dataesr/scientific-paragraphs-categorization huggingface dataset:
The European Nutrient Database and estimation of food and nutrient intakes have been described in detail previously. 31 Mean dietary glycemic index (GI) was calculated using standardized methods. 32 Dietary total fatty acids were calculated as the sum of SFAs, MUFAs, and PUFAs. Total energy intake (TEI) was defined as daily energy from dietary fatty acids, carbohydrates, and protein from plant, animal, and mixed/unknown origin (in kilocalories). Alcohol was not considered a feasible substitution nutrient, and therefore was not included in the definition of TEI but was used as a model covariate. 9, 14 Macronutrients were expressed as their relative contribution to TEI (%TEI). Dietary fiber and GI were energy adjusted with the residual method for analyses of associations with CHD. Dietary SFAs from the following food groups were expressed in %TEI: total dairy (excluding butter) and subtypes milk, yogurt/thick milk, and cheese; added fats and subtypes vegetable (plant) oils, butter, and margarine; total meat and subtypes red, processed, and poultry; cakes and biscuits; sugar and confectionary; cereals; eggs; condiments and sauces; fish; and nuts and seeds.
2 ) the energy partition model. 33 In the nutrient residual model, we evaluated associations per 10 energy-adjusted grams per day from a specific fatty acid to substitute for any other sources of energy; in the energy-partition model, we estimated the effect of adding 10 g/d (90 kcal/d) from a specific fatty acid to existing macronutrient intakes by adjusting for all other macronutrients but not for TEI. We conducted post hoc analyses, repeating primary analysis Model 4 pooling data from all countries together in 1-stage fixed-effects models, rather than country-specific 2stage random-effects modeling, to evaluate similarity in results using these 2 different approaches. We also conducted calibration analyses by correcting observed HRs (95% CIs) from food-frequency questionnaire data for country-specific regression dilution ratios that were estimated among ~8% of participants with single 24hour recall data available.
40 There is also a need to improve databases of sugar contents and GI values of diverse food products in diverse settings. 39 revious observational studies investigating SFAs from food sources with CHD are limited with inconsistent results. Specifically, the inverse association of SFAs from dairy, driven by yogurt and cheese in EPIC-CVD, is consistent with 2 smaller studies that included 1807  11  and 316  18  CHD cases, but not with other large studies reporting null associations. 12, 13, 19, 20 2] [13]  Observational evidence of macronutrient-specific substitutions for fatty acids from different foods in relation to CHD is scarce, and focused on fats from total dairy and/or meat. 19, 21 y contrast, our study modeled several specific macronutrient substitutions for SFAs from fermented dairy, butter, and red meat, finding opposite associations with CHD depending on SFA food sources and the substitution macronutrient.
29 eans (95% CIs) were estimated from multivariable linear regression, adjusted for age, sex and study centre, among participants in the overall subcohort with measured data on all presented lipids (n=16,013), SBP and DBP (n=12,696), CRP (n=16,016) and HbA1c (n=16,426). * All values are means (95% CI), unless specified otherwise. † Geometric means (95% CI). Hazard ratios (HRs) and 95% confidence intervals (CIs) across quintiles of dietary SFAs, MUFAs and PUFAs, where quintiles were based on the overall subcohort, were analysed within each country separately, with age as the underlying time variable and the baseline hazard stratified by sex. Country-specific HRs (95% CIs) were combined in multivariate random-effects meta-analysis to obtain pooled effect estimates and 95% CIs. The multivariable-adjusted HR included adjustment for age at recruitment (years), centre, energy intake (kcal/day), education (low, medium, high), smoking (never, former, current), physical activity (inactive, moderately inactive, moderately active, active), alcohol intake (0, 0-6, 6-12, 12-13, >24 g/day), dietary fibre (g/day, continuous), fruit and vegetable consumption (g/day, continuous), body-mass index (kg/m 2 , continuous), reported history of diabetes, hypertension and hyperlipidemia.
Acknowledgments The authors thank EPIC participants and staff for their contribution to the study. The authors also thank staff from the  EPIC-CVD  and  EPIC-InterAct Coordinating Centers  for sample preparation and data handling, particularly S. Spackman (EPIC-CVD Data Manager) and  N. Kerrison  (EPIC-InterAct Data Manager,  Medical Research Council Epidemiology Unit). The authors thank  Statistics Netherlands  and the  PHARMO Institute  for follow-up data on causes of death and cardiovascular disease in the Netherlands. Author Contributions: The  EPIC-CVD  project was coordinated by J. D. and A. S. B. The current study was conceived and designed by M. S. , L. J. , A. S. B. , J. D. , and N. G. F. The data were analyzed by M. S. , with statistical input from A. W. and S. J. S. The first draft of the article was prepared by M.
