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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule?

Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique “fingerprint” of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy make the testing of this assumption timely. In this paper, we pose the determination of molecular structures from rotational spectra as an inverse problem. Within this framework, we adopt a funnel-based approach to search for molecular twins, which are two or more molecules, which have similar rotational spectra but distinctly different molecular structures. Here we demonstrate that there are twins within standard levels of computational accuracy by generating rotational constants for many molecules from several large molecular databases, indicating that the inverse problem is ill-posed. However, some twins can be distinguished by increasing the accuracy of the theoretical methods or by performing additional experiments.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Development of Streamlined Nuclear Safety Analysis Tool for Spent Nuclear Fuel Applications

To understand the changing nuclear and mechanical characteristics of spent nuclear fuel (SNF) or used nuclear fuel (UNF) and the different storage, transportation, and disposal systems at various stages within the waste management system, different types of analyses are required. These analyses require the use of assorted tools and numerous types of data. Using the appropriate modeling and simulation (M&S) parameters and selecting from the diversity of analytic tools to conduct SNF analyses can be a tedious, error-prone, and time-consuming undertaking for analysts and reviewers alike. A new, integrated data and analysis system was designed to simplify and automate performance of accurate, efficient evaluations for characterizing the input to the overall U.S. nuclear waste management system—the UNF-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS). A relational database has been assembled to provide a standard means by which UNF-ST&DARDS can succinctly store and retrieve M&S parameters for specific SNF analysis. A library of various analysis model templates is used to communicate M&S parameters for the most appropriate M&S application. A process manager facilitates performance of actual as-loaded, assembly-specific, and cask-specific evaluations. Interactive visualization capabilities facilitate data analysis and results interpretation. To date, UNF-ST&DARDS has completed (1) explicit depletion and decay analysis of every fuel assembly (~245 000) discharged from commercial U.S. reactors through June 2013, with 13 cooling time steps (results include isotopic compositions for 142 isotopes, and radiation and thermal source terms); (2) SNF radiation dose rate evaluations at 1 m for all the fuel assemblies discharged through June 2013; and (3) criticality, shielding, thermal, and containment analyses of hundreds of loaded casks. In conclusion, UNF-ST&DARDS also provides various automated report generation capabilities with dynamic figure and table update capabilities based on changes to the Unified Database.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

High-Performance Data Analytics Beyond the Relational and Graph Data Models with GEMS

Graphs represent an increasingly popular data model for data-analytics, since they can naturally represent relationships and interactions between entities. Relational databases and their pure table-based data model are not well suitable to store and process sparse data. Consequently, graph databases have gained interest in the last few years and the Resource Description Framework (RDF) became the standard data model for graph data. Nevertheless, while RDF is well suited to analyze the relationships between the entities, it is not efficient in representing their attributes and properties. In this work we propose the adoption of a new hybrid data model, based on attributed graphs, that aims at overcoming the limitations of the pure relational and graph data models. We present how we have re-designed the GEMS data-analytics framework to fully take advantage of the proposed hybrid data model. To improve analysts productivity, in addition to a C++ API for applications development, we adopt GraQL as input query language. We validate our approach implementing a set of queries on net-flow data and we compare our framework performance against Neo4j. Experimental results show significant performance improvement over Neo4j, up to several orders of magnitude when increasing the size of the input data.

Data Analytics↗
DOE OSTI2021

Enabling Scalable and Extensible Memory-mapped Datastores in Userspace

Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present UMap , a scalable and extensible userspace service for memory-mapping datastores. Furthermore, through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, UMap enables application performance to scale even at high concurrency. We evaluate UMap in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that UMap as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 × . We performed two case studies to demonstrate UMap 's extensibility. First, a new datastore residing in remote memory is incorporated into UMap as an application-specific plugin. Second, we present a persistent memory allocator Metall built atop UMap for unified storage/memory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The Alaska Arctic Vegetation Archive (AVA-AK)

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. Here, we present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High-throughput virtual laboratory for drug discovery using massive datasets

Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Rapid phylogenetic and functional classification of short genomic fragments with signature peptides

