dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: T. F. O. is supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy-EXC  ( 2067/1-390729940 ). V. B. is supported by the  Stichting Parkinson Fonds  (the Netherlands). M. G. S. is supported by the  Bhargava Family Research Chair in Neurodegeneration , the  Department of Medicine at The Ottawa Hospital  and its Foundation. B. M. is supported by  The Michael J. Fox Foundation for PD Research, DFG ,  EU  ( Horizon 2020 ), the  National Parkinson's Foundation, Parkinson Fonds Deutschland , and the  Deutsche Parkinson Vereinigung. L. S. and T. F. O. were supported by  IMPRiND  and  EU (Horizon 2020 ). H. S. was supported by the  Advanced ERC program , The Michael J. Fox foundation, and the  Israel Science Fund. We thank  Dr.
1-2 km) around the ground ruptures, until recently, such deformation remained difficult to recognize and to quantify. Therefore, the nature of the diffuse deformation and the physical processes involved remain strongly debated. Resolving this debate has important consequences for our understanding of the physical processes associated with earthquake rupture as well as for hazard assessment. Here, taking advantage of the exceptional dataset available for the 2019 Ridgecrest earthquake sequence (i. e. ,  Antoine et al. , 2021; DuRoss, 2019; Ross, 2019; Wang et al. , 2020) , we assess the possibility that diffuse deformation accounts for the SSD arising in numerical model inversions. In fact, the Ridgecrest ruptures present, on average, 33% of surface displacement in the form of diffuse deformation at the surface (Fig.
1a  ;  Antoine et al. , 2021) , and a possible SSD ranging between 2 to 45% depending on the models which are considered  (Jin and Fialko, 2020; Wang et al. , 2020). Because of the large variety of rupture models that exist for this earthquake sequence (i. e. ,  Jin and Fialko, 2020; Liu et al. , 2019; Magen et al. , 2020; Milliner and Donnellan, 2020; Ross, 2019; Tung et al. , 2021; Wang et al. , 2020; Yue et al. , 2021) , and given the uncertainties affecting the models' shallower parts, one cannot directly compare the surface observations with the SSD found in existing rupture models. Here, we invert for the source parameters of the 2019 Ridgecrest earthquakes using GNSS, InSAR, and fault offsets derived from optical image correlation (OIC) on a complex-geometry fault model. Incorporating the surface slip measurements at the faults, and especially those derived from optical data with centimeter-level uncertainty and homogeneous sampling, imposes a strong constraint on the shallow slip distribution, and allows us to build a high-resolution slip model of the earthquake sequence with well-quantified SSD estimates for both the Mw6.
We complement this dataset with GNSS and InSAR data that have better constraints of slip distribution at depth  (Wang et al. , 2020). The along-strike model geometry (Figs. 1b  and S1 ) is defined by the trace of the well-documented ground ruptures  (Antoine et al. , 2021; DuRoss, 2019). The along-dip model geometry is considered vertical as suggested by highresolution seismicity data  (Plesch et al. , 2020; Ross, 2019). The joint inversion is performed in a homogeneous elastic half-space, with the Green's functions calculated for each dataset. The data are combined using weight matrixes that reflect the data uncertainty, either uniformly in the case of InSAR and offsets data, or for each data point in the case of GNSS. To minimize the influence of the model regularization on the amplitude of shallow slip  (Causse et al.
Acknowledgments This work was partly supported by  TOSCA CNES  and the  ANR-18-CE31-0012  projects. Yann Klinger  was supported by the  Miller Foundation at UC Berkeley  during his visit at  UC Berkeley. Roland Bürgmann  and  Kang Wang  acknowledge support by the  Southern California Earthquake Center (SCEC). This study contributed to the  IdEx Université de Paris   ANR-18-IDEX-0001.
Open Research Static Global Navigation Satellite System (GNSS) coseismic displacements are available at http://sopac-csrc. ucsd. edu/index. php/ ridgecrestjuly2019 (last accessed March 2023). InSAR coseismic displacement data used in this study are available at https://zenodo. org/record/3475633#. XZvOSudKh7M (last accessed March 2023 ; see also Wang and Bürgmann, 2020). Fault offsets extracted from high-resolution optical image correlation results, MATLAB inversion codes, and slip model derived in this study will be made available on a public repository after acceptance of the manuscript for publication.
