dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, downregulated pathways in stimulated COVID-19 monocytes compared to stimulated healthy donor monocytes included most of the canonical immunological functions expected for innate immune cells upon virus sensing, i. e. , interferon signaling, RIG-I/ MDA5-mediated induction of interferons, activation of TCR signaling in T cells, innate immune functions and interactions with nonlymphoid cells (Fig. 5e  and Dataset 8). The majority of the top 40 genes significantly downregulated in COVID-19 monocytes from these downregulated pathways consisted of different interferons (IFNA1, IFNA2, IFNA14, and IFNB1), interferon-stimulated genes (IFIT3, ISG15, IFIT2, ISG20, IRF7, and MX2) and pathogen-sensing receptors (TLR7, AIM2, Fig. 5f ). This gene signature was functionally confirmed by examining the activation pattern of IRF3 in response to LPS in monocytes from healthy individuals and patients with mild and moderate COVID-19 (Fig.
After 20 h of monocyteplatelet co-culture, we measured the generation of MPA by determining the expression of CD41 in monocytes, which is a standard method to identify MPA (CD41 is a marker of megakaryocytes and it is not expressed on monocytes  48  ). Monocytes from patients with moderate COVID-19 aggregated significantly more platelets that those of healthy individuals or patients with mild disease, functionally supporting the pro-thrombotic signature obtained in the RNA-seq analysis (Fig. 6a-c ). Interestingly, the increase in MPA formation was observed after ex vivo stimulation with UV-inactivated SARS-CoV-2 (Fig. 6a, c ) but also in ex vivo unstimulated monocytes, which was initially surprising due to the lack of hemostasis-related pathways being significantly enriched in the ex vivo RNA-seq datasets (Figs.
2  and 3 ). However, upon examination of the RNA-seq analysis from ex vivo unstimulated monocytes, we identified a number of significantly upregulated genes in COVID-19 monocytes compared to healthy individuals that belonged to pathways that included "Hemostasis" and "Platelet activation, signaling and aggregation" (Dataset 4). Enrichment of none of these two pathways was statistically significant when considering the adjusted p-value, but upregulation of the individual genes was, which could be due to the small number of RNA-seq samples and the large number of pathways to adjust the p-value for. We subsequently took advantage of the available clinical data of the patients for which RNA-seq analysis of monocytes was performed. For these patients we had information of the plasma concentration of D-dimer at the time of blood collection.
D-dimer is routinely used in the clinic as a biomarker for activation of the coagulation and fibrinolysis systems  49  and it has been extensively investigated for the diagnosis, monitoring and treatment of venous thromboembolic diseases, for which it is used routinely  50. Plasma D-dimer concentration positively correlates with venous thromboembolic diseases in the general population and in patients with COVID-19  51. We decided to group samples into high and low plasma D-dimer concentration as a readout of potential thrombotic issues. While most patients whose monocytes were used for RNA-seq had elevated D-dimer concentrations at the time of blood collection (>500 ng/ml, Dataset 9), we could clearly identify two groups of patients: those with D-dimer concentration >1600 ng/ml (High D-dimer, range 1647-20,000 ng/ml) and those with D-dimer concentration <1000 ng/ml (Low D-dimer, range 966-534 ng/ml).
We then examined the expression of genes belonging to the pathways potentially associated with hemostasis and coagulation in both ex vivo RNA-seq data and RNA-seq data from stimulated monocytes in healthy individuals from low and high D-dimer COVID-19 groups (Fig. 6d-f ). Using the RNA-seq datasets from ex vivo isolated monocytes, we examined the expression of those genes that were significantly enriched in two pathways: "Hemostasis" and "Platelet activation, signaling and aggregation" (genes and pathways can be found in Dataset 4). In agreement with the increased capacity to form MPAs, we observed an increased expression of genes associated with these pathways in a D-dimer concentration-dependent manner (Fig. 6d ). Moreover, while not all genes were significantly upregulated in the high D-dimer group, 10 out of 12 showed a trend toward an increased gene expression as D-dimer concentration increased (Fig.
