dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical methods We first calculated mean SBP, mean DBP and prevalence of raised blood pressure for these age groups by sex in each study, taking into account complex survey design and survey sample weights, where relevant. We excluded data points which did not cover complete 10-year age groups, e. g. those in people aged 25-29 years or 60-64 years, to avoid bias in the estimated associations. We also excluded age-sex groups with < 25 participants, because their means and prevalence have larger uncertainty. We then estimated the relationship between the prevalence of raised blood pressure and mean, using a linear mixed effect model, shown below in the equation (where e is the error term), separately by sex. We used probit-transformed prevalence because it provided a better fit to the data than a simple linear model or logit transformation.
The model included age group (10-year age groups from 20-29 to 70-79) and the decade when the data were collected (1985-94, 1995-2004 or 2005-16). We also included interactions between age group and mean blood pressure, between decade and mean blood pressure, and among these three terms, which allowed the prevalence-mean association to vary by age group and over time. We included regional random intercepts to account for the differences in prevalence at any level of mean SBP and DBP by region. The regions, used in previous analyses of cardiometabolic risk factors,  6, [21] [22] [23] [24]  were: Central and Eastern Europe; Central Asia, the Middle East and North Africa; East and South-east Asia; high-income Asia Pacific; high-income Western countries; Latin America and the Caribbean; Oceania; South Asia; and sub-Saharan Africa.
Specifically, we used 1000 draws from the uncertainty distributions of each age-and sex-specific input data point (i. e. mean SBP and DBP and prevalence of raised blood pressure) with uncertainty represented by a normal distribution for mean SBP and DBP and by a binomial distribution for prevalence of raised blood pressure. We then fitted a separate regression to each of the 1000 simulated datasets. We sampled 1000 draws from the joint distribution of the regression coefficients for each of the 1000 fitted regressions (i. e. 1 000 000 sets of regression coefficients). We report the median of the resulting 1 000 000 draws for each coefficient, and their 2. 5th and 97. 5th percentiles as the 95% confidence interval. We also report the median of conditional R 2 from the 1000 fitted regressions.
An analysis of data from the multicountry MONICA Project 32 found that the upper percentiles of blood pressure distribution changed as much as its mean in some communities, and by a larger amount in others. The authors concluded that the decline in blood pressure is mostly a population phenomenon but there was no detailed quantification of the contribution, especially in relation to age, time period or region as was done here with substantially more data. Downward shifts in the whole blood pressure distribution over time have also been reported in a few high-income countries,  [33] [34] [35] [36] [37] [38] [39] [40]  with some studies also finding a larger decline in the upper tail than in the mean of the blood pressure distribution, which is consistent with our results. The strengths of our study include presenting the first global analysis of how much population mean and highblood-pressure individuals have contributed to worldwide trends and variations in raised blood pressure, using a large global database with data from different regions and over time, and using methods that allowed the prevalence-mean association to vary by sex, age group, time period and region.
Acknowledgements The authors would like to thank the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)  (Postdoctoral fellowship internal  PDTS-Resolution 2417 ),  Universidad Nacional de Mar del Plata (UNMdP)  for financial support. We would also like to thank  Dr. Mirian Carmona-Rodríguez  for her valuable contribution. Many thanks also to  Andres Torres Nicolini  for all the assistance he provided during this study.
min -. Measurements were made in triplicate and each repetition was carried out with the pan in a different position in the multi-cell instrument. Peak integration and estimation of gelatinisation parameters were performed using NanoAnalyze software (version 3. 6. 0, TA Instruments 2015 © ) Onset, peak and conclusion temperatures (denoted T o , T p , and T c ) and gelatinisation enthalpy (ΔH) were obtained from each thermogram as described elsewhere  (Bogracheva, Wang et al. 2002).
Logarithm of Slope Analysis Starch digestibility data (i. e. concentration of reducing sugars produced over time) for pure starches and milled particles <0. 25mm was fitted to a first-order equation (eqn. 1), where C t represents the concentration of products (i. e. expressed as maltose equivalents) at a given time (t), C ∞ is the concentration of product at the end of the reaction, and k is the digestibility rate constant. The values of C ∞ and k were obtained from Logarithm of Slope (LOS) plots, as described previously  (Butterworth, Warren et al. 2012 , Edwards, Warren et al. 2014 ). ( ) 2 Starch amylolysis is represented by two consecutive first order reactions, in which the time identifiers indicate the duration of each reaction phase and t int is the intersections between the two phases.
