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At least 199 records · Page 11

Adaptable Standards for Discovery, Access, and Usability of Oak Ridge National Laboratory’s Data Portals and Catalogs

Oak Ridge National Laboratory (ORNL) is leveraging its established capabilities and subject matter expertise in data curation, governance, management, national security, and risk assessment and mitigation to support the US Department of Energy (DOE) Grid Modernization Initiative. Using standards modeled by the National Institute of Standards and Technology (NIST), the Data Curation Network (DCN), the Oak Ridge Leadership Computing Facility (OLCF), and other leading organizations in the fields of energy research, high-performance computing, and national and homeland security, ORNL seeks to provide a federated approach to research data discovery, use, and interoperability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to Ebola Virus Infection, Processed Experimental Dataset Catalog

Ebola virus (EBOV) is high risk biological agent, classified as a Category A priority pathogen (Flaviviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), known to cause hemorrhagic fever with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Ebola virus [NCBITAXON:186536] (Zaire/Makona or Zaire/Mayinga) experimental infection study. Human host samples types include peripheral blood mononuclear cells isolated from blood plasma ["PBMC", BTO:0001025], human hepatoma carcinoma cells ["HUH", BTO:0001950], human umbilical vein endothelial cells ["HUVEC", BTO:0001949], immortalized human hepatocyte cells ["IHH", BTO:0006147], and human histiocytic lymphoma cells ["U937", BTO:0001412].

59 BASIC BIOLOGICAL SCIENCES↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to Influenza A Virus Infection, Processed Experimental Dataset Catalog

Influenza A virus (IAV) is a high risk biological agent, classified as a Category C priority pathogen (Orthomyxoviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), and is known to cause severe respiratory disease with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus [NCBITAXON:11320] experimental infection study. Host sample types include human lung adenocarcinoma cells ["Calu-3", BTO:0002750] and whole mouse lung [BTO:0000763] tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Interferon-Stimulated Response to Virus Infection, Processed Experimental Dataset Catalog

Human Interferon (IFN) alpha, beta, and gamma participate in the body's natural immune response to lethal virus infection and disease.The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013 - 2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Human Interferon (IFN), interferon alpha (IFNα), interferon beta (IFNβ), and/or interferon gamma (IFNγ) stimulated response to an experimental virus infection treatment study. Host sample types include cerebellum ["CB", BTO:0000232], cortical neurons ["CN", BTO:0004102], cortex ["CT", BTO:0000233], dendritic cells ["DC", BTO:0002042], granule cell neurons ["GCN", BTO:0003393], lymph node ["LN", BTO:0000784], and serum ["SE", BTO:0001239] from mouse (Mus musculus) tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

Omics Lethal Human Viruses Project Profiling of the Host Response to MERS-CoV Infection, Processed Experimental Dataset Catalog

Middle East Respiratory Syndrome coronavirus (MERS-CoV) is classified as a Category C priority pathogen (Coronaviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), and is known to cause severe respiratory disease with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV [NCBITAXON:1335626] experimental infection study. Host sample types include human lung adenocarcinoma cells ["Calu-3", BTO:0002750], human bronchial epithelial cells ["Calu-3 clone 2B4"; BTO:0002022], primary human fibroblasts ["FB"; BTO:0000452], primary human airway epithelial cells ["HAE"; BTO:0005571], human microvascular endothelial cells ["HMVE"; BTO:0003123], and whole mouse lung [BTO:0000763] tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to West Nile Virus Infection, Processed Experimental Dataset Catalog

West Nile virus (WNV) is classified as a Category B priority pathogen (mosquito-borne Flavivirus) by the National Institute of Allergy and Infectious Diseases (NIAID), and are known to cause severe infections in humans where lethal host-associated mechanisms are not clearly defined. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013 - 2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding a specific West Nile virus [NCBITAXON:11082] (WNV-NY99 382) experimental infection study. Host sample types include cerebellum ["CB", BTO:0000232], cortical neurons ["CN", BTO:0004102], cortex ["CT", BTO:0000233], dendritic cells ["DC", BTO:0002042], granule cell neurons ["GCN", BTO:0003393], lymph node ["LN", BTO:0000784], and serum ["SE", BTO:0001239] from mouse (Mus musculus) tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗

A Genomic Catalog of Stress Response Genes in Anaerobic Fungi for Applications in Bioproduction