Graph 4: Gini-coefÏcients over time (before and after housing costs) Source: Author's presentation using data from the IFS data spreadsheet on Living standards, poverty and inequality in the UK (accessible at https://ifs. org. uk/living-standards-poverty-and-inequality-uk). 17 Since the 1980s, the Gini-coefficient BHC has remained relatively stable. However, it continued to expand after housing costs (AHC), rising to nearly 0. 40, which is comparatively high. It is beyond the scope of this paper to examine to what extent the British situation concerning housing is peculiar: rising real estate prices, most especially in major urban centres, have been a major feature of neoliberalism in many countries and a key consequence of financial liberalisation. 22  From this point of view, Britain is not alone, although its situation is probably more acute than elsewhere, given that the number of completed "permanent dwellings" has fallen fairly progressively from about 315,000 per year in the 1970s to around 165,000 in the 2010s.
Genetic invalidation of Card9 accelerates atherosclerosis in Apoe -/-mice To gain insight into the immune cells expressing Card9, single-cell analysis of total cells from mouse atherosclerotic aortas (see methods) was performed (Fig. 1A , B and Supplementary Fig. 1 ). Card9 transcript was detected in myeloid cells, and macrophages in particular, including atherosclerosis-associated inflammatory and Trem2 hi /Foamy macrophages (Fig. 1A , B and Supplementary Fig. 1a, b ). Analysis of a second, independent scRNA-seq dataset of Ldlr -/-mouse atherosclerotic aortas  18  corroborated preferential detection of Card9 transcripts in myeloid cells, including macrophages (Supplementary Fig. 2 ). Immunofluorescence staining confirmed that Card9 was expressed in atherosclerotic lesions of Apoe -/-mice at both early (Supplementary Fig.
After thorough PBS washing, the adventitial tissue was removed and the aorta was longitudinally opened to expose the luminal surface for en-face visualization of atherosclerotic lesions after Oil Red O staining. Quantification of Oil Red O positive surface area was performed by a blinded operator. Aortic collagen content was detected using Sirius red staining. Necrotic core surface was quantified after Masson's Trichrome staining. At least 4 sections per mouse were examined for each immunostaining, and appropriate negative controls were used. Morphometric studies were performed using Histolab software (Microvisions)  53. For immunostaining on mouse atherosclerotic plaques, we used antibodies raised against Card9 (AA 274-530), MOMA-2 (macrophage detection, MAB1852, Merck Milllipore®) and CD3 (T cell detection, A0452, Dako®)  53.
The background cholesterol efflux obtained in the absence of any acceptor was subtracted from the efflux obtained with samples. Flow cytometry. Blood and spleen samples were collected at sacrifice for analysis of leukocyte subsets. Myeloid cells were identified as CD45+ CD11b+. Monocytes were identified as CD11b+ CD115+. Among them, classical monocytes were Gr1 high (or Ly6C high ) and non-classical monocytes were Gr1 low (or Ly6C low ). Neutrophils were identified as CD11b+ CD115-Gr1+ (or CD11b+ CD115-Ly6G+). B220 + IgM+ B lymphocytes, CD4+ and CD8+ T lymphocyte subsets were also analyzed. Antibodies raised against CD11b, CD115, Gr1 (Ly6C and G), B220, CD4, CD8a, NK1. 1, CD45, F4/80, CD3ε, MHC II, IgM, CD11c, and CD36 were used for immunostaining and are listed in supplementary data 1. Forward scatter (FSC) and side scatter (SSC) were used to gate live cells excluding red blood cells, debris, and cell aggregates in total blood cells and splenocytes preparations.
Libraries were then equimolarly pooled and quantified by qPCR using the KAPA library quantification kit (Roche). Sequencing was carried out on the NovaSeq 6000 instrument from Illumina using paired-end 2 × 100 bp, to obtain around 100 million clusters (200 million raw paired-end reads) per sample. Raw and normalized counts are provided in Supplementary data. We performed the gene set enrichment analysis using cluster-Profiler v4. 0. 5  54  with selected pathways from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, and Benjamini-Hochberg correction was applied. Used keywords were: "apopto*", "atheroscleros*", "NF-kappa B", "TNF". All pathways with adjusted p-value below 0. 05 were considered as significantly enriched. For each category, median expression of all gene included in core enrichment were calculated for each patient before plotting the heatmap.
