dataesr/scientific-paragraphs-categorization huggingface dataset:
UHPLC-ESI-HRMS Identification of α-Glucosidase Inhibitory Metabolites The P. lentiscus extracts were found to have an α-glucosidase inhibition effect that was more or less impactful according to the plant's living environment (i. e. , mountain or littoral) and the considered organ (i. e. , leaves, stem barks or fruits). With the aim of obtaining a better understanding of this effect, UHPLC-ESI-HRMS analyses were performed to characterize the phytochemical content that could be responsible for α-glucosidase inhibition. Statistical analyses (i. e. , multiblock OPLS) were used for comparing the data of the different extracts and to highlight those for which the metabolic content could be correlated with strong α-glucosidase inhibition activity. This approach led to a total of six compounds. These compounds were putatively identified as phenolic compounds by ESI-HRMS in the HDMS E mode based on accurate mass measurements of the corresponding ions and their fragments.
The UHPLC-ESI-HRMS data results are summarized in Tables  2  and S5. Indeed, these tables include the retention time (RT), observed m/z, mass error value (ppm), molecular formula, characteristic fragment ions used for identification with their intensities and the suggested identity of each compound in agreement with previous studies. Table  S5  in the Supplementary Materials contains more information than Table  2. The tentative Identification of the metabolites most positively correlated with α-glucosidase inhibition was based on UHPLC-QTOF-MS (RT, m/z, molecular formula) data. Using the characteristic fragments obtained after the HDMS E analysis, we were able to propose fragmentation patterns identifying each molecule. The identification process was also based on comparisons using on the available databases, including the KnaPSacK metabolome database, MassBank, Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetFrag, as well as the UNIFI library.
UHPLC-ESI-HRMS Identification of α-Glucosidase Inhibitory Metabolites The P. lentiscus extracts were found to have an α-glucosidase inhibition effect that was more or less impactful according to the plant's living environment (i. e. , mountain or littoral) and the considered organ (i. e. , leaves, stem barks or fruits). With the aim of obtaining a better understanding of this effect, UHPLC-ESI-HRMS analyses were performed to characterize the phytochemical content that could be responsible for α-glucosidase inhibition. Statistical analyses (i. e. , multiblock OPLS) were used for comparing the data of the different extracts and to highlight those for which the metabolic content could be correlated with strong α-glucosidase inhibition activity. This approach led to a total of six compounds. These compounds were putatively identified as phenolic compounds by ESI-HRMS in the HDMS E mode based on accurate mass measurements of the corre-sponding ions and their fragments.
This research was funded by (1) the  French Ministry of Foreign Affairs through Campus France  (No. 973496A ); (2) the " Conseil Régional de Picardie " through the  MTV (Métabo-Typage Végétal ) project;(3) the  University of Picardie Jules Verne  through the  French Ministry of Higher Education and Research; the region "Hauts de France "; and the  European FEDER  fund within the so-called CPER programs devoted to equipment acquisitions. This work was also funded by Algerian authorities though the financial support of the  Tizi-Ouzou UMMTO University  and asocial grant attributed to  Chabha Sehaki  for her PhD. Institutional Review Board Statement: Not applicable.
Flash chromatography was carried out with the indicated solvents using silica gel 60 (60AAC, 35-70 mm). The 1 H (and 1 H decoupled),  13  C, and 31 P NMR spectra were recorded on 600, 500, 400, or 300 MHz spectrometers at ambient temperature using tetramethylsilane as internal reference for 1 H and 13 C spectra and phosphoric acid (85%) as external reference for  31  P NMR. Data were reported as s ¼ singlet, d ¼ doublet, t ¼ triplet, q ¼ quartet, m ¼ multiplet, br. s ¼ broad signal, coupling constant(s) in Hertz, integration. Infrared spectra were recorded on FT-IR instrument, and the data are given in cm  1. Melting points were measured on a Kofler melting points apparatus and are uncorrected. Optical rotation values were determined at 20 C on a polarimeter at 589 nm (sodium lamp). The mass spectra and accurate mass measurements were performed under (ESI) conditions with a micro Q-TOF detector or Orbitrap detector or in the MALDI/ TOF reflection mode using dithranol as a matrix.