ACKNOWLEDGEMENTS The authors acknowledge financial support from the  BBRSC, UK  ( BB/L025566/1 ). We would like to thank  Dr Claire Domoney  ( John Innes Centre, UK ) for providing the pea seeds. We also thank  Mary Parker  ( Institute of Food Research, UK ) for helpful discussions about micrographs during the course of the work and  Henri Tapp , ( Statistician, Institute of Food Research, UK ) for discussion on statistical analysis.
All participants provided written informed consent. The SU. VI. MAX trial was registered at www. clinicaltrials. gov/ct2/show/NCT00272428. The eligibility criteria for the present analysis were: being a participant of the SU. VI. MAX 2 study with available and accurate (at least three 24 h dietary records) information on adherence to the French dietary guidelines (N = 4048), an age of 45-60 years at recruitment  (1994) (1995) , the absence of chronic diseases (cancer, cardiovascular disease or diabetes) at recruitment in 1994-1995 (N = 3083), and the absence of missing values for baseline BMI and for variables necessary to determine HA status in 2007-2009 (N = 2351). Individuals with missing data for baseline covariables-such as sociodemographic factors (gender, age, educational level, occupation, and family situation), smoking status and number of 24 h records-were also excluded, leaving a final sample of 2249 individuals (Figure  1 ).
Timeline of Data Collection All participants included in our analyses were aged 45-60 years at inclusion in the initial SU. VI. MAX study  (1994) (1995). Moreover, they were free of cancer, cardiovascular disease and diabetes at recruitment  (1994) (1995). The dietary data used for the current analyses were collected in 1994-1996, and the anthropometric data used were collected during a clinical examination in 1995-1996. Participants were followed for a median of 13 y, until the SU. VI. MAX 2 observational follow-up point in 2007-2009. Of note, from inclusion  (1994) (1995)  until 2002, participants either received a placebo or a daily dose of antioxidant nutrients for approximately 8 years  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) , according to the design of the initial SU.
Baseline Dietary Data (1994-1996) Participants of the SU. VI. MAX trial were asked to complete one 24 h dietary record every 2 months. Accordingly, computerized questionnaires and an instruction manual including validated photographs of more than 250 foods were provided. Seven possible portion sizes were available to choose from  [46]. A detailed description of dietary data treatment in the SU. VI. MAX study has been published previously  [47]. Briefly, dietary records reporting energy intakes of <100 kcal/d or >6000 kcal/d were considered as invalid. To further account for energy under-reporting, men reporting < 800 kcal/d and women reporting <500 kcal/d across ≥1/3 of valid records were excluded. Baseline questionnaires were used to collect data on alcohol and seafood consumption, as these food groups tend to be consumed less frequently than others.
Data (1995 Data ( -1996) )  Weight was measured to the nearest 0. 5 kg using an electronic scale (Seca, Hamburg, Germany), with participants wearing indoor clothing and no shoes. Height was measured to the nearest 0. 5 cm with a wall-mounted stadiometer under the same conditions. A "BMI status"-variable of 3 categories was created, which distinguished: (a) underweight/normal weight (BMI < 25 kg/m 2 ), (b) overweight without obesity (BMI ≥ 25 and <30 kg/m 2 ), and (c) obesity (BMI ≥ 30 kg/m 2 ).
Main Analysis: Mediating Role of BMI Status in the Association between Diet Quality and HA The PNNS-GS score (modeled as quartiles) was the observed exposure of interest (A), BMI status was the mediator (M) and HA the outcome (Y). Mediation analysis was conducted according to a counterfactual-based method proposed by Lange et al. , which has similarities with marginal structural models, and allows to estimate natural direct and indirect effects  [51]. Briefly, this method includes the following steps: (1) Creation of a new dataset by repeating each observation 4 times, including a new variable A*, which corresponded to each of the 4 PNNS-GS quartiles. This new variable A* was equal to A (the genuinely observed PNNS-GS quartile) for one line per subject and different from A for the 3 other lines; (2) Two multivariable (i.
e. , confounder-adjusted) logistic regression models were applied to the new dataset to estimate the association between PNNS-GS quartiles (A/A*) and BMI status (M), first using the original variable A, and then the new variable A*; (3) 'Individual stabilized weights' were calculated using the predicted probabilities obtained from these two logistic regressions, as follows: W C i = P(M = M i |A = A * i , C = C i )/P(M = M i |A = A i , C = C i ) (1) where i denotes the individual, A the observed exposure, A* the created exposure, M the mediator, and C denotes the different potential confounders; (4) A weighted Poisson regression model was applied to estimate the association between PNNS-GS quartiles (A and A*) and HA. This Poisson model and the above-mentioned logistic regression model were all adjusted for age, gender, smoking status, educational level, occupational status, living situation, antioxidant supplementation, total energy intake and number of 24 h records.