Background: Classification is difficult for shotgun metagenomics data from environments such as soils, where the diversity of sequences is high and where reference sequences from close relatives may not exist. Approaches based on sequence-similarity scores must deal with the confounding effects that inheritance and functional pressures exert on the relation between scores and phylogenetic distance, while approaches based on sequence alignment and tree-building are typically limited to a small fraction of gene families. We describe an approach based on finding one or more exact matches between a read and a precomputed set of peptide 10-mers. Results: At even the largest phylogenetic distances, thousands of 10-mer peptide exact matches can be found between pairs of bacterial genomes. Genes that share one or more peptide 10-mers typically have high reciprocal BLAST scores. Among a set of 403 representative bacterial genomes, some 20 million 10-mer peptides were found to be shared. We assign each of these peptides as a signature of a particular node in a phylogenetic reference tree based on the RNA polymerase genes. We classify the phylogeny of a genomic fragment (e.g., read) at the most specific node on the reference tree that is consistent with the phylogeny of observed signature peptides it contains. Using both synthetic data from four newly-sequenced soil-bacterium genomes and ten real soil metagenomics data sets, we demonstrate a sensitivity and specificity comparable to that of the MEGAN metagenomics analysis package using BLASTX against the NR database. Phylogenetic and functional similarity metrics applied to real metagenomics data indicates a signal-to-noise ratio of approximately 400 for distinguishing among environments. Our method assigns ~6.6 Gbp/hr on a single CPU, compared with 25 kbp/hr for methods based on BLASTX against the NR database. Conclusions: Classification by exact matching against a precomputed list of signature peptides provides comparable results to existing techniques for reads longer than about 300 bp and does not degrade severely with shorter reads. Orders of magnitude faster than existing methods, the approach is suitable now for inclusion in analysis pipelines and appears to be extensible in several different directions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Automatic identification and classification of Palomar Transient Factory astrophysical objects in GLADE

Palomar Transient Factory (PTF) is a comprehensive detection system for the identification and classification of transient astrophysical objects. In this paper, we make two significant contributions to the PTF pipeline. First, we present an experimental study that evaluates a novel implementation of the real-time classifier in GLADE - a parallel data processing system that combines the efficiency of a database with the extensibility of map-reduce. We show how each stage in the classifier maps optimally into GLADE tasks by taking advantage of the unique features of the system - range-based data partitioning, columnar storage, multi-query execution, and in-database support for complex aggregate computation. Second, we introduce a novel parallel similarity join algorithm for advanced transient classification. In conclusion, we implement this algorithm in GLADE and execute it on a massive supercomputer with more than 3,000 threads, achieving more than three orders of magnitude improvement over the PostgreSQL solution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

REE and Coal Open Geodatabase Collection

The REE and Coal Open Database is an online collection of subsurface contextual data from publicly available geological, geochemical, and geospatial resources. These data align to and support execution of NETL’s REE coal assessment method. The database includes basin- and national-level spatial datasets, in addition to other non-spatial data that support the assessment approach. Data in this collection are sourced from a range of authoritative, public sources, including NETL, U.S. Geological Survey (USGS), Energy Information Administration (EIA), and state geological surveys.

coal↗
DOE OSTI2013

Final Report of the Mid-Atlantic Marine Wildlife Surveys, Modeling, and Data

The Wind Program hosted a two-day workshop on July 24-25, 2012 with scientists and regulators engaged in marine ecological survey, modeling, and database efforts pertaining to the waters of the Mid-Atlantic region. The workshop was planned by Federal agency, academic, and private partners to promote collaboration between ongoing offshore ecological survey efforts, and to promote the collaborative development of complementary predictive models and compatible databases. The meeting primarily focused on efforts to establish and predict marine mammal, seabird, and sea turtle abundance, density, and distributions extending from the shoreline to the edge of the Exclusive Economic Zone between Nantucket Sound, Massachusetts and Cape Hatteras, North Carolina.

