dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential expression analysis Differences in gene expression levels were determined for each of the myleoid cell types between C19+ cases at D0, D4, D7 or D14 versus Healthy controls. To assess whether these changes are specific for C19+ cases or are a more general phenomenon as a consequence of acute respiratory distress syndrome, we also compared the upregulated genes with the C19-cases. Differential expression analysis was performed per run on the raw gene expression matrix using Memento v0. 0. 4 (unpublished, Kim MC et al. https://github. com/yelabucsf/scrna-parameter-estimation), after which results were meta-analyzed over all runs. Genes were pre-filtered based on a minimum raw mean expression of 0. 07 within at least 90% of both comparison group. A false discovery rate of <0. 05 was used to determine statistical significance.
Interferon Stimulated Gene Score Method An orthogonal scRNA-seq dataset containing PBMCs stimulated with IFN beta and gamma was used to identify the specific and shared type I and type II ISGs in the cMs (unpublished). The gene list was used to calculate a type I, type II and shared ISG score based on the average gene expression count of the unique or shared type I and type II ISGs. These ISG scores were calculated for each unique combination of cell type, donor and timepoint. Subsequently, ISG scores were averaged over each of the disease categories (C19+ moderate/severe, C19-moderate/severe, healthy control) and then log2transformed. A Welch's T-test was performed to compare the ISG score between C19+ patients and healthy control. Significance was defined as Bonferroni-adjusted p-value < 0.
SARS-CoV-2 detection by clinical qRT-PCR Viral titers were quantified in a subset of the C19+ patients in the UCSF CLIAHUB Clinical Microbiology Laboratory. For this, RNA was extracted from tracheal aspirate samples and used for qRT-PCR as previously described  (40). In short, viral titers were assessed using primers targeting the SARS-CoV-2 N gene (Ct1), E gene (Ct2) and human RNAse P gene (Ct_host, positive control). The Ct value of the viral N or E gene was subtracted from the human RNAse P gene (delta Ct: dCt1 and dCt2) and number signs were reversed to obtain a measurement for viral load. As there was an almost perfect correlation between dCt1 and dCt2 values (Pearson R = 0. 99, p = 1. 9 x 10 -283 ) and dCt2 had the least missing values, viral load is represented by the dCt2 values. dCt2 values as measured in the tracheal aspirate samples were linked to the scRNA-seq PBMC data of the same donor, and the closest possible timepoint (up to 2 days apart).
UM1HG006504 and U24HG008956), the French National Research Agency (ANR) under the "Investments for the Future" program (ANR-10-IAHU-01), the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (ANR-10-LABX-62-IBEID), the French Foundation for Medical Research (FRM) (EQU201903007798), the FRM and ANR GENCOVID project, ANRS-COV05, the Square Foundation, Grandir -Fonds de solidarité pour l'enfance, the SCOR Corporate Foundation for Science, Institut National de la Santé et de la Recherche Médicale (INSERM) and the University of Paris. The work was supported in part by the Intramural Research Program of the NIAID, NIH. P. B. and T. L. V. were supported by the MD-PhD program of the Imagine Institute (with the support of the Fondation Bettencourt-Schueller). Disclosures J.
S. C. ). AleW and Y. S. S. were supported in part by an  NIH  grant  R35-GM134922  and the  Exascale Computing Project  ( 17-SC-20-SC ), a collaborative effort of the  U. S. Department of Energy Office of Science  and the  National Nuclear Security Administration. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  National Institutes of Health (NIH)  ( R01AI088364 ), the  National Center for Advancing Translational Sciences (NCATS) ,  NIH Clinical and Translational Science Award (CTSA) program  ( UL1 TR001866 ), a  Fast Grant  from  Emergent Ventures ,  Mercatus Center at George Mason University , the  Yale Center for Mendelian Genomics  and the  GSP Coordinating Center  funded by the  National Human Genome Research Institute (NHGRI).
