dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversion of BMI prevalence metrics to mean BMI In 0. 5% of our data points, mostly extracted from published reports or from a previous pooling analysis  58  , the mean BMI was not reported but data were available for the prevalence of one or more BMI categories, for example BMI ≥30 kg m -2. To use these data, we used previously validated conversion regressions  65  to estimate the missing primary outcome from the available BMI prevalence metric or metrics. Additional details on regression model specifications along with the regression coefficients are reported at https://github. com/NCD-RisC/ncdriscmethods/.
Statistical model specification As stated earlier, for each data source, we calculated mean height and BMI, together with corresponding standard errors, stratified by sex, age and rural or urban place of residence. For sources that did not stratify the sample on the place of residence, we obtained age-and-sex-stratified data. Each study contributed up to 30 mean BMI data points or 32 mean height data points for each sex, with the exact number depending on how many age groups were represented in the study and whether the study provided data stratified on urban and rural place of residence. The likelihood for an observation at urbanicity level s (urban-only, rural-only or mixed; referred to as stratum hereafter) and age group h, with age z h , from study i, carried out in country j at time t is as follows: ∼ y N a b t u γ z e I p q t r z d n τ ( + + + ( )+ + + [ + + + ], SD / + ), s h i j i j i i ji t i h i s i j i j i i ji h i sh i s h i , , [ ] [ ] [ ], , [ ] [ ] [ ] , , 2 , , 2 i X β i where the country-specific intercept and linear time slope from the jth country (j = 1 … J, where J = 200 which is the total number of countries in our analysis) are denoted a j and b j , respectively.
Urban and rural strata. To model mean height and BMI by urban and rural places of residence, the model included offsets for the two strata. The offsets were captured by country-specific intercept, linear time and age effects, using a centred indicator term (I s i , ): I p q t r z d [ + + + ], s i j i j i i ji h i , [ ] [ ] [ ] where I X = -1 + 2 s i j i t i , urb [ ], [ ] urb        In other words, for data not stratified by place of residence, the model treated the unstratified mean height or BMI as equivalent to the weighted sum of the (unobserved) urban sample mean height or BMI and rural sample mean height or BMI, with the weights based on the proportion of the population of that country living in urban areas in the year of the survey (X j i t i.
Strengths and limitations An important strength of our study is its novel scope of presenting consistent and comparable estimates of urban and rural height and BMI among school-aged children and adolescents, which is essential to formulate and evaluate policies that aim to improve health in these formative ages. We used a large number of population-based studies from 194 countries and territories covering around 99% of the population of the world. We maintained a high level of data quality and representativeness through repeated checks of study characteristics against our inclusion and exclusion criteria, and did not use any self-reported data to avoid bias in height and weight. Data were analysed according to a consistent protocol, and the characteristics and quality of data from each country were rigorously verified through repeated checks by NCD-RisC members.
Acknowledgements This study was funded by the  UK Medical Research Council  (grant number  MR/V034057/1 ), the  Wellcome Trust (Pathways to Equitable Healthy Cities  grant  209376/Z/17/Z ), the  AstraZeneca Young Health Programme  and the  European Commission  ( STOP  project through  EU   Horizon 2020 research and innovation programme  under Grant Agreement 774548). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to the Author Accepted Manuscript version arising from this submission. We thank  W. Dietz ,  L. Jaacks  and  W. Johnson  for recommendations of relevant citations. The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated.
Data availability Estimates of mean BMI and height by country, year, sex, single year of age as well as age-standardized, and place of residence (urban and rural) will be available from https://www. ncdrisc. org in machinereadable numerical format and as visualizations upon publication of the paper. Input data from publicly available sources and contact information for data providers can be downloaded from https://www. ncdrisc. org and Zenodo (https://doi. org/10. 5281/zenodo. 7355601).
Study design This was a community-based intervention as part of the SMC-NUT study, protocol NCT04238845. Details of the SMC-NUT protocol were previously published elsewhere  [21]. During the first month of the 2020 SMC campaign, a baseline census was performed by the Nanoro HDSS to identify all eligible children living within the study catchment area. Children whose parent/guardians consented were visited at home by the field workers during the first month of SMC period from July 13 th to August 12 th , 2020. Inclusion criteria were children under SMC and nutrient supplementation coverage (6 months to 5 years old), permanent residence within the study area, ability to complete the study procedures and willingness of parent/ guardian to participate to the study. Children were not included in case of illness at the time of the enrolment, planned travel or inability to complete the study procedure or unwillingness of parents/ guardians to participate to the study.
