dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations Several limitations of this study should be mentioned. Firstly, the participants enrolled in our study were volunteers in a nutrition cohort and were probably more interested in nutritional issues and had healthier lifestyles issues than the general population. They exhibited particular characteristics when compared to the general French population  (64, 65). As the food exclusion and orgFFQ questionnaires were optional, participants excluding food and high organic food consumers were certainly more willing to fill in these questionnaires. However, the proportion of non-consumers of organic food was similar to the national figures  (66). Second, all data in this study were self-reported, which may be prone to measurement errors and desirability bias  (67). Thirdly, no information was available on the consumption of gluten-free substitutes and their specific nutritional values in our sample.
Acknowledgements The authors thank  Cédric Agaesse  (dietitian);  Thi Hong Van Duong ,  Younes Esseddik  (IT manager),  Régis Gatibelza ,  Djamal Lamri ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Julien Allegre ,  Nathalie Arnault ,  Laurent Bourhis  and  Fabien Szabo de Edelenyi, PhD  (supervisor) (data-manager/statisticians) for their technical contribution to the  NutriNet-Santé study  and  Nathalie Druesne-Pecollo, PhD  (operational coordination). We thank all the volunteers of the NutriNet-Santé cohort.
Funding / Support: Financial Support: This study is part of the  GlutN  project that was supported by the  French National Research Agency (Agence Nationale de la Recherche ) in 2017 ( AAPG ANR 2017 ). The  NutriNet-Santé cohort study  is funded by the following public institutions:  Ministère de la Santé ,  Santé Publique France ,  Institut National de la Santé et de la Recherche Médicale ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Paris 13 University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Study sponsors had no part in study design, collection, analysis, and interpretation of data and the writing of the article and the decision to submit it for publication.
SD: Standard deviation. Downloaded from https://www. cambridge. org/core. INRAE -Toulouse, on 07 Oct 2020 at 14:39:06, subject to the Cambridge Core terms of use, available at https://www. cambridge. org/core/terms. https://doi. org/10. 1017/S0007114520003323 Figure 1. Downloaded N=34,781 subjects completed the food exclusion questionnaire N=616 with coeliac disease N=34,165 subjects without coeliac disease N=698 481 with missing data on socioeconomic variables 217 living overseas N=33,467 subjects with available information on socioeconomic variables N=9,999 did not completed the Org-FFQ Final sample N=23,468 *.
Study design All infants born at the maternity hospital and hospitalized in our NICU were studied retrospectively during two 6-month periods, separated by a 6-month washout period, from May 1 to October 31, 2013 (group 1) then from May 1 to October 31, 2014 (group 2), which occurred before and after the introduction of an optimized nutrition protocol. We performed a longitudinal comparative analysis between these two independent groups. Collection of data from the infants' files was standardized.
Methods This retrospective study was registered by ClinicalTrials. gov under number NCT03217045, and by the French ethic committee "Commission Nationale de l'Informatique et des Libertes" (CNIL) under study number R2015-1 for the Maternity of the CHRU of Nancy. Parents' consent for using the collected clinical data of their infant was obtained and signed at admission. Infants born between 26 to 32 weeks gestation (GA) and admitted to our NICU were included in the study. Infants who died before discharge or presented with any congenital malformation were excluded. The primary outcome measure was to evaluate the impact of introducing a well-defined nutrition protocol on the longitudinal growth of the infants up to the time of discharge. Weight was assessed by daily measurements, every morning, as defined in our routine policy of care.
Use of mother's own milk was promoted, but when it was not available banked donor's milk was used. Milk fortification was initiated from 80 ml/kg/d in both groups. The parenteral solutions were prepared by the hospital pharmacy according to medical prescription. In order to evaluate compliance to the protocol, energy, protein, lipid, carbohydrate, and sodium intake were reported weekly for each patient after collection of the raw data in the patient's individual daily file. Human milk content was assumed to be an average of 70 kcal/ 100 mL with 1. 2 g protein, 4. 2 g fat, and 7 g/100 mL carbohydrate  [23]. Milk fortification and preterm infant formulas were also used and their formula compositions and nutritional content were based on the product labels. Each intake (protein, carbohydrate, lipid) was compared to the theoretical recommendations of each nutritional protocol in use for the period.
