dataesr/scientific-paragraphs-categorization huggingface dataset:
Participant recruitment Consent will be obtained in two stages. 1) Written collective consent for screening for schistosome worm burden will be provided by school directors or health authorities. 2) Written informed consent for children who meet inclusion criteria to participate in the study will be requested from parents or guardians in the home after children have been screened for intensity of infection at school. The informed consent forms and case report forms are provided as part of extended data in Harvard Dataverse  (Noyes, 2021a). Community entry procedures differ in each country as mandated by the respective ethical review committees. In DRC administrative authorities, school inspectors, school directors and teachers were involved, whereas in Ivory Coast administrative authorities, Regional Health Director, Departmental Health Director school inspectors, school directors and teachers were involved.
Meta-analysis Initially data sets from each country with at least 100 families will be analysed independently to identify loci that are associated with populations in each country. Subsequently the data from all four participating countries will be combined in order to undertake a meta-analysis and identify loci that are of smaller effect than are detectable in local studies and that are also consistently associated with disease in multiple African countries. We will use R version 4 for plotting graphics and supplementary data, FBAT V1. 4 will be used for fbat analysis and Plink V1. 90 for transmission disequilibrium tests (TDT).
Prior to running the association tests, we will use the genotype data to optimise the sets of haplotype blocks within each gene to maximise theoretical power to detect associations. Consent to publish. All authors will approve the final manuscript before publication. No institutional consent is required for publication. Data management. Study documents will be kept securely in the institution which will conduct the research, i. e. University of Dschang (Cameroon samples), INRB (DRC), Makerere University(Uganda samples) and University Jean Lorougnon Guédé University (Ivory Coast). These will be in physical lockable cabinets for forms and password protected databases with only study staff and investigators having access. All data will be anonymized before publication in appropriate scientific journals.
Dissemination of study outcomes. No actionable information is expected from the association analysis and individual results will not be returned to participants. General feedback will be shared with study participants and community leaders at public meetings and results shared with policy stakeholders including officials of the National Schistosomiasis Control Programs and Ministries of Health. Sponsors of the research will receive periodic reports on study progress. Analyses will be performed separately for each study population and published independently so that national authorities can have a clear understanding of the genetic risk factors for schistosomiasis in their countries. A meta-analysis of the combined data will also be undertaken and published separately. Study 2: Case-control association study Study site: Lake Albert region, Uganda.
The major difference between Study 1 and Study 2 is the study design. They therefore share the schistosomiasis specific methods described above for study 1; the CCA test, Kato Katz, DNA extraction and genotyping and the panel of genes to be tested. Study 2 will differ from study 1 in recruitment procedures including inclusion criteria, definition of cases and controls and data analysis methods. Below we describe what will be the different in study 2. Recruitment and case definition. In the study site, the population will be screened using the CCA rapid test with results scored visually as trace, +1, +2, +3 (see section on CCA above)  (Casacuberta-Partal et al. , 2019). All individuals will be invited to give a blood sample for DNA extraction and plasma for subsequent worm burden quantification by CAA.
Extreme phenotype sampling will be done, where only the genotypes of the top 25% and bottom 25% of worm burden will be analyzed as described by Li et al. (Li et al. , 2019). Genotyping only the participants with extreme phenotypes reduces the genotyping cost by 50% but has very little effect on power  (Li et al. , 2019). To allow determination of these extreme phenotypes, worm burden will be determined on plasma using the CAA test in the laboratory at UVRI. The CAA test has been described above. Since the case-control design requires controls to be unrelated to the cases, the 25% of participants in the top and bottom tails of the CAA distributions will be included for genotyping. The samples will be genotyped using the H3A chip defined above. The genotype data will be used to filter out individuals who are first cousins or closer and share a phenotype to minimize bias.