17 WIND ENERGY↗
DOE OSTI2019

Full Integration of Lipidomics Data into Multi-OMIC Functional Enrichment

Lipids have numerous roles critical to living systems. Many of these vital functions are tightly linked or regulated by proteins. Even though the function of many lipid-related enzymes is known, direct links between most individual lipid species and the enzymes that metabolize them are severely lacking. This missing connection impedes biological progress. A resource that allows users to quickly identify protein-lipid relationships will advance our understanding of lipid homeostasis and metabolic perturbed systems. Databases are readily available for mapping small molecule metabolites (i.e., polar metabolites) and specific oxidized fatty acid lipids (e.g., arachidonic acid derivatives) to proteins/enzymes known to be directly involved in metabolism of these molecules (e.g. SMPDB, Reactome, HMDB, BioCyc). This enables the integration of metabolite and protein (as well as transcript) data when performing functional enrichment and metabolic modeling allowing researchers to rapidly examine metabolic pathways from multiple omics sources. Currently, linking lipidomics data with proteomics data is conducted using a time-intensive, user defined, manual process. Here we propose creating a tool that will link lipidomics and proteomics data and also conduct functional enrichment analysis and visualization allowing for full statistical integration. Using text-mining approaches, we will extract lipid references from uniprot and create a database linking lipid species to enzymes directly related to their metabolism. We will also extract directional annotation, which will allow even greater specificity when linking data types and will help eliminate false positives from functional associations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Simulation Tools for Characterizing Stress Distribution in Laser Welded Dissimilar Joints

This project focuses on developing a thermo-metallurgical-mechanical modeling method to accurately predict the microstructural evolution and residual stress in laser welding between dissimilar metals, such as HSLA steel and high carbon equivalent (CE) gear steel. The method leverages a comprehensive material database to model the temperature and rate dependent phase transformations, along with their associated effects on material properties, such as thermal expansion and flow stress, throughout the welding process. A key innovation is the incorporation of phase transformation and phase-specific properties, which enhances the accuracy of residual stress predictions. The mixture material in the fusion zone due to the dissimilar metals will also be addressed in the numerical model. This is especially critical in scenarios involving phase transformations in the fusion zone and heat-affected zone (HAZ), where the phase changes can induce substantial residual stress variations. The material database has been generated using JMatPro. The modeling approach is implemented through a custom User Material (UMAT) subroutine, executed with the commercial finite element software Abaqus.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin (Final Technical Report)

This report documents the results of "A Play-Based Exploration of CO₂ Storage in the Illinois Basin" (DE-FE0032366), a project funded by the U.S. Department of Energy Office of Fossil Energy and Carbon Management and conducted by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in partnership with Visage Energy. The project adapted play-based exploration (PBE), a systematic basin-scale evaluation methodology from the petroleum industry, to screen areas of Illinois for commercial geologic carbon storage (GCS) in Cambro-Ordovician strata. The traditional play concept was expanded to encompass three play element groups, subsurface geologic factors, surface features, and societal factors, yielding 24 play elements with defensible suitability criteria applied through a five-tier classification scheme. An integrated geospatial database was assembled from ISGS, MGSC, MRCI, NATCARB, and public data sources, supported by significant data-improvement work including correction of legacy well locations, digitization of more than 1,300 well construction records using the DOE CATALOG team's OGRRE tool, compilation of a statewide 2D seismic database, and production of a refined fault and fold geodatabase.

58 GEOSCIENCES↗
DOE OSTI2022

Multiple Cases of Bacterial Sequence Erroneously Incorporated Into Publicly Available Chloroplast Genomes

Public sequencing databases are invaluable resources to biological researchers, but assessing data veracity as well as the curation and maintenance of such large collections of data can be challenging. Genomes of eukaryotic organelles, such as chloroplasts and other plastids, are particularly susceptible to assembly errors and misrepresentations in these databases due to their close evolutionary relationships with bacteria, which may co-occur within the same environment, as can be the case when sequencing plants. Here, based on sequence similarities with bacterial genomes, we identified several suspicious chloroplast assemblies present in the National Institutes of Health (NIH) Reference Sequence (RefSeq) collection. Investigations into these chloroplast assemblies reveal examples of erroneous integration of bacterial sequences into chloroplast ribosomal RNA (rRNA) loci, often within the rRNA genes, presumably due to the high similarity between plastid and bacterial rRNAs. The bacterial lineages identified within the examined chloroplasts as the most likely source of contamination are either known associates of plants, or co-occur in the same environmental niches as the examined plants. Modifications to the methods used to process untargeted ‘raw’ shotgun sequencing data from whole genome sequencing efforts, such as the identification and removal of bacterial reads prior to plastome assembly, could eliminate similar errors in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Annotation and sequence diversity of transposable elements in common bean (Phaseolus vulgaris)

