dataesr/scientific-paragraphs-categorization huggingface dataset:
INTERGROWTH-21 st project characteristics Large prospective study of 59,137 pregnant women Population based: all institutions providing pregnancy and delivery care in 8 geographically limited urban areas with low rates of adverse perinatal outcomes and low pollution, domestic smoke, radiation, and other toxic substances Sampling of individual women within 8 geographic areas using predefined criteria for construction of standards Participants followed up to age 2 y Pregnancy, neonatal anthropometry, and perinatal conditions recorded for total population (59,137 pregnant women) in 8 geographic areas using standardized procedures, identical equipment, and centrally trained staff Environmental conditions evaluated using special data collection form developed in collaboration with Center for Environmental Research and Children's Health, University of California, following WHO recommendations Excluded from standards only severe maternal or fetal conditions defined a priori A priori data analysis plan based on WHO recommendations to construct human growth standards Use of skeletal growth measures from <14 wk' gestation to age 2 y for comparisons across populations, as recommended by WHO Three complementary data analysis strategies to support pooling data for construction of standards International standards for human growth from <14 wk' gestation to age 2 y International preterm postnatal growth standards as recommended by WHO Preterm postnatal motor development assessment following WHO milestones Published real-time, online data management system Ultrasound equipment selected based on predefined criteria after extensive public consultation according to WHO administrative requirements Ultrasound measures in triplicate and corroborated by newborn anthropometry Ultrasound results masked to operators to eliminate expected result bias Standardized equipment at all sites for ultrasound; maternal, newborn, and child anthropometry Ultrasound machines calibrated with standard phantom Published system of: Training, standardization, and certification of ultrasound operators Quality-control strategy for all maternal and postnatal measures Assessment of intraobserver and interobserver variation of ultrasound fetal biometry Protocols for quality control of ultrasound image review, data monitoring, and random sample remeasurement WHO, World Health Organization.
These and other standards derived from the same cohort, eg, for measuring symphysis-fundal height  29  and maternal weight gain,  30  as well as tools for estimating gestational age in early 2 and late  63  pregnancy have had >65,000 downloads from our website (data up until Nov. 21, 2017) and close to 10,000 health care professionals have been trained using INTERGROWTH-21 st e-learning modules. There is no scientific rationale for using local references instead of standards in clinical practice, and customization based on the color of a mother's skin, the sex of her fetus, or her nationality is unacceptable in the 21st century. Furthermore, classifying any of the 23. 3 million infants born SGA in low-to middle-income countries according to the INTERGROWTH-21 st newborn size standards for gestational age/sex  12  as normally grown by local charts could potentially deprive them of their right to better health care given that most are SGA because of impaired fetal growth due to malnutrition and/or infectious diseases.
The PCR products obtained were transcribed, labelled with Cy3 and Cy5 dyes and fragmented. Finally, the samples were hybridized on the arrays at 62. 5˚C for 16 hours in a rotation oven (Agilent Technologies, Amstelveen, the Netherlands). After the slides were washed and scanned, data was extracted with the Agilent Feature Extraction software (version 9. 1). The data was normalized and analysed using a set of R-based scripts combined with a custom-designed relational database, which operates under the MySQL database management system.
Data filtering Sequencing generated between 13. 4 and 177 million reads per sample. The data set supporting the results of this article is available in the NCBI small reads archive (sra) repository, under accession number SRP043409. The data was filtered, assembled, annotated and classified as previously described  (Davids et al. , 2016). Briefly, the taxonomic origin of the contigs was assigned by alignment of the predicted protein sequences with the NCBI's non-redundant database, retrieving the taxonomic family classification of the protein sequence with the highest similarity. Classification up to family level was chosen, determined in our previous study to be able to have enough confidence in the assignment and still enough precision in the involved community member  [28]. Functional annotation was achieved by assignment of KEGG orthology identifiers to all predicted protein sequences, using the KEGG KAAS server (Moriya et al.
