dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work received financial support from  Agence Nationale de la Recherche  ( BrightRiboProbes ,  ANR-16-CE11-0010-01/02 ) and from the  University of Strasbourg Institute of Advanced Study  ( USIAS, program Translatomix ). This work has also been published under the framework of the  LabEx :  ANR-10-LABX-0036_NETRNA  and benefits from a funding from the state managed by the  French National Research Agency  as part of the  Investments  for the future program. It was also supported by the  Université de Strasbourg  and the  Centre National de la Recherche Scientifique.
Intracellular Bcl-6, c-Maf, TCF1, Foxo1, KLF2, Eomes, and T-bet staining was performed after fixing and permeabilizing the cells with the FoxP3 staining buffer set (eBioscience), whereas pStat5 was detected after permeabilization using BD Cytofix ™ Fixation Buffer. Analyses were performed using FlowJo software (Tree Star, Inc. ). In vitro stimulation MLN cells (1 x 10 6 ) were cultured in 24-well plates pre-coated with purified anti-CD3 (0. 1 μg/ml, Abcam) and with CD28 (0. Immunofluorescence confocal microscopy of tissue sections MLNs were embedded in optimal cutting temperature compound (OCT), sectioned at a 7. 5 μm thickness and stored unfixed at -20 °C until use. Tissue sections were fixed in 4% PFA (15 min at room temperature) followed by acetone (20 min at -20 °C). Slides were submerged in blocking solution (5% normal horse serum, 0.
ACKNOWLEDGEMENTS This work was supported by grants from the  Canadian Institutes of Health Research (CIHR)  ( HBF-123682 ,  HBF-126786 , and  MOP-133476 ), the  ANRS (France) , and by the  Canadian HIV Cure Enterprise  Grant  HIG-133050  from the  CIHR  partnership with CANFAR and IAS. F. M. and H. R. were supported by the " Fondation du CHU de Québec ", and Y. F. by the  ANRS. We also thank  Daphnée Veilleux-Lemieux  and  Anne-Marie Catudal  for their help at the nonhuman primate center ( Quebec City ). J. E. thanks the  Canada Research Chair program  for financial assistance.
Filters were subsequently washed three times with 2 ml of ice-cold washing buffer (Supplemental Table  1 ). The filter-bound radioactivity was determined by scintillation spectrometry using a P-E 1450 Microbeta Wallac Trilux scintillation counter (PerkinElmer). IC 50 values were obtained by fitting the curves with a one-site binding model using GraphPad Prism 7. 00 software. K i values were deduced using the Cheng-Prusoff equation  (Cheng and Prusoff, 1973). Cell Culture. HepG2 cells were cultured in Dulbecco's modified Eagle's medium high-glucose medium supplemented with 10% Squalene-Adenosine Nanoparticles: Ligands or Prodrug? FBS, penicillin (50 U/ml), and streptomycin (50 mg/ml). If not stated otherwise, cells were plated on 24-well plates at a density of 200,000 cells per well and left to adhere overnight before any further procedure.
The detection limit of the HPLC technique was 17 nM for the adenosine, while the quantification limit was 55 nM. This method exhibited linearity (R 2 5 0. 99) over the assayed concentration range (100-1000 nM) and demonstrated good precision, with the relative S. D. being less than 5%. HPLC Conditions. A Waters 717 plus autosampler was used, equipped with a Waters 1525 binary HPLC pump, a Waters 2487 dual l absorbance detector, and a Waters Breeze software. The analysis was performed using a Halo C18 column (4. 6 Â 250 mm, 5 mm; Interchim, France). The injection volume was 40-50 ml, and the elution was carried out at 0. 8 ml/min isocratically for 10 minutes with the column temperature maintained at 30°C. Detection wavelengths were 273 nm for SQAd and 260 nm for adenosine. Intracellular cAMP Levels.
Acknowledgments We thank  Ghozlene Mekhloufi  and  Assia Hessani ,  Stéphanie Denis ,  Juliette Vergnaud , and  Romain Brusini  ( Institut Galien, France ) for experiments involving radioactivity, cell culture experiments, Western blots, and flow cytometry, respectively;  Valérie Nicolas  ( IPSIT, France ) for confocal microscopy;  Delphine Courilleau  ( CIBLOT, IPSIT, France ) for experiments on cAMP; and  Rongfang Liu  ( Division of Medicinal Chemistry, The Netherlands ) for preliminary experiments on radiobinding and cell membrane preparation.