Modeling the Full-Length Structure of CD36 The crystal structure of CD36 (PDB code 5LGD:A) covers only the residues 35-434, which constitute the extracellular domain of this membrane protein. According to UniProt annotation of CD36 (UniProtKB -P16671), the regions 8-29 and 440-461 are transmembrane helices, which are likely to form an antiparallel bundle in the membrane, considering the spatial proximity of the residues 35 and 434 in the crystal structure. Unfortunately, the transmembrane helices, the missed linkers to extracellular domain, and the intracellular termini have no homologs among the proteins with known structure. Thus, automated homology modeling for this protein does not produce any useful results. C-I-TASSER,  47  I-TASSER,  47  and GalaxyTBM 48 servers predict either disordered coils in place of transmembrane helices or highly distorted helices with wrong orientation.
Thus, we used crystal structure of CD36 (PDB code 5LGD:A) as a template for the residues 35-434 and the Figure  12. FTA exert no toxicological effects and malaise in conditioned taste aversion (CTA). In (A), female pathogenfree Wistar rats (8-to 12-week-old) were quarantined for 7 days. After quarantine, rats were randomized and assigned to 3 groups (5 rats/group) where group 1 (control) received the vehicle (0. 3% xanthan gum), group 2 received NKS-3, and group 3 received NKS-5. After an overnight fast, rats were weighed (180-200 g) and dosed orally by gavage with a single limit dose of 2000 mg/kg body weight of NKS-3 or NKS-5. This dosage was selected on the basis of a preliminary study performed with single animal in which no evidence of toxicity was observed at oral dosages of 5000 mg/kg body weight.
The parameters (C max , T max , AUC 0-6 h , Vd SS ) were determined using a standard non-compartmental approach (NCA). Five mice per group. All of the NCA calculations were performed with Kinetica Software (Thermo Scientific, Philadelphia, PA). AUC, area under the plasma concentration-time curve; C max , maximum plasma concentration; T max , the time to reach C max ; Vd SS , volume of distribution (Vd) during steady-state (SS). a Median. level of optimization. Five models were generated for each variant of the transmembrane helices packing, which results in 25 models. The models were ranked by their DOPE scores, and 2 best models were selected. The structures were examined visually, and the model where extracellular domain is more perpendicular to the presumed membrane plane was chosen. At the absence of any experimental data about preferable orientation of the extracellular domain in respect to the membrane, such choice eliminates possible biased interactions between this domain and the lipid head groups as much as possible.
Molecular Dynamics Simulations The CD36 protein was inserted into POPC lipid membrane using CHARMM GUI membrane builder tool. 50 The orientation of the protein followed orientation of the transmembrane helices predicted by PREDDIMER. In addition to 9 glycosylated residues present in the crystal structure, the residue 163 was glycosylated with N-acetyl-D-glucosamine, and the residues 3, 7, 464 and 466 were palmitoylated as suggested by annotation in the UniProt database. Resulting system contained 284 POPC lipids, w40,000 water molecules. One hundred eighteen Na þ and 121 Cl -ions correspond to ionic strength of w0. 15 mol/L and counterbalance the net charge of protein. All palmitoylated residues appeared in the intracellular leaflet of the membrane, which is in agreement with experimental data.
51 The system was equilibrated in several steps. First, the membrane and solution were equilibrated for 50 ns with all protein atoms restrained. Then, the system was equilibrated for 100 ns with the protein backbone restrained. Finally, all restrains were removed, and production run of 200 ns was performed. GPR120 was inserted into the membrane using the same technique. The residue ASN 20 was glycosylated with N-acetyl-D-glucosamine according to annotation in the UniProt database. Resulting system contained 218 POPC lipids, w22,000 water molecules, 45 Na þ ions, and 58 Cl -ions. The membrane and solution were equilibrated for 25 ns with all protein atoms restrained. Then, the system was equilibrated for 50 ns with the protein backbone restrained. Finally, all restrains were removed, and production run of 100 ns was performed.
All molecular dynamic simulations were performed in Gromacs versions 2019. 2 in NPT ensemble at the pressure of 1 atm maintained by Parrinello-Rahman barostat with semiisotropic pressure coupling. The Verlet cutoff scheme was used. Force-switch cutoff of the Van der Waals interactions was used in the region between 1. 0 and 1. 2 nm. Long range electrostatics was computed with the PME method, with the cutoff of explicit short-range electrostatic interactions at 1. 2 nm. Velocity rescale thermostat was used at the temperature of 310K. CHARMM36 force field was used for all components of the system. An integration step of 2 fs was used in all simulations, with the bonds to hydrogen atoms converted to rigid constraints. Analysis was performed by the custom scripts based on Pteros molecular modeling library.