After filtering, the target proportions of the total sample for cases and controls is expected to be at least 20% of the original population sampled. In a multiplicative model; a 20% fraction from each extreme phenotype would give 80% power to detect 35% increase in risk; for MAF 0. 3 with sample size 300 (60 cases and 60 controls genotyped)  (Li et al. , 2019). Most effects in the classical SM1 locus QTL have been shown to be co-dominant  (Abel et al. , 1991; Marquet et al. , 1996)  and therefore a multiplicative model is appropriate. We will therefore aim to recruit at least 500 CAA positive children in Uganda. The case-control recruitment profile is shown in Figure  2  Additional data recorded per participant. The following features will be recorded at the time of sample collection as potential covariates during analysis: age, sex, fever, diarrhea, infections with other parasitic worms will be detected by microscopy as part of the KK procedure, clinical observation for distended abdomen, Mid Upper Arm Circumference (MUAC) and body mass index,BMI (weight divided by the square of height in metres).
Data analysis. A candidate gene case control linear regression analysis will be undertaken in Plink v1. 9 and R 4. 0, with haplotypes as the independent variable and CAA as the dependent variable. Results will be controlled for population structure using principal components also calculated in Plink v1. 9 as covariates. In this sampling design, it is critical to control for population structure  (Panarella & Burkett, 2019) , and genotyping on the whole genome H3A chip will provide sufficient data for a calculation of principal components for this purpose. Age, sex, MUAC and BMI will also be used as additional covariates. Procedures for the KK test will be the same as those of study 1. Dissemination of study outcomes. Study participants will immediately receive their results for testing of schistosomiasis and will be advised on control measures by the health team.
, 2021a)  and observed that Th17 pathway genes were found within the majority of these regions. We will genotype the samples on the H3Africa Whole Genome SNP chip making it possible to scan for associations with any gene in the genome in a Genome Wide Association Study (GWAS). However large numbers of samples are required for a GWAS to compensate for the large number of tests being conducted and, since we only expect to collect 100 families per population for family-based analyses and 100 cases and 100 controls for the case control study, we would only have the power to detect exceptionally large relative risks in a GWAS. By declaring the list of candidate genes in advance of analyzing the data we can establish a much more limited set of hypotheses which we will have the power to test with the number of samples available.
Extended data Harvard Dataverse: Consent forms and CRFs for family based or case control schisto studies. https://doi. org/10. 7910/DVN/ 6OLLGW  (Noyes, 2021a). This project contains the following extended data: - haplotypes in study participants with high worm burdens. This manuscript falls within the scope of this journal, but there should be some adjustments to make it suitable for indexing.
This is an important consideration. We note other helminth infections when screening samples by the KK test but these have been rare so far (<5%). We are aware of evidence that schistosome infections can modify malaria infections and drug response but there seems to be less work on the effect of malaria in schistosomiasis. We will screen for malaria infections if possible and include this as a covariate in the analysis. In the case control study, we actually screened for fever, diarrhea and infections with other parasitic worms as described in section "Additional data recorded per participant". Risk factors of susceptibility/Resistance to Schistosomiasis infection will be valuable information to have. We have a questionnaire that seeks to capture environmental and demographic risk factors for schistosomiasis.
In section "Data management" we stated that "Data will be collected in standardised forms as shown in supplementary data. "An example is the Uganda case control study case report form. Among these are included important variables like age, gender and sanitation facilities. It is not mentioned if the participants will be treated with Praziquantel at the end of the study. We confirm that participants will be treated with Praziquantel. These studies are nested in the National Control Programme, where we sample immediately before treatment with Praziquantel. We stated in the section "Screening procedures" that "All children will be treated with Praziquantel by the national Ministry of Health Mass Drug Administration teams according to the recommendation of the World Health Organization  (World Health Organisation, 2006).
HapStats. 1kg. h3a. ESN. txt (Summary CAM_autorisation recherche schisto. pdf (Cameroon health ministry authorisation for research) CAM_Clairence éthique schistosomiase. pdf (Cameroon CAM_Consent form_Anglais. pdf (Cameroon consent 1,000 genomes dataset and the Illumina H3Africa form in English) with genotypes of SNP that are present in both the ○ SchistoCandGenes. 1kg. h3a. bed (Plink bed file ethics review clearance) ○ Summary. HapStats. 1kg. h3a. YRI. txt (Summary sta-○ getExons. sh (shell script to extract exons for just genes of interest from a larger mart_export. txt (not provided) file which should be downloaded from tistics on each haplotype block in each gene for YRI population) Biomart. Note the script makes assumptions about Data are available under the terms of the Creative Commons --the columns in which data will be found and these Zero "No rights reserved" data waiver (CC0 1.
