dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural analysis of phospholipids by liquid chromatography coupled to mass spectrometry 2. 7. 1. Characterization of phospholipid species PL classes were separated by HPLC under HILIC conditions, and PL molecular species were characterized by coupling the chromatographic system to an Orbitrap Fusion™ Tribrid mass spectrometer (Thermo Scientific, USA) equipped with an EASY-Max NGTM ion source (heated electrospray ionization H-ESI). Positive and negative ions were alternatively monitored by a polarity switching approach coupled with MS/ MS experiments. PL species were characterized based on high-accuracy MS data and MS/MS experiments using the LipidSearch™ software and LIPID MAPS™ database. The Orbitrap mass spectrometer was controlled by Xcalibur™ 4. 1 software. The HILIC-(ESI-MS and MS/MS) conditions were completely described in our recent paper  [25].
Therefore, neutral loss scanning of 185 Da in the positive ion mode was used to quantify PS compounds. The PI species showed in the negative ion mode a fragment at m/z 241 amu, which was identified as inositol phosphate minus one molecule of H 2 O. This fragment was used for the precursor ion scanning to quantify these compounds. Plasmenyl-ethanolamine (PlsE) was quantified in the negative ionization mode by multiple reaction monitoring (MRM) of the parent/fragment transition for each selected plasmalogen. The amount of PL molecular species in each PL class was expressed relative to the total lipids in this class. The data were processed using the Xcalibur software. In addition, corrections were applied to the data for isotopic overlap. More details about the electrospray source parameters and mass spectrometer methods are available in Le Bon et al.
Microstructure analysis The microstructural characteristics of samples were evaluated by light microscopy. Bolus sections of 10 μm thick collected on glass slides were stained for 3 min with a Lugol solution (0. 3%, w/v) and for 3 min more with a Light Green solution (0. 1%, w/v) to stain starch in purple and proteins in green, respectively. Successive 30-s rinses with deionised water were used to remove excess dye. Micrographs were acquired at 20x magnification using an Olympus BX 61 microscope equipped with a high-resolution digital camera (Olympus DP 71) and an Olympus Cell Sens software (Olympus France SAS, Rungis, France).
Statistical analysis The results obtained were analysed by using one-way ANOVA or repeated measures ANOVA, and post-hoc comparisons between in vitro NM boluses and in vitro deficient mastication boluses after simulating in vitro oral and gastrointestinal digestion were made employing Dunnett's multiple comparison test. The results were statistically processed by XLSTAT 2020. 3. 1 software (XLSTAT statistical and data analysis solution, Addinsoft, New York, USA, displayed as the mean of replicates ± standard deviations (SD), and the statistically significant limit was established at p < 0. 05.
Population sample and dietary data To test the validity of the ORCHID, we made secondary analyses on data from the Third French Individual and National Food Consumption Survey 2014-2015 (INCA3) led by ANSES. INCA3 is a cross-sectional survey aimed at estimating the food consumption and eating habits of individuals living in France. The study was carried out between February 2014 and September 2015 among a representative sample of individuals living in mainland France (excluding Corsica and overseas French territories). A total of 3157 eighteen-to seventy-nineyear-old adults participated in the study. Individuals were selected according to a three-stage cluster sampling design (geographical units, households and individuals), based on the 2011 annual national census, with geographical stratification (region, size of urban area) to ensure national representativeness.
The study sample included participants 60 years of age and over (n 1008). Older adults who responded to less than three 24-h dietary recalls or who failed to respond to the selfadministered food propensity questionnaire (FPQ) were excluded from the analyses, leading to a sample of 696 older adults (293 men and 403 women). Participants received a home visit to take anthropometric measures and explain how and when to complete the selfquestionnaire. Dietary intakes were then assessed using a series of three non-consecutive 24-h dietary recalls (two on weekdays and one during the weekend) over the 3 weeks after the home visit. During phone interviews conducted by professional investigators, participants were asked to declare all foods and beverages they had consumed the day prior to the call. Every declaration was computerised using GloboDiet (previously known as EPIC-Soft  (17)  ) by professional interviewers specifically trained in the methods and the software used.
