dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, the relationship between child linear (length/height) and ponderal (weight) growth (in both directions) will be specifically examined throughout the follow-up period and compared across the two groups. Frequency tables and descriptive statistics will be used to describe WASH conditions, the prevalence of food and water contamination, child immune function, and the prevalence and diversity of childhood enteric infections (i. e. , the prevalence of individual enteric pathogens, total number of pathogens detected, proportion children testing positive for at least one pathogen) among children discharged from CMAM treatment. Analytical statistics will be used to compare prevalence data between country locations (when applicable) and assess the association between household WASH conditions, food and water contamination, immune function, enteric infections, and the outcome of interest: the risk of relapse following discharge.
The data collected in the study will be publicly available, with personal identifiable data redacted. This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The contents are the responsibility of Action Against Hunger and do not necessarily reflect the views of USAID or the United States Government. USAID played no role in neither the design of the study, the collection, analysis and interpretation of data, nor the writing of this manuscript.
Electrical Cell-Substrate Impedance Sensing (ECIS) Experiment The 96 w 20 idf ECIS plates were seeded with 20,000 cells per well in 250 µL of media, and the barrier formation was monitored by an ECIS ® Zθ (Z Theta) device (Applied Biophysics, Troy, NY, USA). Three different layouts were performed during this experiment, (A) with the seeding time point, (B) during the log phase approximately 2 h after seeding, and (C) after the barrier formation. The different lipid emulsions concentrations were added by removing 125 µL media and adding 125 µL of 2 × emulsion with the desired final concentration. Multifrequency mode was used for measuring the resistance, and data were collected for at least 72 h using ECIS software (Applied Biophysics, Troy, NY, USA).
Multiplex Cytokines Analysis Cytokines concentration in cell culture supernatants was measured using a Bio-Plex Pro Human Cytokine 17-plex assay (#M5000031YV, Bio-Rad Laboratory, Hercules, CA, USA) according to the pre-optimized protocol based on the methodology provided by the manu-facturer. Assay #M500031YV was used, which detects: G-CSF, IL-4, IL-10, MIP-1beta, GM-CSF, IL-5, IL-12 (p70), TNF-alpha, IFN-gamma, IL-6, IL-13, IL-1beta, IL-7, IL-17A, IL-8, IL-2, MCP-1. Data were collected and analyzed using a Bio-Rad BioPlex 200 instrument equipped with Bio-Plex Manager software version 6. 0 (Bio-Rad Laboratory, Hercules, CA, USA).
Statistical Analysis All data are presented as means ± standard deviation (S. D. ). Statistical analysis was conducted using GraphPad Prism 8 software (GraphPad, San Diego, CA, USA). Statistical comparisons between groups were performed using one-way ANOVA followed by Tukey post-hoc. p values below 0. 05 were considered significant. Two-way ANOVA was used, where each row represents a different time point, and matched values are stacked into a subcolumn with multiple comparisons. Within each row, columns are compared (simple effects within rows). Each cell mean is compared with the control cell mean on that row (control = unstimulated). p values below 0. 05 were considered significant.
Study design and data collection This was a descriptive, longitudinal cohort study of children admitted to the CRENI from October 2016 to November 2017. Study inclusion criteria were: Caregivers of eligible children were invited to participate in the study and provided informed consent at time of admission. At admission, data on demographics, anthropometry, clinical symptoms, and diagnoses were collected. During follow-up, routine clinical data were collected on standardized forms (in conjunction with routine clinical monitoring visits) and included information on clinical status, medical procedures, pharmaceutical prescriptions, and therapeutic feeding. Evaluation of clinical status included use of a pediatric early warning score (PEWS), a system used to monitor pediatric inpatients through close surveillance of specific clinical signs, with a total score indicating severity.
C-Quality and availability of household data in Liberia i It should be noted that the quality and availability of statistics required for evidence-based policymaking and poverty monitoring in Liberia are very limited due to weak institutional capacity and the absence of historical data, especially in the aftermath of the war. In response, the government re-established the Liberia Institute of Statistics and Geo-information Services (LISGIS) as an autonomous agency responsible for producing statistics and spatial data for Liberia. Since 2008 LISGIS has achieved many objectives set out by the National Strategy for Development of Statistics (NSDS), including the completion of major data collection activities. Among these are the Core Welfare Questionnaire Indicator (CWIQ). The most recent and publicly available household surveys in Liberia are CWIQ 2007 and CWIQ 2010.