0 Public domain should be adjusted according to the mart_export. txt file used) dedication). Thousand genomes African samples, together statistics on each haplotype block in each gene for 80% power to detect Haplotype relative Risk of with the number of samples required to have GWD population) - genotyping chip and that are in the candidate genes CAM_Consent form_french. pdf (Cameroon Consent form in French) analysed) 1. 5 or 2. 0 or 3. 0. Also, a worksheet with details of each haplotype in each block) - ○ SchistoCandGenes. 1kg. h3a. bim (Plink bim file CAM_Information sheet. pdf (Cameroon information with identifiers of SNP that are present in both the sheet) 1,000 genomes dataset and the Illumina H3Africa - Software - CIV_Assent form. docx (Ivory Coast Assent form genotyping chip and that are in the candidate ○ ReadMe.
HapsStats. 1kg. h3a. MSL. txt (Sequence of each quently deleted) ○ runBigLD. sh (Wrapper shell script that prepares - haplotype in each haplotype block in each can-UGA_SCHISTO CRF. pdf (Uganda case control study didate gene and counts of observations of each case report form) haplotype for MSL population) input files for runBigLD. R, calls runBigLD. R and also calls makeHaplotypes. pl to process runBigLD. R output) - UGA_Schisto Sample Storage. pdf (Uganda case ○ All. HapsStats. 1kg. h3a. YRI. txt (Sequence of control study sample storage consent) each haplotype in each haplotype block in each Harvard Dataverse: Haplotype Genotyping. https://doi. org/10. 7910/DVN/OVQPCO(Noyes,2021b).
Funding This work was supported by grants from the  ANR  as part of the  second Investissements d'Avenir programme LIGHT4DEAF  ( ANR-15-RHUS-0001 ) and  LabEx LIFESENSES  ( ANR-10-LABX-65 ), LHW-376 Stiftung, the  Fondation pour l'Audition (FPA   IDA05  to CP and  FPA IDA03  to NM) and both the  Royal National Institute for Deaf People and Alzheimer's Research UK  ( RNID G97 ) to NM.
Search strategy The literature search was performed separately for each PICO question in May 2019 by using the Pubmed and Cochrane databases with the search terms presented in Table  4. Existing guidelines were also considered. The aim was to give clear recommendations regarding the indications of therapeutic diets at hospital, rehabilitation center, and nursing home in different settings: e. g. gastroenterology (low-fiber diet, realimentation after gastrointestinal bleeding, pancreatitis, gluten-free diet, FODMAPs, chyle leakage, intestinal lymphagectasia …), endocrinology and nutrition (lowcalorie diet, low sugar diet, particularly in the setting of the risk of malnutrition in acute care obese patients, rare metabolic diseases), cardiology e nephrology-hepatology (low salt diet), geriatrics (diets with texture and consistency modified), hematology (neutropenic diet), as well as indications for high-protein diets.
Recommendation 11 Hospital food diets should be re-evaluated every three to five years according to novel data in nutritional sciences and medicine, but also according to the hospital's focus and needs. Grade of recommendation GPP e consensus (89. 3% agreement). Commentary. This recommendation is based on the best of knowledge. To do this periodic reevaulation of hospital diets and to ensure the well functioning of hospital nutrition organization, some hospitals have installed a nutrition committee consisting of dieticians, nutritionists, nurses, and physicians, but also the manager of the hospital kitchen and possibly the transport logistics.
, 2017). In practice, more classical approaches of kinetic modelling are, therefore, most generally used. A large number of studies is based on the Michaelis-Menten model to describe the kinetics of soluble protein hydrolysis by pepsin  (Ruan, Chi, and Zhang 2010; Luo, Chen, et al. 2018 ) and pancreatic proteases  (Maximova & Trylska, 2015). This approach allows predicting the reaction kinetics by taking into account the effects of the enzyme-to-substrate ratio, the affinity of the protease for the substrate, and several other parameters. However, extensive experimental data are often required to build and validate such models, and a common drawback of Michaelis-Menten models is that they are quite specific for the considered enzyme and substrate. This limits their use in the area of protein digestion as there is a great diversity of edible proteins and food protein structures.