Foods included both simple foods (e. g. apples, meat, etc. ) and complex dishes with multiple ingredients (e. g. apple pie, lasagne, meat and vegetable stew, etc. ). Portion sizes were estimated by participants during the call, using validated photographs from a picture book of food portion sizes and household measures  (16). The nutrient contents of the foods and beverages cited by participants were extracted from the 2016 food composition database of the French Information Centre on Food Quality  (18). Participants were also asked to complete the FPQ describing their usual consumption frequency of approximately sixty foods or food groups over 12 months. FPQ are qualitative FFQ that may be used alongside 24-h dietary recalls to estimate long-term food intake. The FPQ was completed either on paper or directly online.
Calculation of ORCHID score in INCA3 Calculation of the thirteen ORCHID components based on data from the 24-h dietary recalls. Each food associated with the 24-h dietary recalls was categorised into one of the thirteen ORCHID food groups. Each food declaration was considered only if its ORCHID food group's daily consumption was more than half of a standard portion (in g) as defined in the French Nutrition and Health Survey (ENNS) and the French 'Market Research Group for Collective Catering and Nutrition' (GEMRCN)  (21)  (Table  1 ). For example, if less than 25 g of 'cooked ham' per day is declared whereas the standard portion size is 50 g, then the 'cooked ham' consumption is not taken into account. Food taken into account consisted of both simple foods and complex dishes. The categorisation of simple foods into ORCHID food groups was based on the existing INCA3 food categorisation.
About 70 % of the foods were successfully classified among the thirteen ORCHID food groups. The remaining foods were not included in the ORCHID calculation (mostly water, spices, herbs and sauces). Approximately 5 % of the data collected over the three non-consecutive 24-h dietary recalls concerned complex dishes. Complex dishes were disaggregated into ingredients using recipes, and the main ingredients (i. e. those whose sum represented half of the recipe) were identified. Only the main ingredients were sorted into one of the ORCHID food group and each main ingredient was considered as one occurrence only if the quantity consumed of the ORCHID food group represent more than half a standard portion. For each individual and each of the thirteen ORCHID food groups, the ORCHID components are calculated from the total number of consumption occurrences in three 24 recalls.
The details of the scorings are fully described in Table  1  and had been decided based on expert judgements. Calculation of consumption occurrences based on data from the food propensity questionnaire. For the remaining seven ORCHID food groups, the associated ORCHID components are based on data from the FPQ. To overcome missing occurrences due to a lack of response in the FPQ, an imputation was computed using the random forest method  (22)  , taking into consideration individual characteristics, INCA3, food group quantities and FPQ frequencies with no missing values. To maintain consistency with weekly dietary recommendations, the monthly consumption frequencies collected by the FPQ were transformed into weekly frequencies. For each individual and each of the seven ORCHID food groups, the total number of consumption occurrences (based on weekly frequencies) is used to calculate the ORCHID components, using a positive, proportional rating system, as French dietary guidelines advice favouring the consumption of foods in these food groups (Table  1 ).
An example of an ORCHID score's individual calculation is presented in online Supplementary Material 1. A toy data set created from the INCA3 data and the R code are available as online Supplementary Materials 2, 3 and 4. Evaluation of ORCHID score validity. To evaluate the content and construct validity of the score, we applied a strategy commonly used for diet quality scores  (5, 17, 21)  summarised by Bland and Altam  (22). Content validity is met if the score seems complete (i. e. it does not lack any important component), and construct validity is met if the score is associated with the same variables than the object it measures, that is, diet quality. First, content validity was evaluated by (i) examining the distribution of the ORCHID score and its components throughout the sample and (ii) examining the correlation between the ORCHID score and its components.
Also, the FPQ is self-reported. Given that the population in question is of advanced age, participants may be more prone to memory issues. This could potentially affect data accuracy  (36). No correlation was found between the ORCHID score and total monthly household income and living with someone. However, it is offset by the others correlations found (smocking status, level of physical activity, level of study and BMI). Finally, the data did not include a biological sample. Finally, there is no 'gold' standard against which the ORCHID score can be calibrated. However, the use of a detailed strategy based on other measures with converging results strengthened confidence in the ORCHID score's reliability.