6e ). This observation was specific for these two hemostasis-related pathways, as the trend was not observed when we examined the normalized read counts of those genes enriched in another significantly enriched pathway i. e. , "Transmembrane transport of small molecules", with only 5 out of the 14 enriched genes showing a trend towards a D-dimer concentrationdependent increase in expression (Supplementary Fig. 12 ). We also examined the expression (normalized read counts) of those genes enriched in pathways potentially related to a prothrombotic signature in RNA-samples from COVID-19 monocytes stimulated with UV-inactivated SARS-CoV-2, again dividing the samples into low and high D-dimer concentrations in plasma (Fig. 6f ). The pathways tested were "Integrin cell surface interactions", "Extracellular matrix organization", "Response to elevated platelet cytosolic Ca 2+ ", "Signaling by PDGF", "Hemostasis" and "Platelet aggregation (plug formation)" (Genes and pathways can be found in Dataset 7; 81 genes in total).
Endotoxin tolerance signature enriched in COVID-19 monocytes A number of works have suggested similarities between the characteristics of the immune response in COVID-19 patients and those of septic individuals, including multiple organ dysfunction, immunosuppression, coagulopathies and acute respiratory failure  52. To determine the similarities between the transcriptional signature of COVID-19 monocytes with that of sepsis monocytes, we utilized publicly available microarray gene expression data on sepsis monocytes and healthy controls  53  and we tested the estimated fold changes for correlation with those from our ex vivo (Fig. 7a ) and activated (Fig. 7b ) COVID-19 and healthy monocytes. The sepsis dataset compared 6 agematched healthy individuals to 8 adult Gram-negative urinary sepsis patients, with samples taken within 4 h of hospital admission  53.
No clear correlation was observed in any of the two contrasts, which suggests that the transcriptional signature of CD14 + monocytes in moderate COVID-19 is not similar to that of monocytes in sepsis. The lack of cytokine expression, activation of costimulatory receptors, impaired antigen presentation potential and metabolic impairments displayed by moderate COVID-19 monocytes resembled the phenotype observed in LPS-induced tolerance  54. We have previously defined an endotoxin tolerance gene expression signature from publicly available microarray data on monocytes stimulated in vitro with LPS  55  that comprises 398 genes. Out of these, 318 genes were detected in our RNA-seq dataset. We tested for correlation of the endotoxin tolerance signature with ex vivo (Fig. 7c ) and activated (Fig.
7d ) COVID-19 monocytes, and while ex vivo COVID-19 monocytes did not display a clear correlation with the tolerance signature, activated COVID-19 monocytes displayed similar directionality of expression in those genes from the tolerance signature that were detected in the dataset. These data were further confirmed in barcode plots (Fig. 7e ), showing a statistically significant enrichment of the endotoxin tolerance gene signature in the list of differentially expressed genes from stimulated COVID-19 monocytes compared to healthy controls, for both upregulated and downregulated genes. These data are in agreement with the observed diminished response of COVID-19 monocytes to secondary LPS stimulation in both total PBMC (Fig. 4c, f ) and isolated CD14 + monocytes (Supplementary Fig. 14 ). for healthy (n = 14), mild (n = 15) and moderate (n = 10) COVID-19 monocytes stimulated with LPS (mean ± s.
Participants and clinical data collection Disease severity was categorized based on the WHO ordinal classification of clinical improvement, where 0 (uninfected) describes people with no clinical or virological evidence of infection, 1-2 describe ambulatory patients without (1) or with (2) limitation of activities, and 3-4 corresponds to hospitalized patients with no oxygen therapy (3) or oxygen by mask or nasal prongs (4). Peripheral blood was collected from all participants and processed following a common standard operating protocol. For inpatients, clinical data were abstracted from the electronic medical records into summary participant sheets. Participant group characteristics are summarized in Dataset 1. Healthy donors (WHO 0) were Imperial College staff with no prior diagnosis of or recent symptoms consistent with COVID-19, and where possible, were matched in age and sex distribution with COVID-19 patients.
Samples from patients with moderate COVID-19 admitted to hospitals in London (Hammersmith Hospital, Charing Cross Hospital, Saint Mary's Hospital) and eligible to participate in the MATIS trial (NCT04581954)  67  provided consent (ethics approval by the Health Fig. 6  | Monocytes from moderate COVID-19 patients are functionally prothrombotic. Monocytes were isolated from healthy individuals, mild and moderate COVID-19 patients, and left unstimulated or stimulated with UV-inactivated SARS-CoV-2 for 20 h. a Representative dot plots of the expression of CD41 on ex vivo isolated (upper row) or stimulated (lower row) monocytes from healthy individuals (left), mild (middle), and moderate (right) COVID-19 patients (n = 4 individuals per group) after co-culture with freshly isolated platelets from a healthy individual.