Assessment of the use of malaria preventive measures Data on malaria prevention were mainly focused on the use of long-lasting insecticide nets (LLIN). The latter was assessed through the following question addressed to parent/guardian of included children by the field workers: Did the child sleep under an LLIN the night before this visit? A no response was seconded by choice from a drop-down list comprising (i) I have no LLIN in my house, (ii) I have an LLIN in my house, but I have not set it up, (iii) My LLIN is set up, but I have not used it, and (iv) other reason to specify. SMC treatment uptake was not supervised but the number of AQSP tablets taken prior to the time of the field worker's visit was recorded through self-declaration and examination of remaining doses.
Data management and statistical analysis For the baseline, paper Case Record Form (CRFs) was appropriately developed for each participant and data were double entered in the centralized database using REDCap software. Electronic records were backed up and archived daily. Accuracy, reliability, and consistency of electronic records were verified following a standardized procedures of the CRUN data management team. Any action such as creation, modification, or deletion of electronic records by the operator was independently recorded through a control system. Consistency and completeness of records were judiciously verified before individual records were uploaded and centralized via internet into a central database located in the data management section of the IRSS/CRUN. Furthermore, regular consistency checks were performed by the data manager and all the generated queries were transferred to field supervisors for resolution.
This project is part of the EDCTP2 programme supported by the  European Union  (grant number  TMA2018CDF-2365 ). We are grateful to EDCTP2 Association for the financial support through the career development fellowship. We are grateful to  Nutriset, Malaunay, France  for the donation of the PlumpyDoz for the  SMC-NUT  project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Calcium imaging and behavioral recordings For experiments in the linear track, after pre-training, odorants (ethyl acetate, limonene, hexanal diluted to 0. 3% vol. /vol. in mineral oil) were introduced. The field of view (FOV), focus, signal gain, LED intensity, exposition time for GCaMP fluorescence were optimized for each mouse. FOVs selected during base plate implantation were the same for freely-moving recordings, and the same parameters for signal gain, LED intensity and exposition time were used. After setting up optimal imaging parameters, mice were put back into their home cage 20-40 minutes for miniscope habituation before the beginning of the task. Miniscope recordings and nose-poking detections were synchronized using the inscopix data acquisition box. A red LED light located next to the linear track was used to align the beginning and the end of calcium imaging and behavioral recordings.
Data structureFor all odor coding analyses, the output of Inscopix's CNMF-based cell segmentation software was a 2-dimensional time series matrix, with cells in columns and calcium signals in rows. Headfixed calcium signals consisted of 6 30 sec trials for 4 stimuli each. A 30 sec trial contained 10 sec pre-stimulus, 2 sec odor response, and 18 sec post-stimulus phases. For freely moving calcium signals, a trial contained a 1 sec pre-stimulus and 2 sec odor response phase. A total of 48 trials were organized into sets of 12 trials per odor, 6 trials at the right and 6 trials at the left odor port. For subsequent analysis, calcium traces were reorganized into a 4-dimensional dataset (cells, time, trials, odors).
The study was approved by the local ethics committee of the University of  Bonn, Medical Center (No. 214/20). The study protocol conformed to the declaration of Helsinki. Participation in the study was voluntary, participants gave written informed consent, and were paid €40. Based on a prior sample size calculation using G*Power  (Faul et al. , 2009)  (see Supplementary Material 11 for details), we recruited 106 healthy volunteers, half of which were each tested at the Fraunhofer Institute for Process Engineering and Packaging IVV in Freising and the BonnEconLab in Bonn. In Bonn, participants were recruited via the hroot database  (Bock et al. , 2014)  of the BonnEconLab. Data from six subjects were excluded because they did not complete all sessions. Exclusion criteria were: being underweight or overweight/obesity (BMI < 17.