As detailed later on, the evolution of gastric pH is much less trivial to predict from the flux of incoming HCl because of the buffering capacity of the meal. Nevertheless, gastric pH is, in practice, always experimentally monitored when dynamic secretions of HCl are used. Hence, models of macronutrient hydrolysis in dynamic in vitro conditions do not have to explicitly include this buffering effect. They can simply use experimental pH data, either directly using data interpolation or indirectly using an empirical equation that fits the corresponding data. When no food structure effects are expected to occur (e. g. a liquid food that does not phase separate), the pH-dependence of the enzyme activities are the only remaining variables. Although the pH-dependence of enzymes may vary from one substrate to another, one can reasonably build upon experimental data reported in the literature in a first approach.
In the future, it is thus expectable that other in vitro-in silico strategies will be proposed to better project in vitro findings (e. g. nutrient bioaccessibility) into in vivo outcomes (e. g. nutrient bioavailability). Mathematical models based on in vitro digestion data can also be very useful to improve in silico representations of the functioning of the GI tract. Most of the models built on an in vitro approach focus on mechanisms that can hardly be studied in vivo, such as the effects of food particle size, microstructure, and buffering capacity on macronutrient digestion kinetics. Therefore, in vitro based mathematical models such as those presented in this review will certainly enable to improve and refine in silico models of in vivo digestion  (Le Feunteun et al. , 2020 , 2021). It should indeed be understood that the complexity of modelling the digestive process is not only due to the large number of different molecules, enzymes, and biological processes involved.
By providing a common language between in vitro and in vivo observations, mathematical modelling can help anticipating the in vivo consequences of in vitro findings, and vice versa, be useful to improve in vitro methods and data interpretation. Although this area of research remains in its early years, it is clear that further developments in mathematical modelling and in vitro-in silico hybrid strategies will play an important role in the future of food and nutrition sciences. For this, we need to target a food scientist generation not refraining from using mathematical modelling approaches through sufficient training during their educational background.
Acknowledgments The authors would like to acknowledge INFOGEST  COST Action   FA1005  ( 2011-2015 ) and subsequently the INFOGEST network funded by  INRAE  for enabling the networking required to write this review. The authors acknowledge the company " Zucker & Jagdwurst " for their permission to use one of their photo to illustrate complex foods in Fig. 1. S. H. E. Verkempinck is a Postdoctoral Researcher funded by the  Research Foundation Flanders  ( FWO -Grant  no. 1222420N ).
Acknowledgments: We thank  Association Maladie Foie Enfants (AMFE)  (Malakoff, France),  Monaco Liver Disorder (MLD) (Monaco), Association "Pour Louis 1000 Foie Merci" (Fournet-Luisans, France) ,  Association "Il était un foie" (Plouescat, France) ,  Fondation Rumsey-Cartier (Genève, Switzerland)  and  FILFOIE  for their support.
Funding: A. B. S. and E. M. were supported by the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation. A. B was supported by the  German Research Foundation , Grant Number  DFG Project-ID 403224013 ,  SFB 1382 , and  CRC296. T. F. was supported by grants from the  Agence Nationale de la Recherche  ( ANR-15-CE14-0008-01 ) and the  French Association for the Study of the Liver (AFEF). T. A. -S. was supported by grants from  Fondation pour la Recherche Médicale  ( FRM-EQU-2020-03010517 ) and the  Association Mucoviscidose-ABCF2.
Sequencing and metabolomic output In this study, the number of subjects per group whose faecal genomic DNA was available for 16S rRNA sequencing, was as follows: Synbiotic group (n = 44), Prebiotics group (n = 39), Control group (n = 44) and Reference group (n = 26) (Supplementary Table  S1 ). For data analysis and interpretation, these intervention groups were labelled as Synbiotic group, Prebiotics group, Control group and Reference group. Faecal genomic DNA (943 samples) were available for 16S rRNA sequencing from 153 subjects (Supplementary Table  S1 ). The average number of sequencing quality-passed reads mapping to the 16S rRNA Greengenes global rRNA database were 1,267,887 ± 667,938 for the Synbiotic group; 1,233,575 ± 651,949 for the Prebiotics group; 1, 098,486 ± 556,112 for the Control group and 1,197, 828 ± 637,407 for the Reference group (Supplementary Table  S2 ).