Dynamic Light Scattering (DLS) The average size, and size distribution and polydispersity index (PDI) of the NQs and NBs were measured by dynamic light scattering (Zetasizer DTS 1060, Malvern instruments, Malvern, UK) and using the Zetasizer software (Malvern instruments, Malvern, UK). For the reconstruction of the samples of loaded and unloaded nanoparticles, 4 mg of NQs or NBs was diluted in 10 mL of 30% ethanol solution, and then the samples were sonicated for 10 min before reading at a refractive index of 1. 450 and with dispersion optics at 173 °C (λ = 633 nm), in triplicate. The chemical stability of loaded (NQs) and unloaded (NBs) nanoparticles was also measured in the solution. The study was performed in triplicate.
Zeta Potential The surfaces of NB and NQ samples were detected using the Zetasizer DTS 1060 (Zetasizer DTS 1060, Malvern instruments, Malvern, UK) and the Zetasizer software (Zetasizer DTS 1060, Malvern instruments, Malvern, UK). For the reconstruction of the sample for loaded (NQs) and unloaded (NBs) nanoparticles, 4 mg quantities of NBs and NQs were diluted in 10 mL of 30% ethanol, then the samples were sonicated for 10 min  The average size, and size distribution and polydispersity index (PDI) of the NQs and NBs were measured by dynamic light scattering (Zetasizer DTS 1060, Malvern instruments, Malvern, UK) and using the Zetasizer software (Malvern instruments, Malvern, UK). For the reconstruction of the samples of loaded and unloaded nanoparticles, 4 mg of NQs or NBs was diluted in 10 mL of 30% ethanol solution, and then the samples were sonicated for 10 min before reading at a refractive index of 1.
Zeta Potential The surfaces of NB and NQ samples were detected using the Zetasizer DTS 1060 (Zetasizer DTS 1060, Malvern instruments, Malvern, UK) and the Zetasizer software (Zetasizer DTS 1060, Malvern instruments, Malvern, UK). For the reconstruction of the sample for loaded (NQs) and unloaded (NBs) nanoparticles, 4 mg quantities of NBs and NQs were diluted in 10 mL of 30% ethanol, then the samples were sonicated for 10 min before reading at 37 • C in triplicate. For chemical determination, the zeta potential was also determined in samples solubilized in an aqueous solution.
Thermogravimetric Analysis (TGA) The data obtained in the TGA determined the purity and composition of the materials, the drying and ignition temperatures of the materials and the stability temperatures of the samples. In Figure  8 , it can be observed that a high temperature (212-216 • C) was needed to decrease the mass of the NQ sample, demonstrating the corrected dryness of the sample. The NQs enhanced the thermal stability at around 1 to 5 • C when 5% of the mass was lost. Taking into account the controls, high temperatures were still needed for quercetin and inulin to observe any decrement in the mass (186-191 • C and 215-218 • C, respectively) (Table  4 ).
Thermogravimetric Analysis (TGA) The data obtained in the TGA determined the purity and composition of the materials, the drying and ignition temperatures of the materials and the stability temperatures of the samples. In Figure  8 , it can be observed that a high temperature (212-216 °C) was needed to decrease the mass of the NQ sample, demonstrating the corrected dryness of the sample. The NQs enhanced the thermal stability at around 1 to 5 °C when 5% of the mass was lost. Taking into account the controls, high temperatures were still needed for.
4, at 25 • C, R 2 = 0. 9911; pH 7. 4, at 37 • C, R 2 = 0. 993). All experiments were performed in triplicate. Pharmaceutics 2022, 14, x FOR PEER REVIEW 6 of 23 spectrophotometer (λ = 320 nm) [30]. The concentrations of the drug released in the solution were determined at different time intervals, for which aliquots of the volume were extracted as follows: 0, 5, 10, 15 and 30 min. Subsequently, the concentrations were measured every 30 min up to 3 h, and to finish, they were read at 24 and 48 h. A quantity of 1 mL of the solution was extracted for analysis and reintroduced into the PBS buffer medium. Data were plotted and analyzed using Origin (OriginLab Corporation, Northampton, MA, USA) and Excel software (Microsoft 365, Microsoft S. de R. L de C. V, Ciudad de Mexico, Mexico) , and the study was performed in triplicate.