Anaerobic fungi are a potential biotechnology platform to produce biomass-degrading enzymes. Unlike model fungi such as yeasts, stress responses that are relevant during bioprocessing have not yet been established for anaerobic fungi. In this work, we characterize both the heat shock and unfolded protein responses of four strains of anaerobic fungi ( Anaeromyces robustus, Caecomyces churrovis, Neocallimastix californiae , and Piromyces finnis ). The inositol-requiring 1 (Ire1) stress sensor, which typically initiates the fungal UPR, was conserved in all four genomes. However, these genomes also encode putative transmembrane kinases with catalytic domains that are similar to the metazoan stress-sensing enzyme PKR-like endoplasmic reticulum kinase (PERK), although whether they function in the UPR of anaerobic fungi remains unclear. Furthermore, we characterized the global transcriptional responses of Anaeromyces robustus and Neocallimastix californiae to a transient heat shock. Both fungi exhibited the hallmarks of ER stress, including upregulation of genes with functions in protein folding, ER-associated degradation, and intracellular protein trafficking. Relative to other fungi, the genomes of Neocallimastigomycetes contained the greatest gene percentage of HSP20 and HSP70 chaperones, which may serve to stabilize their asparagine-rich genomes. Taken together, these results delineate the unique stress response of anaerobic fungi, which is an important step toward their development as a biotechnology platform to produce enzymes and valuable biomolecules.

Swift, Candice L.↗

The Star Formation Across Cosmic Time (SFACT) Survey. II. The First Catalog from a New Narrowband Survey for Emission-line Objects

Star Formation Across Cosmic Time (SFACT) is a new narrowband (NB) survey designed to detect faint emission-line galaxies (ELGs) and QSOs over a broad range of redshifts. Here we present the first list of SFACT candidates from our pilot-study fields. Using the WIYN 3.5 m telescope, we are able to achieve good image quality with excellent depth and routinely detect ELGs to r = 25.0. The limiting line flux of the survey is ~1.0 × 10 –16 erg s –1 cm –2 . SFACT targets three primary emission lines: Hα, [O III ] λ5007, and [O II ] λ3727. The corresponding redshift windows allow for the detection of objects at z ~ 0–1. With a coverage of 1.50 deg 2 in our three pilot-study fields, a total of 533 SFACT candidates have been detected (355 candidates deg –2 ). We detail the process by which these candidates are selected in an efficient and primarily automated manner, then tabulate accurate coordinates, broadband photometry, and NB fluxes for each source.

79 ASTRONOMY AND ASTROPHYSICS↗

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

Abstract We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ( κ ) from photometric measurements of galaxies along a given line of sight (LOS). The method is of particular interest in strong gravitational time-delay cosmography (TDC), where characterizing the “external convergence” ( κ ext ) from the lens environment and LOS is necessary for precise Hubble constant ( H 0 ) inference. Starting from a large-scale simulation with a κ resolution of ∼1′, we introduce fluctuations on galaxy–galaxy lensing scales of ∼1″ and extract random sight lines to train our BGNN. We then evaluate the model on test sets with varying degrees of overlap with the training distribution. For each test set of 1000 sight lines, the BGNN infers the individual κ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population. For a test field well sampled by the training set, the BGNN recovers the population mean of κ precisely and without bias (within the 2 σ credible interval), resulting in a contribution to the H 0 error budget well under 1%. In the tails of the training set with sparse samples, the BGNN, which can ingest all available information about each sight line, extracts a stronger κ signal compared to a simplified version of the traditional method based on matching galaxy number counts, which is limited by sample variance. Our hierarchical inference pipeline using BGNNs promises to improve the κ ext characterization for precision TDC. The code is available as a public Python package, Node to Joy ⏬ .

79 ASTRONOMY AND ASTROPHYSICS↗

Enabling Catalog Simulations of Transient and Variable Sources Based on LSST Cadence Strategies

The American Astronomical Society. All rights reserved.. The Large Synoptic Survey Telescope (LSST) project will conduct a 10 year multi-band survey starting in 2022. Observing strategies for this survey are being actively investigated, and the science capabilities can be best forecasted on the basis of simulated strategies from the LSST Operations Simulator (OpSim). This paper describes the way in which OpSim simulates a stochastic realization of the sequence of LSST pointings over the survey duration, and is based on a model of the observatory (including telescope) and historical data of observational conditions. OpSim outputs contain a record of each simulated pointing of the survey along with a complete characterization of the pointing in terms of observing conditions, and some useful quantities derived from the characteristics of the pointing. Thus, each record can be efficiently used to derive the properties of observations of all astrophysical sources found in that pointing. However, in order to obtain the time series of observations (light curves) of a set of sources, it is often more convenient to compute all observations of an astrophysical source, and iterate over sources. In this document, we describe the open source python package OpSimSummary, which allows for a convenient reordering. The objectives of this package are to provide users with an Application Programming Interface for accessing all such observations and summarizing this information in the form of intermediate data products usable by third party software such as SNANA, thereby also bridging the gap between official LSST products and preexisting simulation codes.