Hence, the proportion of the association mediated by obesity may be greater in the general population than in our study sample. It should also be noted that it is difficult in observational research to model the natural course over which diet presumably influences BMI status, which may then be involved in the etiology of age-related diseases and functional decline. Here, we considered both BMI status and dietary intakes assessed at baseline, but it can be assumed that dietary habits at earlier ages, including childhood, may have influenced BMI status at baseline. This may be one explanation for the rather small mediating effect of BMI status observed in the current study. In addition, part of the data that was used to define HA status was exclusively collected during the SU. VI. MAX 2 observational study.
Acknowledgments: We thank  Younes Esseddik ,  Thi Hong Van Duong ,  Frédéric Coffinieres ,  Régis Gatibelza ,  Maithyly Sivapalan  and  Paul Flanzy  (computer scientists);  Nathalie Arnault ,  Véronique Gourlet ,  Fabien Szabo ,  Julien Allègre  and  Laurent Bourhis  (data-managers/biostatisticians); and  Cédric Agaesse  and  Anne-Elise Dussolier  (dieticians) for their technical contributions The authors also thank  Stéphane Raffard , who was responsible for standardization of the cognitive evaluation. Sources of support:  Indunil Ruhunuhewa  was supported by an  internship grant (MSc)  from the  University of Paris 13 ;  Karen E. Assmann  and Moufidath Adjibade were supported by doctoral fellowships from the  Ecole Doctorale Galilée, University of Paris 13 ,  Sorbonne Paris Cité. None of the funders had any role in study design, data collection and analysis, decision to publish or writing of the manuscript.
To fill this gap, the current study aims at shedding light on the relationships between breast milk lipidome and growth pattern of preterm infants nourished by their own mother's milk. In the present pilot study, we chose to select two groups of preterm infants, presenting very different growth trajectories during hospital stay, and selected within the ongoing LACTACOL birth cohort (ClinicalTrials. gov Identifier: NCT01493063, acronyme for the clinical study titled "Impact of Breast Milk on the Neurodevelopment of Preterm Newborns"). The aim of the present work therefore was: (i) to assess lipidome in the breast milk received by preterm infants during their first weeks of life; (ii) to evaluate the association between breast milk lipidome and the weight gain of infants between birth and time of discharge, by using multivariate statistical tools that accurately discriminate the two groups of preterm infants; and (iii) to identify a few breast milk lipid species and evaluate their predictive ability on the postnatal weight growth trajectory of the preterm infants, taking into account confounding clinical factors.
Study Design and Population The present pilot study was an ancillary study conducted on sub-groups of infants selected among infants enrolled in the mono-centric prospective population-based LACTACOL birth cohort (registered at www. clinicaltrails. gov as NCT01493063) of preterm infant-mother dyads recruited from October 2011 to March 2016. The main objective of the LACTACOL cohort was to explore the impact of the protein content of breast milk provided to preterm infants during hospital stay on neurodevelopment at 2 years of age. A written consent was obtained from all participants at enrollment. One hundred thirty-eight infants born between 26 and 36 weeks of gestational age, with no major congenital disease except prematurity, were included, for a total of 118 mothers finally enrolled in the LACTACOL cohort (Figure  1 ).
Ethics This research study, referenced BRD/11/02-Y at Nantes University Hospital, was approved, on 28 February 2011, by the National Data Protection Authority (Commission Nationale de l'Informatique et des Libertés, No. 8911009) and, on 19 July 2011, by the appropriate ethics Committee for the Protection of People Participating in Biomedical Research (CPP-Ouest I (Tours, France), reference CPP RCB-2011-AOO292-39). The LACTACOL cohort was registered at www. clinicaltrials. gov under # NCT01493063. The current data were obtained in the corresponding ancillary study N • 3 with the following outcome i. e. , to assess the relationship between the composition of breast milk (lipidome in the present study) and preterm infant's growth trajectory during the first few weeks of life. The milk biobank was approved by the Committee for the Protection of Persons in medical research (approval was granted 24 June 2010, reference CPP CB-2010-03).
