dataesr/scientific-paragraphs-categorization huggingface dataset:
When added to aMDM, LCC-12 antagonized CD80 and CD86 induction more potently than Met, even at a 1000-fold lower dose (e. g. 10 µM LCC-12 versus 10 mM Met) (Fig. 2e ). In contrast, the effect of LCC-4,4 used at 10 µM was moderate, consistent with the reduced capacity of this analog to interact with copper(II). These data support the idea that copper(II) is a mechanistic target of biguanides. Next, we evaluated the effect of LCC-12 in other cell types expressing CD44. Interestingly, expression of activation markers by human CD4 + or CD8 + T lymphocytes, dendritic cells and monocytes, which upregulate CD44 levels upon appropriate stimulation, was reduced upon LCC-12 treatment (Extended Data Fig. 2d-h ). In contrast, the activation of neutrophils, which did not upregulate CD44, was not affected by LCC-12.
4a ). Mass spectrometry revealed the mass of a MDHNA derivative containing an additional oxygen atom, and 2D NMR spectroscopy confirmed epoxidation of the least substituted double bond (Extended Data Fig. 4b ). Thus, copper reprograms the reactivity of hydrogen peroxide towards NADH, protecting the latter from oxidative degradation. To mimic the conditions found in the mitochondrial matrix, we next performed this reaction at 37 °C and pH 8. The rate of the reaction was evaluated by monitoring the concentration of NADH over time. When the reaction was performed in the presence of copper(II) and imidazole as stabilizing copper ligand, NADH was rapidly consumed to yield NAD + (Fig. 4f ), as confirmed by mass spectrometry and NMR spectroscopy (Extended Data Fig. 4c ). This reaction was inhibited by LCC-12 in a dose-dependent manner, whereas the effect of LCC-4,4 and Met was marginal (Fig.
To this end, we quantified mitochondrial NADH and NAD + in aMDM by mass spectrometry-based metabolomics. Mitochondrial NADH levels were higher, whereas NAD + levels were lower in aMDM compared to naMDM, suggesting an enhanced activity of mitochondrial enzymes reliant on NAD + (Fig. 4g  and Supplementary Table  1 ). In agreement with data obtained from our cell-free system, treating aMDM with LCC-12 during activation led to a decrease of both mitochondrial NAD + and NADH (Fig. 4g  and Supplementary Table  1 ). This is consistent with the idea that copper(II) catalyzes the reduction of hydrogen peroxide by NADH to produce NAD + in cells, and that biguanides can interfere with this redox cycling, leading instead to other oxidation by-products. Notably, NADH and copper were found in mitochondria of aMDM at an estimated substrate/catalyst ratio of 2:1, which is even more favorable for this reaction to take place than the 20:1 ratio used in the cell-free system (Fig.
Gene ontology (GO) analysis revealed three main GO term groups, which belonged to inflammation, metabolism and chromatin. Importantly, GO terms of upregulated genes in aMDM included endosomal transport, cellular response to copper ion, response to hydrogen peroxide and positive regulation of mitochondrion organization (Fig. 5a ). Furthermore, we observed striking similarities between the transcriptomics datasets (Extended Data Fig. 5a, b  and Supplementary Table  5 ). In particular, we found increased RNA levels of genes encoding inflammatory cytokines such as IL-6, IL-1b and TNFa, as well as genes encoding proteins involved in the JAK/STAT signaling pathway, the inflammasome and Toll-Like Receptors (TLRs) (Fig. 5b  and Extended Data Fig. 5c ). In addition, aMDM upregulated genes that encode sorting nexin 9 (SNX9), a regulator of CD44 endocytosis 33 , the lysosomal copper transporter 2 (CTR2) and metallothioneins (MT2A, MT1X), which are involved in copper transport and storage (Supplementary Table  3 ).
6e ). Next, we evaluated the effect of LCC-12 in a model of viral infection. K18-hACE2 transgenic mice infected with SARS-CoV-2 displayed increased levels of CD44 in lung tissues (Fig. 6f ). Similarly, lung tissues were characterized by alterations of genes related to mitochondrial metabolism, regulation of chromatin and inflammation (Extended Data Fig. 6a  and Supplementary Table  7 ). In particular, viral infection promoted the expression of inflammatory genes and some genes encoding ATs and iron-dependent demethylases (Extended Data Fig. 6b, c  and Supplementary Table  8 ). Treatment with LCC-12 administered by inhalation perturbed the expression of genes involved in the regulation of chromatin and inflammation (Fig. 6g  and Supplementary Table  9 ), with a reduced expression of inflammatory genes (Fig.
