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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Methods and apparatus to detect and annotate backedges in a dataflow graph

Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.

ChoFleming, Jr., Kermin E.↗
DOE OSTI2023

Methods and apparatus to detect and annotate backedges in a dataflow graph

Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

In–context promoter bashing of the Sorghum bicolor gene models functionally annotated as bundle sheath cell preferred expressing phosphoenolpyruvate carboxykinase and alanine aminotransferase

In-context promoter bashing via genome editing is a route to identify and characterize critical regulatory regions that govern expression of genes of interest. The outcomes of in-context promoter bashing can be used to inform editing strategies to modulate the expression of selected gene models in a desired fashion. Here, we employed in-context promoter bashing to characterize the proximal upstream regulatory regions of sorghum genes encoding phosphoenolpyruvate carboxykinase bundle sheath (SbPEPCK.BS, SbiTx430.01G455400) and alanine aminotransferase bundle sheath (SbAlaAT.BS, SbiTx430.02G006600), two proteins involved in the PCK C 4 pathway. Characterized germinal edits within the targeted regions upstream of these two genes ranged in size from 138 up to 1790 bp. A 138 bp within the SbPEPCK.BS upstream region and a 1643 bp element within the SbAlaAT.BS upstream region were determined to be important for maintenance of transcription levels. No change in development or various physiological parameters was observed in characterized lineages carrying promoter edits. However, significant changes in seed reserves and a reduction in 100-seed weight were consistently observed, under both greenhouse and field environments, in plants carrying an edit in the promoter of SbPEPCK.BS gene, which were significantly reduced in transcript accumulation for this gene.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗
DOE OSTI2023

Annotated genome sequence of a fast-growing diploid clone of red alder ( Alnus rubra Bong.)

Abstract Red alder (Alnus rubra Bong.) is an ecologically significant and important fast-growing commercial tree species native to western coastal and riparian regions of North America, having highly desirable wood, pigment, and medicinal properties. We have sequenced the genome of a rapidly growing clone. The assembly is nearly complete, containing the full complement of expected genes. This supports our objectives of identifying and studying genes and pathways involved in nitrogen-fixing symbiosis and those related to secondary metabolites that underlie red alder's many interesting defense, pigmentation, and wood quality traits. We established that this clone is most likely diploid and identified a set of SNPs that will have utility in future breeding and selection endeavors, as well as in ongoing population studies. We have added a well-characterized genome to others from the order Fagales. In particular, it improves significantly upon the only other published alder genome sequence, that of Alnus glutinosa. Our work initiated a detailed comparative analysis of members of the order Fagales and established some similarities with previous reports in this clade, suggesting a biased retention of certain gene functions in the vestiges of an ancient genome duplication when compared with more recent tandem duplications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Knowledge Network-Based Approach to Facilitate Annotation of Clinical Pathway Component Clusters

Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs’s major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.

Hasan, S M Shamimul↗
DOE OSTI2022

RNA virus annotation pipeline (RVAP) v1.0

The code consists of two scripts that assign functional domains to RNA virus proteins. They achieve this by aggregating the outputs of multiple freely available tools and clustering the domains into high hierarchy clusters.

Camargo, Antonio↗
DOE OSTI2020

Annotated Translated Disassembled Code

Procedure for generating function/library embedding based on disassembled binary data from a graph database and processing those embedding through supervised machine learning techniques.

Beckman, BryanR.↗
DOE OSTI2022

Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).

image↗
DOE OSTI2021

A Publicly Available, Annotated Dataset for Naturalistic Driving Study and Computer Vision Algorithm Development

Oak Ridge National Laboratory developed and implemented a data collection effort to create a dataset for use in evaluating and testing algorithms for analyzing driver behavior under controlled settings for support of the Federal Highway Administration’s Exploratory Advanced Research Program. This collection is called the ORNL Naturalistic Driving Study Sample (ONDSS). The dataset is designed to emulate aspects of the Second Strategic Highway Research Project (SHRP2), which contained a massive naturalistic driving study (NDS) with over 3000 drivers between 2010 and 2013 using their personal vehicles, with over 4300 person-years of data collected [HANKEY].

42 ENGINEERING↗
DOE OSTI2021

U.S. Nuclear Declaratory Policy 2021: the Renewed Debate about Sole Purpose and No-First-Use. Annotated Bibliography

Declaratory policy and public statements about the potential use of nuclear weapons serve many important roles. They provide an assessment of the security environment, and inform the public debate. These statements also enhance deterrence messages and signals towards adversaries, and reassure allies and partners. On the global level, U.S. declaratory policy has the potential to shape international trends and norms, influence nuclear proliferation, and it may also affect the policy decisions of other nuclear possessors. As the Biden administration reviews the elements of U.S. nuclear declaratory policy, the issue of sole purpose and no-first-use is likely to resurface. Previous administrations have examined these policies in multiple rounds of review, and they decided that the time was not right for such declarations. This literature review was prepared to inform the debate by collecting some of the most prominent articles on the topic that highlight the potential risks and benefits of these policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

China and Multi-Domain Strategic Stability (Annotated Bibliography)

Brief summaries of literature relating to four areas: China's Approach to Multi-Domain Complexity, China's Approach to Multi-Domain Strategic Stability, A Cooperative Management Approach, and Risk Mitigation in the Absence of Cooperative Approaches.

97 MATHEMATICS AND COMPUTING↗