dataesr/scientific-paragraphs-categorization huggingface dataset:
Total antioxidant capacity of human serum samples following artichoke leaf extract ingestion. Author Contributions: Conceptualization, F. W. and Y. W. ; methodology, Y. W. , L. B. -W. and F. W. ; formal analysis, F. W. , Y. W. , A. B. -A. , A. V. , L. G. , H. O. , G. C. and E. C. ; NMR investigation: A. B. -A. , A. V. , L. G. and H. O. ; clinical investigation, V. R. , N. M. , S. D. , G. P. and L. B. -W. ; writing Y. W. ; project administration, A. B. -A. , G. R. , L. B. -W. and F. W. All authors have read and agreed to the published version of the manuscript. Funding: This project was funded by EVEAR Extraction. The contribution of Y. W. was supported by INRAE. The contribution of PIC/CIC was supported by Inserm and University Hospital. The contribution of A. V. , L. G. and H. O. was financially supported by University of Rouen Normandy, INSA Rouen Normandy, the Centre National de la Recherche Scientifique (CNRS), European Regional.
From the laboratory reports, we obtained data from stool samples that were requested in medical consultation for detection of parasites, results are registered in handwritten books at the time of receiving samples. No link to the medical records of each patient was possible because of the absence of digital systematization of laboratory or medical records in public health system. We included samples of children from 0 to 12 years old. We included one stool sample per child, per year and the rst sample of the year, regardless of the result. This means that a child could have repeated samples within the period of 5 years but not a repeated sample in one year. This was ensured by Government insurance identi cation system for children with the birthdate and initials of their rst and last names, which allows in a certain way to measure the number of visits that a child makes to a centre, however, it is not systematized and a child can be taken as new if he visits another establishment.
It is worth mentioning that most of the secondary care centers use simple examination as the main diagnosis technique, techniques with higher sensitivity such as Ritchie concentrated technique, molecular tests are barely used and for this reason is not possible to distinguish between E. histolytica and E. dispar. Most hospitals of the public health services in Bolivia do not have a digital system but handwriting notebooks of monthly reports. The third line hospital (city hospital) was able to keep the reports only for two years (2014 and 2015). Transcribing and cleaning process was developed by the main researcher. This study has the approval of the ethical committee of the University of San Simon and the local health direction of the department of Cochabamba. Descriptive data such as mean, standard deviation for age variable and frequencies for the others: area (high valley, low valley, semi-tropical, tropical and city), group of parasites (helminths, protozoan), diagnosis (pathogenic, non-pathogenic, non-parasites observed), season (spring, summer, autumn, winter) and age groups used for analysis took account the paediatric classi cation used in Bolivia to follow child development.
As of December 2020, there were 245 registered clinical trials (ClinicalTrials. gov) which have completed evaluation of prebiotics (alone or in combination with probiotics) on ageing, autism, bariatric surgery, colic, colon cancer, atopic dermatitis, constipation, diarrhoea, infant growth, irritable bowel syndrome (IBS), obesity, and other conditions. The number of studies and investigational targets are suggestive of significant investment in the development of prebiotics as bioactive ingredients or supplements for a range of potential applications.
New Discovery and Research Methodologies Significant developments in technologies and associated methodologies have enabled many advances in the prebiotic and probiotic field in recent years and will continue to play a key role in the future. As the breadth of technologies and bioinformatic techniques expands, increased emphasis is being placed on the reproducibility of microbiome results and removal of bias introduced by protocols. Interpretative variations can enter at many stages, including collection and processing of samples, gene sequencing, database use, and data analyses, any of which may cloud biologically relevant signatures including interindividual variations  [98]. Furthermore, sample size and statistical power will become increasingly important when conducting microbiome studies  [99].