The fixation was stopped with glycine 1 M (Sigma-Aldrich), and the pellet was washed with PBS before being incorporated in CryoFix Gel (BioGnost, France), frozen at -80 °C, and then cut with a microtome (CryoStar NX50, Thermo Scientific). The pellet was stained for 30 minutes with alcian blue 8GX 1% (BioGnost) dye which colors glycosaminoglycans in cartilage. The nucleus was stained for 1 minute with a 0. 1% fast red solution (Sigma-Aldrich), and the section was included in pertex. Images of 10 distinct fields by sample were obtained with an optic microscope (20x objective), and analysis was performed using the ImageJ software application Fiji to quantify the ratio of the alcian blue color surface to the total cell surface. 2. 5. Osteogenic Differentiation. ASC were induced into osteocytes using the StemXVivo Mouse/Rat osteogenic/adipogenic supplement (R&D Systems).
The RNeasy Mini Kit (Qiagen, France) was used to extract RNA, following the provider instructions, from ASC that had been differentiated or not. The reverse transcription was performed using the Takara reverse transcriptase kit (Ozyme, France). RT-qPCR was performed using the TaqMan fast advanced master mix (Biosystems, France). Samples were run in duplicate. Gene expression levels were calculated using the Rotor-Gene Q series software and normalized using the mouse 40S ribosomal protein S17 (Rs17) as the housekeeping gene. The primers used are listed in Table  1. 2. 7. Protein Analysis and Western Blot. ASC were cultivated until confluency and rinsed with PBS after removal of the culture supernatant. Whole cell lysates were prepared by adding per well of 9. 6 cm 2 , 300 μl of lysis buffer containing 1% IGPAL, 0.
The membranes were blocked in saturation buffer (0. 3% Tween, 5% low-fat milk in TBS) for 2 h at room temperature and next incubated overnight at 4 °C with the primary antibody diluted in saturation buffer. The following primary antibodies were used: rabbit anti-UCP2 (1/500, BioLegend, France) and mouse anti-α-tubulin (1/2000, Sigma, France). After washing three times with TBS-0. 3% Tween, the membrane was incubated with the HRP-conjugated anti-rabbit antibody (Bio-Rad, France). After washing, peroxidase activity was detected by chemiluminescence using the Luminata Classico western HRP substrate (Millipore, France). Detection was made using the ChemiDoc XRS+ imaging system (Bio-Rad), and analysis was performed using the Quantity One software (Bio-Rad). Data were normalized relatively to α-tubulin.
Statistical Analysis. All results are expressed as means ± SEM. Student's t-test was used to evaluate the probability of significant differences between the ASC samples isolated from distinct fat pads. One-way ANOVA followed by Tukey's multiple comparison test was used when more than two conditions were compared. A p value < 0. 05 was considered as significant. GraphPad Prism 5. 0 software was used for all statistical analyses. 2. 9. Metabolomic Analyses 2. 9. 1. Exometabolome Sample Preparation. Metabolite concentrations were measured in supernatants of ASC placed for the indicated time in culture medium composed of DMEM without glucose, without pyruvate, without glutamine, and without red phenol (PAN-Biotech, France) supplemented with 25 mM glucose, antibiotics, 20% FCS, 25 mM hepes, nonessential amino acid 1x (PAA, France), 4 mM L-glutamine (Corning, France), 1 mM pyruvate (Dutscher, France), 100 μM ascorbic acid, 5 ng/ml human bFGF, and 5 U/ml heparin sodium.
0 °C. A cooled SampleJet autosampler enabled high throughput data acquisition. A standard 1 H-1D NMR pulse sequence nuclear Overhauser effect spectroscopy (NOESY) with z-gradient and water presaturation (Bruker pulse program noesygppr1d) was recorded on each sample, with a total of 128 transient free induction decays (FID) and a spectral width of 20 ppm, and a relaxation delay was set to 4 seconds. The NOESY mixing time was set to 10 milliseconds, and the 90 °pulse length was automatically determined for each sample (around 13 μs). The total acquisition time of each sample was 12 minutes and 15 seconds. 2. 9. 3. NMR Data Processing. All free induction decays (FIDs) were multiplied by an exponential function corresponding to a 0. 3 Hz line-broadening factor prior to Fourier transform 1 H-NMR spectra which were manually phased and referenced to the glucose doublet at 5.
