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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Metal-free class Ie ribonucleotide reductase from pathogens initiates catalysis with a tyrosine-derived dihydroxyphenylalanine radical

All cells obtain 2'-deoxyribonucleotides for DNA synthesis through the activity of a ribonucleotide reductase (RNR). The class I RNRs found in humans and pathogenic bacteria differ in ( i ) use of Fe(II), Mn(II), or both for activation of the dinuclear-metallocofactor subunit, β; ( ii ) reaction of the reduced dimetal center with dioxygen or superoxide for this activation; ( iii ) requirement (or lack thereof) for a flavoprotein activase, NrdI, to provide the superoxide from O 2 ; and ( iv ) use of either a stable tyrosyl radical or a high-valent dimetal cluster to initiate each turnover by oxidizing a cysteine residue in the α subunit to a radical (Cys•). The use of manganese by bacterial class I, subclass b-d RNRs, which contrasts with the exclusive use of iron by the eukaryotic Ia enzymes, appears to be a countermeasure of certain pathogens against iron deprivation imposed by their hosts. Here, we report a metal-free type of class I RNR (subclass e) from two human pathogens. The Cys• in its α subunit is generated by a stable, tyrosine-derived dihydroxyphenylalanine radical (DOPA•) in β. The three-electron oxidation producing DOPA• occurs in Escherichia coli only if the β is coexpressed with the NrdI activase encoded adjacently in the pathogen genome. We conclude the independence of this new RNR from transition metals, or the requirement for a single metal ion only transiently for activation, may afford the pathogens an even more potent countermeasure against transition metal-directed innate immunity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Structure of lipoprotein lipase in complex with GPIHBP1

Lipoprotein lipase (LPL) plays a central role in triglyceride (TG) metabolism. By catalyzing the hydrolysis of TGs present in TG-rich lipoproteins (TRLs), LPL facilitates TG utilization and regulates circulating TG and TRL concentrations. Until very recently, structural information for LPL was limited to homology models, presumably due to the propensity of LPL to unfold and aggregate. By coexpressing LPL with a soluble variant of its accessory protein glycosylphosphatidylinositol-anchored high-density lipoprotein binding protein 1 (GPIHBP1) and with its chaperone protein lipase maturation factor 1 (LMF1), we obtained a stable and homogenous LPL/GPIHBP1 complex that was suitable for structure determination. We report here X-ray crystal structures of human LPL in complex with human GPIHBP1 at 2.5–3.0 Å resolution, including a structure with a novel inhibitor bound to LPL. Binding of the inhibitor resulted in ordering of the LPL lid and lipid-binding regions and thus enabled determination of the first crystal structure of LPL that includes these important regions of the protein. It was assumed for many years that LPL was only active as a homodimer. The structures and additional biochemical data reported here are consistent with a new report that LPL, in complex with GPIHBP1, can be active as a monomeric 1:1 complex. The crystal structures illuminate the structural basis for LPL-mediated TRL lipolysis as well as LPL stabilization and transport by GPIHBP1.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A distinct RNA recognition mechanism governs Np 4 decapping by RppH

Dinucleoside tetraphosphates, often described as alarmones because their cellular concentration increases in response to stress, have recently been shown to function in bacteria as precursors to nucleoside tetraphosphate (Np 4 ) RNA caps. Removal of this cap is critical for initiating 5' end-dependent degradation of those RNAs, potentially affecting bacterial adaptability to stress; however, the predominant Np 4 decapping enzyme in proteobacteria, ApaH, is inactivated by the very conditions of disulfide stress that enable Np 4 -capped RNAs to accumulate to high levels. Here, in this study, we show that, in Escherichia coli cells experiencing such stress, the RNA pyrophosphohydrolase RppH assumes a leading role in decapping those transcripts, preferring them as substrates over their triphosphorylated and diphosphorylated counterparts. Unexpectedly, this enzyme recognizes Np 4 -capped 5' ends by a mechanism distinct from the one it uses to recognize other 5' termini, resulting in a one-nucleotide shift in substrate specificity. The unique manner in which capped substrates of this kind bind to the active site of RppH positions the δ-phosphate, rather than the β-phosphate, for hydrolytic attack, generating triphosphorylated RNA as the primary product of decapping. Consequently, a second RppH-catalyzed deprotection step is required to produce the monophosphorylated 5' terminus needed to stimulate rapid RNA decay. The unconventional manner in which RppH recognizes Np 4 -capped 5' ends and its differential impact on the rates at which such termini are deprotected as a prelude to RNA degradation could have major consequences for reprogramming gene expression during disulfide stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Protein folds vs. protein folding: Differing questions, different challenges

We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Learning dynamical systems from data: An introduction to physics-guided deep learning

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electron transfer in polysaccharide monooxygenase catalysis