Acknowledgements RR expresses his gratitude to  J. -M. Lehn  whose inspiring achievements has been instrumental to this work. This work was supported in part by  INSERM  and  CNRS. We thank the  PICT-IbiSA@BDD Imaging Facility of Institut Curie , member of the  France-BioImaging national research infrastructure  ( ANR-10-INBS-04 ) for the use of microscopes, the cytometry platform of  Institut Curie ,  C. Gaillet ,  J. Sampaio Lopes  and  D. Guillemot  for technical assistance, the  ICP-MS platform at Institut de Physique du Globe de Paris ,  K. Bailly  from the  Cytometry and Immuno-Biology (CYBIO) platform of Institut Cochin. G. D. P. thank the  University of Bath  for support. This research made use of the  Balena High Performance Computing (HPC) Service at the University of Bath. V. G.
thanks the  UPSaclay, CNRS  and  Ecole Polytechnique  for financial support. This work has been funded by the following: The  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  grant agreement No  647973  (RR), The  Foundation Charles Defforey-Institut de France (RR) ,  Ligue Contre le Cancer (RR and GK Equipes Labellisées) ,  Region IdF  for NMR infrastructure and financial support (RR),  FHU Sepsis and RHU records (DA) ,  Agence National de la Recherche Projets blancs AMMICa   US23/CNRS UMS3655 ,  Association pour la recherche sur le cancer, Association "Ruban Rose" ,  Cancéropôle Ile-de-France ,  Fondation pour la Recherche Médicale , A donation by  Elior ,  Equipex Onco-Pheno-Screen ,  European Joint Programme on Rare Diseases (EJPRD) ,  Gustave Roussy Odyssea , The  European Union   Horizon 2020 Projects Oncobiome and Crimson ,  Fondation Carrefour ,  Institut National du Cancer (INCa) ,  Institut Universitaire de France ,  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ), The  Leducq Foundation , The  RHU Torino Lumière, Seerave Foundation ,  SIRIC Stratified Oncology Cell DNA, Repair and Tumor Immune Elimination (SOCRATE) ,  SIRIC Cancer Research and Personalized Medicine (CARPEM) , This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001 ,  Institut de Physique du Globe de Paris  is supported by  IPGP multidisciplinary program PARI and Paris-Region IdF  ( SESAME grant  agreement No [ 12015908 ]), This work was granted access to the HPC resources of CINES under the allocation 2020-A0070810977 made by  GENCI , FT thanks for the support by  University of Lille  and  Institut Pasteur de Lille.
Data and code availability RNA-seq data are available on the National Center for Biotechnology Information website with accession reference GSE160864 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE160864; enter token wvqxgwgojxcdboz into the box). Analysis scripts are available at https://github. com/bioinfopf-curie/MDMmetals All data are available in the main text or the supplemental materials.
Acknowledgement None. We thank very much  Dr. Zhige Zhang  and colleagues for their interest in our recently published 'ESPEN guideline on hospital nutrition' and for their lauding words [1]. We also noticed theirs words of criticism to which we would like to reply briefly. worsening. Therefore, screening and assessment for malnutrition according to the "GLIM criteria" is a need that exists independently from symptoms and will of the patients [2]. In conclusion, we hope that you and many others will profit from this guideline and thank again for the comments.
Statistical Data Analysis Analysis of variance (ANOVA), Tukey's Honest Significant Difference (HSD) multiple comparison test (p < 0. 05), Principal Component Analysis (PCA), word cloud and Partial Least Squares (PLS) regression were performed using XLSTAT software (ADDINSOFT, Paris, France). For each sensory descriptor, ANOVA was performed with the product and panellist as the main effects, as well as their interaction, to investigate the performance of the panel and determine which descriptors can significantly discriminate the products  [50]. The model used was as follows: descriptor intensity = panellist + product + panellist*product + error (random panellist effect). The ability of the panel to discriminate products was measured by the F product, and the panel was discriminating if the product effect was significant.
These analyses were performed after calculating the average of the two repetitions for the sensory profiling data for each panellist and each product. Partial Least Squares (PLS) regression analysis was carried out to determine the correlation between the sensory profile results (exclusion of non-discriminant and texture descriptors) and the hedonic score of the 12 gels. In addition to the PLS, a word cloud representing the most frequently cited terms was created from the terms cited by consumers to explain what they liked or disliked in the different products (the word size was proportional to the frequency of citation). The first step consisted of synthesising whole sentences into keywords and grouping together words with the same semantic origin. Words describing the intensity level of descriptors ("too much", "not enough", or "very") were not considered.
