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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Antiviral Effect of Antimicrobial Peptoid TM9 and Murine Model of Respiratory Coronavirus Infection

New antiviral agents are essential to improving treatment and control of SARS-CoV-2 infections that can lead to the disease COVID-19. Antimicrobial peptoids are sequence-specific oligo-N-substituted glycine peptidomimetics that emulate the structure and function of natural antimicrobial peptides but are resistant to proteases. We demonstrate antiviral activity of a new peptoid (TM9) against the coronavirus, murine hepatitis virus (MHV), as a closely related model for the structure and antiviral susceptibility profile of SARS-CoV-2. This peptoid mimics the human cathelicidin LL-37, which has also been shown to have antimicrobial and antiviral activity. In this study, TM9 was effective against three murine coronavirus strains, demonstrating that the therapeutic window is large enough to allow the use of TM9 for treatment. All three isolates of MHV generated infection in mice after 15 min of exposure by aerosol using the Madison aerosol chamber, and all three viral strains could be isolated from the lungs throughout the 5-day observation period post-infection, with the peak titers on day 2. MHV-A59 and MHV-A59-GFP were also isolated from the liver, heart, spleen, olfactory bulbs, and brain. These data demonstrate that MHV serves as a valuable natural murine model of coronavirus pathogenesis in multiple organs, including the brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Interchangeability of the Assays Used to Assess the Activity of Anti-SARS-CoV-2 Monoclonal Antibodies

The recent global COVID-19 pandemic caused by SARS-CoV-2 lasted for over three years. A key measure in combatting this pandemic involved the measurement of the monoclonal antibody (mAb)-mediated inhibition of binding between the spike receptor-binding domain (RBD) and hACE2 receptor. Potency assessments of therapeutic anti-SARS-CoV-2 mAbs typically include binding or cell-based neutralization assays. We assessed the inhibitory activity of five anti-SARS-CoV-2 mAbs using ELISA, surface plasmon resonance (SPR), and four cell-based neutralization assays using different pseudovirus particles and 293T or A549 cells expressing hACE2 with or without TMPRSS2. We assessed the interchangeability between cell-based and binding assays by applying the Bland–Altman method under certain assumptions. Our data demonstrated that the IC50 [nM] values determined by eight neutralization assays are independent of the cell line, presence of TMPRSS2 enzyme on the cell surface, and pseudovirus backbone used. Moreover, the Bland–Altman analysis showed that the IC50 [nM] and KD [nM] values determined by neutralization/ELISA or by SPR are equivalent and that the anti-spike mAb activity can be attributed to one variable directly related to its tertiary conformational structure conformation, rate dissociation constant Koff. This parameter is independent from the concentrations of the components of the mAb:RBD:hACE2 complexes and can be used for a comparison between the activities of the different mAbs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Recommendations for Uniform Variant Calling of SARS-CoV-2 Genome Sequence across Bioinformatic Workflows

Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

TDCOSMO - XVII. New time delays in 22 lensed quasars from optical monitoring with the ESO-VST 2.6m and MPG 2.2m telescopes

We present new time delays, the main ingredient of time delay cosmography, for 22 lensed quasars resulting from high-cadence r-band monitoring on the 2.6 m ESO VLT Survey Telescope and Max-Planck-Gesellschaft 2.2 m telescope. Each lensed quasar was typically monitored for one to four seasons, often shared between the two telescopes to mitigate the interruptions forced by the COVID-19 pandemic. The sample of targets consists of 19 quadruply and 3 doubly imaged quasars, which received a total of 1918 hours of on-sky time split into 21 581 wide-field frames, each 320 seconds long. In a given field, the 5-σ depth of the combined exposures typically reaches the 27th magnitude, while that of single visits is 24.5 mag – similar to the expected depth of the upcoming Vera-Rubin LSST. The fluxes of the different lensed images of the targets were reliably de-blended, providing not only light curves with photometric precision down to the photon noise limit, but also high-resolution models of the targets whose features and astrometry were systematically confirmed in Hubble Space Telescope imaging. This was made possible thanks to a new photometric pipeline, lightcurver, and the forward modelling method STARRED. Finally, the time delays between pairs of curves and their uncertainties were estimated, taking into account the degeneracy due to microlensing, and for the first time the full covariance matrices of the delay pairs are provided. Of note, this survey, with 13 square degrees, has applications beyond that of time delays, such as the study of the structure function of the multiple high-redshift quasars present in the footprint at a new high in terms of both depth and frequency. The reduced images will be available through the European Southern Observatory Science Portal.Key words: methods: data analysis / surveys / distance scale

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Hands-On Curriculum for Training in HPC Cluster Deployment and Management

This paper presents the design, methodology, and outcomes of the High-Performance Computing Technologies (HPCT) course, a hands-on training program focused on the system-side of HPC cluster deployment and administration. Delivered as part of the Master in High Performance Computing (MHPC) program, the course introduces students to key concepts in cluster configuration, including networking, software stack provisioning, job scheduling, and monitoring. Initially taught in person, the course was transitioned to an online format during the COVID-19 pandemic. This shift led to the development of openly available instructional material and a flipped-classroom approach that continues to support both in-person and hybrid delivery. All course materials are publicly available at www.hpc.temple.edu/mhpc/hpc-technology/index.html. By documenting the structure, infrastructure, and evolution of HPCT, this paper offers a model for accessible HPC system training that supports workforce development in computational science.