( UM1HG006504 and U24HG008956), the French National Research Agency (ANR) under the "Investments for the Future" program (ANR-10-IAHU-01), the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (ANR-10-LABX-62-IBEID), the French Foundation for Medical Research (FRM) (EQU201903007798), the FRM and ANR GENCOVID project, ANRS-COV05, the Square Foundation, Grandir -Fonds de solidarité pour l'enfance, the SCOR Corporate Foundation for Science, Institut National de la Santé et de la Recherche Médicale (INSERM) and the University of Paris. The work was supported in part by the Intramural Research Program of the NIAID, NIH. P. B. and T. L. V. were supported by the MD-PhD program of the Imagine Institute (with the support of the Fondation Bettencourt-Schueller). Disclosures J.
Flares of known astronomical sources and new transient phenomena occur on different timescales, from sub-seconds to several days or weeks. The discovery potential of both serendipitous observations and multi-messenger and multi-wavelength follow-up observations could be maximized with a tool which allows for quickly acquiring an overview over both persistent sources as well as transient events in the relevant phase space. We here present COincidence LIBrary for Real-time Inquiry (Astro-COLIBRI), a novel and comprehensive tool for this task. Astro-COLIBRI's architecture comprises a RESTful API, a real-time database, a cloud-based alert system and a website as well as apps for iOS and Android as clients for users. The structure of Astro-COLIBRI is optimized for performance and reliability and exploits concepts such as multi-index database queries, a global content delivery network (CDN), and direct data streams from the database to the clients to allow for a seamless user experience.
Astro-COLIBRI evaluates incoming VOEvent messages of astronomical observations in real time, filters them by userspecified criteria and puts them into their MWL and MM context. The clients provide a graphical representation with an easy to grasp summary of the relevant data to allow for the fast identification of interesting phenomena and provides an assessment of observing conditions at a large selection of observatories around the world. We here summarize the key features of Astro-COLIBRI, the architecture and used data resources. We specifically provide examples for applications and use cases. Focussing on the high-energy domain, we showcase how Astro-COLIBRI facilitates the search for high-energy gamma-ray counterparts to high-energy neutrinos and scheduling of follow-up observations of a large variety of transient phenomena like gamma-ray bursts, gravitational waves, TDEs, FRBs, and others.
The Astro-COLIBRI platform A large amount of information is available on known astrophysical objects and on transient phenomena. Often due to historical reasons, this wealth of information is unfortunately spread over many systems, databases, and collaborations. For example, SIMBAD  [1] , NED  [2] , etc. provide detailed information about known sources and the associated archival observations. The Gamma-Ray Coordinates Network (GCN  [3, 4] ) is providing an efficient real-time stream of announcements of mainly GRBs, GW, and high-energy neutrinos, while the Transient Name Server (TNS  [5] ) is providing similar information for SNe, TDEs, FRBs, etc. This is complemented by collaborations operating monitoring instruments like Fermi-LAT that provide analyses of their data at various timescale (e.
g. the Fermi All-sky Variability Analysis (FAVA  [6] )) and community platforms like the Astronomers Telegram (ATEL  [7] ) enable sharing of reports on observations. This huge, and increasing, amount of information can be used to efficiently select the most promising candidate events for scheduling of deeper follow-up observations, but keeping track of these various sources of information in real-time (and partially in stressful situations while operating an instrument in the middle of the night) can be time-consuming and error-prone. Without replacing the individual, expert-run systems, Astro-COLIBRI provides a top-level layer enabling access to the most important information at a glance. To fulfil this promise, Astro-COLIBRI is built as a modular, cloud-based infrastructure that is using up-to-date technologies like a central RESTful API, a real-time database, a cloud-based alert system and a website as well as apps for iOS and Android as clients for users.
The structure of Astro-COLIBRI (cf. Fig. 1 ) is optimized for performance and reliability and exploits concepts such as multi-index database queries, a global content delivery network (CDN), and direct data streams from the database to the clients to allow for a seamless user experience. Dedicated listeners are subscribed to the most relevant alert networks using the IVAO  [8]  standard protocols and each incoming VOEvent message is analyzed, filtered, and classified in real-time. The API is using dedicated, cloud-based databases and access to external APIs to put the incoming alert into its multi-wavelength (MWL) and multi-messenger (MM) context. The deeply customizable clients provide a graphical representation with an easy to grasp summary of the relevant data to allow for the fast identification of interesting phenomena and provide an assessment of observing conditions at a large selection of observatories around the world.