Common bean (Phaseolus vulgaris) is an important legume crop grown and consumed worldwide.With the availability of the common bean genome sequence, the next challenge is to annotate the genome and characterize functional DNA elements. Transposable elements (TEs) are the most abundant component of plant genomes and can dramatically affect genome evolution and genetic variation. Thus, it is pivotal to identify TEs in the common bean genome. In this study, we performed a genome-wide transposon annotation in common bean using a combination of homology and sequence structure-based methods.We developed a 2.12-Mb transposon database which includes 791 representative transposon sequences and is available upon request or from www.phytozome.org. Of note, nearly all transposons in the database are previously unrecognized TEs. More than 5,000 transposon-related expressed sequence tags (ESTs) were detected which indicates that some transposons may be transcriptionally active. Two Ty1-copia retrotransposon families were found to encode the envelope-like protein which has rarely been identified in plant genomes. Also, we identified an extra open reading frame (ORF) termed ORF2 from 15 Ty3-gypsy families that was located between the ORF encoding the retrotransposase and the 3 LTR. The ORF2 was in opposite transcriptional orientation to retrotransposase. Sequence homology searches and phylogenetic analysis suggested that the ORF2 may have an ancient origin, but its function is not clear. These transposon data provide a useful resource for understanding the genome organization and evolution and may be used to identify active TEs for developing transposon-tagging system in common bean and other related genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mining Thermophile Photosynthesis Genes: A Synthetic Operon Expressing Chloroflexota Species Reaction Center Genes in Rhodobacter sphaeroides

Photosynthesis is the foundation of the vast majority of life systems, and is therefore the most important bioenergetic process on earth. The greatest diversity of photosynthetic systems is found in microorganisms. However, our understanding of the biophysical and biochemical processes that transduce light into chemical energy is derived from a relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of predicted proteins that catalyze the initial photochemical reactions deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for the expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42–90 °C) springs available through the JGI IMG database, to generate a resource of diverse sequences that are potentially adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

Chloroflexota↗
DOE OSTI2017

Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

1. To develop a comprehensive model characterization framework to describe epidemiological models in an operational context. 2. To apply the framework to characterize “operational” models for specific infectious diseases and provide a web-based directory, the biosurveillance analytics resource directory (BARD) to the global infectious disease surveillance community. Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory(LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. We offer this framework and an associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models. Such a framework could help the understanding of diverse models by various stakeholders with different preconceptions, backgrounds, expertise ,and needs, and can foster greater use of epidemiological models as tools in infectious disease surveillance. We define, “operational” as the application of an epidemiological model to a real-world event for decision support and can be used by experts and non-experts alike. The term “model” covers three major types, risk mapping, disease dynamics and anomaly detection. To develop a framework for characterizing epidemiological models we collected information via a three-step process: a literature search of model characteristics, a review of current operational infectious disease epidemiological models, and subject matter expert (SME)panel consultation. We limited selection of operational models to five infectious diseases: influenza, malaria, dengue, cholera and foot-and-mouth disease (FMD). These diseases capture a variety of transmission modes, represent high or potentially high epidemic or endemic burden, and are well represented in the literature. We also developed working criteria for what attributes can be used to comprehensively describe an operational model including a model’s documentation, accessibility, and sustainability. To apply the model characterization framework, we built the BARD, which is publicly available (http://brd.bsvgateway.org).A document was also developed to describe the usability requirements for the BARD; potential users (and non-users) and use cases are formally described to explain the scope of use. 1. Framework for model characterization. The framework is divided into six major components (Figure 1):Model Purpose, Model Objective, Model Scope, Biosurveillance(BSV) goals, Conceptual Model and Model Utility; each of which has several sub-categories for characterizing each aspect of a model.2. Application to model characterization Models for five infectious diseases—cholera, malaria, influenza, FMD and dengue were characterized using the framework and are included in the BARD database. Our framework characterized disparate models in a streamlined fashion. Model information could be binned into the same categories, allowing easy manual comparison and understanding of the models.3. Development of the BARD. Our model characterization framework was implemented into an actionable tool which provides specific information about a model that has been systematically categorized. It allows manual category-to-category comparison of multiple models for a single disease and while the tool does not rank models it provides model information in a format that allows a user to make a ranking or an assessment of the utility of the model. With the model characterization framework we hope to encourage model developers to start describing the many features of their models using a common format. We illustrate the application of the framework through the development of the BARD which is a scientific and non-biased tool for selecting an appropriate epidemiological model for infectious disease surveillance. Epidemiological models are not necessarily being developed with decision makers in mind. This gap between model developers and decision makers needs to be narrowed before modeling becomes routinely implemented in decision making. The characterization framework and the tool developed (BARD) area first step towards addressing this gap.

97 MATHEMATICS AND COMPUTING↗