Effect of high protein and high fat diets on microbiota function profiles To focus only on functions that are differentially expressed within the microbiota as a function of the different diets, we employed an in house R script to detect KEGG modules that are differentially expressed. Remarkably, the different fat levels in the diets (HF versus LF) were not significantly correlated to any differentially expressed KEGG modules. In contrast, the comparison of samples derived from NP and HP diet fed mice enabled the detection of KEGG modules that displayed significantly different expression levels. The KEGG modules enriched in the NP diet derived samples were all associated with sugar metabolism, whereas the modules enriched in the HP diet derived samples were consistently associated with protein metabolism (Table  4 ).
Effect of high protein and high fat on microbial community activity The microbial families Lachnospiraceae, Erysipelotrichaceae and Clostridiaceae dominated in the overall activity associated with the degradation of proteins, supporting their high relative contribution in the total mRNA activity profile in all the samples (S3A Fig). Erysipelotrichaceae has relatively higher abundances in the HP diets, while the abundances of Lachnospiraceae decreased in the HP diets. Many of the butyrate producing bacteria belong to Lachnospiraceae, Ruminococcaceae and Eubacteriaceae and all of these families tended to decrease in the HP, supporting earlier studies in rodents  [13, 15, 14]. To further support an eventual role of microbial groups in protein fermentation, the metatranscriptome datasets were mined for genes involved in proteolysis (S3B Especially the Erysipelotrichaceae displayed an increased contribution to the overall protein degradation, which was most prominently detected in samples obtained from mice that were fed the HPLF diet, whereas a similar trend was observed in the samples obtained from HPHF-fed mice (Fig  3 ).
This may either be due to their low in situ expression levels or incorrect functional annotation, or taxonomic misclassification of the predicted proteins. Many of the inaccuracies in assignment of function and taxonomic origin of a sequence derived from the mouse cecum is likely due to inaccurate annotation assignments in the NCBI database as well as the substantial dissimilarity between the reference genomes and the metatranscriptome data obtained from the microbial community residing in the murine intestinal tract. Additionally, the limited depth of metatranscriptome analysis could also provide a limitation in the detection of complete and/or lowly expressed pathways. Overall, members of the Erysipelotrichaceae appeared to benefit most significantly from the HPLF diet, and the expression profiles assigned to this group confirmed its focus on pathways for the degradation of a several amino acids.
Notably, the expression of genes encoding butyrate-kinase and butyryl-CoA:acetate-CoA-transferase that are also involved in the crotonyl-CoA pathway was not detected in Erysipelotrichaceae, which is likely due to the erroneous annotations of the homologous acetate-kinase and other SCFA transferase functions in this bacterial group  [36] [37] [38]. Genes that encode the enzymes required for propionate production appeared barely expressed, and were exclusively assigned to the bacterial families Porphyromonadaceae and Sphingomonadaceae. The very low expression detected for the propionate production pathway may indicate that for a more complete reconstruction of the microbiome activity profiles, metatranscriptome datasets with a higher depth of analysis would be required. Analogously, we failed to detect the expression of genes encoding enzymes involved in BCFA production, which may also require a higher depth of metatranscriptome analysis considering that concentrations of these metabolites were 2-3 orders of magnitude lower than those of acetate, propionate and butyrate (Fig  1 ).
In conclusion, the data presented here provide clear metabolic indications concerning the microbial groups involved in protein catabolism in the intestine, but more complete understanding of the precise role of these microbes will require a better understanding of their physiological characteristics and may also require metatranscriptome datasets with a higher depth of analysis. enzyme catalizes a step in the glycolytic pathway and its gene was transcribed at a lower level in the HPLF diet, which was mainly due to the decreased expression from Lachnospiraceae. In each of the microbial families unique expression patterns were found, which are indicated within the grey circles. Lachnospiraceae strongly expressed genes coding for enzymes involved in the conversion of phosphoenolpyruvate to oxaloacetate, and lipid biosynthesis activity.
The eight WASH behaviors included 1) child's body hygiene; 2) fecal contamination-free child play areas; 3) safe disposal of child's faces; 4) household drinking water treatment; 5) transport of water from the source to the point-of-use; 6) safe water storage; 7) knowledge of the key times for hand washing; and 8) food hygiene. Randomization and masking. Health centers were stratified in pairs (intervention and control) according to the monthly number of SAM admissions (historic data from the year 2013) to obtain a balanced number of enrolments in the two arms. We randomly extracted one letter of the alphabet and we assigned within each pair the intervention to the HC with the first letter of its name closest to this letter. Masking of participants was not possible because of the nature of the intervention.