The metagenomics and metatranscriptomics data were generated from a subset of the stool samples collected at day 3 and/or day 5 (Fig. 1 ). The samples were selected randomly from 10 subjects per group from the clinical study's biobank. The metagenomics sequencing run produced, on average, 8. 6 million paired-end reads per sample, or 4. 3Gb data per-sample. After quality control and removal of host sequences, on average, 6,864,061 (79. 7%) paired-end reads were used for downstream metagenomics analysis. At the end of the classification analysis, on average, 88. 93% (66. 78-94. 97%) of the paired-end reads could be classified to the kingdom Bacteria. The metatranscriptomics sequencing run produced, on average, 11. 9 million paired-end reads per sample, or 2. 4Gb data per-sample. After quality control and removal of ribosomal RNA sequences, on average, 1, 212,609 (10.
12%) paired-end reads were categorized as non-ribosomal RNA reads and used for downstream metatranscriptomics analysis. The resulting analysis showed that, on average, 60. 31% (40-75. 28%) of the non-ribosomal RNA reads could be classified to the kingdom Bacteria. The metabolomics data were generated from a subset of the stool samples collected at day 3 and/or day 5 (Fig. 1 ). The samples were selected randomly from 10 subjects per group from the clinical study's biobank. The 10 samples per group were then pooled before Fig. 1  Infographic depicting the study design. Depiction of the multi-omics approach used to investigate the functional impact of mode of delivery and nutritional interventions on the infant gut microbiome. In this study, the infants were mixed fed. Subjects from each group of infants born by elective C-section (Synbiotic, Prebiotics and Control) received the study product corresponding to their allocated group in addition to breastfeeding.
The group of vaginally born infants or Reference group was also mixed fed and a control formula was provided. This allowed us to ascertain that observed differences between study arms and the reference group resulted from mode of delivery and nutritional interventions. In this study, the number of subjects per group whose faecal genomic DNA was available for 16S rRNA sequencing, was as follows: Synbiotic group (n = 44), Prebiotic group (n = 39), Control group (n = 44) and Reference group (n = 26) and those numbers are reflected in Figure  1  metabolomics profiling. The metabolomics dataset represented four pools reflecting the intervention and reference groups.
Delayed colonization by keystone colonizers in C-section born infants 16S rRNA data were used to determine the effect of mode of delivery on the development of the infant gut microbiota. We applied a constrained supervised ordination method, db-RDA and PERMANOVA to test the hypothesis that mode of delivery influences the pioneer bacterial colonization of the infant gut. The ordination plots (Fig. 2a  and b ) show that the first two components separate samples from the Control (C-section birth) and Reference (vaginal birth) groups collected at day 3 and 5. The bacterial communities of both groups of infants were compared across time points, a statistically significant difference was observed between day 3 and week 16 (Supplementary Table  S3 ). Pairwise group comparisons between the Control and Reference groups showed that Bifidobacterium and Bacteroides were the predominant genera that separate both groups (Supplementary Fig.
002 and P = 0. 041, respectively). We hypothesized that anaerobiosis was a key microbial metabolic trait among infants born vaginally, whereas aerobiosis was a key microbial metabolic trait among C-section born infants. A synbiotic intervention supports species diversity and Bifidobacterium abundance in elective C-section born infants Multivariate analysis was used on 16S rRNA data to determine the relationship between the nutritional intervention and the bacterial colonization of the infant gut. The ordination plots (Fig. 2a, b  and c ) show that the taxonomic composition of the Synbiotic group is distinct from the Control group at day 3, day 5 and week 2 (Supplementary Table  S3 ). Similar results were observed when we compared the Synbiotic and the Prebiotic groups between day 3 and week 4 (Supplementary Table  S3 ).