Statistics Normally distributed data, assessed by a Shapiro-Wilk test of normality, are presented as mean values with SD; non-normally distributed data are presented as medians with the range of values. To evaluate differences between groups, we used the Student t test for continuous variables and the Chi 2 test or Fisher exact test when appropriate for categorical variables. For continuous variables not normally distributed, we used the Mann-Whitney U test. Categorical data are presented as actual numbers or percentages. Postnatal evolution of growth variables was assessed longitudinally by z-score. Observed differences were considered statistically significant if P < 0. 05. All analyses were performed with SYSTAT 13 software (2009, Systat Software Inc 1 , San Jose CA, USA).
However, this difference may also be due in part to the fact that males had lower weight z-scores at birth than females in our population. Therefore, we further analyzed the data accounting for the weight of the infants at birth using delta z-score from birth. We did not find significant differences anymore but delta z-score were stable with time and always above -1, without significant difference between sex but with a trend of a worse evolution in female from Day 56 (Fig  3B ). This suggests that the introduction of a nutrition protocol updated on the last guidelines had an impact on nutrient delivery, but the impact on growth was not what we expected because of low compliance to the protocol. In addition, as we worked on a cohort we might have had a lack of power. We evaluated the effects of our new protocol on clinical outcomes other than growth and did not find any difference except a higher rate of surgery for PDA in Group 2, and longer doxapram treatment in Group 2.
To induce obesity and insulin resistance, male Ucp2 DLysM mice age 9-12 weeks and their male floxed littermates were placed on a high-fat diet for 16 weeks. To calculate the power, previous data on fasting glucose values were used. Fasting glucose values of mice fed a high-fat diet may differ on average 3 mM (6 8-11 mM) compared with mice fed a low-fat diet. Differences in responses might lead to an S. D. around 2 mM or higher. To perform the power calculation, we used a one-way analysis of variance with a significance level of 0. 05 and a power of 90%, leading to an estimation of around n = 11 mice needed per group. To allow for the compensation of unforeseen circumstances or potential loss of mice during the study, n = 12 mice were included per group. Thus, 12 mice per genotype were randomly allocated to a standardized high-fat diet or a low-fat diet for 16 weeks (D12451 and D12450H, Research Diets, New Brunswick, NJ, USA; g-irradiated with 10-20 kilograys).
Flow cytometry Stromal vascular cells were stained with antibodies against CD45-ECD (Beckman Coulter), F4/80-FITC, CD206-APC, CD11c-PE-Cy7, and CD11b-PE (BioLegend, San Diego, CA, USA). Samples were measured on a flow cytometer (FC500, Beckman Coulter) and results were analyzed using Kaluza analysis software 2. 1 (Beckman Coulter).
Real-time PCR and microarray For cells and liver tissue, total RNA was isolated using TRIzol ® Reagent (Invitrogen, Thermo Fisher Scientific). For gWAT, total RNA was isolated with the RNeasy Micro Kit (Qiagen, Venlo, the Netherlands). The iScript cDNA kit was used to synthesize cDNA (Bio-Rad) according to manufacturer's instructions. The CFX384 Touch TM Real-Time Detection System (Bio-Rad) was used to perform real time PCR, using a SensiMix TM -(BioLine, London, UK) based protocol. Human B2M and mouse 36b4 expression were used to normalize values for human and mouse samples, respectively. The microarray datasets used for Fig. 1A  were described earlier  (10, 33).
Data and statistical analysis Data are represented as mean 6 S. D. as indicated in the legend. Statistical analyses were carried out using the unpaired Student's t test or two-way analysis of variance followed by Bonferroni's post hoc multiple comparisons test, if genotype and diet or genotype and treatment were both found significant (GraphPad Software, San Diego, CA, USA). A value of p , 0. 05 was considered statistically significant.
Acknowledgments: Special thanks go to  Ruud Woutersen  for participating in the workshop and giving feedback on this review. The authors also want to thank  Dirk van Aken  and  Hubert Noteborn  for participating in the workshop, and we thank  Nikita Zhukovskiy  ( WRX ) for the excellent graphics and visual communication. We thank  Anna K. Undas  at  Business Unit Contaminants and Toxicology, Wageningen Food Safety Research, The Netherlands , for providing data and images regarding E171 particle characterization.