To limit judgment bias, the research and HC staff members were different. Whereas the HC staff collected nutritional and morbidity data in OTP, research staff was responsible of collecting WASH data. It was not possible to blind research staff, but they rotated so they covered different groups. Recruitment started 1 month after allocation of each HC to either group. Sample size. Program data indicated that 20% of cases were relapse cases at admission but the relapse rate postrecovery remained unknown in the study area. Based on other studies,  14, 15  we estimated a cumulative true relapse rate of 12% during the 6 months after successful discharge. Furthermore, we estimated a coefficient of variation of the true rate between clusters within each group (k) of 0. 17 based on the number of admitted cases in the 20 clusters as reported in program data in 2013.
Using the formula presented by Hayes et al. ,  16  an alpha error of 5% and a beta error of 20%, we needed 1,400 cured children from 20 clusters to detect a 5% difference between the two groups. Data collection. The study recruitment phase lasted from April to December 2015 and the follow-up phase finished by May 2016. Ten field monitors were recruited locally and received a technical training in Mao on appropriate data collection, water quality tests, and how to promote the WASH kit. The HC staff members as part of their routine procedures collected weekly data on sociodemographic characteristics, nutritional status, morbidity, and treatment. Height was measured to the nearest 1 mm (UNICEF measuring scale) and weight was measured to the nearest 100 grams (calibrated Salter hanging scale for children weighing 10 kg or more and a calibrated electronic scale (type: UNISCALE) for children weighing less than 10 kg).
Mid-upper-arm-circumference was measured to the nearest 1 mm using an arm circumference measuring tape. Height-for-age Z (HAZ) scores and WHZ scores were calculated based on the UNISEX Z scores table, as used by the Chad Ministry of Health. 17 Water, sanitation and hygiene variables were collected by the field monitors at the HC through a standardized questionnaire, at admission and discharge. During the OTP phase, two home visits in the intervention group aimed to measure water quality (by turbidimetry and presence of chlorine residual), to collect information on the adherence to the WASH package, and to provide extra promotion session on the usage of the WASH kit. In the study area, chlorine was not available, and therefore, no data were collected in the control group. Turbidity was measured with a two-part turbidity tube to assess the suitability of the chlorine intervention.
Free chlorine (residual) was measured with a pooltester with a detection limit of 0. 1 mg/L. According to ACF standards,  18  correct turbidity and correct chlorine residual were defined between 0 and 20 nephelometric turbidity units and ³ 0. 5 mg/L, respectively. Acceptance of chlorinated water was self-reported by the mother and defined as "at least the child drinks the chlorinated water. " In addition, cured children from both groups received home visits 2 and 6 months after OTP discharge to assess whether the child relapsed and to collect data on hygiene knowledge and related care practices. All collected forms were inserted in an open data kit database with integrated validity checks. Data were double entered by two different data managers and checked for consistency and accuracy by the project manager.
Statistical analysis. All analyses were conducted in STATA 13 (Stata Corp, College Station, TX) on an intention-to-treat basis by using the full dataset. Statistical significance was set at < 5% with a two-sided test. For anthropometric values, World Health Organization standards with recommended flag limits were used (±6 standard deviations [SD] for HAZ and ±5 SD for WHZ. 19 We compared categorical and continuous variables at baseline with clustered χ 2 tests and clustered t tests, respectively (clustering was at HC level). Kaplan-Meier multilevel survival analysis was used to display the length of stay, with time-to-recovery as censoring event and a maximum stay of 12 weeks. Multilevel mixed-effects linear regression models at individual level were used for all outcomes, with a random effect for clusters to account for the clustered design.
