dataesr/scientific-paragraphs-categorization huggingface dataset:
Full-scan mass spectra were acquired in the m/z range of 80-1,000. The ionization source parameters for positive and negative ion modes were as follows: capillary temperature 320°C, spray voltage 3. 5 kV, sheath gas 30 (arbitrary units), auxiliary gas eight (arbitrary units), probe heater temperature 310°C, and S-Lens RF level was set at 55 V. MS/MS experiments were performed using higher energy collisioninduced dissociation (HCD) and the normalized collision energy applied was ramped from 10% to 40%. To ensure good repeatability of the analysis, a quality control sample (QC) was formed by pooling a small aliquot of each biological sample. The QC sample was analyzed intermittently (because of the small number of samples, QC was analyzed one out of every three samples) for the duration of the analytical study to assess the variance observed in the data throughout the sample preparation, data acquisition, and data pre-processing steps.
NMR Samples of the thawed tissue (~15 mg) were placed into a 30-μl disposable insert, in which 10 μl of D2O was added to provide a field-lock signal. The disposable insert was then inserted into a 4-mm ZrO2 HRMAS rotor for HRMAS NMR spectral acquisition. All NMR experiments were carried out on a Bruker Avance III spectrometer operating at 400 MHz for the 1H frequency equipped with a 1H/13C/ 31P HRMAS probe. Spectra were obtained at 278 K with a spin rate of 4 kHz. To attenuate the NMR signals of macromolecules, the Carr-Purcell-Meiboom-Gil NMR sequence with an overall spin echo time of 150 ms was employed, preceded by a water presaturation pulse during a relaxation delay of 2 s. For each sample, 256 free induction decays of 32 k data points were collected using a spectral width of 6,000 Hz.
The free induction decays were multiplied by an exponential weighting function corresponding to a line broadening of 0. 3 Hz and zero-filled once before the Fourier transformation. Subsequently, the spectra were phased and baseline-corrected manually and calibrated to the alanine methyl signal (δ = 1. 47 ppm). To facilitate NMR signal assignment, 2D NMR experiments using 1H-1H TOCSY, 1H-13C HSQC, and online databases (Human Metabolome Database,  Wishart et al, 2013)  were employed. The 1H 1D NMR spectra were directly exported to the AMIX 3. 8 software (Bruker Biospin GmbH) and divided into buckets with a width of 0. 001 ppm. To remove the effects of variations in the water suppression efficiency, the region between 4. 70 and 5. 20 ppm was discarded. The obtained NMR dataset X-matrix (40 observations × 8,001 buckets) was then normalized to the total spectral intensity and subjected to multivariate statistical analysis using SIMCA-P + v.
16 software (Umetrics). Initially, principal component analysis (PCA) of the 1H NMR spectral data was performed to check the homogeneity of the dataset, group clustering, and detect potential outliers. After PCA, a supervised orthogonal partial least squares discriminant analysis was applied to the X-matrix, in which we defined a Y-matrix as that of sample classes to target metabolic differences between the groups of interest. The resulting score and loading plots were used to visualize the discriminant features. Leave-one-out internal cross-validation was performed to calculate R2Y and Q2 values representing the explained variance of the Y matrix and the predictive ability of the model, respectively. Model validation was performed by random permutation of the Y matrix with n = 999 times and using a CV-ANOVA P-value from SIMCA-P + v.
Cells were resuspended in FACS Buffer before analysis on BD LSR Symphony or Fortessa. Mitotracker Deep Red and MitroTracker Green were used following the manufacturer's protocol to assess the membrane mitochondrial potential and mitochondrial mass, respectively. The OVAtetramer (SIINFEKL-H2-K b ) was purchased from the NIH tetramer core facility and incubated with cell suspension before cell surface labeling for 1 h at 4°C. Beads were added to the cell suspension for calibration and normalization. Flow cytometry data were analyzed using FlowJo 10. 8. The gating strategies for the analysis of myeloid and lymphoid cells are indicated in Figs S1E and S4C, respectively.