The flow rate was 800 µL/min, the injection volume was 10 µL, and the column was maintained at 40 °C. The flow from LC was split using an analytical fixed flow splitter (split ratio = 1:1, post-column) from Analytical Scientific Instruments (El Sobrante, CA, USA). The liquid chromatography system was controlled by STANDARD INSTRUMENT INTEGRATION (SII) software based on Dionex Chromeleon TN 7. Characterization of phospholipid species. The ORBITRAP FUSION TRIBRID mass spectrometer (Thermo Scientific, USA) was used for high-resolution analyses to identify phospholipid molecular species in olfactory tissues, as described previously  85. This instrument was equipped with an EASY-MAX NG Ion Source (Heated Electrospray Ionization H-ESI) and was controlled by XCALIBUR 4. 1 software. Positive and negative ions were monitored alternatively by switching the polarity approach with a spray voltage set to 3,500 V in positive and negative ion modes.
The identification of all PL species was performed using the high-accuracy data and the information collected from fragmentation spectra with the help of LIPIDSEARCH software (version 4. 1. 16) and the LIPID MAPS database. Quantification of phospholipid species. Samples were diluted to the appropriate concentration of 25 µg/mL PLs in CHCl 3 /CH 3 OH (1:1, v/v) for analysis. The LC system under HILIC conditions was coupled to a triple quadrupole mass spectrometer (Thermo Finnigan TSQ Quantum) equipped with a standard electrospray ionization source to quantify phospholipid molecular species. Specific acquisition methods in positive and negative ion modes were optimized and used according to the studied compounds. PC, PlsC and SM species were quantified in positive ion mode by precursor ion scanning of m/z 184 amu, which corresponds to the choline head group.
PE species lose their ethanolamine phosphate head group as a neutral fragment of 141 Da. Therefore, neutral loss scanning of 141 Da in positive ion mode was used for the selective detection and quantification of PE. Similarly, PS species lose their serine-phosphate head group as a neutral fragment of 185 Da. Therefore, neutral loss scanning of 185 Da in positive ion mode was used to quantify PS compounds. PI species show in negative ion mode a fragment at m/z 241 amu identified as inositol phosphate minus one molecule of H 2 O. This fragment was used for precursor ion scanning to quantify these compounds. The quantification of PlsE was performed in negative ionization mode by multiple reaction monitoring (MRM) of the parent/fragment transition for each selected plasmalogen. The data were processed using XCALIBUR software.
Acknowledgements The authors acknowledge the staff of the animal facility of the  Centre des Sciences du Goût et de l' Alimentation (Dijon, France)  for animal care. This work was supported by the  Groupe Lipides et Nutrition (GLN) , the  Conseil Régional Bourgogne Franche-Comté , the  European Regional Development Fund (FEDER) , the  Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE) , and the  Fondation de France/Fondation de l'Oeil.
Background: Many small and malnourished infants under 6 months of age have problems with breastfeeding and restoring effective exclusive breastfeeding is a common treatment goal. Assessment is a critical first step of case management, but most malnutrition guidelines do not specify how best to do this. We aimed to identify breastfeeding assessment tools for use in assessing at-risk and malnourished infants in resource-poor settings. Methods: We systematically searched: Medline and Embase; Web of Knowledge; Cochrane Reviews; Eldis and Google Scholar databases. Also the World Health Organization (WHO), United Nations International Children's Emergency Fund (UNICEF), CAse REport guidelines, Emergency Nutrition Network, and Field Exchange websites. Assessment tool content was analysed using a framework describing breastfeeding 'domains' (baby's behaviour; mother's behaviour; position; latching; effective feeding; breast health; baby's health; mother's view of feed; number, timing and length of feeds).
Databases and search terms: Articles were identified by searching electronic database Medline and Embase via Ovid interface (full search strategy in Extended data  (Kerac et al. , 2020) ). Key words and MeSH terms were selected by the review on The Lancet Breastfeeding Series (The Lancet  Series, 2016)  and a recent similar review on feeding assessment tools  (Howe et al. , 2008). Searches were finalised in March 2018. This updated an earlier search done as part of the original MAMI project performed on PubMed, Web of Knowledge, Cochrane Review, Eldis and Google scholar databases which concluded in November 2013. In that original search, highly relevant journals were also searched directly: Maternal and Child Nutrition, International Breastfeeding Journal, Journal of Human Lactation, and BMC Family Practice.