Acknowledgments: We would like to thank all study participants, the regional health authorities of Kanem, the district health authorities of Mao and Mundo, the  State of Chad , the  ACF Chad Mission  and ASRADD members, our field staff, health staff of all participating health centers, community health workers, and drivers. We are thankful to  Dr. Moussa Sogoba  for leading the fieldwork and to  Philippe Pillon  for his support in the coordination. We are particularly grateful to  Alain Ngarmarde  for the work on the data quality and to  Hannah Thompson  for English editing. This publication was produced by  PATH  through support provided by the  UK Aid  from the  UK Government ; however, the views expressed do not necessarily reflect the  UK Government 's official policies.
Patients A total of 111 Tunisian patients consulting for coronary disorders were recruited from F. Bourguiba University Hospital (Monastir, Tunisia), and were clinically examined by coronary angiography at the department of Cardiology. A Gensini score was calculated for all patients in order to estimate the severity of coronary artery disease (CAD). CAD patients have a clinical history of unstable angina, previous acute coronary syndromes with or without persistent ST-segment elevation in addition to the presence of several risk factors for cardiovascular disease. The following data were also obtained: age, sex, and the presence of risk factors (cigarette, smoking, hypertension, diabetes mellitusand dyslipidemia Body mass index (BMI) was calculated from the equation: BMI = weight/height 2 (kg/m 2 ).
Coronary angiographic data and Gensini score All coronary angiography results were interpreted for the presence, extent and severity of CAD, by two experienced interventional cardiologists. Patients were considered as CAD patients according to angiographic results (≥ 50% obstruction in ≥1 coronary artery). Gensini score was used to assess the severity of coronary artery disease: it grades narrowing of the lumen of the coronary artery and scores it as 1 for 1-25% narrowing, 2 for 26-50% narrowing, 4 for 51-75%, 8 for 76-90%, 16 for 91-99% and 32 for a completely occluded artery. This score was then multiplied by a factor according to the importance of the coronary artery. The multiplication factor for a left main stem (LMS) lesion was 5. It was 2. 5 for proximal left anterior descending artery (LAD) and proximal circumflex artery (CX) lesions, 1.
Quantifying Benefits and Risks to HEU Children of Maternal ART Dr. Renaud Becquet (Bordeaux University) presented results from a collaborative individual patient meta-analysis with the objective to assess mortality risk in HEU children as well as to estimate the contribution of identified risk factors in mediating poor HEU child outcomes. More than 19,000 HEU children were included in this analysis that pooled data from 21 clinical trials and observational studies in Africa and Asia  (12). Seventy five percent of included children were born in sub-Saharan Africa and 70% were born prior to 2005. Mortality occurred early at a median (interquartile range) of 113 (38-244) days with cumulative incidence [95% confidence interval (CI)] for mortality at 6, 12, and 24 months of 3. 1% (2. 8-3. 3), 4.
ACKNOWLEDGMENTS We wish to acknowledge the contributions of the CARMA and mother-child pairs who participated in the CARMA study. CARMA co-investigators included:  Ariane Alimenti  (The  University of British Columbia [UBC]), Ari Bitnun (University of Toronto ),  Jason Brophy  ( University of Ottawa ),  Matthew Budd  (UBC),  Jennifer Bowes  ( Children Hospital of Eastern Ontario [CHEO]), Kristina Calli (UBC ),  John C. Forbes  ( UBC ),  Carole Gentile  ( University of Ottawa ),  Anthony Y. Y. Hsieh  ( UBC ),  Patricia Janssen  (UBC),  Fatima Kakkar  ( CHU Sainte-Justine and Université de Montréal ),  M. E. Suzanne Lewis  (UBC),  Evelyn Maan  ( Oak Tree Clinic ),  Micah Piske (UBC) ,  Annie A. Qiu  ( Oak Tree Clinic ),  Laura J. Sauve  ( UBC ),  Lindy Samson  ( University of Ottawa ),  Joel Singer  (UBC),  Hugo Soudeyns  ( CHU Sainte-Justine and Université de Montréal ).