23 ppm using TopSpin 2. 2 (Bruker GmbH, Rheinstetten, Germany). TSP was not used for data processing in this study. For multivariate analyses, residual water signal (4. 85-4. 67 ppm) was excluded. Spectra were divided into 0. 001 ppm-wide buckets over the chemical shift range (-0. 2; 9. 5 ppm) using the AMIX software (Bruker GmbH). 2. 9. 4. Spectra Analyses. Identification of the metabolites was carried out from the 1D NMR data using the software Chenomx NMR Suite 8. 0 (Chenomx Inc. , Edmonton, Canada) and confirmed from analysis of 2D 1 H-1 H TOCSY, 1 H- 13 C HSQC, and 1 H J-Resolved NMR spectra recorded with standard parameters. The measured chemical shifts were compared to reference shifts of pure compounds using the HMDB database  [23]. Relative metabolite concentrations were determined using Chenomx software by manual fitting of the proton resonance lines for the compounds available in the database.
The linewidth used in the reference database was adjusted to the width of one component of the alanine doublet. A pure standard lactate solution (1 g/l, Fisher) was used as an external concentration reference and exploited using the ERETIC2 utility from TopSpin (Bruker GmbH, Rheinstetten, Germany) to add a digitally synthesized peak to a spectrum  [24]. Concentrations in the cell culture media are presented as absolute, not normalized data. They are apparent concentrations, as endogenous and FCS proteins were not removed from the NMR samples. 2. 9. 5. Multivariate Data Analyses. Multivariate analyses were performed on NMR spectra buckets in the absence of any normalization using SIMCA-P 13 (Umetrics, Umea, Sweden) with Pareto scaled variables. Principal component analysis (PCA) was used to derive the main sources of variance within the data set, assess sample homogeneity, and exclude biological or technical outliers.
To get a clear view on the metabolic differences between cell types, while avoiding the complexity induced by the production or consumption status of each metabolite, the analyses were performed on the absolute value of the NMR spectra variation to the original culture medium. PCA unsupervised multivariate data analyses were first used to evaluate the dataset homogeneity and potential sample class discrimination. The dataset showed good homogeneity, though one sample from V-ASC appeared as a strong outlier on the PCA score plot and was subsequently removed from further analyses (this sample presented ethanol contamination, data not shown). A straightforward discrimination between S-ASC and V-ASC was observed on the PCA unsupervised model (Figure  4(a) ). Remarkably, the first principal component of this model could explain alone 68.
4% of the variance within the dataset. Those untargeted results indicate significant differences of the metabolic profile between both cell types. To specifically target the NMR regions discriminating S-ASC and V-ASC, a supervised analysis by O-PLS-DA  [32, 33]  was conducted on the 9700 NMR spectral variables. We obtained a strongly discriminating O-PLS-DA model (Figure  4(b) ). The analysis of NMR regions presenting the most differentially expressed peaks pointed out 10 metabolites which concentration in the culture medium was the most influenced by the presence of the distinct ASC. Glucose, leucine, valine, glutamine, tyrosine, phenylalanine, lactate, and acetate appeared to vary more in V-ASC than in S-ASC culture supernatants, while citrate and alanine varied more in S-ASC than in V-ASC culture supernatants (Figure  4(c) ).
Depot-Specific Features of ASC Metabolic Signatures. Careful analysis of the 1 H-1D and 2D 1 H-1 H and 1 H- 13 C NMR spectra provided the identification of 29 metabolites that were present in the S-and V-ASC supernatants after 72 h culture and that belong to a variety of biochemical classes (amino acids, sugars, and metabolic intermediates). We did not detect cell type-specific metabolites, but concentrations determined using the Chenomx software revealed quantitative differences (Table  S1 ). A scheme illustrating the main anabolic and catabolic pathways and key metabolites is presented in Figure  4(d). In this study, we analyzed changes at a given time in the composition of the cell culture supernatants in comparison  with the cell-free culture medium placed in the same conditions. Different from the intracellular compartment, the cell culture supernatant is not a homeostatic compartment.