Single-cell analysis of Card9/CARD9 expression patterns. Singlecell RNA sequencing (scRNA-seq) datasets of immune cells from mouse aortic atherosclerotic plaques, reported in various publications  [55] [56] [57] [58] [59] [60] [61]  , (see ref. 34 for details), were pooled and integrated using canonical correlation analysis (CCA) in Seurat v4. 3. 0  62, 63. All datasets used for analysis were pre-processed as follows: cells containing >200 detected genes, and genes detected in at least 3 cells were included in the analysis using the 'CreateSeuratObject' function with 'min. features = 200' and 'min. cells=3'. Quality control filtering was further performed to remove dead/damaged cells with a high proportion of mitochondrial transcripts, and outlier cells with high UMI numbers (probable doublets/mutliplets).
For mitochondrial transcripts, a < 5% cutoff was applied for all datasets, except for the data from Williams et al. 60  and from Gil-Pulido et al. 61  , where <7. 5% and <10% cutoffs were applied, respectively. Pre-processing code for aortic leukocyte datasets can be found as supplemental files of Zernecke et al. 34. All data were log normalized using the 'NormalizeData' function in Seurat with default parameters. Data integration was performed using a canonical CCA workflow in Seurat with default parameters. After CCA integration, data were scaled using 'ScaleData', and principal component analysis was performed using 'RunPCA'. Dimensional reduction was performed using 'RunUMAP' with 30 principal components. Clustering was performed using 'FindNeighbors' with 30 principal components, and 'FindClusters' with a resolution of 0.
4. Immune cell annotations are based on Zernecke's works  34, 64. Mouse scRNA-seq data given in Supplementary Fig. 2  were obtained from Pan et al. 18  and downloaded from Gene Expression Omnibus GSE155513, pre-processed in cellranger-6. 1. 2, and further analyzed in Seurat v4. 3. 0  63. We used data from Ldlr -/-mice fed normal chow or a western diet for 8, 16 or 26 weeks (i. e. the following data from Gene Expression Omnibus GSE155513: GSM4705592, GSM4705593, GSM4705594, GSM4705595, GSM4705596, GSM4705597, GSM4705598, GSM4705599). Individual datasets were pre-processed with quality control filtering in Seurat: cells containing >200 detected genes, and genes detected in at least 3 cells were included in the analysis using the 'CreateSeuratObject' function with 'min. features = 200' and 'min.
cells = 3'. Quality control filtering was further performed to remove dead/damaged cells with a high proportion of mitochondrial transcripts (>10%), and outlier cells with high UMI numbers. All data were log normalized using the 'NormalizeData' function in Seurat with default parameters. Data were pooled and batch corrected using Harmony  65  within Seurat. 2000 highly variable genes were identified using 'FindVariableFeatures' (with selection. method = "vst"). Data were scaled using 'ScaleData' with default parameters, and principal component analysis performed using 'RunPCA' with default parameters, and batch corrected using 'RunHarmony' with default parameters. Dimensional reduction was performed using 'RunUMAP(reduction = "harmony", dims = 1:20)', and clustering was performed at a 0. 4 resolution using 'FindNeighbors(reduction = " harmony", dims = 1:20)' followed by 'FindClusters(resolution = 0.
2)'. Positive marker genes for each cluster were identified using 'FindAllMarkers'. Human scRNA-seq data given in Fig. 7  were obtained from total cells of human atherosclerotic coronary arteries  33  and analyzed in Seurat v3  62  starting from the author provided cell-count matrix (downloaded from Gene Expression Omnibus GSE131778). Cells containing <200 detected genes were excluded, and genes detected in at least 3 cells were included in the analysis using the 'Create-SeuratObject' functions with 'min. features = 200' and 'min. cells = 3'. Further quality control filtering was performed and cells with >5% mitochondrial transcripts were excluded, as well as cells with outlier number of UMIs (nCount_RNA > 15,000). A total of 10,934 cells were analyzed. As a pre-analysis indicated a substantial patient-driven batch effect, we performed batch correction using Harmony 65 within Seurat, considering each patient as an independent sample.
33. In Supplementary Fig. 15e-g , the expression of CARD9 was examined in scRNA-seq data of mononuclear phagocytes from human coronary and carotid arteries in an integrated dataset described in details in Zernecke et al. 34. Western blot. Proteins from BMDMs were extracted by pipetting in ice-cold lysis buffer (NaCl 150 mM, HEPES 20 mM, EDTA 1 mM, EGTA 1 mM, NP-40 0. 25% (Vol/Vol)) supplemented with protease (1 tab/ 10 ml, Roche) and phosphatase (Na 4 VO 3 2 mM) inhibitors). Protein extracts were briefly sonicated (5 pulses, amplitude 20, Vibracell 75021) and quantified by BCA (Thermofisher Scientific). Equal protein amounts were separated by SDS-PAGE (10% acrylamide) and transferred onto nitrocellulose membrane (0. 2 μm, Biorad). For LC3b detection, SDS-PAGE separated proteins were transferred onto polyvinylidene difluoride membranes.