The resulting XCMS [m/z; RT] features for each sample was subsequently manually sorted out according to their quality of integration and filtered by a 30% relative SD cutoff within the repeated pooled QC injections (see Dunn and co-workers' recommendations  [53] ). Thereafter, accurate mass measurement of each putative metabolite was submitted to LIPID Metabolites and Pathways Strategy (LipidMaps ® , www. lipidmaps. org) annotation. Moreover, the use of all ion fragmentation, when reverse phase chromatography was applied, helped us identify the proposed lipids by examination of the (pseudo) tandem mass spectrometry spectrum generated  [47] , combined with the use of an in-house reference databank  [54].
Statistical Analyses Statistical analyses were carried out using GraphPad Prism ® software version 6. 00 (La Joya, CA, USA), SIMCA P ® version 13 (Umetrics AB, Malmö, Sweden) and R version 3. 2. 5 (R Development Core Team, 2013, R Foundation for Statistical Computing, Vienna, Austria; http://www. R-project. org). For all data analyses, the significance level (α) was set to 5%, and 10% for Multiple Linear Regression. Multivariate statistical models were applied on pre-processed QC-filtered lipidomic mother x time (rows) by [m/z; RT] features (columns) data matrix considering the a priori structure into "faster" vs. "slower" infants' growth groups. Lipidomic features were column-wise centered and scaled with a Log Pareto scaling  [55]. A statistical workflow (Figure  2 ) was set up to: (i) select the most reliable lactation points; (ii) identify the lipid biomarkers providing a clear separation between the two infant postnatal growth subgroups; (iii) check the selected biomarkers predictive ability; and (iv) confront them to the various confounding clinical variables.
As a matter of fact, MG PLS-DA model revealed the time lactation points presenting the best differentiation between the two groups ("faster" vs. "slower" infant growth). Thereafter, only the most discriminant time lactation points were considered in the subsequent analyses and the significance of the growth factor was assessed. To cope with multi-colinearity inherent in metabolomics data  [58, 59] , the Analysis of Variance-PLS (AoV-PLS) procedure proposed by El Ghaziri et al. [60]  was applied. Step 2: Identification of the lipid species biomarkers. AoV-PLS aimed at: (i) exploring how "faster" vs. "slower" preterm infants' growth structure was explained by milk lipidomic signatures; and (ii) pinpointing the most discriminant features related to the infants' growth pattern during hospital stay.
This selection was performed on the basis of the variables of importance (for the milk clustering) indices (VIP > 1. 0)  [61]  reflecting the contribution of each variable ([m/z; RT] feature) to the separation of the two infant groups. The appropriate number of components retained for the AoV-PLS model was chosen on the basis of the RMSEP (root mean square error of prediction) value obtained with a leave-one-out cross-validation procedure. In a subsequent stage, the selected AoV-PLS components were subjected to a Fisher's linear discriminant analysis (LDA) to assess the significance of this model with respect with the two a priori groups ("faster" vs. "slower" infant growth) (function linDA of the DiscriMiner package in R  [62] ). Step 3: Validation of the predictive ability of the selected biomarkers.
Thereafter, only the most discriminant time lactation points were considered in the subsequent analyses and the significance of the growth factor was assessed. To cope with multi-colinearity inherent in metabolomics data  [58, 59] , the Analysis of Variance-PLS (AoV-PLS) procedure proposed by El Ghaziri et al. [60]  was applied. Step 2: Identification of the lipid species biomarkers. AoV-PLS aimed at: (i) exploring how "faster" vs. "slower" preterm infants' growth structure was explained by milk lipidomic signatures; and (ii) pinpointing the most discriminant features related to the infants' growth pattern during hospital stay. This selection was performed on the basis of the variables of importance (for the milk clustering) indices (VIP > 1. 0)  [61]  reflecting the contribution of each variable ([m/z; RT] feature) to the separation of the two infant groups.
