dataesr/scientific-paragraphs-categorization huggingface dataset:
com). Reconstructed PET images are analyzed with a pipeline developed by CATI, according to a method previously described. The mean activity in the pons and whole cerebellum regions was used as reference for individual voxel normalization in the partial volume effect corrected images. Standard uptake value ratios (SUVR) were calculated for each of 12 cortical regions of interest (cingulum posterior right and left, cingulum anterior right and left, frontal superior right and left, parietal inferior right and left, precuneus right and left, temporal mid right and left), as well as the global average SUVR. For Aβ-PET data from the CATI pipeline the threshold identified to categorize our cohort in Aβ-PET positive or Aβ-PET negative was 0. 7918  [14, 15]. The characteristics of the 70 individuals included in this study are provided in the table below (Table  1 ).
Data analysis All statistical analyses were performed using the R software program (version 3. 4. 4) [16]. To assess the predictive ability of the Cq values, we conducted a sparse partial least squares discriminant analysis (sPLS-DA)  [15]  as implemented in sPLS-DA function of the R package mixOmics 22. The use of sPLS-DA is motivated by its dimension reduction effectiveness through the construction of a small set of orthogonal components (latent variables) summarizing the manifest Cq values. All of the Aptamarker values and the ratios between them are considered variables. We only computed the ratios in one direction. This still means however that the number of variables far exceeds the number of samples analyzed and as such increases the risk of spurious relationships between variables and variance for Aβ brain deposition.
The sparse component of this package enables us to reduce the number of variables and dimensions in which variance is considered to mitigate this problem. A predictive model was built based on a random sample of 54 individuals from the 70 individuals analyzed. One individual sample was excluded from the analysis as an outlier. This left an additional 15 individual samples as a test set. For data analysis, ratios of all Cq values from the 54 samples were determined and added to the Cq values alone as a combined dataset. This data set was normalized to zero mean and standard deviation of unity for each Cq value or Cq value ratio. This normalized matrix was then used as explanatory variables in sPLS-DA to account for the Aβ brain lesion status, categorized as negative "neg" or positive "pos" based on a SUVr threshold of 0.
This work has been performed during his previous position at Sorbonne University, Paris, France. HHampel is an employee of Eisai Inc. and serves as Senior Associate Editor for the Journal Alzheimer's & Dementia and does not receive any fees or honoraria since May 2019; before May 2019 he had received lecture fees from Servier, Biogen and Roche, research grants from Pfizer, Avid, and MSD Avenir (paid to the institution), travel funding from PLOS ONE | https://doi. org/10. 1371/journal. pone. 0243902 January 4, 2021 2 / 14 Lilly and company, Cytox Ltd. , GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. This work has been performed during his previous position at Sorbonne University, Paris, France. At Sorbonne University he was supported by the AXA Research Fund, the "Fondation partenariale Sorbonne Universite" and the "Fondation pour la Recherche sur Alzheimer", Paris, France.
g. , general practitioners and medical specialists, pharmacists, dentists, municipalities)  54. Recruitment is still open. Participants are tracked using an online platform connected to their email address and questionnaires are completed online on a dedicated website (https:// etude-nutri net-sante. fr). All participants provide an electronic informed consent. The NutriNet-Santé study is registered on ClinicalTrials. gov as NCT03335644 and is conducted according to the Declaration of Helsinki guidelines and is approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm) and the "Commission Nationale de l'Informatique et des Libertés" (CNIL n°908,450/n°909,216).
Data collection. At inclusion and at least each year thereafter, participants were asked to complete a set of five questionnaires related to sociodemographic and lifestyle characteristics (e. g. , date of birth, sex, educational Dietary assessment. At inclusion, and every six months thereafter (to vary the season of completion) participants completed a series of three non-consecutive, web-based 24 h-dietary records (validated against an interview by a trained dietitian  59  and against blood and urinary biomarkers  60, 61  ), randomly assigned over a twoweek period (two week-days and one weekend day). Participants reported all foods and beverages consumed for the three main meals and on any additional eating occasions. Portion sizes were estimated by participants using validated photographs or usual serving containers  62.