EPIC-Greece was supported by the  Hellenic Health Foundation (Greece). M. S. , N. J. W. , N. G. F. , and F. I. acknowledge support from  MRC Epidemiology Unit  ( MC_UU_00006/1  and  MC_UU_00006/3 ). N. J. W. and N. G. F. acknowledge support from  NIHR* Cambridge BRC: Nutrition, Diet, and Lifestyle Research Theme  ( IS-BRC-1215-20014 ) and NGF is a  NIHR Senior Investigator Award  holder. M. S. was also supported by  BHF  for part of this work while working in the  BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. R. Z. -R. thanks the " Miguel Servet" program  ( CP15/00100 ) from the  Institute of Health Carlos III  (co-funded by the  European Social Fund-European Social Fund Investing in Your Future). A. W.
was supported by a  BHF-Turing Cardiovascular Data Science Award  and by the  European Commission-Innovative Medicines Initiative (BigData@Heart). R. C. was funded by a  MRC-Newton  project grant to study genetic risk factors of cardiovascular disease among Southeast Asian people and the  Academy of Sciences Malaysia  (grant no. MR/P013880/1 ) and a  United Kingdom Research and Innovation-Global Challenges Research Fund   Project Grant  to study risk factors of noncommunicable diseases in Bangladesh. J. D. holds a  BHF Professorship  and a  NIHR Senior Investigator Award. The views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care. 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.
Instrumental analysis of yolk membrane strength Instrumental analysis of yolk membrane strength was determined in fresh and 35-day-old eggs. The yolk membrane strength was determined on an Instron Universal Testing Machine, Model 5544 (Instron Corporation, High Wycombe, UK) with a puncture probe. The cross head speed was set at 50 mm/min and the load at 2 kN. The data were obtained using Merlin software, Series IX (Merlin, Richardson, USA). The strength was expressed as the force (N) needed to break the vitelline membrane.
Instrumental colour analysis of egg yolk Instrumental colour analysis of yolks (CIEL*a*b* colour system) was performed on fresh eggs only using a KonicaMinolta Colorimeter CM 2600d (Minolta, Osaka, Japan) and standard illuminant light source D65 for average daylight (including the ultraviolet wavelength region). Lightness (L*), redness (a*), yellowness (b*) and chroma (C*) of the sample were calculated using available software (Spectra Magic 3. 61). Three measurements were made directly on each egg yolk surface.
Acknowledgements This research has been financially supported by the  Institutional Research Support for VETUNI Brno. Further support was provided by the  RECETOX research infrastructure  (the  Czech Ministry of Education, Youth and Sports ;  LM2018121 ) and  CETOCOEN EXCELLENCE Teaming 2  project ( Horizon 2020 ;  857560  and the  Czech Ministry of Education, Youth and Sports ;  CZ. 02. 1. 01/0. 0/0. 0/17_043/0009632 ). The authors thank the  International Poultry Testing, Ustrašice, Czech Republic  for cooperation within experimental part of this research.
71 ± 0. 17% and 3. 18 ± 0. 14% BCFAs, respectively  (Wang, 2017) , relative to total fatty acids. Few data exist on the ingested amount of BCFAs. In the USA in 2011, the consumption of milk, cheese and beef contributed to a daily dietary intake of about 400 mg of branched fatty acids  (Ran-Ressler et al. , 2011). Consumption of chocolate contributed to about 6 mg BCFA/day (Ran-Ressler et al. , 2014).
Food content and daily intake Numerous branched FAHFA families have been detected in food of plant origin (fruits, vegetables and cereals)  (Zhu et al. , 2018; Liberati-Cizmek et al. , 2019)  and of animal origin (egg, chicken, beef, caribou, moose)  (Yore et al. , 2014; Pham et al. , 2019). Abundance of each FAHFAs varies according to the type of food considered. Total FAHFAs range from 45 to 320 ng/g in fresh food. Branched FAHFAs were also detected in caribou meat and moose at very high doses (50 mg/g) compared to other food sources. Branched FAHFAs are present in breast milk, although at very low concentrations  (Kuda et al. , 2018). To our knowledge, no data are available on the amount of FAHFAs ingested daily. Moreover, absorption and bioavailability of dietary FAHAs are unknown.