Pharmacokinetic Parameters NSK-3 and NSK-5 pharmacokinetics data were analyzed via a non-compartmental approach (Kinetica; Thermo Scientific, Philadelphia, PA) to determine the major pharmacokinetic parameters (ie, C max , T max , AUC last , T1/ 2 , clearance, and V ss ). Parameters were derived for each animal and then summarized as means or medians (for time). A second approach, called population pharmacokinetic, was applied to propose an optimal limited sampling strategy approach that is based on non-linear mixed effect modeling (Monolix; Lixoft, Paris). The aim is to propose a model that, via a compartmental approach, underlies the entire time course of the concentrations versus time by differential equations.
An indifferent electrode (negative polarity) was attached to the skin overlying the cranium with an alligator clip and served as a reference. Throughout the surgery and nerve recordings, body temperatures were maintained at 37 with a thermal pad. Signals were first amplified and passed through a bandpass filter (300-3000 Hz) by an AC differential amplifier (DAM 80; WPI, USA). Thereafter, signals were digitized (sampling rate: 1000 samples/s), integrated with a time constant of 1. 0 s, and then sorted for on-line data collection using a laboratory interface and software (PowerLab 4/30; ADInstruments Pvt Ltd, Australia). All electrical activity was also displayed on an oscilloscope (Hitachi, Japan). The unit activities were also assessed simultaneously by the use of an audio analyzer instrument (Fredrick Haer, USA).
All taste solutions were mixed daily from reagent grade chemicals (Sigma-Aldrich and VWR) and presented at room temperature. Stimuli consisted of NKS-3, NKS-5, LA, and OA at 1, 2, and 4 mmol/L concentrations. In addition, 0. 1 mol/L NH 4 Cl mixed in distilled water was applied before and after each concentration series, with a given taste stimulus serving as a reference stimulus. The anterior half of the mouse tongue was enclosed in a flow chamber. Solutions were delivered into the flow chamber through input tubes by gravity flow and flowed over the tongue for a controlled period (0. 1 ml/s). For data analysis, the magnitude of the integrated neural activities was measured and averaged at 6 different time points for 1 minute after stimulus (at 5, 15, 25, 35, 45, and 55 seconds). Finally, data were normalized to the response magnitude of 0.
Indirect Calorimetry Indirect calorimetry was performed using a computercontrolled, open-circuit system (Columbus Oxymax Comprehensive Lab Animal Monitoring System). Mice were individually tested during 24 hours at 23 C ± 0. 5 C after being acclimated to the measurement cage over 5 days. Outdoor air reference values were sampled every 8 measurements. Gas sensors were calibrated before experiment with gas standards containing known concentrations of O 2 , CO 2 , and N 2 (Air Liquide -Certified accuracy ±1%). Data from 5 separate experimental runs were combined. Oxygen consumption (VO 2 ), carbon dioxide production (VCO 2 ), total and locomotor activity, and food intake were recorded in time intervals of 20 minutes during the whole measurement period. Energy expenditure.
Acknowledgements The authors thank  Miss Charmaine Bastian Joseph ,  Miss Anoucheka Bories ,  Baptiste Rugeri , and  Mr Anthony-Damien Desirée  for their technical assistance. Present affiliation for  Julia Leemput :  Team PADYS ,  UMR UB/AgroSup/ INSERM U1231 , Lipides,  Nutrition & Cancer ,  Université de Bourgogne-Franche Comté (UBFC), Dijon, France.
Funding Supported by financial support from the SATT (Société d'Accélération du Transfert de Technologies) Grand-Est (Dijon) that financed two projects (ImmorTasteCell and FaTasteAnalogues). This project was also supported by the University of Burgundy as BQR (bonus-qualité-recherche). The DRRT (délégation régionale à la recherche et à la technologie) at Dijon granted a project on the synthesis of fat taste agonists. The BFC (Burgundy Franche Comté) Région also granted a post-doc scholarship to one of the authors (MS). The BFC (Burgundy Franche Comté) Région also sanctioned a project "Tasty Lipids" in the category "Envergeure" that helped realize research work. A grant for the recruitment of a technician is also acknowledged from LipStick Excellence laboratory (ANR-11-LABX-0021-LipSTIC). SY was supported by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no.
(H) DGAT inhibition (DGATi)-induced changes in PGE 2 production in serum-starved control cells (empty) and in cells overexpressing ATGL (ATGL OE ) or cPLA 2 a (cPLA 2 a OE ), without and with additional stimulation of LD biogenesis by hGX sPLA 2 pretreatment. Neutral lipid content was quantified by Nile Red staining and flow cytometry (B), PGE 2 levels were determined in cell supernatants using ELISA (B, F, G, H). Data are means AE SEM of two (B, A549 cells) or at least three independent experiments *, P < 0. 05; **, P < 0. 01; ***, P < 0. 001 (nested one-way ANOVA with Sidak adjustment (C, box plots), two-way ANOVA with Tukey (B, C, H, F), Holm-Sidak (D, E) or Dunnet (G) adjustment). serum starvation (Supp. Figure  6A ). These data suggest that a significant portion of hGX sPLA 2 -released PUFAs that are incorporated into TAGs in serum-fed cells are gradually released via lipolysis during serum starvation and are re-esterified into phospholipids.