79 ASTRONOMY AND ASTROPHYSICS↗

IceCube Search for Neutrinos Coincident with Compact Binary Mergers from LIGO-Virgo’s First Gravitational-wave Transient Catalog

Using the IceCube Neutrino Observatory, we search for high-energy neutrino emission coincident with compact binary mergers observed by the LIGO and Virgo gravitational-wave (GW) detectors during their first and second observing runs. In this paper, we present results from two searches targeting emission coincident with the sky localization of each GW event within a 1000 s time window centered around the reported merger time. One search uses a model-independent unbinned maximum-likelihood analysis, which uses neutrino data from IceCube to search for pointlike neutrino sources consistent with the sky localization of GW events. The other uses the Low-Latency Algorithm for Multi-messenger Astrophysics, which incorporates astrophysical priors through a Bayesian framework and includes LIGO-Virgo detector characteristics to determine the association between the GW source and the neutrinos. No significant neutrino coincidence is seen by either search during the first two observing runs of the LIGO-Virgo detectors. We set upper limits on the time-integrated neutrino emission within the 1000 s window for each of the 11 GW events. These limits range from 0.02 to 0.7 GeV cm -2 . We also set limits on the total isotropic equivalent energy, E iso , emitted in high-energy neutrinos by each GW event. These limits range from 1.7 × 10 51 to 1.8 × 10 55 erg. We conclude with an outlook for LIGO-Virgo observing run O3, during which both analyses are running in real time.

79 ASTRONOMY AND ASTROPHYSICS↗

Data Catalog Project - A Browsable, Searchable, Metadata System

Modern experiments are typically conducted by large, extended, where researchers rely on other team members to produce much of the data they use. The experiments record very large numbers of measurements which can be difficult for users to find, access and understand. We are developing a system for users to annotate their data products with structured metadata, providing data consumers with a discoverable, browsable data index. Machine understandable metadata captures the underlying semantics of the recorded data, which can then be consumed by both programs, and interactively by users. Collaborators can use these metadata to select and understand recorded measurements.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

MetaCat - metadata catalog for data management systems

Metadata management is one of three major areas and parts of functionality of scientific data management along with replica management and workflow management. Metadata is the information describing the data stored in a data item, a file or an object. It includes the data item provenance, recording conditions, format and other attributes. MetaCat is a metadata management database designed and developed for High Energy Physics experiments. As a component of a data management system, it’s main objectives are to provide efficient metadata storage and management and fast data items selection functionality. MetaCat is supposed to work on the scale of 100 million files (or objects) and beyond. The article will discuss the functionality of MetaCat and technological solutions used to implement the product.

Mandrichenko, Igor↗

The eROSITA Final Equatorial-Depth Survey (eFEDS)

In 2019, the eROSITA telescope on board the Russian-German satellite Spectrum-Roentgen-Gamma (SRG) began to perform a deep all-sky X-ray survey with the aim of identifying ~100 000 clusters and groups over the course of four years. As part of its performance verification phase, a ~140 deg 2 survey, called eROSITA Final Equatorial-Depth Survey (eFEDS), was performed. With a depth typical of the all-sky survey after four years, it allows tests of tools and methods as well as improved predictions for the all-sky survey. As part of this effort, a catalog of 542 X-ray selected galaxy group and cluster candidates was compiled. Here, in this paper, we present the optical follow-up, with the aim of providing redshifts and cluster confirmation for the full sample. Furthermore, we aim to provide additional information on the dynamical state, richness, and optical center of the clusters. Finally, we aim to evaluate the impact of optical cluster confirmation on the purity and completeness of the X-ray selected sample. We used optical imaging data from the Hyper Suprime-Cam Subaru Strategic Program and from the Legacy Survey to identify optical counterparts to the X-ray detected cluster candidates. We make use of the multi-component matched filter cluster confirmation tool (MCMF), as well as of the optical cluster finder CAMIRA to derive cluster redshifts and richnesses. MCMF provided the probabilities with which an optical structure would be a chance superposition with the X-ray candidate. These probabilities were used to identify the best optical counterpart as well as to confirm an X-ray candidate as a cluster. The impact of this confirmation process on catalog purity and completeness was estimated using optical to X-ray scaling relations as well as simulations. The resulting catalog was furthermore matched with public group and cluster catalogs. Optical estimators of the cluster dynamical state were constructed based on density maps of the red-sequence galaxies at the cluster redshift. By providing redshift estimates for all 542 candidates, we construct an optically confirmed sample of 477 clusters and groups with a residual contamination of 6%. Of these, 470 (98.5%) are confirmed using MCMF, and 7 systems are added through cross-matching with spectroscopic group catalogs. Using observable-to-observable scaling and the applied confirmation threshold, we predict that 8 ± 2 real systems have been excluded with the MCMF cut required to build this low-contamination sample. This number agrees well with the 7 systems found through cross-matching that were not confirmed with MCMF. The predicted redshift and mass distribution of this catalog agree well with simulations. Thus, we expect that these 477 systems include >99% of all true clusters in the candidate list. Using an MCMF-independent method, we confirm that the catalog contamination of the confirmed subsample is 6 ± 3%. Application of the same method to the full candidate list yields 17 ± 3%, consistent with estimates coming from the fraction of confirmed systems of ~17% and with expectations from simulations of ~20%. We also present a sample of merging cluster candidates based on the derived estimators of the cluster dynamical state.

79 ASTRONOMY AND ASTROPHYSICS↗