High-resolution NMR multivariate data analyses unveil significant differences in extracellular metabolite variations between S-ASC and V-ASC at 75% confluency and cultivated for additional 72 h in media containing 25 mM glucose, 4 mM glutamine, 1 mM pyruvate, and nonessential amino acids as determined by NMR absolute spectra bins after subtraction of the culture medium signal analysis performed on NMR spectra buckets without normalization to the cell number. At the time of supernatant collection, cells were at confluency. alternative substrates such as glutamine and fatty acids  [39]. Preventing OXPHOS activation, UCP2 expressed in embryonic stem cells favors their maintenance and is repressed as pluripotency is lost  [40]. Measurements of Ucp2 transcripts (Figure  5 (d)) and UCP2 protein (Figures  5(d )) in ASC revealed the higher expression in V-ASC than in S-ASC, which is consistent in V-ASC cells with a reduced pyruvate routing into the mitochondria, an elevated lactate secretion, and a majored role of glycolysis for ATP production.
Figure4: Multivariate statistical analyses of the differences between S-and V-ASC exometabolomes. High-resolution NMR multivariate data analyses unveil significant differences in extracellular metabolite variations between S-ASC and V-ASC at 75% confluency and cultivated for additional 72 h in media containing 25 mM glucose, 4 mM glutamine, 1 mM pyruvate, and nonessential amino acids as determined by NMR absolute spectra bins after subtraction of the culture medium signal analysis performed on NMR spectra buckets without normalization to the cell number. At the time of supernatant collection, cells were at confluency. (a) Untargeted principal component analysis (PCA) readily evidences the cell type as a major origin of the dataset variance. Score plot of the PCA model (PC1 and PC2) (n = 11, R 2 = 0 962, and Q 2 = 0 843 on 5 principal components).
Acknowledgments We thank  Stéphanie Chanon  from the  CarMeN Laboratory , for her help in the ImageJ analysis of histological images. This study was supported by a grant from  INRA  ( Institut National de la Recherche Agronomique) -CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement ): Glofoods programme and from recurrent funding from  INSERM ,  Université Claude Bernard Lyon 1 , and  INRA  to the  CarMeN laboratory  ( INSERM U1060-INRA 1397 ). Camille Lefevre  was supported by a PhD fellowship from the  French Ministry of Higher Education and Scientific Research.
4. Events of interest are selected using a two-tiered trigger system  [43]. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage. Events used in this analysis are selected at the trigger level based on the presence of jets in the detector. The level-1 trigger algorithms reconstruct jets from energy deposits in the calorimeters. The particle-flow (PF) algorithm  [44] , aims to reconstruct and identify each individual particle in an event. The physics objects reconstructed include jets (clustered with a different algorithm), electrons, muons, photons, and also the missing-p T vector.
Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F. R. S. -FNRS and FWO (Belgium) under the "Excellence of Science -EOS" -be. h project n. 30820817; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lend ület ("Momentum") Programme and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS programme of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus programme of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).
Notably, EVs from SA-treated cells possessed higher levels of RNA-binding proteins and Hsp70 (Extended Data Fig. 1c ), likely reflective of a stress response  22. Compared to healthy subjects (n=38), plasma EV RNA levels were significantly increased in patients with localized (n=15) and metastatic (n=19) tumors (Fig. 2a  and Extended Data Tables  1, 2 ), being positively associated with proinflammatory protein levels in EwS patients. To examine the RNA content, plasma EV RNA preparations from the EW cohort were interrogated using whole transcriptome RNA sequencing (RNAseq). Twelve of fifteen samples in this cohort yielded high-quality RNAseq libraries. Distribution of normalized uniquely mapped non-ribosomal RNAseq reads revealed a high prevalence of exonic non-coding and intergenic RNAs (Fig.