Thanks to high-throughput sequencing technologies, these methods can now be used to perform Genome-Wide Selection Scans (GWSS) using massive Single Nucleotide Polymorphism (SNP) datasets  (6) (7) (8). Although GWSS have identified cohorts of genes associated with past or ongoing adaptive processes, they are not designed to identify gene coadaptation, that is the adaptive interactions between genes through epistatic selection, because the genetic background on which adaptive mutations occur is not considered in statistical frameworks  (9). Few theoretical studies have addressed the issue of simulating the behavior and detecting epistatic selection in population samples using DNA polymorphisms. Simulations of two-locus epistatic models with different degrees of recombination (i. e. 0 < c < 0.
This is particularly the case in highly inbred or predominantly selfing species  (20). At a given locus, Mangin and collaborators proposed to uncorrelate the observations by multiplying the vector of genotypes by  -1 2 , where  is the kinship (or relatedness) matrix among individuals. is built with the   covariance for all pairs (i,j) of individuals. is the average number of identical genotypes between individuals i and j, in a genome-wide SNP dataset. This measure of Identity In State (IIS) is a good proxy of Identity By Descent (IBS) with SNP markers since the latter follow an infinite site mutation model. It follows that, since r is the Pearson correlation coefficient, rv can be computed as ( -1 2   ,  -1 2   ) and corPC1v as ( -1 2 1  ,  -1 2 1  ).
Otherwise, the sign of r is not interpretable, but the p-value of the test can be computed on either side of the null distribution. Likewise, whether the ancestral/derived allele status of the SNPs is known or not, the sign of corPC1 (or corPC1v) is not interpretable since PC1 reflections imply identical ranking of individuals genotypes (or relatedness) in a given genomic region (see for instance  (51) ). R scripts to implement the statistical test based on   ,    ,  1 or  1  , along with an example dataset, are available at https://github. com/leaboyrie/LD_corpc1.
For PC1 calculation on each gene, imputed SNP data were required. Gene-based imputation was performed using the TASSEL software  (69) , where each missing base was imputed with the accession that shares the longest haplotype surrounding the base, on a window of 30 SNPs maximum  (56). For calculation of  1  , the kinship matrix V of the 262 individuals was estimated based on the genome-wide SNP dataset with MAF 5%. In human, GWESS was performed by using the dataset of 494,764 SNPs (431,951 with a Minor Allele Frequency of 5%) with no missing data from the HGDP-CEPH Human Genome Diversity Panel on a world-wide sample (America, Asia, Europe, Middle East, North Africa, Subsaharian Africa and Oceania) of 940 individuals belonging to 57 populations from 23 countries  (70, 71). The genome version (i.
The corPC1 and corPC1v measures were calculated on windows of 10 kbp (i. e. ± 5 kbp on either side of the focal SNP). For rv and corPC1v, the kinship matrix was calculated at each time-point measure based on the simulated SNPs on the four chromosomes dataset, after filtering for a 5% Minor Allele Frequency (MAF). No MAF filtering was performed in the genomic regions of the two focal SNPs before calculating LD-based measures. We performed 1,000 simulations of each scenario described above, which allowed us to get the empirical distributions of all LD-based statistics. GWESS with SNP data in Medicago truncatula and humanGWESS was performed in M. truncatula using a raw dataset of 22,079,533 SNP markers identified on the eight chromosomes of the species by the Medicago HapMap Project on a collection of 262 accessions (see http://www.
Acknowledgements This work was supported by the " DeCoD " project funded by the  French Agence Nationale de la Recherche  (grant number  ANR-16-CE20-0017-01 ). The PhD position of  Léa Boyrie  was funded by the  "DeCoD" project. We thank the bioinformatics platform Toulouse Midi-Pyrenees (Genotoul). This work was performed in the LRSV (Toulouse, France), part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  (grant number  ANR-10-LABX-41 ). We thank  Carole Laffont  ( IPS2 ,  CNRS, Gif-sur-Yvette, France ) for providing results about MtCLE13 expression. Work in the  Florian Frugier laboratory  has benefited from a  French State grant  (Saclay Plant Sciences, grant number  ANR-17-EUR-0007 ,  EUR SPS-GSR ) and  ANR  grant  PSYCHE  (grant number  ANR-16-CE20-0009-01 ). We thank  Pierre-Marc Delaux  for useful criticisms and comments on the manuscript.