Data acquisition was carried out using the MassLynx 4. 1 software (Waters), and the lipid classes were analysed with the Lipid Data Analyser 1. 6. 2 software. The data were normalised for recovery, extraction and ionisation efficacy by calculating analyte/internal standard ratios (AU) and expressed as percentage composition. To determine the changes in the phospholipid and TAG profiles in ATGL-depleted and cPLA 2 a-depleted cells, MDA-MB-231 cells were seeded in complete medium on 6-well plates at 3 Â 10 5 cells/well and reverse transfected with ATGL-specific and/or cPLA 2 a-specific siR-NAs, as described above. After 24 h, the cells were washed and grown for 48 h in complete medium in the presence or absence of 10 nM sPLA 2. The cells were then washed twice with DPBS and serum starved for the following 24 h in RPMI-1640 medium containing 0.
1% formic acid, 8 mM phosphoric acid) to 100% solvent B (2-propanol; 10 mM ammonium acetate, 0. 1% formic acid, 8 mM phosphoric acid). The column compartment was kept at 50 C. A Q-TOF mass spectrometer (6560 Ion Mobility; Agilent, Santa Clara, CA, USA) equipped with electrospray ionisation source (Dual AJS) was used for detection of the lipids in positive and negative Q-TOF mode. Lipid data were acquired at MS1 level using the MassHunter Data Acquisition software (B. 09; Agilent, Santa Clara, CA, USA) and initial feature analysis was performed in MassHunter Mass Profiler (V10. 0, Agilent). A manually curated, in-house lipid library of naturally occurring lipids, containing w1000 MS2-identified/characterized lipid species, was used for lipidfeature identification based on accurate precursor mass (< 5 ppm) and retention time (< 0.
2 min). All identifications were inspected manually and RT and m/z of identified lipid species were used as target list for peak integration in MassHunter Quantitative Analysis (B. 09. 00). Peak areas were blank subtracted, single-point quantified using the respective internal standard, normalized using the determined protein amount of the respective sample and expressed as fmol/mg protein. Lipidomic data were analysed by multiple t-test analysis of logtransformed data to compare two conditions at a time, using Graph-Pad Prism 9. 0. 2 (GraphPad Software, USA). Lipid ontology enrichment analysis was performed using the LION/web enrichment and principal component analysis modules  [83]. Lipidomics data were deposited in the MetaboLights database under study ID MTBLS7786. 4. 11. Thin layer chromatography of radiolabelled cellular lipids MDA-MB-231 cells were seeded in complete medium on T-25 flasks at a density of 5 Â 10 5 cells/flask.
LD counting and diameter analysis Confocal microscopy images of BODIPY 493/503 stained LDs were used for computer image analysis with the ImageJ software (National Institutes of Health, USA) and the LD Counter Plugin (https://doi. org/10. 5281/zenodo. 2581434). Analysis was performed on 32-bit twodimensional images, whereby the numbers and sizes of the LDs were determined according to the plugin instructions. LD diameters were calculated from the LD surface areas. Analyses were performed on at least 40 cells/sample. Data were loaded into the Prism 9. 0. 2 software (GraphPad Software, USA), with the geometric means of the LD diameters and numbers calculated per individual cell in the samples, followed by the statistical analysis. 4. 16. Cell proliferation MDA-MB-231/Luciferase-2A-RFP cells were seeded in complete medium in 96-well plates at 5 Â 10 3 cells/well in at least triplicates.
A549 cell proliferation was determined after 48 h using the direct cell proliferation assay kits (CyQUANT; Invitrogen, USA). MDA-MB-231/Luciferase-2A-RFP cell proliferation was determined by measuring RFP fluorescence emission (excitation, 558 nm; emission, 583 nm) on a microplate reader (Infinite M1000; Tecan, Austria). The final data were obtained after subtracting the background signal of the blank samples that contained culture medium without cells. 4. 17. Clonogenic assay MDA-MB-231 cells were seeded in 24-well plates in complete medium as described above. After 24 h, the cells were treated with 20 mM T863 and/or 20 mM PF-06424439 and left for 24 h. Cells were then washed and harvested for cell counting. 500 cells/well were seeded in 6-well plates and colony formation followed for nine days in complete medium containing 20 mM T863 and/or 20 mM PF-06424439.