Numbers in each dot plot represent CD41 gMFI. b, c Summary of CD41 gMFI on unstimulated (b) or stimulated (c) monocytes after co-culture with healthy donor platelets (n = 4 individuals in each group). Boxes in b and c extend from the 25 th to the 75 th percentiles and whiskers extend down to the minimum and up to the maximum values. d RNA-seq datasets from ex vivo isolated monocytes from moderate COVID-19 patients were grouped into low and high D-dimer concentrations. Heatmap of z-score-transformed normalized read counts of significantly upregulated genes in "hemostasis" and "platelet activation, signaling, and aggregation" pathways in healthy (n = 6), low D-dimer concentration (n = 4) and high D-dimer concentration (n = 6) moderate COVID-19 monocytes. Gene expression values are scaled by column, and each row represents one individual.
e Summary of normalized gene counts of the genes in d, shown as mean±s. e. m. f RNA-seq datasets from stimulated monocytes from moderate COVID-19 patients were grouped into low and high D-dimer concentrations. Heatmap of z-scoretransformed normalized read counts of significantly upregulated genes in "Integrin cell surface interactions", "Extracellular matrix organization", "Response to elevated platelet cytosolic Ca 2+ ", "Signaling by PDGF", "Hemostasis" and "Platelet aggregation (plug formation)" pathways in healthy (n = 12), low D-dimer concentration (n = 8) and high D-dimer concentration (n = 6) moderate COVID-19 monocytes. Gene expression values are scaled by column, and each row represents one individual. One-way ANOVA with Tukey's correction for multiple comparisons for b, c, e. *p < 0.
05, **p < 0. 005. Source data are provided as a Source Data file. Research Authority, London-Surrey Borders Research Ethics Committee) and blood was collected 3-14 days after disease onset and 0-2 days after hospitalization and positive PCR, and before study treatment initiation. Moderate patients displayed mild or moderate COVID-19 pneumonia, defined as grade 3 or 4 WHO severity. Samples were collected from March 2020 to February 2021 and none of the participants had received a COVID-19 vaccine.
Ex vivo stimulation assays PBMC were thawed and rested for 2 h at 37 °C in complete media. 250,000 PBMC were plated in polystyrene plates (Corning) to prevent unspecific stimulation of monocytes by adherence to the plastic between sepsis and healthy controls is plotted against the log 2 (FC) for ex vivo COVID-19 monocytes vs. healthy control monocytes, and the points are colored according to the significance and direction of effect in the COVID-19 contrast (grey, not significant; red, significantly upregulated; blue, significantly downregulated). Correlation plot of endotoxin-induced tolerance gene signature and ex vivo (c) or stimulated (d) COVID-19 vs. healthy monocyte signature. Each point represents a gene detected in both the endotoxin gene signature public dataset and our COVID-19 vs.
healthy RNA-seq dataset. The log 2 (FC) between endotoxin tolerance and LPS response is plotted against the log 2 (FC) for COVID-19 vs. healthy monocytes, and the points colored according to the significance and direction of effect in the COVID-19 contrast as in a. Some of the most differentially expressed genes in the COVID-19 vs. healthy monocyte dataset are identified in the plot. e Barcode plot showing enrichment of the endotoxin tolerance (ET) gene set in the differential gene expression results for SARS-CoV-2-stimulated COVID-19 vs healthy monocytes. The ranked test statistics from DESeq2 for the SARS-CoV-2-stimulated COVID-19 vs. healthy contrast are represented by the central shaded bar, with genes downregulated in COVID-19 on the left and upregulated genes on the right. The ranks of the ET gene set within the COVID-19 contrast are indicated by the vertical lines in the central bar.
RNA-seq analysis RNA-sequencing was performed by the Oxford Genomics Centre. PolyA-enriched strand-specific libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kits (Illumina). All samples were pooled together and 150 bp PE reads were sequenced on a Novaseq system, resulting in a median read count of 28 million per sample. Raw data was processed using the Sanger Nextflow RNA-seq pipeline. Briefly, reads were aligned to the reference genome (GRCh38. 99) using STAR v2. 7. 3  72  in the two-pass mode (using ENCODE recommended parameters) and gene expression was quantified using featureCounts  73. Mapping table and quality control metrics from FastQC and RNA-SeQC 74 indicated high data quality for all samples with no outliers detected. RNA-seq data analysis was performed in R v4.