0. 1  (Zheng et al, 2017) , which performs filtering, barcode counting, and unique molecular identifier counting. After QC, all count matrices were loaded and processed together using the R package Seurat (6. 1. 0)  (Hao et al, 2021). A second QC was performed to evaluate potential batch-related biases, perform HTO demultiplexing, and identify poor-quality cells to be excluded before downstream analyses. Cells with less than 1,000 counted unique molecular identifiers or more than 5,000 detected genes (potential doublets), and cells with more than 10% mitochondrial gene expression (apoptotic cells) or less than 5% ribosomal genes were excluded from the analyses. Datasets from two independent experiments were pooled and analyzed. Five metaclusters were identified: Myeloid cells, lymphoid cells, APC cells including DC cells and B cells, and an undefined metacluster call "Others" which include tumor cell, fibroblasts, and stromal cells.
For subsequent analyses, the metaclusters of the four immune cell types were recomputed separately. After normalization (lognormalize) and centering, the 2,000 most variable features were identified and used for dimensionality reduction by PCA (50 PCs). Further dimensionality reduction was performed using uniform manifold approximation, projection (UMAP) embedding, and cell clustering using the Louvain algorithm  (McInnes et al, 2020 Preprint). Marker genes for each cluster were extracted using the Wilcoxon method with the Seurat function "FindMarkers. " Functional enrichment analyses were performed using the clusterprofiler  (Yu et al, 2012)  and fgsea  (Korotkevich et al, 2021  Preprint) R packages, and the external database Kyoto Encyclopedia of Genes and Genomes pathways  (Kanehisa et al, 2022).
GSEA enrichment analyses and figures were performed with the "fgsea" R package against selected reference gmt files from MSigDB  (Subramanian et al, 2005; Liberzon et al, 2015). Heatmaps were generated using the R package complex heatmap  (Gu et al, 2016)  and other custom figures were generated using  Wickham (2016). RNA velocity analysis was performed using scVeloAll  (Bergen et al, 2020)  (detailed parameters for analyses are available in technical reports). For further characterization of NK cells, the results of a differential expression analysis from a previous study  (Vienne et al, 2021)  comparing ILC and NK cells were downloaded. The top 20 up-regulated and downregulated genes (sorted by P-value) from this study were used to create gene module scores in Seurat, projected on the previously computed uniform manifold approximation, projection for the NK cell population.
RAW sequencing data are available from GEO accession number https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE221164. The bioinformatics code for analyses and reports generation is hosted on a GitHub repository: https://github. com/ CIML-bioinformatic/PNlab_Sarcoma. Resulting data, containers, reports, and figures for analyses of individual samples and merged datasets are available on Zenodo with DOIs as follows:  Miallot et al (2023b Miallot et al ( , 2023c Miallot et al ( , 2023d, 2023e, 2023f), 2023e, 2023f).
Immunofluorescence For immunofluorescence experiments, tumors were harvested, washed, and fixed in Antigenfix solution (Diapath) for 2 h with agitation. After several steps of PBS 1X washing, samples were placed in PBS containing 30% sucrose overnight and included in OCT. After saturation with PBS 2% BSA, samples were stained by overnight incubation at 4°C with anti-CD8b (Clone 53-5. 8; BioXcell) and MHC-II (2G9; BD-Pharmingen) mAbs. After incubation with donkey anti-rabbit Cy3 serum and streptavidin Alexa488 (BioLegend), samples were mounted in a mounting medium. Images were acquired using a Zeiss LSM780 confocal microscope and analyzed using ZEN 2. 3 software.
Immunohistology Histological analysis of osteosarcoma, and MCA tumors was realized on 5-µm-thick slice mounted in SuperFrost-Plus (VWR). After deparaffinization and rehydration, hematoxylin-phlowin-safran staining was performed to discriminate between the tumor and surrounding tissues. CD8, CD3, and CD31 were stained by immunohistochemistry on the Lyon-Est Research Anatomopathology platform on an automated staining system Ventana Discovery XT (Ventana Medical Systems; Roche). Slides were digitized using Histech 3D slide scanner. The slide scans were analyzed using QuPath v0. 3. 0 software  (Bankhead et al, 2017). After manual annotation of the tumor area, a specific script for IHC and hematoxylin-phlowin-safran staining detection was created to quantify the labeled immune cells and the rate of necrotic tissue present in the tumors.