Taste and Aroma Properties Guiding Gel Acceptability To identify the sensory descriptors guiding the acceptability of the products, the data from the sensory profile and the consumer study were combined. The objective was to determine the descriptors that most decreased the acceptability of the products. A PLS regression was performed to explain the hedonic appreciation scores (dependent variable) using the intensities of these descriptors (explanatory variables)  [75]. The texture descriptors and the yeast, broth and mushroom non-discriminant aroma descriptors were excluded from the prediction. The PLS regression model (R² = 0. 855; Q 2 (cum) = 0. 819 for a single component) indicated significant descriptors (Figure  4 ). The descriptors grilled, salty and umami were not significant. Potato, cereal and haricot bean descriptors contributed positively to the appreciation of the gels.
Taste and Aroma Properties Guiding Gel Acceptability To identify the sensory descriptors guiding the acceptability of the products, the data from the sensory profile and the consumer study were combined. The objective was to determine the descriptors that most decreased the acceptability of the products. A PLS regression was performed to explain the hedonic appreciation scores (dependent variable) using the intensities of these descriptors (explanatory variables)  [75]. The texture descriptors and the yeast, broth and mushroom non-discriminant aroma descriptors were excluded from the prediction. The PLS regression model (R 2 = 0. 855; Q 2 (cum) = 0. 819 for a single component) indicated significant descriptors (Figure  4 ). The descriptors grilled, salty and umami were not significant. Potato, cereal and haricot bean descriptors contributed positively to the appreciation of the gels.
Bitterness is a flavour that is identified well by consumers, even without training, which explains why they had no difficulty in identifying it as a factor. The word bland was more related to aroma perception. Indeed, the consumers often said that the gels were salty or bitter but also bland (for example: "Bland, no particular flavour, leaves some bitterness in the mouth"). The green note, aftertaste and salty taste were cited 78, 60 and 56 times, respectively (data not shown). The green note consisted mostly of pea, green legume and vegetal notes. Consumers did not all agree with each other; some liked the green note, while others did not. The aftertaste was described as "persistent", "unpleasant", or "pleasant", but no reference to a specific taste or aroma was made. A recommendation for the next sensory profile of pulses would be to evaluate the descriptor before and after swallowing to identify those responsible for the aftertaste.
Acknowledgments: The authors are indebted to  Mitali Kulkarni  ( Junia ISA, Lille, France ) for the gel formulation and the sensory profiling help provided during her internship. They thank  Chantal Septier  ( CSGA, INRAE, Dijon, France ) for her technical advice for sensory tests,  Françoise Durey  for the proper functioning of the experimental kitchen and all the sensory and consumer panellists. INRAE, Regional Council of Bourgogne Franche-Comté  and the  European Regional Development Fund (FEDER)  are thanked for equipment funding.
The resolution of the mass spectrometer was 18,000. With SpiderMass direct analysis no lock mass correction can be performed, resulting in a slight decrease in mass accuracy. All data were binned to 0. 1 Da. For the standards, the analysis was conducted from 1 µL of standard deposited with 1 µL of glycerol onto a Prosolia Omni TM glass slide. To create the calibration curves based on diagnostic MS/MS fragment ions, different CBDA/∆ 9 -THCA ratios were used by increasing the number (100% ∆ 9 -THCA, 2:8 CBDA: ∆ 9 -THCA, 3:7 CBDA: ∆ 9 -THCA, 4:6 CBDA: ∆ 9 -THCA, 5:5 CBDA: ∆ 9 -THCA, 6:4 CBDA: ∆ 9 -THCA, 7:3 CBDA: ∆ 9 -THCA, and 100% CBDA). In all cases, 1 µL of standard was deposited with 1 µL of glycerol. For the plants, different regions were analyzed, including the flower, bulb, sugar leaves, and along the fan leaf, by directly placing the laser at the right focal distance from the laser probe.
For each sample, the acquisition was composed of 10 laser shots consecutively fired (i. e. , 1 s) repeated 3 times with a 10 s pause between each series. Ten laser shots resulted in one individual MS spectrum. The laser energy was fixed at 4 mJ/pulse and the spot size was 500 µm. The data were acquired in both polarities in the sensitivity mode in the m/z 100-2000 mass range. Four independent biological repetitions were realized for each cannabis plant subtype. The raw data were then imported into "Abstract Model Builder"-AMX (version 1. 0. 1972. 0, Waters, Hungary). The classification model was built using individual spectra (on average 3 per sample) and by subjecting them to principal component analysis (PCA) and linear discriminant analysis (LDA). The classification model was built using a mass range of 100-1000 m/z with 0.