To assess daily intakes of macronutrients, micronutrients, alcohol, and total calories, dietary consumption data were linked to the NutriNet-Santé food composition database which contains more than 3,500 generic items  63. Besides, for each industrial product, the brand and commercial name were collected. Intakes from composite dishes were estimated by referring to French recipes as defined by nutrition professionals. Baseline habitual dietary intakes were averaged from all 24 h-dietary records provided during the first two years of follow-up (at least two 24 h records mandatory for inclusion in the study). Identification of dietary under-reporting was identified based on the method proposed by Black, by using the basal metabolic rate and Goldberg cut-off, and under-reporters of energy intake were excluded  64.
Food additives. The determination of food additive exposure is described in detail in Appendix 1. Briefly, for each food (or beverage, candy, chewing-gum) consumed, the presence of each food additive (qualitative composition data) and if relevant, its dose (quantitative composition data) were searched by the research team (no specific knowledge on food additives or their classification was required from the participants). To determine the qualitative presence/absence of food additives, three databases were used: OQALI  65  , a national database hosted by the French food safety agency (ANSES) to characterize the quality of the food supply, Open Food Facts, an open collaborative database of food products marketed worldwide  66  , and Mintel GNPD  67  , an online database of innovative food products in the world.
Statistical analyses. Descriptive analyses were weighted according to the 2016 French national census report data by using the CALMAR macro run by sex and based on categories for age, socio-professional status and housing area  68. Intakes of each food additive were described in mg per day as well as in mg/kg bodyweight per day (% of consumers, mean, SD, median, 95 th percentile). Toxicity of each food additive is assessed by EFSA to determine its Acceptable Daily Intake (ADI), which is then used to set maximum authorized levels in the different food groups. However, additives without a specified ADI can be used quantum satis, i. e. , with 'no limitations other than current good manufacturing practice'. Proportion of participants exceeding the ADI  69  (when available) were calculated for each food additive.
Clusters of participants were described in terms of socio-demographic characteristics, food additive intake and food consumption. Regarding food and food additive consumption, means adjusted for energy intake and number of dietary records were used for description. Comparisons between clusters were performed by Chisquare tests or linear regression, as appropriate. Partial correlation corresponds to the degree of association between two variables, controlling for other variables. To visualize the partial correlations between food additives, a network was generated using the glasso package  74  which computes a sparse gaussian graphical model with graphical lasso  75. It can be interpreted as follows: when two food additives are connected by a blue line, it means that they are more consumed by the same participants, when they are connected by a red line, it means that they are rarely consumed by the same participants.
Values are percentages unless stated otherwise. IPAQ was available for 91,675 participants, education for 99,725, smoking status for 106,242 and proportion of organic food for 28,075 participants. *Comparisons between clusters using Chi-square tests or linear regressions, as appropriate. **Adjusted for energy intake and number of 24 h dietary records. Continued main mixtures identified). In a previous work consisting in the exploration of the Open Food Facts database, we identified clusters of additives found in food products of the French market  30. The mixtures of food additives identified in the present work resulted 1) from the co-occurrence of several additives in a same industrial product (as shown previously  30  ) and 2) from the co-consumption of various food products within usual dietary patterns.
Percent Strengths of this study included the large sample size and the accuracy of dietary intake data used to estimate additive exposure at the individual level, which is necessary for future etiological studies (population-based    simulations are not appropriate for this purpose). Indeed, repeated 24 h records allowed us to collect detailed information on > 3500 generic foods/beverages, each declined in dozens of commercial brands, which is a strength compared to previous nutritional studies. Three complementary databases were used to determine qualitative additive composition and thousands of assays were performed and complemented by EFSA and GSFA data to retrieve information on quantitative doses. However, some limitations should be acknowledged. First, not all food additives could be covered due to a lack of quantitative data for some additives.