Posada Correa, Fernando [ORNL] (ORCID:000000022565↗
DOE OSTI2024

Enhancing Nuclear and Radiological Security in Polarized Times: Safeguarding Against Extremist Insider Threats

Domestic violent extremism has been on the rise in recent years, fueled by growing political polarization, the COVID-19 pandemic, and the spread of misinformation online. This concerning trend raises an important question: has this increase in extremist violence manifested in more incidents involving radioactive or nuclear (RN) materials and a corresponding security concern? To explore this question, we analyzed existing data on events involving chemical, biological, radiological, or nuclear materials over time. Specifically, we examined the rate of RN-related incidents, the targets of these events, and the ideologies motivating the perpetrators by using the Violent Non-State Actor Chemical, Biological, Radiological, and Nuclear Event Database from the National Consortium for the Study of Terrorism and Responses to Terrorism. Our analysis aimed to determine whether the broader rise of violent extremism has translated to an elevated risk of attacks involving RN materials. Results of the analysis revealed that there was no clear increase in events involving RN material in the dataset. However, given the rarity of these events, this conclusion does not indicate that such a trend might not appear in coming years if violent extremism continues to rise. To address the potential risk presented by violent extremism, in addition to quantifying the frequency of RN events, we also reviewed traits that could make organizations more susceptible to insider threats from radicalized employees. Focusing on the literature around counterproductive workplace behavior, we identified organizational characteristics that might increase the risk of malicious acts committed by radicalized insiders. These risk factors include abusive leadership, situational constraints, and organizational injustice. Finally, using these organizational risk characteristics, we discuss interventions that organizations with RN material can take to reduce their risk of attacks stemming from violent extremism while still protecting individual rights to privacy and civil liberties. Overall, this article provides an overview of the intersections between rising domestic extremism, insider threats, and risks posed by RN materials. We present current data on trends and patterns in this space as well as practical guidance for organizational resilience.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM: Preprint

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Evaluating Electric Vehicle Public Charging Utilization in the United States Using the EV WATTS Dataset: Preprint

The utilization of electric vehicle (EV) charging in the United States is a rapidly evolving puzzle further complicated by changes in travel habits brought on by the global COVID-19 pandemic. The EV WATTS dataset is the largest publicly available dataset on the utilization of EV charging in the United States and provides key insights into the changing patterns of public vehicle charging and vehicle use as EV range increases, commute patterns evolve, and more infrastructure becomes available.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Empowering Women in Biomedical Informatics: Pivoting for Success in the Post-Pandemic World

The COVID-19 pandemic and the isolation measures implemented to contain its spread have exacerbated pre-existing barriers to advancement for women in biomedical informatics. In 2020, the Women in AMIA (WIA) Networking, Mentoring, and Lifecycle Committee administered a survey to identify resources and needs to assist women in informatics. Part of this survey identified several factors impacted by the pandemic: 1) work-life boundaries disappeared and caring for family while working from home became increasingly challenging; 2) isolation, stress and safety of well-being was heightened during the pandemic impacting physical and mental health; 3) diverse working environment and modality brought unforeseen challenges in collaborations and unified communications. These challenges present an opportunity for promoting career development for women professionals in biomedical informatics. This WIA-sponsored panel brings together leaders in the field to highlight their personal experiences and strategies to support women professionals.

Dua, Prerna↗
DOE OSTI2025

Identification and Exploration of a Series of SARS-Cov-2 M Pro Cyano-Based Inhibitors Revealing Ortho-Substitution Effects within the P3 Biphenyl Group

Starting from a simple scaffold hopping exercise based on our previous exploration of cysteine protease inhibitors against legumain, compound 6a was identified as a starting point for the development of a SARS-CoV-2 main protease (M Pro ) inhibitor. Compound 6a displayed submicromolar biochemical potency in the ultrasensitive assay developed by Drag and coworkers. Through an iterative structure−activity relationship campaign, we discovered an unexpected improvement in both biochemical and cellular potency through the incorporation of an ortho substituent within the P3 benzamide. X-ray crystallography revealed that incorporation of the ortho substituent caused a subtle but important binding enhancement of the P1 glutamate group within the M Pro S1 pocket. While incorporation of the ortho substituent improved the potency, the off-target selectivity against a panel of cysteine proteases and cell activity remained suboptimal. Further scanning of the P2 core revealed that incorporation of the 3.1.0 proline could address these issues and afford compound 22e, a highly potent and cellularly active M Pro inhibitor.

COVID-19 infection↗
DOE OSTI2023

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

We develop a tractable nonparametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. Employing the inferred reproductive rates, we estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences

We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. In conclusion, solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Review of Rattlesnake Venoms

Venom components are invaluable in biomedical research owing to their specificity and potency. Many of these components exist in two genera of rattlesnakes, Crotalus and Sistrurus, with high toxicity and proteolytic activity variation. This review focuses on venom components within rattlesnakes, and offers a comparison and itemized list of factors dictating venom composition, as well as presenting their known characteristics, activities, and significant applications in biosciences. There are 64 families and subfamilies of proteins present in Crotalus and Sistrurus venom. Snake venom serine proteases (SVSP), snake venom metalloproteases (SVMP), and phospholipases A2 (PLA2) are the standard components in Crotalus and Sistrurus venom. Through this review, we highlight gaps in the knowledge of rattlesnake venom; there needs to be more information on the venom composition of three Crotalus species and one Sistrurus subspecies. We discuss the activity and importance of both major and minor components in biomedical research and drug development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Chimeric Forecasting

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Bayesian Melding, COVID-19↗