Analysis of VNN1 expression in STS clinical samples We analyzed our database  (Bertucci et al, 2022)  including 1,455 cases of clinically annotated STS clinical samples with available normalized gene expression profiles. VNN1 expression was available in 1,377 cases that were included in our analyses. Data were gathered from 15 public datasets (Fig  S9C ), all samples were from operative specimens of previously untreated primary tumors, and the gene expression profiles were generated using DNA microarrays or RNA sequencing. We first searched for robust clusters of coexpressed genes, and the one including VNN1, by applying a WGCNA to the 224 TCGA samples, the 8,989 genes filtered based on a SD > 1, and the following parameters: biweight midcorrelation and a power of 13 to construct a signed gene co-expression network.
Ontological analysis of the VNN1-containing cluster was done using the clus-terProfiler package, assessing enrichments against the 50 biological functions from the Hallmark database in MSigDB  (Yu et al, 2012; Liberzon et al, 2015). Significantly enriched Hallmark functions were defined by an adjusted P-value of 5%. Then, VNN1 mRNA expression was analyzed as discrete variable (high versus low) using two different cut-offs: (i) mean expression level of the whole series: "VNN1-low" if < mean, "VNN1-high" if > mean; (ii) mean expression level of the whole series ±0. 5 SD: "VNN1-low-sd" if < mean-0. 5 sd, "VNN1-high-sd" if > mean + 0. 5 SD. As we found a link between VNN1 and immunity in our mouse model, we searched for correlations between VNN1 tumor expression and several immunity-related variables.
Statistical analysis The sample sizes were designed to minimize the number of individual experimental units (mice or samples) and obtain informative results and appropriate material for downstream analysis. This represents five mice per group and experiments were typically performed twice, unless otherwise stated in the figure legends. GraphPad Prism 7 software was used for the statistical significance assessment. The Gaussian distribution was tested using the D'Agostino-Pearson omnibus normality test. When passing the normality test, the t test was used. Otherwise, the Mann-Whitney U test was used. Differences were considered statistically significant when ****P < 0. 0001, ***P < 0. 001, **P < 0. 01, *P < 0. 05.
Acknowledgements The  ANR PLBIO19-015 INCA_13754  Grant supported the work realized in the  P Naquet ,  J-C Martin ,  L Shintu , and  J-Y Blay  teams and RM funding. We thank the core facilities involved in this project:  CIML Flow Cytometry ,  CIML Genomics Platform ,  CIML Animal facilities , and  Imagimm. B Malissen , and  S Henri  were supported by the  DCBIOL LabEx  (grants  ANR-11-LABEX-0043  and  ANR-10-IDEX-0001-02 PSL ). A Roger  and  S Ugolini  were supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program , under grant agreement No. 648768 , from the  Agence Nationale de la Recherche (ANR)  (No. ANR-14-CE14-00 09-01 ), and the  Fondation pour la Recherche M édicale (FRM). This work was also supported by institutional grants from  INSERM ,  CNRS ,  Aix-Marseille University , and  Marseille-Immunopole  to CIML.
Guillaume Charbonnier  performed bioinformatics analysis of the TCGA database. We thank  Thomas Vannier  from the  CENTURI Multi-Engineering Platform  for preliminary work on the datamining of the cancer databases. The project leading to this publication has received funding from  France 2030 , the  French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ), and from the  Excellence Initiative of Aix-Marseille University -A*MIDEX. J-Y Blay  is supported by funds from  NetSARC ,  LYRIC  ( INCA-DGOS 4664 ),  LYon Recherche Innovation contre le CANcer ,  European Clinical trials in Rare Sarcomas  ( FP7-278742 ), and the  European Reference Network for Rare Adult Solid Cancer. We thank  A Carrier  and  T Nguyen  for their assistance with the use of the  Cell Culture Platform facility (Centre de Recherche contre le Cancer de Marseille , U1068) in charge of the Seahorse platform.
As shown in Figs 6F and S12B, depolarized computed for the myeloid metacluster delineated 11 clusters: neutrophils, monocytes, Mono-TAM (A, B, C), inflammatory TAM, TAM TNFα/NFκB, moDC, CAF, M2, and Ki67 TAM. (G, H) Pearson's residuals of TAM and APC cell clusters from single-cell dataset obtained from CD45-enriched cells from tumors harvested at day 20 and D28 of PBS or Pant-treated mice (n = 2). Cell subset enrichment is represented on a colored scale from red (enriched in PANT) to blue (enriched in PBS). (I) Quantification of cell contacts between CD8β + and MHC II + cells on D24 tumor sections shown in Fig S7E. Mann-Whitney test; ****P-value < 0. 0001. (J) Proteomic profiling of tumor lysates represented by enrichment in Pant versus PBS condition (n = 4). Source data are available for this figure.