Acknowledgement. The authors acknowledge the financial support of the  French Lipid Nutrition Group  for the work conducted on 9-PAHPA and 9-OAHPA (Benlebna et al. , 2020a(Benlebna et al. , , 2020b(Benlebna et al. , , 2020c)). We thank all colleagues who have contributed and/or who are co-authors of our publications cited in this review. Conflicts of interest. The authors declare that they have no conflicts of interest in relation to this article.
Funding Benoit Chassaing  laboratory is supported by a Starting Grant from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (Grant No. ERC-2018-StG-804135 ), a  Chaire d'Excellence  from  IdEx Université de Paris  ( ANR-18-IDEX-0001 ), an  Innovator Award  from the  Kenneth Rainin Foundation  and the  National Program "Microbiote" INSERM. Nicolas Barnich's laboratory  is supported by the  Ministère de la Recherche et de la Technologie ,  INSERM  ( UMR 1071 ),  INRAe  ( USC-2018 ), the  French government 's  IDEX-ISITE initiative 16-IDEX-.
Statistical Analysis Statistical analysis was performed with the statistical software package SPSS Version 23. 0 for Windows (SPSS Inc. , Chicago, IL, USA). The Kolmogorov-Smirnov test was used to examine if variables were normally distributed. Levene's test was employed to check the assumption of homoscedasticity. The unpaired two-sample t-test was used to compare the means of two independent groups. For multiple comparison, the differences among the variables examined were checked using a one-way analysis of variance (ANOVA) followed by Bonferroni's post hoc test. A significance level of p ≤ 0. 05 was adopted.
Methods: In June 2020, we searched 3 databases (Pubmed, Web of Science, EMBASE) to identify studies from all years on older adults at home and receiving home-delivered meal services (population). The following outcomes were considered: nutritional status (Body Mass Index, weight, undernutrition) and nutritional intake. Any nutritional intervention, comparator, and study design were relevant for inclusion. Results: Forty-eight original studies met the inclusion criteria, most of them being published after the year 2000 (n = 34) and undertaken in the USA (n = 32). The selection includes 30 cross-sectional and 18 longitudinal studies. The main findings of this review are the following: (1) home-delivery meal older recipients are at high risk of undernutrition; (2) providing home-delivery meals may improve the nutritional status and nutrient intake; (3) this improvement is even higher when the home-delivery meal service is improved, for instance by providing dietetic counseling or adding supplementary snacks/meals or enriched food.
Eligibility Criteria The PICOS (Population, Intervention, Comparator, Outcome, Study design) eligibility criteria were as follows  (11) : Population. Only older adults living at home and receiving home-delivered meal services were eligible. We excluded from the review (1) studies on older people residing at nursing homes or in the hospital and (2) studies that not display specific data and results for home-delivered meal recipients (e. g. , studies that display results from a mixed sample including home-delivered meal recipients and recipients of other care services such as home helper or congregate meals). Intervention. Any nutritional intervention was relevant for inclusion (e. g. , studies providing additional food items to regular meals-on-wheels, or studies providing dietary guidance).
Information Sources and Search Strategy After repeated attempts and adjustments, a search strategy combining both thesaurus and free-text terms was developed to retrieve articles of interest in the following databases: PubMed, Web of Science (WOS), and EMBASE (Supplementary File 1). Separate title, abstract, and keyword searches were conducted for older people, home-delivered meal service, and nutritional outcomes on June 2020. The results for the three separate search strings were combined to identify relevant titles. Afterward, references from selected articles and systematic reviews were checked manually for further screening in case they have been not identified during the whole search process. After removing the duplicates, titles, abstracts, and full texts were screened by two independent reviewers against the agreed inclusion and exclusion criteria.
b Data were extracted from baseline data. Five studies reported data on weight loss or underweight. In two large cohort studies conducted in HDM recipients, one from North Carolina, USA  (45) , n = 1,026, and a second one from Texas, USA  (46) , n = 908, respectively 26% and 43% of the respondents reported an unintended weight change of 10 pounds in the last 6 months. Herndon  (41)  reported that 11% HDM recipient had lost at least 10 pounds (4. 5 kg) in the last 6 months. In a study conducted with 244 HDM recipients from New York state, Roe  (55)  reported that 33 and 11% of the respondents were respectively underweight and overweight. However, O'Dwyer et al. (43)  reported that only 3% of HDM recipient were underweight (<18. 5 kg/m 2 ). Ten articles explored food intake in HDM recipients-all these studies were conducted in North America but one in Australia (Table  3 ).