In case of three-way interaction, we calculated corresponding BFs by dividing the BF of a model with the three-way interaction by the BF of a model without it. As a robustness check, we varied the prior width parameter from r = 0 to r = 1 and visually checked resulting BFs. If not otherwise stated, the analysis was considered robust. Statistical analyses were conducted using R 4. 1. 0 (R Core Team, 2021). Linear mixed models were performed using lme4  (Bates et al. , 2015) , lmerTest  (Kuznetsova et al. , 2017) , and emmeans  (Lenth, 2022)  packages. Bayesian analysis was conducted with the BayesFactor package  (Morey & Rouder, 2021). Figures were created using ggplot2  (Wickham, 2016). Statistical tables of linear models are provided in the Supplementary Material.
Funding This work was supported by four Competence Clusters Nutrition Research funded by the German Federal Ministry of Education and Research (BMBF): enable (01EA1409A to J. F. ), DietBB (01E1809B to B. W. ), NutriAct (01EA1408A-G to K. O. ), and nutriCARD (01E01001 to B. P. ). Additional support was provided by the German Academic Scholarship Foundation to D. S.
Acknowledgments We thank our participants as well as technicians and colleagues  Laura Höckmeier ,  Holger Gerhardt ,  Clemens Overkott ,  Nayara Atella-Hödtke ,  Jana Paus ,  Emir Kavukcu , and  Lennard Schneidewind  who helped conduct the study and  Sally Arnhardt  for enhancing the figures of the manuscript. The present work was performed in partial fulfillment of the requirements for obtaining the degree "Dr. rer. biol. hum. " at  Friedrich-Alexander Universität Erlangen-Nürnberg  (D. S. ).
These findings could suggest cognitive effect of a MI in early stage on the continuum of Alzheimer disease (AD). However, MAPT-AV45 and FINGER studies had several methodological limitations: (1) the long-term impact of MI was not evaluated after interruption of the interventional program to test durability, (2) the sub-group size of MAPT-AV45 was relatively low, and (3) APOE ε4 status used in FINGER to define at-risk subjects for cognitive decline cannot be considered as a diagnosis biomarker of AD. To date, amyloid level assessed by PET and cerebrospinal fluid (CSF) measures of Aβ isoforms are the most widely used amyloid biomarkers. Screening by amyloid PET is difficult to generalize in real-world settings given its cost and limited access. Blood-based biomarkers are less invasive and cost-effective options for identification of at-risk subjects eligible for these non-pharmacological interventions  [5].
In a subgroup of the MAPT study, amyloid blood assays have been performed from the MAPT biobank to determinate amyloid status of the participants. These data are an opportunity to validate encouraging findings from MAPT-AV45 and to assess the possibility of such preventive trials based on blood biomarkers in the future. Moreover, two additional years of clinical observation were performed after completion of the MAPT interventional program to track durability of the intervention once discontinued. Therefore, we evaluated the longterm cognitive effect over a 36-month treatment period and at 60 months, 24 months after discontinuation of non-pharmacological intervention in the subgroup characterized by blood biomarkers.
Study design and participants All subjects included in the present analysis were participants, from the MAPT and MAPT-PLUS studies, that were tested for amyloid blood biomarkers (Fig. 1 ). MAPT was a multicenter (13 memory centres in France and Monaco), randomized, placebo-controlled, 3-year trial whose objective was to assess effect of MI and omega 3 on cognitive performance. MAPT-PLUS was a 2-year observational and optional extension of MAPT after completion of the interventional program  [1]. The objective of MAPT-PLUS was to evaluate the long-term cognitive effect of MAPT preventive strategies. This additional follow-up was systematically offered to MAPT participants during the end-of-study visit. Based on MAPT inclusion criteria, subjects included in the present analysis were non-demented, 70 years old and over, and fulfilled one of the following three criteria: spontaneous memory complaint, limitation in one instrumental activity of daily living, or slow gait speed  [2].
Procedures Participants took two capsules of either the placebo or omega 3 daily. The active supplement used was V0137, an oil mixture containing natural fish oil with a minimum of 65% docosahexaenoic acid (400 mg) and a maximum of 15% eicosapentaenoic acid (no more than 112. 5 mg). As described previously, MI program consisted of 12 2-h group sessions focusing on three domains (cognitive stimulation, demonstrations of physical activity, and nutritional advice) and a preventive consultation for the management of cardiovascular risks at baseline, 12 and 24 months  [2]. This interventional program lasted 3 years, and 2-year observational follow-up was added in MAPT-PLUS. Clinical visits in MAPT and MAPT-PLUS were scheduled every 6 or 12 months to assess physical and functional conditions and adherence  [1].