1 in Rstudio Server. Features that did not have at least 10 reads in at least 6 samples (the size of the smallest biological subgroup) were filtered out using the genefilter package  75  , resulting in a processed data set on 16,328 features. Principal component analysis (PCA) with the prcomp function was used to explore the relationship between samples, after the filtered gene counts were transformed using a regularized log transformation from the DESeq2 76 package. Differential gene expression analysis was carried out using DESeq2, comparing unstimulated monocytes from COVID-19 patients (n = 10) to unstimulated monocytes from healthy controls (n = 6), and SARS-CoV-2-stimulated monocytes from COVID-19 patients (n = 14) to stimulated monocytes from healthy controls (n = 12). Genes with FDR < 0.
05 and a fold change (FC) ≥ 1. 5 were deemed significantly differentially expressed. Pathway enrichment analysis was performed using Fisher's exact test in XGR  25  with annotations from Reactome, using all genes retained in the processed RNA-seq data as the background, and employing the xEnrichConciser options. An adjusted p-value (FDR) threshold of 0. 05 was used to identify significantly enriched pathways. Pheatmap package was used to draw heatmaps illustrating variation in gene expression across samples. For testing the enrichment of the sepsis signature in our datasets, publicly available microarray gene expression data on sepsis patients and healthy controls were accessed using GEOquery (GSE46955)  53. Gene expression between patients and controls was compared using limma  77  , for both the unstimulated and stimulated conditions.
Subsequently, the estimated fold changes were tested for correlation with those from the COVID-19 vs. healthy control results. Where multiple probes were available for the same gene in the microarray dataset, the top-ranked probe was selected for the comparison. For comparison to the endotoxin-induced tolerance signature, we have previously defined an endotoxin tolerance gene signature  78  from publicly available microarray data on in vitro LPS-stimulated monocytes. Briefly, two datasets (GSE15219  55  and GSE22248 79 ) were accessed through GEO. Genes that were differentially expressed following a single LPS treatment (LPS response genes), and that were also differentially expressed between singly-and doubly-stimulated cells were identified. This resulted in an endotoxin tolerance gene signature comprising 398 genes, of which 318 were detected in the RNA-seq dataset.
Acknowledgements We thank the participants who volunteered for this study and the clinical teams of the COVIDITY and MATIS studies for patient recruitment and blood collection. We thank  Dr. Parisa Amjadi  and  Ms. Radhika Patel  for their help with flow cytometry sorting. A. K. M. is a Wellcome Trust Ph. D. scholar. K. L. B. and E. E. D. are funded by the  Wellcome Trust  [ 108413/A/ 15/D]. For the purpose of Open Access , the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. We thank the  Wellcome Sanger Institute's Human Genetics Informatics (HGI)  team for mapping the RNA sequencing reads. This work was funded by a  Rosetrees Trust  grant ( M971 ) to M. D. V.
Data availability RNA-seq datasets generated during the current study have been deposited in the European Genome-Phenome Archive under accession code EGAD00001009800. Reference genome used was GRCh38. 99 was used as reference genome. Gene expression data from sepsis patients was obtained from the Gene Expression Omnibus (GSE46955). Microarray data from in vitro LPS-stimulated monocytes (endotoxin tolerance signature) were also obtained from the Gene Expression Omnibus (datasets GSE15219 and GSE2224879). The raw numbers for charts and graphs are available in the Source data file whenever possible. Source data are provided with this paper.
Introduction Continuous seismograms are time series of the ground motion recorded at a single location and provide a vast amount of information about processes occurring at the Earth's surface and interior. The recorded ground motion at a given location results from the convolution of the medium's impulse response-expressed as the Green's function-and the seismic waves emitted by various sources, often simultaneously. Thus, continuous seismograms are goldmines to study the medium's properties or sources in time. However, unmixing source or medium changes is often not easy, especially if source and medium changes coincide. For instance, seismic recordings in the vicinity of volcanoes, where many different source and medium effects occur, are challenging and complex datasets to analyze. To better explore continuous seismic data, seismologists developed many data processing tools to extract valuable information for the task at hand.