7  The Brief Dementia Screening Indicator (BDSI) is a 7-item weighted instrument that includes age group, education, BMI, depressive symptoms, stroke, diabetes, and requiring assistance with money or medication. 5 The Australian National University Alzheimer Disease Risk Index (ANU-ADRI) is a selfreport instrument that includes 11 risk and 4 protective items (risk: age group, sex, low education, high BMI, total cholesterol, diabetes, traumatic brain injury, depressive symptoms, smoking, low social networks, and occupational pesticide exposure; protective: cognitively stimulating activities, alcohol consumption, physical activity, and fish intake). 6 Unlike CAIDE scores, the BDSI and ANU-ADRI risk scores have been developed using risk factor data on older adults. ] [7] [8] [9] [10]  Although higher C statistics have been reported for some new algorithms, these lack validation in independent study populations and are not currently used in health care settings.
Study Design and Oversight This observational study was based on 2 prospective cohort studies, the UK Biobank study and the Whitehall II study. For estimation of 10-year dementia risk, we used data from the UK Biobank study, a nationwide study of half a million participants aged between 38 and 73 years and living in the UK. 13  Participants volunteered in 21 assessment centers across England, Wales, and Scotland using standardized procedures. Baseline clinical examinations, including measures for the assessment of dementia risk, were conducted between March 13, 2006, and October 1, 2010. Participant follow-up via linked electronic health records of the UK National Health Service (NHS) started at baseline and ended on September 30, 2021, in England; July 31, 2021, in Scotland; and February 28, 2018, in Wales.
The NHS provides most of the health care in the UK, including inpatient and outpatient care, and record linkage is undertaken using a unique NHS identifier held by all UK residents. Selection of participants for this study is shown in Figure  1. We included participants who did not have dementia at baseline, had complete data on at least 1 of the dementia risk scores, and were linked to electronic health records from hospitalizations or mortality. We excluded participants from Wales whose baseline assessment was less than 10 years before the end of the availability of the hospital records. This study was conducted using the UK Biobank Resource under Application Numbers 60565 and 22627 (data accessed December 9, 2022). In supplementary analysis for CAIDE, data on midlife risk factors and old-age dementia were obtained from the Whitehall II study of 10 308 UK civil servants, established in 1985 to 1988.
Baseline Data and Dementia Follow-up Baseline assessments included all components of the CAIDE, BDSI, and ANU-ADRI scores, measured according to standard operating protocols, as detailed in the eMethods in Supplement 1. To characterize the study populations, we measured age, sex and ethnicity. Ethnicity was self-defined as Asian or Asian British, Black or Black British, multiple, White (all with subcategories), or other in UK Biobank and as Black, South Asian, White or other in the Whitehall II study. Dementia was ascertained using primary and secondary diagnoses from linked hospital admission data and from underlying and contributory causes of death being dementia (eMethods in Supplement 1). A diagnosis of all-cause dementia consisted of the following International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes: F00 to F03, F05.
Statistical Analysis To exclude prevalent dementia from the analysis and participants with potentially compromised capacity to accurately respond to self-administered risk questionnaires due to preclinical or undiagnosed dementia at the time of risk assessment, we excluded participants with a record of hospitalization due to dementia at baseline (88 participants [0. 02%] of all baseline participants in UK Biobank; no participants in the Whitehall II study) or within 12 months after baseline (40 participants [0. 01%] in the UK Biobank; no participants in the Whitehall II study). We determined 4 dementia risk scores (CAIDE clinical version, CAIDE-APOE-supplemented version, BDSI, and ANU-ADRI) for each participant using standard formulas and analyzed each risk score separately, excluding participants with missing data on any of the items in the risk score under investigation (eTable 1 in Supplement 1).
As the CAIDE scores are designed for long-term prediction of dementia in middle-aged people, we tested 20-year risk prediction in the Whitehall II study. Risk scores were assessed at midlife and incident dementia was assessed over the follow-up. We used Stata MP statistical software version 17 (StataCorp) for data analyses. Syntax for the analyses is provided in the eMethods in Supplement 1. P values were 2-sided, and statistical significance was set at P =. 05. Data analysis was conducted from July 5, 2022, to April 20, 2023.