Weight and height The height and weight of the participants were reported by caregivers during the first survey interview. To limit bias in the assessment of the weight status based on reported values, data below the 1st or above the 99th percentiles of the WHO child growth standards  (14)  were excluded. Age-specific and sex-specific BMI Z-scores were then calculated, based on the WHO child growth standards for children aged 0 to 2 years  (15)  , using the WHO R package  (14).
Dietary data Quantitative dietary data were collected during face-to-face interviews carried out during four home visits. The data were recorded in a 4-d food diary, covering 3 consecutive weekdays and 1 d during the weekend. During the interviews, parents or caregivers were asked, with the help of a paper food diary, to recall all food and drink given to their child in the previous 24 h before the visit. Portion sizes were estimated using the photographic atlas of food portions prepared for the Emirate of Abu Dhabi nutritional survey  (16)  and using household measures (glasses, cups, etc. ) or were provided directly as a weight, volume or standard unit (such as a commercial food portion). For breastfed children, WHO estimates of the mean quantity of breastmilk produced per d for developing countries  (17, 18)  were attributed to each child according to their age.
USDA values  (19)  were used for breastmilk composition. Information on whether the participants had received micronutrient supplements was also collected. However, as no information regarding the characteristics or quantities of the supplements was recorded, these data were not included in the analysis. Assessments of dietary and nutrient intakes and nutrient adequacy Dietary intake (in g/d or ml/d) was assessed from the data recorded in the 4-d food diary after the classification of the consumed foods into 28 groups. Milk formulas included infant formula (IF) adapted for children from birth to 6 months, FOF from 6 to 12 months and YCF from 1 to 3 years. Macronutrient and micronutrient intakes were then evaluated for children above 6 months of age using food composition databases  (20, 21)  , Danone data for infant milk formulas and baby cereals, and a nutritional survey tool  (22)  for traditional recipes.
Study population Data from 446 participants were included in the analysis: 100 aged 0 to 6 months, 94 aged 6 to 12 months and 252 aged 12 to 24 months. The characteristics of the analysed sample population are provided in Table  1. The mean BMI Z-score for infants aged from 0 to 12 months was in the recommended range (-2, 1)  (14). However, children aged over 12 months had a mean Z-score of 1•0, indicating that they were at risk of being overweight. Closer analysis of the data on case-by-case basis revealed that among the participants for whom anthropometric data were reported (n 225), 54 % had a normal weight status, 17 % were at risk of being overweight, 14 % were overweight, 5 % were obese, 7 % were stunted and 3 % were severely stunted. The most highly represented SEC among caregivers was the lowest class (C2), and the most highly represented sample region was Algiers, followed by Oran.
The dedicated training on dietary data collection provided to all interviewers as well as the age of the target population are likely to have limited this bias in our study. However, although children with implausible intakes were excluded from our analyses, the difficulty of quantifying breastmilk consumption and composition may have led to the underor overestimation of adequacy prevalence values. In addition, the nutrient composition of the milk formulas consumed by the participants in our study was estimated from Danone data rather than from mean compositions of milk formulas from other manufacturers to avoid missing values; however, as the nutrient content of milk formulas is tightly regulated, it is unlikely that any small differences in composition between formula milks made by other manufacturers would have had an impact on the results obtained.
Metabolic Activity Short-chain fatty acid (SCFA) concentrations were analyzed in different colon compartments during AX microcapsule fermentation, and means were calculated for the last 4 days of the treatment period and compared with the control period (Figure  5 ). Supplementation of AX microcapsules gave an overall increase of around 12% in SCFA production (p < 0. 05) (Figure  5a ). This increase of SCFA level upon cross-linked AX treatment is consistent with findings previously reported  [10, 24]. The total SCFA concentration (206. 4 mM, data not shown) measured in the AX microcapsules treatment was divided into 59% acetate, 30% propionate, and 11% butyrate. The percentages of propionate increase significantly in the transverse and descending colon, while the production of butyrate in descending colon was the highest (Figure  5b ).