The NMR  1  H is performed at the CCRMN Lyon platform on a Bruker 400 MHz and the spectra are analyzed with the software Mestrenova (Version: 6. 0. 2-5475. 2009 Mestrelab Research S. L. ). Elemental analysis. The analysis is made by the "Pôle Isotopes & Organique" of the Institut des Sciences Analytiques, UMR 5280. In vitro efficacy of copper extraction by chelating dialysis. Chemicals. The standard Cu solution for the ICP-MS is provided by SCP-Science: ICP Standard Cu 50,000 ug/mL 140-041-295 SCP Science. The dialysate solution used for the experiment is the Hemosol B0 produced by Baxter. The other chemicals used for the preparation of the copper II solution is the sodium chloride (purity > 99. 8%, CAS 7647-14-5) provided by Carl Roth, the sodium hydrogen carbonate (analytical reagent grade, CAS 144-55-8) provided by Fisher Chemical and the citric acid (ACS reagent > 99.
5 min, T5 min, T7. 5 min (in duplicate, blue sampling site); sampling of the effluent at T5 min (yellow sampling site), (iii) 30 min at 250 mL/h: sampling of the Cu solution at T10 min, T20 min, T30 min (in duplicate, blue sampling site); sampling of the effluent at T20 min (yellow sampling site) and (iv) 60 min at 50 mL/h: sampling of the Cu solution at T0, T30 min, T60 min (in duplicate, blue sampling site); sampling of the effluent at T30 min (yellow sampling site). Both experiments were conducted with and without Chitosan@DOTAGA. Copper quantification by ICP-MS. ICP-MS analysis were performed using a Perkin Elmer NexION2000 equipped with Syngistix software (Licensed software by Perkin Elmer. Version: 2. In vivo proof of concept on healthy sheep. This study was conducted in accordance with the Guide for the Care and Use of Laboratory Animals and approved by the Ethics Committee of VetAgroSup (Campus vétérinaire de Lyon) with the agreement 1548-V2.
Metal extraction quantification. Blood samples were collected at day 0, 7 and 10 in the morning and day 1 and 3 before and after dialysis using Vacuette tubes (NH Trace Elements Sodium Heparin 6 mL; Greiner, Kremsmünster, Autriche). ICP-MS analysis were performed using a Perkin Elmer NexION2000 equipped with Syngistix software and a ESI SC-FAST Sample Introduction in Kinetic Energy Discrimination (KED) mode. The samples are prepared by dilution in an aqueous solution of HNO 3 1% v/v. Statistics. Both blood analysis evolution according to samples type ("before HD, "after HD", "N day(s) after HD") and dialysis bag metals evolution according to time (hours) were tested by non-parametric Kruskal-Wallis tests (krustal. test from the vegan package, Oksanen et al. 32  ), followed by Dunn tests (kwAllPairsDunnTest from the PMCMRplus package, Pohlert  33  ) for multiples comparison when significant.
Periodic acid Schiff (PAS) staining (Cat No. C062AA, DiaPath SpA, Martinengo, Italy) was also performed following the manufacturer's instructions to reveal glycogen accumulations. The images were analyzed with ImageJ software, using the "Color deconvolution" plug-In and the "H-PAS" vector. Red-purple color corresponding to the positive PAS staining and the VNSE surface were selected to obtain a percentage of PAS-positive areas in the selected VNSE surface.
T/ABE125, or Anti-goat, Cat No. F/AGL125, ScyTek Laboratories, Logan, UT, USA), followed by a 10 min streptavidinperoxidase incubation (Cat No: 12694067, Invitrogen, Carlsbad, CA, USA). Visualization was obtained with 3,3 diaminobenzidine tetrahydrochloride (ImmPACT ® DAB Peroxidase Substrate, Cat No. SK4105, Vector Laboratories, Burlingame, CA, USA) and a hematoxylin counterstain. The sections were finally dehydrated through alcohols, cleared in xylene, and mounted. For each antibody, as negative controls, the primary antibodies were replaced with a nonimmune rabbit or goat serum, while for positive controls for Ki-67 and cleaved caspase 3, murine lymph node sections were included in each IHC run. The software ImageJ was used to measure IHC positivity of each antibody in the VNSEs. To quantify the OMP and G proteins, the plugin "Color deconvolution" was used in Image J to measure the positive pixels as it allows for extrapolation of the DAB staining in the manually selected VNSE area.