Acknowledgements AFJ would like to thank the  ORS PACA  and LEHA statistician teams and members of MS-nutrition for their advice. This research received no specific grant from any funding agency, commercial or not-for-profit sectors. R. P. , E. O. V. , R. G. , C. D. , N. D. and A. F. J. conceived and designed the ORCHID study. R. P. and A. F. J. performed the statistical analysis, interpreted results and wrote the manuscript. E. O. V. , R. G. and C. F. supported the analyses and writing of the paper. All authors reviewed the manuscript and contributed to the discussion. There are no conflicts of interest.
Statistical analyses 2. 5. 1. Data handling Baseline c-peptide, insulin, and glucose values were obtained for each participant by averaging their measured concentrations of the day for the respective analyte across -17, -12, -7, and -2 min; note the averaged baseline value is equivalent to the participant's reported fasting concentration. To determine changes in c-peptide (Δ c-peptide), insulin (Δ insulin), and glucose (Δ glucose) from baseline, the participant's average baseline value was subtracted from each of their poststimulus time point values (i. e. , concentration at 2, 4, 6, and 8 min) for the respective analyte. Group mean Δ values for 2, 4, 6, and 8 min were then calculated. Additionally, for all four stimuli, each individual's highest Δ c-peptide and Δ insulin concentration reached over 2-8 min was identified.
Data analyses Mean and standard deviation were used to describe participant age, anthropometric measurements, and fasting concentrations of plasma cpeptide, insulin, and glucose. The following planned analyses were performed: 1) To test if age, anthropometric measurements, and fasting concentrations of c-peptide, insulin, and glucose averaged across conditions differed between two sexes, independent samples t-tests were conducted. 2) To determine whether fasting concentrations of c-peptide, insulin, or glucose differed across the four test conditions, repeated-measures analysis of variance (ANOVA) tests were conducted separately for each analyte with target stimulus as a factor. 3) To determine whether Δ c-peptide, Δ insulin, and Δ glucose concentrations increased from baseline, one-sample t-tests (one-tailed) were performed separately for each post-stimulus time point (2, 4, 6, and 8 min).
Acknowledgements We would like to thank  Sandra Uesugi, RN , for assistance with blood collection, and  Hung Nguyen  for assistance with glucose analyses. We would also like to thank  John Glendinning  for his critical reading of the manuscript. AJP is an  ARCS (Achievement Rewards for College Scientists) Scholar  and received the  Rose Marie Pangborn Sensory Science Scholarship  while conducting this research.
Urban BMI excess increased slightly more in middle-income countries in east and southeast Asia, Latin America and the Caribbean, and Middle East and north Africa, a trend that was the opposite of the convergence in BMI of  adults in these same regions  21. Additional analyses of data collected by the NCD Risk Factor Collaboration (NCD-RisC) for young adults (20-29 and 30-39 years) showed that the shift from a small divergent trend to convergence of BMI between urban and rural areas happens in young adulthood (Extended Data Figs. 6  and 7 ), a period during which there is substantial, but variable, weight gain among population subgroups  31. These shifts in trends from adolescence to young adulthood might be a result of changes in diet and energy expenditure that accompany changes in household structure, social and economic roles and the living environment  [32] [33] [34].