Alternatively, this phenotype could be due to epigenetic imprinting, leading to changes in the general gut homeostasis. The causative role of given microbes in inducing and sustaining undernutrition is supported by two studies in mice, which reproduced the main hallmarks of PEE by chronic undernutrition and gavage with a given set of pathobionts. Further, it was possible to transfer the PEE phenotype by inoculating germ-free mice with feces of affected animals. Likewise, feces from stunted children inoculated into germ-free mice led to stunting in the recipient mice  [26, 38, 39]. Human data for this second hypothesis is therefore urgently needed as to assess the pathophysiological mechanisms underlying these interactions in greater detail and to identify potential interventions. In AFRIBIOTA, we focus on children falling under the second etiology: children with or without linear growth delay in apparently good health at the time point of inclusion.
1 ). Many of the factors underlying PEE and stunting are tightly linked, such as parasite burden, infection, socioeconomic status and access to health care. Therefore, it is crucial to collect as much metadata as possible for each child to correct for these factors and analyze the different influences independently from each other. Only a tightly controlled study group will allow correction for a maximum of confounding factors and truly shed light on the pathophysiological associations observed upon PEE. To this purpose, in AFRIBIOTA, we decided to include a larger number of children rather than performing a longitudinal study on fewer children. In addition to its correlation with stunting, PEE is also linked to other long-term sequelae, including psychomotor delay, diminished oral vaccine performance  [55]  and increased risk of cardiovascular diseases later in life  [47].
Biological measurements and tests performed We measure different interacting entities that might play a role in the pathophysiology of child stunting and PEE (Fig. 3 ). They include the pro-and eukaryotic microbial community in the small intestine as well as in gastric aspirates and feces; gut atrophy; the mucosal and systemic immune response; micronutrient deficiencies; asymptomatic enteropathogens and parasite carriage; gut leakiness and atrophy and bacterial translocation; and the micro-and macro-environment of the child. For each child, feces, urine and blood are collected. For stunted children (200 children/country), we also collect gastric and duodenal aspirates. We apply both culture techniques and NGS (16S, 18S, ITS amplicon sequencing, metagenomics) to determine the community structure of the small intestinal aspirates, hence generating unprecedented data about the small intestinal community structure in children living in low-income countries.
Understanding the broader environment of children with PEE In both countries, a medical anthropological study is conducted in the recruitment districts to evaluate children's social and environmental living conditions. Using an observation grid, we observe hygienic practices, including hand washing, food preparation and consumption, and water storage. Further, data about the medical history of the child, the mother's pregnancy, child feeding and care practices as well as household characteristics are collected with a standardized questionnaire. The data of these questionnaires will be brought into dialogue with household-level participant-observations. We will use additional medical anthropological methods to produce analyses (semi-structured, open-ended interviews, focus groups, …) of the children's social relations (with caregivers, other family and neighbors) and economic and environmental conditions (Table  3 ).
AFRIBIOTA does have some limitations-importantly, it is a cross-sectional rather than a longitudinal study. This choice, mainly due to financial constraints, will only allow establishing associations but not causality. Nevertheless, we will include almost 1000 children from two distinct countries and will be able to integrate many different aspects of the syndrome in each child. Further, AFRIBIOTA will also generate basic microbiological data and a biobank, which will allow experimental validation of causality in animal models. We hypothesize that, in stunting and PEE, the whole microbial community rather than just isolated members might contribute to morbidity ("ecological Koch's postulates", reviewed in  [102] ). Therefore, causality can only be experimentally proven by using human samples.
Acknowledgements The authors wish to thank all participating families, the  AFRIBIOTA  Consortium, including all field workers, laboratory engineers, technicians, administrative support persons, doctors and nurses, the participating hospitals in  Bangui and Antananarivo (Complexe Pédiatrique de Bangui, Centre Hospitalier Universitaire Mère-Enfant de Tsaralalàna (CHUMET), Centre Hospitalier Universitaire Joseph Ravoahangy Andrianavalona (CHUJRA)  and  Centre de Santé Maternelle et Infantile de Tsaralalàna) , the  Office National de Nutrition de Madagascar  and the  Office Régional de Nutrition Analamanga , the  Direction de Lutte contre les Infections Sexuellement Transmissibles de Madagascar , the nutrition section of  UNICEF in Bangui, Central African Republic , the local health centers in Bangui ( St.