Statistical Analyses Data analyses used SAS 9. 4 software (Copyright© 2021-2012 by SAS Institute Inc. , Cary, NC, USA). The significance threshold was classically fixed at 5%. The principal explanatory variable was the age of the mice (3, 10, or 24 months). Sex (M/F) and strains (inbred/outbred) were also collected to see if the effect of age on the VNO degeneration was different depending on the sex and/or strains. For that, age * sex and age * strains interactions were included in the models in addition to age, sex, and strains factors. Two VNSEs (from left and right parts of each VNO) were available from each mouse. Thus, when possible, each mouse was considered as a random factor in the models. For OMP expression, accumulated glycogen and Gαo protein, a general linear mixed model was applied.
Characterization of lipoplexes Size and zeta potential. The average hydrodynamic size of lipoplexes was determined by the dynamic light scattering (DLS) method employing the Zetasizer Nano ZS (ZEN 3600, Malvern Instruments, Malvern, UK). The Zeta (ζ) potential was determined by electrophoretic light scattering (ELS) using a Zeta dip cell (ZEN 1002) immersed into the sample. The measurements were performed at 25 • C using the Smoluchowski model. The results were analyzed using the Zetasizer Software 7. 12 (Malvern Instruments, Malvern, UK). Gel retardation assay. The efficacy of shRNA plasmid complexation by cationic liposomes was investigated, at different charge ratios (+/-), by electrophoretic separation on 1% agarose gels stained with Midori Green, as previously described  [40]. The samples were mixed with 6× Loading Buffer (0.
no. sc-365,154) or mouse anti-GAPDH antibody (1:1000, Ambion cat. no. AM4300). After three washes of 10 min with TBST, the membranes were incubated with mouse IgG kappa binding protein (m-IgGκ BP) conjugated to Horseradish Peroxidase (HRP) (1:1000, Santa Cruz Biotechnology cat. no. sc-516,102) at room temperature for one hour. After washing in TBST, the blots were developed with SuperSignal™ West Dura Extended Duration Substrate (Thermo Fisher Scientific cat. no. 34076) and the detection of the protein bands was performed using the image reader LAS-4000 (General Electrics, Healthcare, UK). The optical density (OD) of the protein bands was quantified using Image J (National Institutes of Health, Bethesda, MD, USA) and RAGE expression was normalized to that of GAPDH. The data were expressed as mean ± S.
sc-365,154, or NF-κB p65, Abcam, cat no. ab16502) diluted 1:100 in 1% BSA/PBS. The next day, the slides were incubated with the appropriate secondary antibody (Alexa Fluor 594 chicken anti-mouse, Invitrogen, cat no. A21201, and Alexa Fluor 594 donkey anti-rabbit, Invitrogen, cat no. A21207, respectively) diluted 1:1000 in 1% BSA/PBS. After rinsing, the slides were mounted with ProLong Gold Antifade Mountant with DAPI (ThermoFisher-Scientific, P36931). Fluorescence micrographs were captured using XM10 camera attached to a Olympus IX81 inverted microscope (IX81-ZDC) and the cellSens Dimension software (Olympus), using the same acquisition settings throughout the experiment. Image processing was performed using Fiji Is Just ImageJ (NIH) freeware, with the same protocol between samples. Images acquired with 20× and 40× magnification were stitched together with the Stitching (Linear blending) plug-in in order to obtain whole detailed images.
Statistical analysis The results are expressed as mean ± standard deviation (SD). Statistical analyses were carried out using GraphPad Prism 7 software (GraphPad Software, La Jolla, CA) and statistical differences were evaluated with unpaired two-tailed t-test for the comparison of two groups or one-way ANOVA with multiple comparisons post-hoc Tukey test for comparison of three or more groups. Statistically significance of differences: *p < 0. 05, ** p < 0. 01, *** p < 0. 001.
Effect of P-selectin targeted lipoplexes on endothelial cells: In vitro studies 3. 2. 1. Lipoplexes are not cytotoxic for endothelial cells Potential cytotoxicity induced by the exposure of quiescent and LPSactivated EC (bEnd. 3 cell line) to Psel-lipo/shCTR lipoplexes for different intervals (24, 48 and 72 h) was assessed using XTT assay. The data showed that the viability of quiescent and LPS-activated bEnd. 3 cells was not significantly affected by the incubation with P-selectin targeted lipoplexes (Fig. 1D ) or non-targeted ones (Supplementary Fig. S1 ) formed with the shRNA control plasmid (1 μg shCTR/ ml) at various charge ratios R = 4, 6, 8 and 10. The data show that lipoplexes are cyto-compatible and can be further used in transfection experiments.