Allele frequencies and the r and rv LD measures were calculated at the two SNP positions every 20 generations on the total population (500 diploid individuals distributed in two isolated populations). The corPC1 and corPC1v measures were calculated on windows of 10 kbp (i. e. ± 5 kbp on either side of the focal SNP). For rv and corPC1v, the kinship matrix was calculated at each time-point measure based on the simulated SNPs on the four chromosomes dataset, after filtering for a 5% Minor Allele Frequency (MAF). No MAF filtering was performed in the genomic regions of the two focal SNPs before calculating LD-based measures. We performed 1,000 simulations of each scenario described above, which allowed us to get the empirical distributions of all LD-based statistics. GWESS with SNP data in Medicago truncatula and humanGWESS was performed in M.
6) : with Kr a constant depending both on the material to grind and the grinder, and Sp1 and Sp2 the specific surfaces of the particle before and after breakage. In case of coarse milling of lignocellulosic materials, this model been validated for wood chip, alpha chops and pellet milling  [24, 75]. Here, the milling energy has been calculated from the active operating power of the vibratory ball mill multiplied by the milling time, and the specific surface area of the ball milled flax powder was determined from the particle size distribution. Figure  11  presents the evolution of milling energy with the specific surface area created (Specific surface area of the sample minus those of the raw flax) together with a linear fit of the experimental data according to the Von Rittinger models. One can note that the Von Rittinger model fits very well the experimental data with a correlation coefficient R 2 greater than 0.
Acknowledgment Jacqueline Vigouroux  ( INRA, Nantes ) and  Cécile Sotto  from the plant transformation platform of the JRU IATE are gratefully acknowledged for their helpful advice and assistance in carbohydrate analysis and grinding experiments respectively. The authors also thank the  French national research Network 'GDR 3710 INRA/ CNRS SYMBIOSE -Synthons et matériaux biosourcés' , for its financial support.
Dietary data The dietary information was extracted from the FFQs. Intakes at the level of food items were directly taken from each question/ answer and then multiplied with a gender-specific portion size factor  [20]. The dietary variables were represented in three levels (from broad to specific): food category (FC), food group (FG) and food item  (FI). The mapping between specific food items, their food groups and food categories has previously been established  [20]  (Supplementary Table  ST1 ). For instance as a FC, fruits, is made up by three FGs, respectively, citrus fruits, other fruits and nuts. One specific FG, citrus fruits, comprises two FIs: orange and grape fruits. Beverages are part of the dietary variables divided into nine groups: coffee, tea, water, soft drink, fruit juice, vegetable juice, beer, wine, spirit or brandy at the food group level.
In parallel, association mining was performed at the FG level to reveal the gender-specific cohort trends for the 47 food groups, and at the FI level to reveal the gender-specific cohort trends for the 174 food items. Analysis II: The gender-specific data sets were clustered twice into subgroups according to their dietary intake (FG or FI, separately), lifestyle factors and anthropometric traits. Association mining was performed for each subgroup to reveal associations between annual waist or weight change and FG, and associations between annual waist or weight change and FI. Gender-specific cohort association rules were compared to subgroup association rules to reveal dietary patterns in which one or two dietary factors did display a conflicting effect on the anthropometric changes, when comparing subgroups to the overall trend in each gender-specific cohort.
The software for making statistical tests was written in Python as well. In Analysis I only the Association Mining part was used as a benchmark that was carried out for the full gender-specific cohorts (as in the previous study  [20] ). For Analysis II (see Fig. 1   Two types of associations were defined: (a) direct association between a specific diet factor and an anthropometric outcome ("1to-1" relationship), and (b), joint effect exhibited by two diet factors and one anthropometric outcome ("2-to-1" relationship). Distinguishing these two association types allows us to identify cases of conflicting rules (explained below).