Plasma Aβ42/Aβ40 immunoprecipitation/mass spectrometry assay methods Plasma samples of 0. 46 ml were assessed to test plasma Aβ 42 and Aβ 40 levels by immunoprecipitation mass spectrometry as previously described  [9, 22]. Aβ levels were analyzed and calculated by integrated peak area ratios to known concentrations of the internal standards using the Skyline software package  [23]. Aβ42/Aβ40 cutoff (≤ 0. 0107) has been defined, by Randall Bateman laboratory at Washington University School of Medicine in Saint-Louis, to discriminate as accurately as possible negative and positive amyloid participants in comparison to PET  [24]. Indeed, many subjects included in the present analysis (n = 233) were participants from MAPT-AV45 with amyloid PET (Fig. 1 ). In the MAPT-AV45 study, the positivity threshold for amyloid PET was set at SUVr > 1.
Statistical analysis Analysis was completed in the intention-to-treat (ITT, n = 483) and per-protocol (n = 457) populations. The ITT population consisted of all randomly assigned participants who completed a cognitive composite score at baseline and a minimum of one post-baseline visit. Per-protocol population excluded all major protocol violations at baseline and during follow-up  [2]. Efficacy in subgroups according to amyloid blood status was assessed by post-hoc analysis. We used the same statistical method as for the work carried out to determine the cognitive effect of MAPT interventions according to PET amyloid status  [4]. Linear mixed-model repeated-measures analyses were used including baseline, 6, 12, 24, 36, 48, and 60-month follow-up data to assess between-group differences in the change in cognitive composite score from baseline to 12, 36, and 60 months.
Time was used as a continuous variable, with a cubic trajectory, because the terms time 2 and time 3 were significant. For each linear mixed model, we included subject-specific random effects to consider the intra-subject correlation: a random intercept to consider the heterogeneity of the composite score at baseline and a random slope to consider the heterogeneity of the slopes between subjects. In the unadjusted linear mixed models, we included these fixed effects: intervention group by their amyloid blood status (8 categories), time, and interaction between group and time  [4]. Then, to test the difference of the effect of the intervention between the negative and positive amyloid blood groups, we used the estimates of the interaction term parameters with the ESTIMATE command from the SAS MIXED procedure.
All the models were completed with and without adjustments for gender, age, educational level, CDR global score, and APOE ε4 genotype. All p values were presented before and after adjustment for multiple comparisons (using the Hochberg procedure) and the statistical significance was set at a P value < 0. 05. All confidence intervals were two-sided with a 95% confidence level. All statistical analyses were achieved using SAS software version 9. 4 (SAS Institute Inc, Cary, NC).
Enrollment and rates of study completion Among the 1680 participants in MAPT and 1503 in its biobank, 483 amyloid blood assays were performed for this analysis at 12 (448 subjects) and 24 months (35 subjects). These subjects (n = 483) were selected from MAPT biobank based on their participation in the MAPT-AV45 study and an available blood sample as close as possible to the baseline visit. Subjects were enrolled in the MAPT biobank from October 2009. The mean time interval between blood collection and baseline visit is 12. 99 ± 3. 15 months. From 483 subjects included in this analysis, 323 subjects had observational data at 48 months and 299 at 60 months in MAPT-PLUS. The flow chart of participants in this analysis is shown in Fig. 2. The ITT population included 161 positive and 322 negative amyloid subjects based on plasma Aβ42/Aβ40 ratio.
Confocal laser scanning microscopy (CLSM) The model IFF microstructure was observed on inverted microscope using a CLSM system (Leica SP8, Heidelberg, Germany) as described by  Kergomard et al. (2021)  using a 40 × water-immersion objective. Three fluorescent dyes were used to localize proteins, apolar or polar lipids and were added in 200 μL model IFF at least 10 min before observation. 10 µL of model IFF was then deposited on a glass slide. The Leica LAS X software (Wetzlar, Germany) was used for images collection and analysis.