A higher propionate production by the breakdown of AX in the more distal colon has previously been described  [25]. The decrease of butyrate in the ascending and transverse colon was in good agreement with the reduction in Bacteroides/Prevotella genera abundance (1-2 log CFU, Table  1 ) in this region, which are typical butyrate producers  [26]. The production of SCFA is considered beneficial for the host because, among other benefits, it stimulates colonic blood flow, protects the host against pathogens, improves insulin sensitivity, and reduces cholesterol synthesis  [27, 28]. consistent with findings previously reported  [10, 24]. The total SCFA concentration (206. 4 mM, data not shown) measured in the AX microcapsules treatment was divided into 59% acetate, 30% propionate, and 11% butyrate.
From the plasma glucose curve, the hypoglycemic effect was determined by calculating the area above the curve (AAC 0-48 h ) using the linear trapezoidal method. Pharmacological availability (PA) was estimated as the percentage of AAC of oral formulation compared to subcutaneous injection. The main pharmacokinetic parameters (C max , T max , and area under the curve (AUC 0-48 h ) were calculated from the representation of the serum insulin levels vs. time, using the PKsolver software, pKSolver®, a solver add-in for Excel®(Microsoft, Redmond, WA, USA)  [45]. The relative oral bioavailability (Fr, expressed in percentage) of the oral insulin-loaded AX microcapsules against the subcutaneous administration of insulin was estimated as the ratio between the areas under the curve for the oral (AUC oral ) and subcutaneous (AUC sc ) administration.
Sampling and data collection The baseline sample collection took place in August 2020, nine months after the last mass drug administration by the NSCP. Class registers were used to randomly select the study participants, taking into account the sex ratio in each school, and an identi er number was randomly allocated to each child throughout the follow-up period. Urine samples were collected in sterile 150 ml containers between 10 am and 2 pm. A urine container with the corresponding identi er was given to each child, and they were asked to go individually to the latrine and ll the container with their urine. The containers were checked to ensure that at least ten millilitres could be obtained for the quanti cation of eggs. If the volume collected was too low, the child was asked to drink water and wait until urinating again.
Data analysis The prevalence of S. haematobium was calculated as the number of children who were positive over the total number screened. Intensity of infection was classi ed as "light" if the number of eggs/10 ml was between 1-49 eggs and "heavy" if it was more than 50 eggs  [45]. At the baseline (S0), we aimed to test the effect of the type of water access, namely, irrigation canal (Guia and Khodit), lake (Mbane) or river (Dioundou and Ndiawara), and also the age and the sex of the child with regards to parasite prevalence and parasite egg load. Since our sampling design displayed an underlying spatial structure in villages, we used generalised linear mixed models (glmm) with villages set as random effect. The rst model aimed at explaining the parasite prevalence (measured at the individual level, i.
e. 0 or 1) and, hence, a binomial error term distribution was used. The second model aimed at explaining the parasitic load (measured at the individual level and only for children who had been parasitised), and a negative binomial error term distribution was used, since over-dispersion was detected. The two glmm were adjusted using the R package glmmADMB  [46]  and the signi cance of the results was examined using a type II Anova. The level of re-infection between water accesses types were compared using the Chi-squared test. In the case of a p-value < 0. 05, the difference was considered as statistically signi cant.
The balloon was filled to obtain a LV end-diastolic pressure (LVEDP) of 5-8 mmHg during basal conditions. A second pressure transducer, located above the aorta, was employed for recording the coronary pressure (CP). Hemodynamic parameters were monitored and analyzed using a PowerLab data acquisition system (AD Instruments, Sydney, New South Wales, Australia) and the performance variables were measured every 10 min.