Data Availability Statement The seismic data were downloaded from  Steinmannn et al. (2020)  and the temperature data were provided by the Meteorological Institute of Hamburg. The temperature data can be retrieved by contacting the Meteorological Institute of Hamburg through https://wettermast. uni-hamburg. de/frame. php?doc=Impressum. htm. The main code for calculating the scattering coefficients, features, and linkage matrix can be found under https://zenodo. org/badge/latestdoi/460424596. The work relies heavily on the python packages ObsPy  (Beyreuther et al. , 2010) , scikit-learn  (Pedregosa et al. , 2011) , and SciPy  (Virtanen et al. , 2020). The map was produced with map tiles by Stamen Design, under CC BY 3. 0. Data by OpenStreetMap, under ODbL.
, 2005; Mearns et al. , 2012; Solman et al. , 2013; Takle et al. , 1999; van der Linden and Mitchell, 2009). Particularly, the global scale of the CORDEX initiative, which has engaged an unprecedented number of modeling groups, resulted in the generation of large ensembles of regional climate projections over the fourteen official continental-scale domains that are the backbone of CORDEX activities worldwide (see Figure  1a ), with different modeling centers contributing to different domains, and simulations performed at resolutions ranging from ~50 km (default in most domains) to ~12 km. Moreover, the common archiving protocol  (Christensen et al. , 2020)  and the collaboration with the Earth System Grid Federation (ESGF,  Cinquini et al. , 2014)  have made freely available a massive dataset of regional climate change projections driven by a subset of the global simulations provided by the Coupled Model Intercomparison Project phase 5 (CMIP5) under different emission scenarios.
CORDEX regional projections constitute the state-of-the-art dataset for regional climate change impact and adaptation studies (an updated inventory with existing simulations -both those published on ESGF and those available from modeling centersis available at the CORDEX website 6 ). In this context, the latest report of the IPCC required globally homogeneous data for regional climate change assessment worldwide. However, at an early stage in the preparation of the Working Group I (WGI) contribution to the Sixth Assessment Report (AR6) in 2019, data availability on ESGF was patchy and heterogeneous across domains. This spatial heterogeneity was partially alleviated by the CORDEX-CORE initiative  (Coppola et al. , 2021b; Giorgi et al. , 2022; Gutowski Jr. et al. , 2016; Remedio et al. , 2019; Teichmann et al.
, 2021) , providing homogeneous future regional climate projections across most domains at ~25 km resolution for a few RCMs nested into a number of selected driving GCMs. However, in spite of this massive community effort, the publicly available ensembles from the ESGF were too small in some domains. One of the reasons for the apparent lack of CORDEX simulations on ESGF over certain 6 https://cordex. org/data-access/regional-climate-change-simulations-for-cordex-domains domains was the inherent complexity of the data publishing protocol and the lack of human resources to undertake this task. Strong metadata re-formatting and quality checks are required before publication of model simulations on ESGF. Many modeling groups did not have the funds and/or expertise to accomplish such a task. This paper presents the work carried out under the Copernicus Climate Change Service (C3S) over the past three years to address this problem and assemble a worldwide CORDEX dataset aligned with IPCC AR6 activities and timelines.
The mosaic approach avoids potential artifacts that may arise in overlapping regions, but may result in small ensembles for regions where information is available from multiple domains. For instance, simulations from the South Asia domain might not accurately represent the climate of central Africa (e. g. if models were tuned to perform best in the South Asia region). This approach was used to present global CORDEX-CORE results  (Coppola et al. , 2021a)  and in the worldwide assessment in the IPCC AR6 Atlas chapter  (Gutiérrez et al. , 2021) , as illustrated in Figure  1b. This paper is organized as follows. Firstly (Sect. 2), we describe the world-wide CORDEX dataset, including the protocol followed to consolidate this global dataset based on CORDEX output (Sect. 2. 1), the description of the resulting dataset (hereafter, the C3S CORDEX dataset; Sect.
2. 2), and the new metadata compiled to describe the simulations (Sect. 2. 3); moreover, Sec. 2. 4 describes how this dataset was used in the IPCC AR6 report as a new line of regional evidence. Secondly (Sect. 3), we investigate the consistency of the regional climate change projections, particularly in areas where different domains overlap (Sec. 3. 1), and compare the two above mentioned approaches to produce regional climate information (Sec. 3. 2).