1 binning, 1 × 10 3 threshold intensity, applied normalization to the TIC, and background subtraction. A non-parametrical oneway ANOVA (Kruskal-Wallis) followed by Dunn's test was performed to calculate the significant differences between the normalized intensities for discriminative ions using GraphPad Prism 5. The putative assignments of flavonoids, waxes, fatty acids, and lipids were made through the METLIN and ALEX123 databases  [30, 31]. The cannabinoid peaks were selected for MS/MS analysis with a 0. 1 Da isolation window. MS/MS was performed with collision-induced dissociation (CID) using argon as the collision gas and a collision energy set to 25 V. The fragmentation spectra were then further used to evaluate the presence of CBDA and ∆ 9 -THCA based on the ratios of neutral losses 18 Da/44 Da and 44 Da/46 Da.
1% and acetonitrile/formic acid 0. 1% and taken to MS/MS analysis on a triple quadrupole Agilent instrument. The identification of the lipid mediators in the samples was based on matching of their retention time with the retention time of pure authentic standards injected in the same conditions. Calibration curves were obtained using the same authentic standards, and quantification was carried out based on peak areas from multiple reaction monitoring (MRM) transitions. Peak detection, integration, and quantitative analysis were done using Mass Hunter Quantitative Analysis Software (Agilent Technologies). Quantification results were expressed in pg/mL.
The membrane was then saturated in blocking buffer (5% fat dry milk dissolved in a Tris-buffered saline and 0. 1% Tween-20 solution (TBST) and incubated overnight at 4°C with primary antibodies. After the washes, membranes were incubated for 1h at RT with secondary antibodies coupled with HRP and washed again. Staining was revealed using the Supersignal ™ West Pico PLUS Chemiluminescent Substrate kit (ThermoFisher Scientific). Images were analysed and bands densitometry measured using the ImageJ software. The following antibodies dilutions were used: anti-5-LO 1:200 (Cayman, No. 160402), anti-15-LO1 1:200 (SantaCruz, sc-133085), anti-15-LO2 1:200 (SantaCruz, sc-271290), anti-12-LO 1:500 (Abnova, H00000242-A01), and anti-b actin 1:5000 (Sigma-Aldrich, A5316). Secondary antibodies were diluted at 1:10000.
Supplemental Figure  2 -a: Proportional hazard assumption testing using rescaled Schoenfeldtype residuals for total TFAs and type 2 diabetes risk Supplemental Figure  3 : Detailed decision tree for TFAs composition assignment with reference to the NNDSR (*National Nutrient Database for Standard Reference of the United States). A TFA composition value was attributed to all food items that were likely to contain some. Indeed, when the exact match with the USDA composition table was not found for a particular food item, the value of the closest food/beverage equivalent was selected. For composite food dishes, the amount of TFAs was estimated using validated French recipes by summing TFA amounts contained in the different ingredients composing the dish.
Food containing trans fatty acids (TFAs) No: Belongs to the categories "water", "coffee", "alcoholic beverages", "fruits", "compotes and cooked fruits", "dried vegetables", "rice", whole wheat or cracked wheat" → TFAs value =0 Participants were asked to declare major health events though the yearly health questionnaire, through a specific health check-up questionnaire every six months, or at any time through a specific interface on the study website. They were also asked to declare all currently taken medications and treatments via the check-up and yearly questionnaires. A search engine with embedded exhaustive Vidal® drug database is used to facilitate medication data entry for the participants. Besides, our research team was the first in France to obtain the authorization by Decree in the Council of State (n°2013-175) to link data from our general population-based cohorts to medico-administrative databases of the National health insurance (SNIIRAM database).
Thus, data from the NutriNet-Santé cohort are linked every year to medicoadministrative databases of the SNIIRAM, providing detailed information about the reimbursement of medication and medical consultations. Regarding type 2 diabetes specifically: all 969 cases were primarily detected through the declaration by the participants of a type 2 diabetes diagnosed by a physician and/or diabetes medication use, in follow-up questionnaires. The questions were: "Have you been diagnosed with type 2 diabetes (if yes, indicate the date of diagnosis)" and "Are you treated for type 2 diabetes?". ATC codes considered for type 2 diabetes medication were A10AB01, A10AB03, A10AB04, A10AB05, A10AB06, A10AC01, A10AC03, A10AC04, A10AD01, A10AD03, A10AD04, A10AD05, A10AE01, A10AE02, A10AE03, A10AE04, A10AE05, A10AE30, A10BA02, A10BB01, A10BB03, A10BB04, A10BB06, A10BB07, A10BB09, A10BB12, A10BD02, A10BD03, A10BD05, A10BD07, A10BD08, A10BD10, A10BD15, A10BD16, A10BF01, A10BF02, A10BG02, A10BG03, A10BH01, A10BH02, A10BH03, A10BX02, A10BX04, A10BX07, A10BX09, A10BX10, A10BX11, A10BX12.