Membranes were then blocked with Tris Buffered Saline supplemented with 0. 1% Tween-20 (TBST) and 2% BSA (2 h at room temperature), then incubated with primary antibodies (overnight, 4 °C) diluted following the manufacturer's recommendations. After three washes in TBST, membranes were incubated with species-specific horseradish peroxidase-conjugated secondary antibodies (1:8000 dilution, 45 min at room temperature). After three washes in TBST, the peroxidase activity was detected using Clarity Western ECL Substrate (Biorad) using Cytiva's ImageQuant Fluor 800. The migration position of transferred proteins was compared to the PageRuler Prestain Protein Ladder (10 to 170 kDa, Thermo Fisher Scientific). Densitometric analysis was performed using ImageJ software (NIH). Phosphorylated protein signals were normalized on total protein levels, whereas non-phosphorylated proteins were normalized on β-actin protein levels.
Briefly, PCR of the 16 S DNA was performed with 5 ng of genomic DNA according to the manufacturer's protocol (Metabiote), with bar-coded primers (Metabiote MiSeq Primers) to a final concentration of 0. 2 μmol/l, with an annealing temperature of 50 °C for 30 cycles. Purification of the PCR products was performed with Agencourt AMPure XP-PCR purification system (Beckman Coulter, Brea, CA, USA), and quantified following the manufacturer's instructions. The samples were multiplexed at equal concentrations. Sequencing was performed on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) using a 250 bp paired-end sequencing protocol at GenoScreen. Raw paired-end reads were subjected to the following processes: (1) quality filtering using the PRINSEQ-lite PERL script  67  , by truncating the bases from the 3′ end, that did not exhibit a quality <30, based on the Phred algorithm and (2) searching for and removing both forward and reverse primer sequences using CutAdapt, with no mismatches allowed in the primer sequences.
Only sequences where perfect matching forward and reverse primers were detected were included. 16S sequence analysis. Sequences were quality filtered using the dada2 software package (version 1. 12. 1)  68  in the R programming language (R version 3. 6. 1) to produce amplicon sequence variants (ASVs). Taxonomic classification was performed using the Silva reference database (version 132)  69. Bacterial ASVs that could not be assigned to Phylum-level taxonomy were excluded. Alpha diversity was estimated using the number of observed species and the Shannon diversity index. Raw sequence data are accessible in the Sequence Read Archive (accession number pending). Beta diversity analysis was performed on proportion-normalized data using the Bray-Curtis index. Assessment for significant differences between clusters was performed using PERMANOVA with the adonis function in the vegan package (version 2.
Statistical analysis Graphs and statistical analyses were performed using Prism software (Graphpad). Values are expressed as mean ± s. e. m. Differences between values were examined using the nonparametric two-tailed Mann-Whitney, Kruskal-Wallis tests when appropriate and were considered significant at P < 0. 05. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
Acknowledgements This work was supported by  Inserm  (H. A. O. and Z. M. ),  Nouvelle Société Française d'Athérosclérose  (Y. Z. ), the  Fondation pour la Recherche Médicale  (H. A. O. and S. T. ),  la Fondation Lefoulon-Delalande  (Y. S. Z. ),  la Fondation de l'avenir , The  European Research Council  (Z. M. ), and the  British Heart Foundation  (Z. M. ). C. Cochain was supported by the  Interdisciplinary Center for Clinical Research (IZKF, Interdisziplinäres Zentrum für Klinische Forschung) ,  University Hospital Würzburg  (Project  IZKF-E-353 ). C. C. , A. E. S. , and A. Z. are supported by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) Project -ID 453989101-SFB1525. S. C. M. received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  846519  and by the  Fondation Lefoulon-Delalande, Paris, France.
High-throughput sequencing was performed by the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan   Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOS-Inserm_12554 ). Data management, quality control, and primary analysis were performed by the Bioinformatics platform of the  Institut Curie. " A. P. and M. M. were supported by the  French National Research Agency (ANR)  under the  "Investments for the future" program  ( ANR-10-IAHU-01 ), the  ANR-FNS LTh-MSMD-CMCD  ( ANR-18-CE93-0008-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), and the  National Institute of Allergy and Infectious Diseases of the NIH  (grant no.