Differential scanning calorimetry The crystallization behavior of oil phases was characterized using a differential scanning calorimeter (DSC Q200, TA Instruments, New Castle, USA). Cooling was carried out with a refrigerated cooling system (TA Instruments, New Castle, USA) with Nitrogen as purge gas. The TA Instruments Software was used to record and analyze the thermograms. 10 mg of each oil mixtures were loaded into aluminum DSC pans with inverted lids (TA Instruments, New Castle, USA) and sealed. An empty, hermetically sealed, aluminum pan was used as reference. The temperature range was adapted to each oil mixture as follow: isothermal during 10 min at 60 • C, 40 or 30 • C for POM, COM and RPOM or DFOM, respectively followed by cooling and isothermal at -40 • C for 5 min. The scan rate was 5 • C.
25 mm with 0. 2 μm film thickness; Chrompack, Mid-delburg, Netherlands), and helium (1 mL. min -1 ) was used as carrier gas. Fatty acid methyl esters (FAME) were analyzed by flame ionization detector and ChromCard software data system (version 2005, Thermo FisherScientific, Massachusetts, USA). The column temperature started from 150 • C, reached 225 • C with a rise of 5 • C. min -1 and was maintained for 10 min. The injector and detector temperatures were and 270 • C, respectively. FAME were identified using an external standards of methyl esters mixture.
The infrared signal from the membrane was detected with an Odyssey imaging system CLx (LI-COR Biosciences). An initial scan at a low resolution (337 m) was performed to locate and verify the quality of the signal. This region was then scanned at a higher resolution (42 m). Quantifications were performed using ImageStudioLite software. To assess the le v els of selenoprotein mRNAs, we performed quantitati v e re v erse transcription-PCR (RT-qPCR) experiments as described previously  ( 23 , 25 , 55 ). Total RNAs were re v erse transcribed using the qScript cDNA Synthesis kit (Quanta Bio) according to the manufacturer's instructions. Real-time PCR was performed in triplicate using FastStart Uni v ersal SYBR ® Green master mix (Roche Applied Science) on a StepOne Real-Time PCR System (Applied Biosystems).
Protein extraction and analysis by western blot Cellular protein extracts were harvested in a lysis buffer (25 mM Tris-HCl, pH 7. 8, 2 mM DTT, 2 mM EDTA, 1% Triton X-100 and 10% glycer ol). Then, pr otein concentrations were measured using the DC kit protein assay kit (Biorad) in micr oplate assays. Equal pr otein amounts (30 g) were separated on 4-12% Bis-Tris NuPAGE Nov e x Midi Gels and transferred onto nitrocellulose membranes using the iBlot ® DRy blotting System (Ther-moFisher Scientific). Membranes were probed with the indicated primary antibodies and horseradish peroxidase (HRP)-conjugated anti-rabbit or anti-mouse secondary antibodies. Antibodies wer e pur chased from Abcam (GPX1, #a b108427; GPX4, #a b125066; TXNRD1, #a b124954; TXNRD2, #a b180493; SELENOH, #a b151023; SE-LENOO, #ab172957; SELENOT, #ab176192) and Sigma (SELENOS, #HPA010025; Actin, #A1978, HRPconjuga ted goa t anti-ra bbit IgG , #A6154, HRP-conjugated goat anti-mouse IgG, #A9044).
The chemiluminescence signal was detected using an ECL Select detection kit (GE Healthcare) in the Chemidoc Imager (Biorad). Data quantifications were performed with ImageLab Software (Biorad, Version 6. 0. 1). The top part of the membranes wer e r eblotted with anti-actin antibodies following the same procedure. Values were expressed relati v e to the signal obtained for actin.
In silico prediction of C65G mutation-induced changes in tRNA [Ser]Sec structure The analysis of the two-dimensional (2D) structure of tRNA [Ser]Sec , as depicted in Figure  1  A, suggests that the substitution of the C65 nucleotide with a G may disrupt a Watson-Crick C:G base pairing, and may also impose a steric hindrance due to the presence of two purine residues facing each other. Howe v er, it is possib le that two G residues ma y f orm various types of non-Watson-Crick base pairs, as described previously  ( 61 ). To determine whether this could also occur in this case and pre v ent a complete destabilization of the cloverleaf tRNA folding, we conducted in silico analyses to predict possible RNA structures. To accomplish this, we used RNAComposer, an online software that provides 3D structure models based on the sequence and secondary structure topological folding of RNA  ( 62 ).