Example use-case: observations of GRBs One of the most common transient events of interest for the high-energy astrophysics community are GRBs. Providing information on their detections and enabling well-informed decision to trigger follow-up observations with ground or space-based observatories is therefore obviously a core feature of Astro-COLIBRI. A dedicated server with extensive performance monitoring is constantly listening to a redundant list of IVOA VoEvent brokers. Upon the announcement of a new GRB by any of the most active X-ray monitors (Fermi-GBM, Swift-BAT, INTEGRAL), the Astro-COLIBRI pipeline outlined in Fig. 1  is activated. The (python based) logic in the API searches the Astro-COLIBRI databases for existing entries of the same event and classifies the incoming information both following the expected sequence of messages from each observatory as well as using the data provided in the alert itself (e.
g. the GCN Pkt_Ser_Num, references to previous messages on the same event, etc. ). If appropriate, the databases are updated to always contain the newest and highest precision information on each event. Thanks to the established direct data streams between the real-time database and the clients (both web and smartphone apps), the latter are updated automatically with negligible delays. The name of a new GRB (e. g. GRB YYMMDDA/B/. ) is typically only communicated by the detecting observatories via GCN circulars. Dedicated listeners parsing the associated email messages allow adding this information fully automatically. While the announcement of the detection of GeV emission by Fermi-LAT is also being treated automatically, further information can easily be added manually by the Astro-COLIBRI admins.
Displayed via the web and smartphone frontends, it immediately provides a visual indication of previous transients and known GeV gamma-ray emitters within the neutrino uncertainty circle and the region around that. The provided card-view also includes the angular distance to the neutrino event to allow for a first quantitative assessment. The cone search result is also rapidly available in a machine-readable format via the dedicated API endpoint /cone_search  [10]. While the visibility plots allow assessing the observability of the given event by the userselected observatory, the information box provides a detailed overview of the event information like the false-alarm rate (FAR) and the probability of the event to be of astrophysical origin ( astro ). Further information can be found in the original VoEvent file announcing the event that is provided via a call to the 4PiSky database and via links to collections of GCN notices and circulars related to the event.
The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2. 2021 is an update of the previous version, GLODAPv2. 2020 (Olsen et al. , 2020). The major changes are as follows: data from 43 new cruises were added, data coverage was extended until 2020, all data with missing temperatures were removed, and a digital object identifier (DOI) was included for each cruise in the product files. In addition, a number of minor corrections to GLODAPv2. 2020 data were performed. GLODAPv2. 2021 includes measurements from more than 1. 3 million water samples from the global oceans collected on 989 cruises.
A full list of variables included in the product is provided in Table  1. The oceanographic community largely adheres to principles and practices for ensuring open access to research data, such as the FAIR (Findable, Accessible, Interoperable, Reusable) initiative  (Wilkinson et al. , 2016) , but the plethora Table  1. Variables in the GLODAPv2. 2021 comma separated (csv) product files, their units, short and flag names, and corresponding names in the individual cruise exchange files. In the MATLAB product files that are also supplied a "G2" has been added to every variable name. of file formats and different levels of documentation, combined with the need to retrieve data on a per cruise basis from different access points, limit the realization of their full scientific potential. In addition, the manual data retrieval is time consuming and prone to data handling errors  (Tanhua et al.
, 2021). For biogeochemical data there is the added complexity of different levels of standardization and calibration and even different units used for the same variable such that the comparability between datasets is often poor. Standard operating procedures have been developed for some variables  (Dickson et al. , 2007; Hood et al. , 2010; Becker et al. , 2020) , and certified reference materials (CRMs) exist for seawater TCO 2 and TAlk measurements  (Dickson et al. , 2003)  and for nutrients in seawater (CRMNS;  Aoyama et al. , 2012; Ota et al. , 2010). Despite this, biases in data still occur. These can arise from poor sampling and preservation practices, calibration procedures, instrument design, and inaccurate calculations. The use of CRMs does not by itself ensure accurate measurements of seawater CO 2 chemistry  (Bockmon and Dickson, 2015) , and the CRMNS have only become available recently and are not universally used.