2b ). Among the most prevalent species was the previously reported 7SL/SRP RNA  24  , a core component of the signal recognition particle, as well as diverse long and short interspersed nuclear elements (LINEs and SINEs), long terminal repeat/ human endogenous retroviruses (LTR/ERVs) and satellite RNAs (Extended Data Table  4 ), many of which were elevated in patients with metastasis, although only satellite transcripts reached statistical significance (Fig. 2c  and Extended Data Fig. 2a ). While most of the detected repeat-derived EV RNAs had limited or no coding capacity, some are annotated in the genome-based endogenous viral protein-coding elements (gEVE) database  25. Based on their high coverage with respective RNAseq reads, these RNAs, including ERV1, ERV2/HERV-K and ERV3/ERV-L and LINE-1 (L1), contain extended open reading frames (ORFs) for various retroviral polypeptides with predicted sizes ranging from 10 to more than 100 kDa, including reverse transcriptase (RT), envelop (Env), polymerase (Pol) and L1's ORF1 and ORF2 (Extended Data Table  5a ).
Bio-Plex proinflammatory protein profiling Proinflammatory protein profiling of blood plasma (12 μl/reaction) or CM from the cell lines (50 μl/reaction) was done using the magnetic bead-based multiplex immunoassay Bio-Plex 200 system and the 37-plex human inflammation panel (171AL001M, Bio-Rad) following manufacturer's recommendations. Each CM sample was prepared in duplicate and experiments were repeated at least three times using different EV preparations. Plasma proinflammatory protein profiles were generated from three independent measurements of the same specimens. Standard and sample cytokine concentrations were then calculated using the Bio-Rad Bio-Plex 200 software. Background corrected signals for each cytokine were normalized to the mean signal intensity obtained for each sample and were plotted using the mean and standard deviation from three independent experiments.
Positive control probe RPL13a AF647 (Type 1) was used for optimization; see Extended Data Table  7  for the full list of reagents used. For flow cytometry, cells were re-suspended in 200 μl of staining buffer (554657, BD Pharmingen) and analyzed using a BD Fortessa X-20 SORP (BD Biosciences) and BD FACSDiva 8. 0. 2 software. Healthy donor and EwS patients' samples were run in parallel. Positive events were identified based on isotope control antibodies, fluorescence minus one (FMO) controls and excluding non-viable FVD-positive cells and cell aggregates. Gating strategy is shown in Extended Data Fig. 11.
Target probes were then hybridized for 2 h at 40°C followed by ViewRNA signal amplification. Signal detection was performed sequentially, using Fast Blue and Fast Red substrates, and slides were then counterstained with hematoxylin. Signal was considered specific if normal kidney tissue and non-probe controls stained in parallel showed less than 1 dot per 20 cells. Regions of interest were captured at x100 magnification using a Leica DM2000LED microscope and LAS software with a pixel resolution of 0. 04 μm/pixel. Stain separation of the fast blue, fast red and nonspecific background signals were achieved using a python implementation of the independent component analysis approach  54. The resulting deconvoluted images were analysed using a custom rule set built in the object-oriented machine learning software Developer XD (Definiens AG, Munich, Germany).
Bioinformatics and statistical analysis Paired-end strand-specific 100 or 125-bp reads were aligned to human genome build 37 (hg19/GRCh37) using the BWA aligner v. 0. 7. 13 with subsequent filtering of ribosomal and tRNA reads, followed by removal of secondary alignments and reads with mapping score less than 30. Transcript expression was quantified as the numbers of fragments per million (FPM). Based on annotations from the Known Genes track of UCSC browser, reads were assigned to different transcript classes using bedtools suite 2. 17. 0. Analysis of repeat RNAs was done with hg19-compliant RepeatMasker track annotations. To quantify expression of different transcript classes, overlapping annotations were resolved giving priority to features in a hierarchical order: miRNA > piRNA > lincRNA > Repeatmasker annotations > UTRs > exon > intron.
The annotated reads were partitioned into four bins: exonic coding, exonic non-coding (including 7SL RNAs, snRNAs, lncRNAs, 5' and 3'UTRs), intronic and intergenic with the results plotted using R statistical environment (https://www. R-project. org/). Coverage for each transcript was calculated with bedtools, and figures were generated using the ggplot2 package  55. No statistical methods were used to predetermine sample size. All statistical analyses were performed using R with packages obtained from Bioconductor (https://www. bioconductor. org) and/or from CRAN (https://cran. rproject. org/web/packages/). RNAseq and ddPCR data were analyzed with Shapiro-Wilk test (to verify normal distribution of data), followed by the unpaired t-test on log-transformed values with subsequent Benjamini & Hochberg (BH) adjustment.