Recently developed instruments like the Prospector System (GALT, CA, USA), which allow the simultaneous cultivation and screening of isolates based on specific phenotypes, the ability to use substrates of interest like prebiotics, or the generation of metabolites, will rapidly advance the collection, characterisation, and application of novel strains. Advances in bioinformatic platforms and parallel-computing workflows have enhanced our ability to convert the terabytes of sequence data into useable data in a fraction of the time, allowing for generation of probiotic databases and large-scale analyses of probiotic strains  [111, 112]. Similarly, movement of some software and wrapper programs into more user-friendly graphical user interfaces, rather than only being available through cumbersome command lines, has also helped to remove analysis barriers for probiotic and prebiotic effects.
Conventionally, phenotypic methods of probiotic identification are used in commercial quality control laboratories, which provide low resolution, often to the genus level only  [119]. The availability of whole-genome sequencing (WGS) and next-generation sequencing (NGS) have changed studies of microbial communities and are routinely used in food quality and safety practices  [120]. However, these techniques are not currently widely adopted in quality control laboratories, despite their potential to enable accurate identification of probiotics in multistrain mixes, as well as the presence of potential contaminants  [121, 122]. Increased utilisation will depend on time-to-results reduction, cost effectiveness, and curation of databases v  [123]. Microbial viability is classically measured through culturability, that is, colony-forming units (CFUs); however, this method has significant limitations, including an inability to enumerate organisms in a viable but nonculturable state  [124].
Study technique, participants and settings The study design was a secondary analysis of cross-sectional survey data from RDHS 2014/2015 that was retrospectively carried out for investigating the effects of antenatal care visits on birth weight of the newborn in Rwanda. The RDHS data collection fieldwork was conducted from November 9, 2014, to April 8, 2015. The data entry, editing, and cleaning was completed by May 15, 2015, and the final survey report was completed in March 2016. A total of 8,004 pregnant women who were to receive antenatal care interventions before delivery were recruited. The interviewed women were of reproductive age (15-49 years). This study was conducted in Rwanda, a small country located in the Central and Eastern Africa bordered by the Republic Democratic of Congo to the West, Uganda to the North, Tanzania to the East and Burundi to the South.
To collect the data of this household-based survey, mothers who had the youngest children, age five years or less, were interviewed to provide data related to birth weight for their children. The data for this survey were collected using a two-stage sampling strategy for enrolling participants. These stages were cluster sampling design and the sampling frame. The sampling frame was composed of the list of the enumerators' areas (EAs) that covered the entire country. All residents in selected households were eligible to be interviewed. At the first stage of this study, 492 clusters were randomly selected (113 in urban and 379 in rural areas). At the second stage of this study, the systematic sampling technique that focused on selecting the households was applied. Then, a fixed number of 26 households were selected randomly from each cluster and a total of 12,792 households were selected for the final sample for this study.
Research tools and data collection RDHS 2014/2015 collected data at the national level using household-based survey data on birth weight retrospectively collected from the mothers. The data collection was completed by trained data collectors who used face-to-face interviews, asking mothers eligible for this study to provide a detailed birth history for children born in the preceding five years. Recruitment included stratified sampling, two stages of cluster sampling designs. The first stage was characterized by selecting the participants from the samples frame constructed from enumeration whereas the second stage involved the systematic sampling of the households. These were listed from each cluster to ensure that an adequate number of the completed individuals were obtained. 44 Participants were interviewed based on the measurement of the DHS program.
Explanatory variables Based on the literature review and the structure of the RDHS 2014/2015 dataset, the independent variables were found. The main explanatory variable was the number of the antenatal care visits for the pregnant women. Pregnant women who attend none or one ANC visit were considered to have inadequate number of visits, those who attended two or three ANC visits were considered to have intermediate, and those who attended four to seven were taken as adequate number of visits. 7] [48]  This study used different covariate variables selected based on the previous epidemiological studies, reviewing the suitable published demographic and epidemiological studies and the available information provided in the demographic health survey (DHS) datasets with the consideration of the potential confounders.