Acknowledgements The authors warmly thank all the volunteers of the NutriNet-Santé cohort. We also thank  Thi Hong Van Duong ,  Régis Gatibelza , and  Jagatjit Mohinder  (computer scientists),  Julien Allègre ,  Nathalie Arnault ,  Laurent Bourhis  (data-managers/biostatisticians), and  Sandrine Kamdem, MD  (physician), for their technical contribution to the  NutriNet-Santé study. IARC disclaimer: Where authors are identified as personnel of the  International Agency for Research on Cancer / World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer / World Health Organization.
Iron isotopes are fractionated by multiple biological processes, which offers a novel opportunity to study iron homeostasis. The determination of Fe isotope composition in biological samples necessitates certified biological reference materials with known Fe isotopic signature in order to properly assess external reproducibility and data quality between laboratories. We report the most comprehensive study on the Fe isotopic composition for widely available international biological reference materials. They consist of different terrestrial and marine animal organs (bovine, porcine, tuna, and mussel) as well as apple leaves and human hair (ERC-CE464, NIST1515, ERM-DB001, ERM-BB186, ERM-BB184, ERM-CE196, BCR668, ERM-BB185, ERM-BB124). Previously measured Fe isotopic compositions were available for only two of these reference materials (ERC-CE464 tuna fish and ERM-BB186 pig kidney) and these literature data are in excellent agreement with our data.
Although research fields such as geochemistry benefit from numerous extensively characterised international reference rock materials, metal isotope compositions of certified reference materials of biological origin are very scarce [e. g. ,  (50) (51) (52) ]. In this context, we have determined the Fe isotope composition in nine widely available international biological standards. For seven of them, there is no existing published value for Fe isotope composition. Moreover, due to occasional or definitive unavailability of the international standards such as IRMM-014 which is used as reference, well-characterised biological reference materials are essential to compare different datasets. This data could therefore be used in future biological and/or medical studies to cross calibrate values between laboratories and as quality control.
The dataset can be divided into two groups with human hair (ERM-DB001), apple leaves (NIST1515) and organs of marine animal origin (tuna fish ERC-CE464 and mussel tissue BCR668) presenting δ 56 Fe values ranging from -0. 35 to -0. 71‰ (Figure  2 ). The five reference materials sampled from bovine and porcine organs present more fractioned Fe isotope compositions compared to the IRMM-014 standard, with δ 56 Fe values from -1. 48 to -2. 27‰. Pork (ERM-BB124) and bovine (ERM-BB184) muscles present very close δ 56 Fe values of -1. 92 ± 0. 02‰ and -1. 97 ± 0. 07‰ respectively suggesting that iron fractionation processes in both organisms are similar. On the other hand, bovine blood (ERM-CE196) and liver (ERM-BB185) yield significantly different δ 56 Fe values of -2. 27 ± 0. 12‰ and -1. 48 ± 0. 17‰, respectively, suggesting the existence of one or several processes fractionating Fe isotopes between the three biological reservoirs.
CONCLUSIONS We report novel high-precision Fe isotope MC-ICPMS measurements for nine international biological reference materials: NIST1515 (apple leaves), ERC-CE464 (tuna fish), BCR668 (mussel tissue), ERM-BB184 (bovine muscle), ERM-CE196 (bovine blood), ERMBB-185 (bovine liver), ERM-BB124 (Pork muscle), ERM-BB186 (pig kidney), ERM-DB001 (human hair). For the two standards previously analysed, our data are in excellent agreement with literature. This dataset contains Fe isotope compositions of widely available international reference materials covering a large range of different biological matrices, which will be useful for future works aiming at investigating biological processes through the Fe isotopic system.
FUNDING JS thanks the financial support of the  French National Research Agency  ( ANR  Project  VolTerre , grant no. ANR-14-CE33-0017-01 ). FM acknowledges funding from the  European Research Council  under the  H2020 framework program / ERC  grant agreement  637503  (Pristine), and the  ANR  through a chaire d'excellence  Sorbonne Paris Cité. Parts of this work were supported by  IPGP  multidisciplinary program PARI, and by  Région Île-de-France SESAME  Grant no. 12015908 ,  EX047016  and the  IdEx Université de Paris  grant,  ANR-18-IDEX-0001  and the DIM ACAV+.