The secondary quality control focuses on deep data, in which natural variability is minimal. Adjustments are applied to the data to minimize cases of bias that could be confidently established relative to the measurement precision for the variables and cruises considered. Key metadata are provided in the header of each data file, and full cruise reports submitted by the data providers are accessible through the GLODAPv2 cruise summary table (https: //www. ncei. noaa. gov/access/ocean-carbon-data-system/ oceans/GLODAPv2_2021/cruise_table_v2021. html, last access: 7 July 2021). GLODAPv2. 2021 builds on earlier synthesis efforts for biogeochemical data obtained from research cruises, GLO-DAPv1. 1  (Key et al. , 2004; Sabine et al. , 2005) , Carbon dioxide in the Atlantic Ocean (CARINA)  (Key et al.
, 2016) , instigated in the early 2000s as part of the Climate and Ocean -Variability, Predictability and Change (CLIVAR) program and since 2007 organized as the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP)  (Sloyan et al. , 2019). GLODAPv2 is now updated regularly using the "living data process" of Earth System Science Data to document significant additions and changes to the dataset. There are two types of GLODAP updates: full and intermediate. Full updates involve a reanalysis, notably crossover and inversion, of the entire dataset (both historical and new cruises) and all data points are subject to potential adjustment. This was carried out for GLODAPv2. For intermediate updates, recently available data are added following quality control procedures to ensure their consistency with the cruises included in the latest GLODAP release.
Except for obvious outliers and similar types of errors (Sect. 3. 3. 1), the data included in previous releases are not changed during intermediate updates. Additionally, the GLODAP mapped climatologies  (Lauvset et al. , 2016)  are not updated for these intermediate products. A naming convention has been introduced to distinguish intermediate from full product updates. For the latter the version number will change, while for the former the year of release is appended. The exact version number and release year (if appended) of the product used should always be reported in studies rather than making a generic reference to GLODAP. Creating and interpreting inversions, as well as other checks of the full dataset needed for full updates, are too demanding in terms of time and resources to be performed every year or 2 years.
1) and secondary (Sect. 3. 2) quality control (QC). These procedures are the same as for GLODAPv2. 2020, aiming to ensure the consistency of the data from the 43 new cruises with the previous release of this data product (in this case, the GLO-DAPv2. 2020 adjusted data product). For GLODAPv2. 2021 we have also added a basin identifier to the product files, where 1 is the Atlantic Ocean, 4 is the Arctic Mediterranean Sea, 8 is the Pacific Ocean, and 16 is the Indian Ocean. These regions are abbreviated AO, AMS, PO, and IO, respectively, in the adjustment table. Data in the Mediterranean Sea are classified as AO. The basin identifier is now added to the product files to make it easier for users to identify in which ocean basin an individual cruise belongs without having to use one of the four regional files.
Data assembly and primary quality control The data from the 43 new cruises were submitted directly to us or retrieved from data centers: typically the CLIVAR and Carbon Hydrographic Data Office (https://cchdo. ucsd. edu, last access: 3 June 2021), National Center for Environmental Information (https://www. ncei. noaa. gov, last access: 3 June 2021), and PANGAEA (https://pangaea. de, last access: 3 June 2021). Each cruise is identified by an expedition code (EXPOCODE). The EXPOCODE is guaranteed to be unique and constructed by combining the country code and platform code with the date of departure in the format YYYYMMDD. The country and platform codes were taken from the ICES (International Council for the Exploration of the Sea) library (https://vocab. ices. dk/, last access 3 June 2021). The individual cruise data files were converted to the WOCE exchange format: a comma-delimited ASCII format for CTD and bottle data from hydrographic cruises.