Yes In addition to the diagnosis of type 2 diabetesM and/or a medication report, two sources of confirmation were considered. First, linkage with the SNIIRAM database confirmed more than 80% of the cases surveyed (ICD-10 codes E11). Second, among participants who provided a blood sample at the clinical/biological examination, 85. 3 % of those with elevated fasting blood glucose (i. e. , >1. 26 g/L 9 ) had consistently reported a diagnosis of type 2 diabetesM and/or medication. However, elevated blood glucose without any declaration of type 2 diabetes diagnosis or treatment was not considered specific enough to classify the participant as a type 2 diabetes case.
Supplemental Method 2: Multiple Imputation by Chained Equations Missing values for covariates were handled using the Multiple Imputation by Chained Equations (MICE) method 2 (20 imputed datasets) for the following covariates: smoking status (0. 07% missing), level of education (0. 86%), physical activity (13. 81%), occupation (0. 86%), prevalence of dyslipidemia (0. 44%), BMI (1. 56%), family history of diabetes (1. 39% missing), marital status (0. 25% missing), prevalence of cardiovascular disease (0. 44% missing).
Statistical Analysis The results reported are the averages of three replications, unless otherwise stated. Statistica 6. 0 software (2001, StatSoft, Tulsa, OK, USA) was used to calculate one-way ANOVA (analysis of variance) couples with Tukey's honest significant difference and to obtain the bloc plots. Before the ANOVA analysis, the data were normalized using the following formula: zi = (ximin(x))/(max(x)min(x)) × 100  (2)  where zi: the ith normalized value in the dataset; xi: the ith value in the dataset; min(x): the minimum value in the dataset; max(x): the maximum value in the dataset.
3%). To confirm the data obtained by GC-FID, lipid classes were separated by TLC. The S9 samples showed two main spots at the end of elution; according to the comparison with other samples, the first one (lower Rf) is related to the triglycerides, and the last one (higher Rf) presented the same elution of the ethyl esters present in the fish oil concentrates (Figure  3 ). To confirm the identity, the spot was scraped off and analyzed by GC-MS, confirming the presence of ethyl esters of omega-3 fatty acids. Briefly, the The TAG range content was 0-97. 6%. Samples S10, S13, and S20 did not contain TAG because they were formulated with concentrated fish oil containing ethyl esters of EPA and DHA. Sample S5 showed a TAG content of 47%; this low content was strictly related to the source of the oil.
Finally, sample S9 formulated with fish oil reported a very low content of TAG (5. 3%). To confirm the data obtained by GC-FID, lipid classes were separated by TLC. The S9 samples showed two main spots at the end of elution; according to the comparison with other samples, the first one (lower Rf) is related to the triglycerides, and the last one (higher Rf) presented the same elution of the ethyl esters present in the fish oil concentrates (Figure  3 ). To confirm the identity, the spot was scraped off and analyzed by GC-MS, confirming the presence of ethyl esters of omega-3 fatty acids. Briefly, the fragment at m/z 108, typical of omega-3 fatty acids, was detected. Moreover, the fragments at m/z 79, 91, and 119 were found for EPA ethyl ester, and the fragments at m/z 79, 91, and 117 were detected for DHA ethyl ester.
Background: Starchy vegetables, including white potatoes, are often categorized as "lower-quality" carbohydrate foods, along with refined grains, 100% fruit juices, sweetened beverages, and sugars, snacks and sweets. Among "higher-quality" carbohydrates are whole grains, non-starchy vegetables, legumes, and whole fruits. Objective: To apply multiple nutrient profiling (NP) models of carbohydrate quality to foods containing >40% carbohydrate by dry weight in the USDA Food and Nutrient Database for Dietary Studies (FNDDS 2017-18). Methods: Carbohydrate foods in the FNDDS (n = 2423) were screened using four recent Carbohydrate Quality Indices (CQI) and a new Carbohydrate Food Quality Score (CFQS-4). Cereal products containing >25% whole grains by dry weight were classified as whole grain foods.