The exploration started with the analysis of the three carrier-proteins of thyroid hormones, namely thyro-binding globulin (TBG)  [9] , Alb, and TTR. The analysis of TTR was performed using a radial immunodiffusion procedure with partigen immunoplates (Behringwerke, A. G. , Germany) containing anti-TTR antibodies. The study revealed the following conclusions: (1) Both TTR and Alb indices changed in the same direction during the active stage of PCM as well as during the recovery period but with steeper slopes for TTR, indicating its higher level of sensitivity to protein depletion and repletion. (2) For unknown reasons at that time, TTR plasma concentrations were found to be systemically more depressed in children with KW than in those with MR. The main data from this first study were published in 1972  [10] , stressing the particular importance of TTR for the early detection of marginal PCM states and for the follow-up and outcome of any severe nutritional disorder.
Data Analysis For protein quantification, data were analyzed using Progenesis LC-MS software (Nonlinear Dynamics, v4. 0. 4441. 29989, Newcastle, UK) as previously described by  Obry et al. , 2014 [48]. Peptide identification was performed using Mascot (Matrix Science, version 2. 2. 04) against the database restricted to Glycine max from Uniprot. Mascot search results were imported into Progenesis. For each condition, the total cumulative abundance of the protein was calculated by summing the abundances of peptides. Proteins identified with fewer than 2 peptides were discarded. Only the proteins presenting a p-value ≤ 0. 05 and varying by 1. 5-fold in their average normalized abundances between different conditions were retained.
Data Analysis After acquisition, metabolomic data were processed using Progenesis QI software (v3. 0, Waters). Metabolite abundance was calculated using peak area and was normalized to all compound ions. Identification was carried out using the PlantCyc database (v15. 0. 1)  [49]  with a mass tolerance of 10 ppm. For identified metabolites, experimental MS2 spectra were compared to the MS-DIAL reference MS/MS database (v14) [50] when possible, otherwise they were compared to the theoretical fragmentation spectrum. Multivariate analysis was performed on identified metabolites using EZInfo software (v3. 0. 3. 0, Umetrics, Umeå, Sweden). Raw data were mean-centered and Pareto scaled  [51, 52]. Partial least squares discriminant analyses (OPLS-DA)  [51, 53]  were carried out to classify samples with and without PEP-13 elicitation.
The influence of each metabolite on the classification was calculated by the variable influence on projection (VIP)  [51, 53]. Metabolites with VIP > 1 have an above-average influence. In this study, only metabolites with VIP > 2 were kept for further analysis. For metabolites detected both in positive and negative modes, the one with the higher mean abundance was kept. Heatmaps were generated using Morpheus (https://software. broadinstitute. org/morpheus (accessed on 1 October 2020)). For a better understanding of metabolic regulation induced by PEP-13 elicitation, pathway analysis was performed using the open-source software MetaboAnalyst 5. 0  [54]. For each pathway, the p-values of metabolite set enrichment analysis and pathway impact of topology analysis were calculated using KEGG database  [55].
radiata) and a PR-5 protein (Uniprot blast 90% identity: B6ZHC0 locus GLYMA_01G217700, Glycine max). Furthermore, genes and proteins over-expressed upon PEP-13 elicitation were involved in defense response, including PTI activation, cell wall and mucilage reinforcement and DAMP production were observed. However, the number of differentially expressed proteins was not high between elicited and constitutive conditions. In order to explain this observation, a time-related explanation was hypothesized. Indeed, our samples were collected with the same protocol for each experiment (transcriptomic, proteomic and metabolomic), so the exposure time to the PEP-13 elicitor was the same for gene, protein and metabolite experiments (5 days). This timing allowed us to microscopically observe a response of the RET-an accumulation of root AC-DC (data not shown)-as shown previously in the literature upon elicitation and infection of plants  [37, 39].
Nevertheless, it may be possible that a first response to PEP-13 has already occurred, and most RNA transcripts and proteins might already have been degraded. Another hypothesis concerning the low number of differentially expressed proteins might be the use of post-translational modifications (PTM) upon defense response  [98] , which could not be detected with our proteomic method. Indeed, although the Mascot database is able to recognize PTM as peptides with cysteine carbamidomethyl and methionine oxidation, our analysis was not designed to detect these modifications. Thus, undetected PTM in our data might be involved in PEP-13 elicitation response. In the literature, high dephosphorylation of some extracellular proteins was shown on elicited maize cell cultures  [99] , indicating that phosphorylation and dephosphorylation might be involved in the defense response.