Based on the insights from the literature and availability in the datasets, the following variables were included as potential confounders: maternal age (<19 years, 20-34 years and 35-49 years); maternal height (1: <160 cm; 2: ≥160 cm), parity (i. e. birth order), maternal weight (1: <80 kg; 2: 80-89 kg; and 3: ≥90 kg), body mass index (BMI), residence (0: urban; 1: rural); educational attainment (0: illiterate; 1: primary; 2: secondary and higher); household wealth status (0: poor, 1: middle and rich); place of delivery, marital status, maternal occupation, tetanus injection during the pregnancy (0: yes; 1: no), intake iron during the pregnancy (0: yes; 1: no), sex of the child (0: female; 1: male), sex of household head (0: female; 1: male), wanted pregnancy when became pregnant (0: yes; 1: no), blood pressure taken during pregnancy (0: yes; 1: no) provided anti-malaria drugs during pregnancy (0: yes; 1: no), urine sample taken during pregnancy (0: yes; 1: no), received counseling or health education related to nutrients (0: yes; 1: no).
Strengths and limitations The present research has numerous prominent strengths. The first strength is that the RDHS 2014/2015 used the validated and standardized tools for interviewing the eligible participants. Second, the research data about birth weight that we used were obviously verified through records and recall bias was prevented. However, several limitations also warrant discussion. First, it was limited to a study design that did not actually explore for modifiable factors of ANC visits, such as HIV status of pregnant women, maternal gestational age, prenatal depression, receiving the supplementary vitamins (like vitamin A) during the pregnant period, maternal weight gain during pregnancy, eclampsia, gestational diabetes, high blood glucose level during pregnancy, antiretroviral (ART) for HIV-positive women and reducing transmission of HIV from pregnant mother to child.
Second, birth weight data was available for only babies whose weight was measured at birth. This limited us for estimating the real prevalence. Third, the selection bias resulted in the underestimation of the association between ANC and birth weight. This was because not all babies' weight was measured. The other limitation was that the qualitative aspects data from the participants was not included for exploring some factors and to triangulate the findings of the quantitative methods used. Additionally, as the methods used for measuring birth weight were not validated by the survey team, the misclassification could occur in this research. As we do not know the exact timing of the birth weight measurement, this causes misclassification.
Data availability Underlying data Underlying data for 'Regular antenatal care visits were associated with low risk of low birth weight among newborn in Rwanda: Evidence from the 2014/2015 Rwanda Demographic Health Survey (RDHS) Data was owned by the DHS program that can be obtained from https://dhsprogram. com/methodology/survey/survey-display-468. cfm. The electronic data is available from the DHS program under its terms of use. Before downloading the data, the main author of this study registered as DHS user for reasons laid out on the DHS program website and dataset access was only granted for legitimate purpose of this research.
Sufficiency and tolerable intakes We searched for tolerable intakes established by national and international governmental or advisory agencies pertinent to residents of the United States and France using governmental databases, Google Scholar, and Google. For each of the published tolerable intakes that we found, we examined the supporting documentation for the tolerable intake and identified the key study or studies that formed the basis or bases cited for the tolerable intake. We reviewed each of these key studies to determine the type of study, the type and duration of exposure, the number and age of subjects, and whether health effects were measured.
RfD = In justifying the UF of 1, the U. S. EPA wrote, "The use of 1 as the UF is based on the following considerations. Manganese is an essential trace element for human health. The information used to derive the RfD was collected from many large human populations consuming normal diets over an extended period of time. The available data suggest that as long as physiological systems are not overwhelmed, humans exert effective homeostatic control over manganese so that body burden is kept relatively constant even when the concentration of manganese in the diet varies"  [10]. The U. S. EPA notes that the RfD for ingestion of Mn is based on "dietary information described by  WHO (1973) ,  Schroeder et al. (1966) , and NRC (1989)"  [10]. The National Research Council (NRC) reference  [29]  used by the U.