25 e wording of questions in the UK Biobank survey did not exactly match those in the BDSI and the ANU-ADRI, and this could have affected our findings. We used linkage to electronic health records from high-coverage national registries to define dementia. It is possible that not all incident dementia was captured in these records, but previous research suggests high sensitivity and specificity and that use of linked data may have little effect on risk factor associations. 27 The CAIDE scores were designed for 20-year rather than 10-year prediction of dementia. 7 While data for longterm follow-up were not available in UK Biobank, a supplementary analysis using a 20-year follow-up of the Whitehall II study participants suggested a similar or worse risk stratification for CAIDE compared with age alone.
CT Protocol All patients underwent postoperative and follow-up examinations with a three-phase CT scan. Image analysis and measurements were performed using three-dimensional imaging software (OSIRIX software, Geneva, Switzerland). The maximal aortic diameter measurements were performed on the perpendicular axis according to the centerline using a semiautomated centerline algorithm at three different levels of the aorta (ascending aorta, aortic arch and descending thoracic aorta). FL patency was assessed as FL enhancement anywhere in the downstream aorta on arterial or venous-phase CT. The FL was also considered patent if only partial thrombosis was observed, while disappearance of the FL was considered to indicate complete FL thrombosis. Complete aortic remodeling was defined as complete aortic healing.
Results In this study, 357 patients were treated for acute type A aortic dissection, the hospital mortality rate was 12. 6% (45/357), 88 patients (24. 6%) had no residual dissection, and 24 were lost to follow-up (6. 7%). In total, 200 survivors of RAD comprised the study group. The mean age was 62. 2 +/-11. 6 years, 72. 5% (145) were male, and 6. 0% (12/200) had Marfan and related syndromes. Demographic data are presented in Table  1. The rate of aortic-related death was 0. 5% (1/200). The Kaplan-Meier estimated survival rates at 1, 2, 3 and 5 years were 99. 5%, 97. 1%, 96. 1% and 91. 1%, respectively (Figure  1 ).
Context. V838 Monocerotis is a peculiar binary that underwent an immense stellar explosion in 2002, leaving behind an expanding cool supergiant and a hot B3V companion. Five years after the outburst, the B3V companion disappeared from view, and has not returned to its original state. Aims. We investigate the changes in the light curve and spectral features to explain the behaviour of V838 Mon during the current long-lasting minimum. Methods. A monitoring campaign has been performed over the past 13 years with the Nordic Optical Telescope to obtain optical photometric and spectroscopic data. The datasets are used to analyse the temporal evolution of the spectral features and the spectral energy distribution, and to characterise the object. Results. Our photometric data show a steady brightening in all bands over the past 13 years, which is particularly prominent in the blue.
Optical photometry The bases of this paper are the data collected since 2009 with the 2. 56 m Nordic Optical Telescope 1 (NOT) equipped with the cryogenically cooled Alhambra Faint Object Spectrograph and 1 http://www. not. iac. es Camera 2  (ALFOSC). Bessel U BVR filters and interference filter i (NOT ID no. 12) with a central wavelength of 797 nm and FWHM 157 nm were exploited. The Bessel filters are equivalent to the Johnson-Cousins U BVR C system. The measurements in ALFOSC i filter were calibrated using the fixed colour terms provided at the ALFOSC website 3  , which were obtained using the Landolt standard stars in the standard U BVR C I C system, and therefore take into account the transformation to I C system. Observations were conducted through normal observing programmes 4  , and on 2019 November 29 via the FINCA observing school.
Several measurements were obtained in each season (in earlier years once a month, in later years once every six months), with B and R C bands being more frequent, approximately every two weeks. In total V838 Mon was observed on 100 nights with a typical seeing of 1. 0. Our complementary photometric data, obtained with the telescopes at the Tartu Observatory (TO) and South African Astronomical Observatory (SAAO) between the years 2002 and 2019, are described in Appendix A. All the data were bias and flat-field corrected using either the Interactive Data Language (IDL) for data taken up to 2007 or (IRAF) 5  for data collected afterwards. For the telescopes at TO dark current correction was also performed. For all the different datasets the magnitude measurements were done using aperture photometry, which provides reliable measurements during all states of V838 Mon (e.