Data and statistical analysis Please refer to figure legends for sample size and statistical tests performed. Publicly available cancer genomics data from the Cancer Genome Atlas (TCGA) consortium, the Pan-Cancer Atlas Studies  [86] , were analysed using cBioPortal  [87, 88]. Data were plotted and statistical analyses performed using Prism 9. 0. 2 (GraphPad Software, USA). Unless otherwise indicated, the data are presented as means AE SEM of at least three independent experiments. Statistical significance was determined using t-tests, one-way or two-way ANOVA, followed by Bonferroni, Sidak or Tukey's multiple comparison tests. P values < 0. 05 were considered as statistically significant.
ACKNOWLEDGEMENTS We are grateful to  Mauro Danielli ,  Leja Perne ,  Ana Temprano Lopez ,  Petra Hru  sevar,  Ana Kump ,  Ema Gu  stin and  Belen Vilanova Baeza  for their technical help,  Dr. Mojca Pavlin  ( University of Ljubljana, Slovenia ),  Dr. Brett McKinnon  ( Berne University Hospital, Switzerland ),  Dr. Merce Miranda  ( Joan XXIII University Hospital Tarragona, Spain ) and  Dr. Boris Rogelj  ( Jo zef Stefan   Institute, Slovenia ) for kindly providing cell lines. We thank  Dr. Chris Berrie  for critical reading of the manuscript. The results shown here are in part based upon data generated by the TCGA Research Network:https://www. cancer. gov/tcga. This work was supported by the  Slovenian Research Agency  young researcher ( 1000-15-106 ) and postdoctoral grants ( Z3-2650 ) to E.
Study design IMMUNOCITRE was a double-blind randomized placebo-controlled trial involving four intensive care units (ICUs) in France. The study protocol was approved by an independent ethics committee (Comité de Protection des personnes, France; reference: 2016-895) and by regulatory authorities (reference: 150806B-42). Signed informed consent was obtained from all the participants or their legally authorized representatives prior to enrollment and randomization. This trial was registered at ClinicalTrials. gov, No. NCT02864017. Because flow cytometry analysis for immune monitoring required live cell samples, patients were included only from Monday to Wednesday so that each sample (day 1, day 3, and day 7) fell on a regular workday.
Randomization and masking Patients were randomized in a 1:1 ratio to either L-citrulline (intervention group) or placebo (control group). The randomization sequence was generated using SAS 9. 4 software (SAS Institute, Cary, NC, USA) and used blocks of size 6, stratified by site. To maintain blinding, treatments were prepared by a nurse from another unit who was not involved in the clinical trial.
Protocole changes Day 28 mortality was measured, as a post hoc analysis of our data since day 28 mortality was not listed as a prespecified outcome in our trial. As additional immune parameters, three unspecified exploratory outcomes were added to the trial and were evaluated on day 1, day 3, and day 7: number of circulating monocytic myeloid-derived suppressor cells (M-MDSCs), which was evaluated only in one center (Rennes); total lymphocyte count; and indoleamine-pyrrole 2,3-dioxygenase (IDO) activity. During the peer review process, changes have been made to the statistical analysis plan. A sensitivity analysis on the primary endpoint has been added: an Analysis of Covariance (ANCOVA) adjusting for the SOFA score at day 1 was used to analyze SOFA score at day 7. For secondary endpoint, a log transformation was applied to deal with non-normal distribution of the following variables: arginine, citrulline, glutamine, ornithine, and kynurenine.
4 ± 1. 0 kPa and 30. 2 ± 0. 5 kPa for the roughest TMS, and 31. 8 ± 0. 2 kPa and 30. 6 ± 0. 2 kPa for the smoothest. No major differences could be noted between both samples, confirming that all TMSs had the same rigidity. The roughness of the TMSs was analyzed by profilometry (Dek-takXT®, Bruker, USA). Tested samples included one TMS per class of roughness (six samples). Three one-dimensional profiles were performed for each TMS analyzed (each was 30 mm long), with a 45 • rotation between one profile and the next, in order to cover as well as possible the area of interest for tribology measurements. The profilometry probe was a stylus with a 2 μm radius, the force was 1 mg, and the achieved resolution was 1. 66 μm/pt. , at a constant speed of 500 μm. s -1. The data from each one-dimensional profile (signals of the measured asperity height as a function of the tip scan distance) was processed individually on MATLAB (The MathWorks, Natick, Massachusetts, USA).