Acknowledgments This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This study received institutional support from  Better Life Laboratories, Inc. ,  Norwich University , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de Physique Nucléaire et de Physique des Particules  ( IN2P3 ), and the  Centre d'Etudes Nucléaires de Bordeaux Gradignan (CENBG). We thank  Deborah Ahlers  and  Tammy Hunt  from  Norwich University  for their valuable assistance with this project.
RESULTS AND DISCUSSION General features of data sets included in the meta-analysis. A collection of 124 shotgun metagenomic data sets from four different studies  (19) (20) (21) (22) (23)  was retrieved from publicly available repositories (Table  S1 ). Out of these, 67 corresponded to fecal samples of preterm infants suffering from NEC, while the remaining 57 samples were acquired from premature infants considered healthy overall. More precisely, among the NEC subjects, 53 were newborns with confirmed NEC at the time of sampling, while 14 fecal samples were collected prior to NEC diagnosis (Table  S1 ). Gestational age ranged from 23 to 39 weeks, corresponding to a birth weight between 900 and 3,010 g. Notably, only four infants showed a gestational age or birth weight longer than 32 weeks and 1,500 g, respectively (Table  S1 ).
While 11 infants did not display any intestinal morbidity (for a total of 24 fecal samples), 7 infants developed NEC, which was found in 12 fecal samples before and 11 fecal samples after NEC development and diagnosis (Table  S2 ). Overall, a total of 171 fecal samples of preterm infants, encompassing 64 cases of ongoing NEC, 26 patients that later established NEC but did not show symptoms at the time of collection, and 81 healthy control samples, were evaluated, representing one of the largest shotgun metagenomics data collections to date. All the data sets were reanalyzed using the same analysis pipeline, i. e. , METAnnotatorX  (25). Gut microbiota variability between cases of NEC and healthy subjects. In order to highlight the differences in gut microbiota composition between infants with manifested NEC symptoms and healthy subjects, we compared the microbiota composition of the retrieved 64 NEC samples with the 81 healthy samples, while the 26 pre-NEC samples were investigated separately (see below).
In order to explore the gut microbial communities before and during NEC, we performed one of the largest shotgun metagenomics meta-analyses, including 124 publicly available data sets of preterm infant fecal samples along with 47 shotgun metagenomics data sets obtained from fecal samples of 18 preterm infants collected across the first month of life in the framework of this study at the NICU Croix Rousse University Hospital. Statistically validated hierarchical clustering analysis allowed the identification of five archetypical recurring taxonomic profiles named preterm community state types encompassing both healthy controls and NEC-affected preterm infants. Investigation of the NEC subpopulation of each PT-CST revealed a common trend of biodiversity loss accompanied by a marked increase in renowned (opportunistic) pathogens.
MATERIALS AND METHODS Ethical statement. The human study protocol was approved by the local Ethics Committee (Comité de Protection des Personnes Sud-Est IV, Lyon). Data collection. To perform a meta-analysis, we retrieved four publicly available data sets from studies involving the taxonomic evaluation of the gut microbiota in preterm infants with and without NEC (PRJNA46337, PRJNA376566, PRJNA63661, and PRJNA273761). Remarkably, we selected shotgun metagenomics data sets achieved by the Illumina sequencing platform to obtain high resolution and limited input data variability. Accordingly, we collected 124 fecal samples from five different neonatal intensive care units (NICU) in the United Stated, corresponding to 57 infants considered overall healthy and 67 declared affected by NEC. Additional 47 fecal samples from 18 premature infants enrolled in NICU at Croix Rousse University Hospital of Lyon, France, were considered between September 2014 and November 2014.
genprobio. com/). DNA library preparation was performed using the Nextera XT DNA sample preparation kit (Illumina, San Diego, CA), following the manufacturer's instructions. One nanogram input DNA from each sample was used for library preparation. The isolated DNA underwent fragmentation, adapter ligation, and amplification. The ready-to-go libraries were pooled equimolarly, denatured, and diluted to a sequencing concentration of 1. 5 pM. Sequencing was performed on NextSeq 550 instrument (Illumina, San Diego, CA), following the manufacturer's instructions, using the 2 by 150 bp high output sequencing kit and spike-in of 1% PhiX control library. Shotgun metagenomics analysis of the fecal samples produced an average number of 3,314,112. 39 6 6,556,669. 69 reads per sample. The raw data in fastq format, including those retrieved from publicly available shotgun data sets, were submitted to quality filtering for the removal of reads with an average quality of ,25.