After normal mastication (NM), the PSD of the collected food boluses was analyzed by sieving and was considered as the reference curve to reach during the programming step. The dynamic in vivo data were used to program the AM 2 masticator apparatus and other mechanical parameters were progressively adjusted by comparison between in vivo and in vitro granulometric curves until they were superimposed. Artificial saliva containing 261 UI/mL of alpha-amylase from porcine pancreas (A3176 Sigma®, France) and 2. 16 g/L of mucin from porcine stomach type II (M2378 Sigma®, France) in mineral water (Volvic®, France) was introduced (1 mL) at the beginning of the masticatory sequence (Roger-Leroi, Mishellany-Dutour,  Woda, Marchand, & Peyron, 2012). As observed in vivo, the temperature of the masticatory chamber was regulated at 36 • C.
Data analysis Statistical analysis was performed with SPSS software (IBM SPSS Statistics). The normality of the distribution of the dependent variables was verified. First, to validate the programming of the masticator apparatus, One-Way repeated-measure ANOVAs were performed in a General Linear Model design (with sieves as repeated factor) to test if in vivo and in vitro particle size distribution were not significantly different for the boluses of each type of pastas. Other One-Way repeated-measures ANOVAs were performed to test if differences existed between particle size distributions measured in boluses obtained after NM and DM, and between particle size distributions obtained for the two types of pastas according to the type of masticatory sequence. Several One-Way ANOVAs were used to test the existence of difference for all the other variables characterizing the boluses collected after NM and DM and GI digestion of the two types of pastas.
As a result, many DDIs have been developed and used in the literature, either as proxies of nutritional adequacy of the diet or as recommendations to maintain optimal health (6-10). The most recent example is the Minimum Dietary Diversity for Women of Reproductive Age, which is calculated based on a single 24-h recall  (11)  and has been shown to be effective in predicting adequacy for 11 micronutrients in 9 large datasets from different countries  (12). While several comprehensive or systematic reviews have been carried out, the most recent ones focusing on obesity and body adiposity  (8) (9) (10) , there is a lack of comprehensive information about how dietary diversity is conceptualized and measured across contexts, and whether the proposed indicators are associated with dietary adequacy and health outcomes.
Data extraction All studies identified as suitable were extracted using Zotero (version 4. 0. 28. 7). We conducted a 2-stage screening process to select the studies to be fully reviewed. During the first stage, titles and abstracts were examined by 2 authors (EOV and AB) and irrelevant studies were excluded from further review. At the second stage, a full-text screen was performed by 2 authors (EOV and ALP) and evaluated using the Population, Interventions, Comparators, Outcomes, and Study design (PICOS) criteria for inclusion and exclusion  (16). All disagreements regarding eligibility were resolved by discussion.
Acknowledgments We thank  Mary Arimond  and  Megan Deitchler  for their constructive feedback. We also thank the 2 anonymous reviewers whose valuable comments and suggestions helped improve and clarify this manuscript. The authors' responsibilities were as follows-EOV, FM, and YM-P: designed the study; EOV, ALP, and AB: conducted the literature search and selected relevant articles; EOV, ALP, and GB: cross-checked data extracted from articles included in the review; EOV and ALP: drafted the figures, tables, and manuscript; FM, MM, MS, and YM-P: provided critical review; and all authors: read and approved the final manuscript.
