preprint has been peer-reviewed and recommended by  Peer Community In Animal Science  (https://doi. org/10. 24072/pci. animsci. 100009).
Data collection Caregivers of children hospitalized for complicated SAM who provided written consent to participate in the study were purposively selected. An interview guide was developed by study staff consisting of open-ended and probing questions to explore caregiver knowledge, perceptions and practices related to hospital hygiene. The interview guide was piloted prior to data collection for cultural relevance and clarity. Individual interviews were conducted from October to November 2017 with 28 caregivers in the local language. Interviews were conducted in a quiet space of the health facility by trained study staff and lasted no more than one hour each. Audio recordings were transcribed verbatim, and then translated into French using standard word processing software. Each transcript was assessed for data quality before analysis.