Polysaccharide monooxygenase (PMO) catalysis involves the chemically difficult hydroxylation of unactivated C–H bonds in carbohydrates. The reaction requires reducing equivalents and will utilize either oxygen or hydrogen peroxide as a cosubstrate. Two key mechanistic questions are addressed here: 1) How does the enzyme regulate the timely and tightly controlled electron delivery to the mononuclear copper active site, especially when bound substrate occludes the active site? and 2) How does this electron delivery differ when utilizing oxygen or hydrogen peroxide as a cosubstrate? Using a computational approach, potential paths of electron transfer (ET) to the active site copper ion were identified in a representative AA9 family PMO from Myceliophthora thermophila ( Mt PMO9E). When Y62, a buried residue 12 Å from the active site, is mutated to F, lower activity is observed with O 2 . However, a WT-level activity is observed with H 2 O 2 as a cosubstrate indicating an important role in ET for O 2 activation. To better understand the structural effects of mutations to Y62 and axial copper ligand Y168, crystal structures were solved of the wild type Mt PMO9E and the variants Y62W, Y62F, and Y168F. A bioinformatic analysis revealed that position 62 is conserved as either Y or W in the AA9 family. The Mt PMO9E Y62W variant has restored activity with O 2 . Overall, the use of redox-active residues to supply electrons for the reaction with O 2 appears to be widespread in the AA9 family. Furthermore, the results provide a molecular framework to understand catalysis with O 2 versus H 2 O 2 .

Sayler, Richard I. (ORCID:000000017252707X)↗
DOE OSTI2025

Combining MicroED and native mass spectrometry for structural discovery of enzyme–small molecule complexes

With the goal of accelerating the discovery of small molecule–protein complexes, we leverage fast, low-dose, event-based electron counting microcrystal electron diffraction (MicroED) data collection and native mass spectrometry. This approach, which we term electron diffraction with native mass spectrometry (ED-MS), allows assignment of protein target structures bound to ligands with data obtained from crystal slurries soaked with mixtures of known inhibitors and crude biosynthetic reactions. This extends to libraries of printed ligands dispensed directly onto TEM grids for later soaking with microcrystal slurries, and complexes with noncovalent ligands. ED-MS resolves structures of the natural product, epoxide-based cysteine protease inhibitor E-64, and its biosynthetic analogs bound to the model cysteine protease, papain. It further identifies papain binding to its preferred natural products, by showing that two analogs of E-64 outcompete others in binding to papain crystals, and by detecting papain bound to E-64 and an analog from crude biosynthetic reactions, without purification. ED-MS also resolves binding of the CTX-M-14 β-lactamase, a target of active drug development, to the non-β-lactam inhibitor, avibactam, alone or in a cocktail of unrelated compounds. These results illustrate the utility of ED-MS for natural product ligand discovery and for structure-based screening of small molecule binders to macromolecular targets, promising utility for drug discovery.

MicroED↗
DOE OSTI2020

Airborne Release Fractions from Surrogate Nuclear Waste Fires Containing Lanthanide Nitrates and Depleted Uranium Nitrate in 30% Tributyl Phosphate in Kerosene

Airborne contaminants from fires containing nuclear waste represent significant health hazards and shape the design and operation of nuclear facilities. Much of the data used to formulate DOE-HDBK-3010-94, “Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities,” from the U.S. Department of Energy, were taken over 40 years ago. The objectives of this study were to reproduce experiments from Pacific Northwest Laboratories conducted in June 1973 employing current aerosol measurement methods and instrumentation, develop an enhanced understanding of particulate formation and transport from fires containing nuclear waste, and provide modeling and experimental capabilities for updating current standards and practices in nuclear facilities. A special chamber was designed to conduct small fires containing 25 mL of flammable waste containing lutetium nitrate, ytterbium nitrate, or depleted uranium nitrate. Carbon soot aerosols showed aggregates of primary particles ranging from 20 to 60 nm in diameter. In scanning electron microscopy, ~200-nm spheroidal particles were also observed dispersed among the fractal aggregates. The 200-nm spherical particles were composed of metal phosphates. Airborne release fractions (ARFs) were characterized by leaching filter deposits and quantifying metal concentrations with mass spectrometry. The average mass-based ARF for 238 U experiments was 1.0 × 10 -3 with a standard deviation of 7.5 × 10 -4 . For the original experiments, DOE-HDBK-3010-94 states, “Uranium ARFs range from 2 × 10 -4 to 3 × 10 -3 , an uncertainty of approximately an order of magnitude.” Therefore, current measurements were consistent with DOE-HDBK-3010-94 values. ARF values for lutetium and ytterbium were approximately one to two orders of magnitude lower than 238 U. Furthermore, metal nitrate solubility may have varied with elemental composition and temperature, thereby affecting ARF values for uranium surrogates (Yb and Lu). In addition to ARF data, solution boiling temperatures and evaporation rates can also be deduced from experimental data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Key Results from Detailed Nondestructive Examinations of 25 Pressurized Water Reactor High Burnup Spent Nuclear Fuel Rods