1) Background: This systematic review and meta-analysis aimed to assess the effects of folic acid supplementation on oxidative stress markers. (2) Methods: Online database including PubMed, Scopus, Web of Science, and Cochrane were searched up to January 2021, to retrieve randomized controlled trials (RCTs) which examined the effect of folic acid supplementation on markers of oxidative stress. Meta-analyses were carried out using a random-effects model. I 2 index was used to evaluate the heterogeneity of RCTs. (3) Results: Among the initial 2322 studies that were identified from electronic databases search, 13 studies involving 1013 participants were eligible. Pooled effect size from 13 studies indicated that folic acid supplementation elicits a significant rise in serum concentrations of glutathione (GSH) (WMD: 219.
Search Strategy To find relevant publications earlier than January 2021, two independent investigators performed a systematic literature search in the following online databases: PubMed, Scopus, Web of Science, and Cochrane. The following terms were used in the search: "folate" OR "folic acid" OR "Vitamin M" OR "Vitamin B9" OR "Folacin" OR "Folvite" OR "Pteroylglutamic Acid" OR "folates" OR "tetrahydrofolates" OR "Formyltetrahydrofolates" AND "Oxidative stress" OR "malondialdehyde" OR "MDA" OR "Glutathione" OR "GSH" OR "Total Antioxidant Capacity" OR "TAC" OR "total antioxidant status" OR "TAS" OR "NO" OR "nitric oxide". There were no date and language restrictions included in each of the database searches. Furthermore, a manual review of all reference lists from related papers generated from each database search was additionally performed.
Inclusion Criteria The title and abstract of all identified studies from the database and manual searches were screened for study eligibility. Studies were included in the current meta-analysis if they (1) were randomized controlled trials (RCTs) or placebo-controlled trials, (2) included adult participants ≥18 years old, (3) administered oral folic acid alone (e. g. , not in combination with other nutrients), (4) had an intervention duration of at least two weeks, and (5) reported the means and standard deviations (SD) of serum concentrations of NO, MDA, GSH, and TAC at baseline and after the study for both the intervention and control groups.
Data Extraction Data extraction was conducted independently by two researchers using a standardized data collection form  [19]. The following information was extracted from each eligible study: author's name, year of publication, country of study origin, number, sex, and age of participants, study design, type and dosage of folic acid, baseline (pre) and postintervention serum concentrations of NO, MDA, GSH, and TAC, and duration of the study. When necessary, data across RCTs reported in dissimilar units were converted to the units presented in this paper in order to perform mean effect size comparisons.
Results A total of 2322 studies were found from the initial databases and manual references search. From these, 655 duplicates were identified across databases and removed. After screening titles and abstracts for inclusion and exclusion criteria, 1647 studies were excluded for being unrelated or not meeting inclusion criteria (n = 1282), animal studies (n = 245), and review articles (n = 120). Following evaluation of full-text articles from remaining RCTs indicated seven studies did not report required information and thus were excluded from consideration. Finally, 13 studies meeting all inclusion criteria that were not already excluded moved forward for analysis in the current systematic review and dose-response meta-analysis (Figure  1 ).
METHODS Study Population The EPIC-CVD study is a case-cohort study nested within EPIC, a prospective cohort including ~520 000 men and women recruited from 23 study centers in 10 European countries between 1992 and 2000. 26, 27 equests to access the supporting anonymized data may be made by qualified researchers trained in human participant confidentiality protocols by following the instructions at http://epic. iarc. fr/acces s/index.
Assessment of Dietary Intake Habitual food consumption over the past year was assessed by center-specific food frequency questionnaires or diet histories, either self-administered or assessed in face-to-face interviews. 27 Validity of each dietary questionnaire was assessed in subgroups of participants within centers using a reference method of monthly 24-hour recalls or weighed food records. 30 In analyses comparing intakes based on the 2 instruments, correlation coefficients were between 0. 31 and 0. 87 for total fat (except they were lower in Greece, 0. 09 in men and 0. 26 in women), 0. 39 to 0. 84 for SFA, 0. 28 to 0. 89 for MUFA, and 0. 26 to 0. 89 for PUFA. 30 All dietary nutrient data were collected in a cohort-specific manner and standardized centrally within the EPIC consortium. The European Nutrient Database was established to facilitate comparability of the dietary exposures.