Data availability The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. All the raw data generated in this study are provided in the Source Data file. RNA-sequencing data of blood monocytes generated for this report has been deposited in Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE221782). Mouse scRNA-seq data were downloaded from Gene Expression Omnibus GSE155513  18  , GSM4705592  18  , GSM4705593 18 , GSM4705594  18  , GSM4705595  18  , GSM4705596 18 , GSM4705597 18 , GSM4705598  18  , GSM4705599  18  , and GSE131780  33. Microbiota 16s RNA data are accessible with the following link https://www. ncbi. nlm. nih. gov/sra/ PRJNA986053. Human carotid atherosclerosis scRNA-seq data were downloaded from https://figshare.
(2011;  who claimed to have solved some of the incompleteness problems of bright galaxies), we find 28 440 galaxies, among which 26 966 are in both catalogues, 1474 are only in the NSA table and 1224 are only in the ELP table. The differences between the NSA and the ELP may be due to a number of factors. The NSA was built on the basis of several catalogues: Sloan Digital Sky Server (DR8), NASA Extragalactic Database, Six-degree Field Galaxy Redshift Survey, Two-degree Field Galaxy Redshift Survey, ZCAT and ALFALFA, while the ELP table (DR12) was built from the SDSS and BOSS galaxies. Our analysis considers all (29 590) targets found in either of the two catalogues. We further integrate this sample with the updated Zwicky catalogue (UZC,  Zwicky et al. 1961; Falco et al. 1999) , the VCC catalogue  (Binggeli et al.
1985) , the UGC catalogue  (Nilson 1973) , the ALFALFA survey α. 100  (Durbala et al. 2020) , the optical spectroscopic survey by  Ho, Filippenko, and Sargent (Ho et al. 1993 , 1995 , 1997) , the spectra taken by us with the Loiano 1. 5 m telescope (e. g. Gavazzi et al. 2013b) , and the spectra taken with the MUSE IFU by  Consolandi et al. (2017) ,  Fossati et al. (2019) ,  Pedrini et al. (2022). We dub the database obtained in this way as the 'SPRING' database, which amounts to 30 597 unique objects. Its sky distribution is given in Fig. 1. We claim that our SPRING catalogue is a robust and exhaustive compilation of all galaxies in the northern spring sky, including the brightest ones. For example, all 29 galaxies in the Messier catalogue projected in this region are listed in our database; also, we cross-correlated our catalogue with the UZC  (Falco et al.
Photometry Photometric optical data are available for all galaxies in the sample. We took Petrosian (u, g, r, i, z) magnitudes from the SDSS. The GALEX ultraviolet (NUV and FUV) data  (Martin et al. 2005; Morrissey et al. 2007)  were taken from  Voyer et al. (2014)  for Virgo galaxies and from the Revised Catalog of GALEX Ultraviolet Sources  (Bianchi et al. 2017 ) otherwise; 25 526 galaxies A118, page 3 of 26 have NUV magnitudes, 24 545 have FUV, and 24 405 have both. For six galaxies, for which SDSS Petrosian u, g, r, i, z magnitudes were not available, we took B and V magnitudes from the NASA/IPAC Extragalactic Database (NED). Since a number of galaxies in the ELP/NSA tables have their photometry taken in off-nuclear positions, their given magnitudes are sometimes not representative of the whole galaxy.
When the g and i magnitudes were not available, we employed the B and V magnitudes instead, and calculated the stellar mass as log(M star /M ) = -1. 075 + 1. 837(B -V) + log l V. (3) The SPRING catalogue was cross-matched with H I-data from the GOLDMine database (GM,  Gavazzi et al. 2003) , the Westerbork Coma Survey (WCS,  Molnár et al. 2022) , the ALFALFA survey (AA,  Haynes et al. 2011; Durbala et al. 2020) , and the HyperLeda catalogue (LEDA,  Makarov et al. 2014). We cross-matched our sample by coordinates separation with the criteria cδz < 200 km s -1 (for all datasets), δθ < 5 for GM, AA, and WSB and δθ < 10 for LEDA. This choice was motivated by the lower accuracy of LEDA celestial coordinates; non-unique associations were visually inspected. H I data are available for 9461 objects (∼30%).