Adjustments for salinity, TCO 2 , TAlk, and pH are always additive, while adjustments for oxygen, nutrients, and the halogenated transient traces are always multiplicative. Except where explicitly noted (Sect. 3. 3. 1), adjustments were not changed for data previously included in GLODAPv2. 2020. Crossover comparisons, multi-linear regressions (MLRs), and comparison of deep-water averages were used to identify offsets for salinity, oxygen, nutrients, TCO 2 , TAlk, and pH (Sect. 3. 2. 2 and 3. 2. 3). As in GLODAPv2. 2020, but in contrast to GLODAPv2 and GLODAPv2. 2019, the evaluation of the internal consistency of the seawater CO 2 chemistry variables was not used for the evaluation of pH (Sect. 3. 2. 4). As in GLODAPv2. 2020 we made extensive use of two predictions from two empirical algorithms -CArbonate system And Nutrients concentration from hYdrological properties and Oxygen using a Neural-network version B (CANYON-B) and CONsisTency EstimatioN and amounT (CONTENT)  (Bittig et al.
Merging of sensor and bottle data Salinity and oxygen data can be obtained by analysis of water samples (bottle data) and/or directly from the CTD sensor pack. These two measurement types are merged and presented as a single variable in the product. The merging was conducted prior to the consistency checks, ensuring their internal calibration in the product. The merging procedures were only applied to the bottle data files, which commonly include values recorded by the CTD at the pressures where the water samples are collected. Whenever both CTD and bottle data were present in a data file, the merging step considered the deviation between the two and calibrated the CTD values if required and possible. Altogether seven scenarios (Table  4 ) are possible for each of the CTD O 2 sensor properties individually, in which the fourth and sixth never occurred during our analyses but are included to maintain consistency with GLODAPv2.
As an example of crossover analysis, the crossover for TCO 2 measured on the two cruises 320620170820 (P06E), which is new to this version, and 49NZ20030803, which was included in GLODAPv2, is shown in Fig. 3. For TCO 2 the offset is determined as the difference, in accordance with the procedures followed for GLODAPv2. The TCO 2 values from 320620170820 are comparable, with a weighed mean off-set of 0. 84 ± 3. 12 µmol kg -1 compared to those measured on 49NZ20030803. For each of the 43 new cruises, such a crossover comparison was conducted against all possible cruises in GLO-DAPv2. 2020, i. e. , all cruises that had stations closer than 2 arcdeg distance to any station for the cruise in question. The summary figure for TCO 2 on 320620170820 is shown in Fig. 4. The TCO 2 data measured on this cruise are 2.
15 ± 1. 04 µmol kg -1 higher when compared to the data measured on nearby cruises included in GLODAPv2. 2020. This is well within the initial minimum adjustment limit for TCO 2 of 4 µmol kg -1 (Table  3 ) and as such does not qualify for an adjustment of the data in the merged data product. All other variables show the same high consistency (not shown); thus, no adjustment is given to any variable on cruise 320620170820 in GLODAPv2. 2021. This is supported by the CANYON-B and CONTENT results (Sect. 3. 2. 5). Note that adjustments, when applied, are typically round numbers (e. g. , -3 not -3. 4 for TCO 2 and 0. 005 not 0. 0047 for pH) to avoid communicating that the ideal adjustments are known to high precision.
pH scale conversion and quality control Altogether 13 of the 43 new cruises included measured pH data, and none required adjustment (Sect. 4. 2). All new pH data were reported on the total scale and at 25 • C, and so no scale and/or temperature conversion was necessary. For details on scale and temperature conversions in previous versions of GLODAPv2, we refer the reader to  Olsen et al. (2020). In contrast to past quality control of GLODAP pH data, evaluation of the internal consistency of CO 2 system variables was not used for the secondary quality control of the pH data of the 13 new cruises; only crossover analysis was used, supplemented by CONTENT and CANYON-B comparisons (Sect. 3. 2. 5). Recent literature has demonstrated that internal consistency evaluation procedures are subject to errors owing to incomplete understanding of the thermodynamic constants, major ion concentrations, measurement biases, and potential contribution of organic compounds or other unknown protolytes to alkalinity.