Online content Any methods, additional references, Nature Portfolio reporting summaries, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-023-05772-8. We did not estimate the difference between rural and urban height and BMI for countries classified as entirely urban (Bermuda, Kuwait, Nauru and Singapore) or entirely rural (Tokelau). d HUN SRB SVN MN E BIH MK D HR V SV K BG R AL B P O L R O U C Z E E S T B L R L V A L T U R U S M D A U K R U Z B A Z E A R M G E O T K M K A Z K G Z M N G T J K Q A T K W T A R E L B N B H R IR Q S A U O M N E G Y LB Y SY R PS E IR N TU R JO R DZA TUN YEM MAR CHN PRK TWN BRN THA MYS MD V KH M LA O VN M ID N PH L M M R TL S K O R S G P J P N U S A N Z L A U S C A N G B R IR L A U T F IN D E U S W E IS L C H E D N K L U X B E L G R L N O R N L D C Y P G R C E S P IS R A N D M LT IT A PR T FR A BO L PE R EC U BHS ATG BLZ KNA BRB V CT SUR PRI HTI LC A GR D BM U DM A TT O C U B D O M G U Y JA M M E X V E N H N D S L V P A N N IC C R I C O L G T M C H L U R Y A R G B R A P R Y P N G V U T F J I S L B C O K N IU N R U T O N T U V P LW P Y F AS M KI R W SM FS M MH L TK L AFG NPL LKA BTN PAK IND BGD GAB COG GNQ CAF AG O CO D MU S SY C SS D M O Z K EN Z M B T Z A C O M D J I B D I M D G U G A S O M R W A S D N M W I E R I E T H S W Z Z A F N A M Z W E B W A L S O C M R S T P B E N C IV T G O G H A M R T S LE M LI CP V GN B GM B TC D BF A GIN LBR NER NGA SEN E BG R PO L M KD C ZE R O U A L B E S T B L R R U S L V A L T U U K R M D A U Z B A Z E G E O A R M T K M K A Z K G Z M N G T J K Q A T A R E L B N B H R IR Q O M N E G Y S A U LB Y SY R PS E JO R TU R IRN DZ A TUN YEM MAR KWT CHN TWN PRK BRN MYS THA MD V KH M LA O ID N PH L VN M M M R TL S K O R JP N S G P U S A A U S N Z L C A N G B R IR L F IN A U T S W E D E U IS L D N K B E L C H E L U X N O R G R L N L D C Y P G R C E S P IS R A N D IT A PR T M LT FR A BO L EC U PE R BHS ATG BLZ KNA BRB V CT SUR PRI HTI LC A GR D DM A TT O DO M C U B G U Y JA M B M U V E N M E X P A N H N D S L V N IC C R I C O L G T M C H L U R Y A R G B R A P R Y P N G V U T F J I S L B C O K N IU T K L T O N T U V P LW P Y F AS M KI R W SM FS M MH L NR U AFG BTN NPL LKA PAK IND BGD GAB COG GNQ CAF AG O CO D MU S SY C SS D KE N ZM B M O Z T Z A C O M B D I M D G D J I S O M U G A R W A S D N M W I E R I E T H S W Z Z A F N A M L S O Z W E B W A C M R S T P B E N C IV T G O G H A M LI M R T SL E G NB GM B TC D CP V BF A LBR GIN NER NGA SEN.
Methods We estimated trends in mean height and BMI for children and adolescents aged 5-19 years from 1990 to 2020 by rural and urban place of residence for the 200 countries and territories listed in Supplementary Table  1. We pooled, in a Bayesian meta-regression, repeated cross-sectional population-based data on height and BMI. Our results represent estimates of height and BMI for children and adolescents of the same age over time (that is, for successive cohorts) in rural and urban settings for each country.
Data sources We used a database on cardiometabolic risk factors collated by NCD-RisC. Data were obtained from publicly available multi-country and national measurement surveys, for example, Demographic and Health Surveys (DHS), WHO-STEPwise approach to Surveillance (STEPS) surveys, and those identified through the Inter-University Consortium for Political and Social Research, UK Data Service and European Health Interview & Health Examination Surveys Database. With the help of the WHO and its regional and country offices as well as the World Heart Federation, we identified and accessed population-based survey data from national health and statistical agencies. We searched and reviewed published studies as previously detailed  54  and invited eligible studies to join NCD-RisC, as we did with data holders from earlier pooled analyses of cardiometabolic risk factors  [55] [56] [57] [58].
The NCD-RisC database is continuously updated through all the above routes and through periodic requests to NCD-RisC members to ask them to suggest additional sources in their countries. We carefully checked that each data source met our inclusion criteria, as listed below. Potential duplicate data sources were first identified by comparing studies from the same country and year, followed by checking with NCD-RisC members that had provided data about whether the sources from the same country and year, with similar samples, were the same or distinct. If two sources were confirmed as duplicates, one was discarded. All NCD-RisC members were also periodically asked to review the list of sources from their country to verify that the included data met the inclusion criteria and were not duplicates.