Subsequently, human DNA was removed by reads mapping on the Homo sapiens genome, obtaining an average of 13,891. 71 6 3,580. 60 reads per sample that were submitted to downstream analysis. Following that, taxonomic profiling of retained reads was performed with the METAnnotatorX bioinformatics platform  (25). Remarkably, in order to homogenize the sequencing data from different studies, we selected only those produced through Illumina sequencing method. Taxonomic classification of each sequenced read was achieved using MegaBLAST  (81) , employing the curated nonredundant sequence database of genomes retrieved from the National Center for Biotechnology Information (NCBI). For each metagenomic sample, species richness, i. e. , biodiversity, represented the number of gut-associated bacterial taxa whose sequenced reads had a relative abundance greater than 0.
5%. Similarities between samples (beta-diversity) were calculated by Bray-Curtis dissimilarity based on species abundance. The range of similarities is calculated between values 0 and 1. Principle-coordinate analysis (PCoA) representation of beta-diversity was performed using ORIGIN 2021 (https://www. originlab. com/2021). In the PCoA, each dot represented a sample distributed in tridimensional space according to its own bacterial composition. Functional prediction. Functional profiling of sequenced reads was obtained through the METAnnotatorX bioinformatics platform  (25). In addition, enzymatic reactions profiles were predicted using METAnnotatorX based on the MetaCyc database  (82). Microbial cooccurrence and network analyses. Covariance analysis involving the 233 bacterial species obtained by shotgun profiling of the 171 infant fecal samples was accomplished employing Kendall's tau rank covariance analysis  (83).
Using software Gephi (https://www. gephi. org/), such correlation coefficients were then exploited to build a force-driven network, where bacterial species, represented in the form of nodes, are connected by edges. Each node size is proportional to the number of interactions of a specific taxon, i. e. , the node degree, while the edge color indicates the type of interaction, i. e. , positive (green) or negative (red). Statistical analyses. Sample clustering based on different predominant taxa was achieved by hierarchical clustering analysis (HCL) using bacterial abundance information at the species level and was then calculated through TMeV 4. 8. 1 software using Pearson correlation as a distance metric. The data obtained were represented in the form of a cladogram. ORIGIN 2021 (https://www.
Subjects and Samples Human milk samples were provided by participating mothers in a study conducted in the Obstetrics and Gynecology Department of "Hospital Universitario Virgen de las Nieves" from Granada (Spain). Ethics were approved by the relevant scientific committee and the trial was registered at ClinicalTrials. gov (accessed on 15 April 2019) (number NCT): NCT02811172. Details of the study were explained to all mothers who voluntarily gave written consent to participate. Eighteen lactating women were recruited for HM collection, with each mother donating three samples (colostrum, transitional and mature milk). The main characteristics of sampled mothers are shown in Table  1. Colostrum corresponded to milk from the 3rd to the 5th post-partum day, transitional milk to days 9-13 and mature milk from day 17 thereafter.
Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Identification of Bands by In-Gel Digestion Undigested (control) and digested human milk samples were dissolved at 1 mg of protein/mL in sample buffer and analyzed on 12% Bis-Trispolyacrilamide gels (Crite-rion_XT, Bio-Rad, Hercules, CA, USA). In gel digestion and analysis on an Autoflex speed MALDI-TOF/TOF (Bruker Daltonics, Bremen, Germany) instrument was performed as previously reported  [22]. MASCOT v2. 4 software (MatrixScience, Boston, MA, USA) was used to carry out protein identification searches against a homemade database of human milk proteins selected from the Uniprot database (https://www. uniprot. org/ accessed on 15 April 2019).