The appropriate number of components retained for the AoV-PLS model was chosen on the basis of the RMSEP (root mean square error of prediction) value obtained with a leave-one-out cross-validation procedure. In a subsequent stage, the selected AoV-PLS components were subjected to a Fisher's linear discriminant analysis (LDA) to assess the significance of this model with respect with the two a priori groups ("faster" vs. "slower" infant growth) (function linDA of the DiscriMiner package in R  [62] ). Step 3: Validation of the predictive ability of the selected biomarkers. To determine the more robust putative biomarkers of infant growth during hospital stay, lipid species that had been selected on the basis of AoV-PLS/LDA VIP indices in a multivariate scope were submitted to: (i) a subsequent univariate Mann-Whitney U-test; (ii) multiple testing filtering (FDR); and (iii) receiver operating-characteristic (ROC) curve (GraphPad Prism ® ).
A Distinct Breast Milk Lipidomic Signature Is Associated with Infant Growth Rate during Hospital Stay Selected features of lipidomic LC-HRMS (ESI + /ESI -) profiles performed on breast milk samples from Week 2 to Week 7 of lactation were processed with MG PLS-DA (Step 1). On the corresponding score plots obtained in positive (Figure  3 ) and negative (Figure  S1 ) ionization mode (3451 and 903 features respectively, 118 observations), the milk samples associated with the two groups ("faster" vs. "slower" growth) are plotted separately for each weekly sampling time point (i. e. , Weeks 2 to 7). The first three sampling points at Weeks 2, 3 and 4 (i. e. , from the 12th to the 24th day (median values)) allowed the best separation between the two groups of milks. This result led us to further restrict the multivariate analysis to data from samples obtained between Week 2 and Week 4.
Acknowledgments: This work was part of the  LACTACOL  project. The LACTACOL projet was funded by  Région Pays-de-la-Loire  ( 2011-2012 ) and by the  "Fonds Européen de Développement Economique et Régional" (FEDER , Grant  38395 , Project  6226 ,  2013-2015 ). We thank lactating mothers for having accepted donation of breast milk for the LACTACOL protocol, and  Laure Simon , and  Evelyne Gauvard  for caring for mothers and their infants as well as guarantying the collection of maternal milk. We also thank the  Biological Resource Centre for biobanking (CHU Nantes ,  Hôtel Dieu ,  Centre de Ressources Biologiques (CRB) ,  Nantes, F-44093 , France (BRIF:  BB-0033-00040 )) for storing human milk samples in the milk biobank ( DC-2009-982 ).
A C C E P T E D M A N U S C R I P T Analysis was done using GraphPad Prism version 6. 00 for Windows, GraphPad Software, La Jolla California USA. Data are expressed as mean + SEM. Normal distribution was tested with d'Agostino and Pearson omnibus, Shapiro-Wilk, and Kolmogorov-Smirnov normality tests. If at least one of them was passed, statistical analysis was performed using two-tailed Student's t-test or ANOVA and Holm-Sidak's post hoc test. Otherwise non-parametric Mann and Whitney or Kruskal-Wallis' and Dunn's tests were used. p < 0. 05 was considered as statistically significant.
Acknowledgements. The Girault's lab is affiliated with the  Paris School of Neuroscience (ENP)  and the  "Biology for Psychiatry" laboratory of excellence "Bio-Psy  labex". Imaging was carried out at the  Institut du Fer à Moulin "Cell  and  Tissue Imaging  facility". We are grateful to the  Banc de Teixits Neurològics (Biobanc-HC-IDIBAPS)  for providing brain samples from control subjects and AD patients.
Acknowledgments: The authors acknowledge the participants and chief of the  Tshino Nesengani (Mukondeleli) village, Limpopo Province, South Africa. One of the authors is supported by the  South African DST/NRF Centre of Excellence in Human development. Another author is supported in part by the  South African NRF  through grant number  113366. Any opinion, finding and conclusion or recommendation expressed in this material is that of the author(s) and the  NRF  does not accept any liability in this regard.
Statistics Data were analysed with GraphPad Prism 6. 0 (GraphPad Software, Inc. , La Jolla, CA, USA). Results are shown as mean ± SEM. Normality was tested using the D'Agostino-Pearson test. Normally distributed data were analysed by one-way ANOVA followed by Tukey's test, while data without normal distribution were analysed using Kruskal-Wallis ANOVA and subsequent Dunn's tests. Correlation analyses were assessed using Pearson's correlation coefficient. A P-value of <0. 05 was considered significant.