For each data source, we recorded the study population, the sampling approach, the years of measurement and the measurement methods. Only data that were representative of the population were included. All data sources were assessed in terms of whether they covered the entire country, one or more subnational regions (that is one or more provinces or states, more than three cities, or more than five rural communities), or one or a small number of communities (limited geographical scope not meeting above national or subnational criteria), and whether participants in rural, urban or both areas were included. As stated in the sections on the statistical model, these study-level attributes were used in the Bayesian hierarchical model to estimate mean height and BMI by country, year, sex, age and place of residence using all available data while taking into account differences in the populations from which different studies had sampled.
All submitted data were checked by at least two independent individuals. Questions and clarifications were discussed with NCD-RisC members and resolved before data were incorporated into the database. Anonymized individual data from the studies in the NCD-RisC database were re-analysed according to a common protocol. We calculated the mean height and the mean BMI, and the associated standard errors, by sex, single year of age from 5 to 19 years and rural or urban place of residence. Additionally, for analysis of height, participants aged 20-30 years were included, assigned to their corresponding birth cohort, because mean height in these ages would be at least that when they were aged 19 years given that the decline in height with age begins in the third and fourth decades of life  59. All analyses incorporated sample weights and complex survey design, when applicable, in calculating summary statistics.
For studies that had used simple random sampling, we calculated the mean as the average of all individuals within the group and the associated standard error (s. d. divided by the square root of sample size); for studies that had used multistage (stratified) sampling, we accounted for survey design features, including clusters, strata and sample weights, to weight each observation by the inverse sampling probability and estimated standard error through Taylor series linearization, as implemented in the R 'survey' package  60. Computer code was provided to NCD-RisC members who requested assistance. For surveys without information on the place of residence, we calculated summary statistics stratified by age and sex for the entire sample, which represented the population-weighted sum of rural and urban means; data on the share of population in urban and rural areas were from the United Nations Population Division  61.
Data inclusion and exclusion Data sources were included in the NCD-RisC height and weight database if the following criteria were met: measured data on height and weight were available; study participants were 5 years of age or older; data were collected using a probabilistic sampling method with a defined sampling frame; data were from population samples at the national, subnational or community level as defined above; and data were from the countries and territories listed in Supplementary Table  1. We excluded all data sources that were solely based on self-reported weight and height without a measurement component because these data are subject to biases that vary by geography, time, age, sex and socioeconomic characteristics  [62] [63] [64]. Owing to these variations, approaches to correcting self-reported data may leave residual bias.
We also excluded data sources on population subgroups for which anthropometric status may differ systematically from the general population, including the following: studies that had included or excluded people based on their health status or cardiovascular risk; studies in which participants were only ethnic minorities; specific educational, occupational or socioeconomic subgroups (with the exception noted below); those recruited through health facilities (with the exception noted below); and females aged 15-19 years in surveys that sampled only ever-married women or measured height and weight only among mothers. We used school-based data in countries and age-sex groups with school enrolment of 70% or higher. We used data for which the sampling frame was health insurance schemes in countries where at least 80% of the population were insured.
Finally, we used data collected through general practice and primary care systems in high-income and central European countries with universal insurance because contact with the primary care systems tends to be as good as or better than response rates for population-based surveys. We excluded participants whose age was <18 years and whose data were not reported by single year of age (<0. 01% of all participants) because height and weight may have nonlinear age associations in these ages, especially during growth spurts. We excluded BMI data for females who were pregnant at the time of measurement (<0. 01% of all participants). We excluded <0. 2% of all participants who had recorded height: <60 cm or >180 cm for ages <10 years; <80 cm or >200 cm for ages 10-14 years; <100 cm or >250 cm for ages ≥15 years, or who had recorded weight: <5 kg or >90 kg for age <10 years; <8 kg or >150 kg for ages 10-14 years; <12 kg or >300 kg for ages ≥15 years, or who had recorded BMI: <6 kg m -2 or >40 kg m -2 for ages <10 years; <8 kg m -2 or >60 kg m -2 for ages 10-14 years; <10 kg m -2 or >80 kg m -2 for ages ≥15 years.