The High Burnup Spent Fuel Data Project, sponsored by the U.S. Department of Energy Office of Nuclear Energy, is focused on understanding the effects of long-term storage and transportation on high burnup (HBU) (>45 GW days per tonne uranium) light water reactor fuel. The project includes 32 HBU spent nuclear fuel (SNF) assemblies (the project assemblies) that are stored in a typical independent spent fuel storage installation (ISFSI) and 25 “sister rods”—9 SNF rods that were removed from the fuel assemblies prior to insertion to the ISFSI and 16 SNF rods removed from similar HBU assemblies. The sister rods provide a baseline of the condition of the HBU rods before loading, drying, and long-term dry storage. The project assemblies will be inspected after 10 years, and the physical state of the stored rods will be compared with the condition of the sister rods to identify any changes in physical properties during the dry storage period. Our report focuses on key results from the nondestructive postirradiation examinations of the sister rods and summarizes the results of detailed visual examinations, gamma scans, dimensional measurements, and eddy current liftoff measurements of the combined Chalk River unidentified deposits and oxide layer on the waterside surface of the rod. Additionally the data are used to calculate fuel rod and pellet stack growth rates, estimated remaining fuel rod plenum volumes, and the percentage change in fuel rod cladding diameter.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Simulated Performance of the Micro-Pocket Fission Detector in the Advanced Test Reactor Critical Facility

The Micro-Pocket Fission Detector (MPFD) is a small-form–factor real-time fission chamber. MPFD performance has been simulated in the Advanced Test Reactor Critical Facility (ATRC), located at Idaho National Laboratory (INL). Here, the neutron and gamma-ray flux profiles and magnitudes were simulated using Monte Carlo N-Particle (MCNP) in the near-core B-8 irradiation position. These simulations were performed at 69 discrete axial locations inside the B-8 position for three separate orientations of the nearby hafnium outer shim control cylinders and at a power level of 700 Wth. The resulting neutron and gamma-ray flux values were used to determine the MPFD response for various fissile masses and detector gas pressures. The optimal gas-operating pressure was determined to be between 30 and 60 psig. The required fissile-layer mass was determined to be between 1–2 µg of 235 U. Additionally, the gamma-ray to fission-fragment interaction rate was determined to be 4.42 × 10 -3 with average energy deposition for gamma rays and fission fragments in 30 psig argon gas to be 1 keV and 9.5 MeV, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Microfluidics-based separation of actinium-225 from radium-225 for medical applications

Separation of 225 Ra (t 1/2 = 15 d) from its daughter isotope 225 Ac (t 1/2 = 10 d) is necessary to obtain pure 225 Ac for cancer alpha-therapy. In this study, microscale separation of 225 Ra from its daughter 225 Ac using BioRad AG50X4 cation exchange resin was achieved with good reproducibility across microdevices, and ≥90% purity was achieved for 225 Ac, which is comparable to conventional chromatography. These results indicate the potential for greater use of microfluidics for biomedical radiochemistry. The modularity of the system and its compatibility with different resins allows for quick and easy adaptation to the various needs of a separation campaign.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Synchrotron Radiation and Neutrons in Art and Archaeology (SR2A) Conference 2016

Here, the seventh edition of the international conference on Synchrotron Radiation and Neutrons in Art and Archaeology (SR2A 2016) was held September 6–8, 2016, at the Stock Exchange Room of The Art Institute of Chicago, USA. The conference was jointly organized by seven research laboratories and museums; more precisely, the Center for Scientific Studies in the Arts (NU-ACCESS) of Northwestern University, the Art Institute of Chicago, the Field Museum Chicago, the Advanced Photon Source (APS), the Oriental Institute Chicago, the Detroit Institute of Arts, and the Indianapolis Museum of Art, in close interaction with the SR2A International Committee. Nine years after the organization of the first SR2A conference in Grenoble, the Art Institute hosted the second biennial interdisciplinary meeting in the US.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Regional Economic Impacts of the Los Angeles 100% Renewable Energy Transition

To help mitigate greenhouse gas (GHGs) generation from burning fossil fuels, many state and local governments are requiring utilities to dramatically increase the share of electricity generated from renewable sources. The City of Los Angeles has set a target of 100% renewable energy by 2045 and has formulated a plan that considers nine potential alternative scenarios that differ by technology, location, and timing. Each scenario has a unique set of local investments, operating and maintenance (O&M) costs, and concomitant rate structures. In this study we develop and apply a computable general equilibrium (CGE) model built specifically for LA to estimate and compare the economic impacts for each of the scenarios over time relative to a reference case. We find differences in economic impacts across scenarios, depending on the level and timing of investment and O&M expenditures, as well as differences in the relative rate changes across scenarios. Results show that employment and economic output are positively correlated with greater capital and O&M spending, while higher electricity rates can dampen economic activity. Several scenarios generate positive economic impacts relative to the reference case, showing that the transition need not have harmful economic impacts, and all scenarios generate a number of other positive co-benefits, such as reduced damage to health from the reduction of ordinary air pollutants. The net employment impacts from 2026 to 2045 across the scenarios range from a low of 3,600 job-year losses annually to 4,700 job-year gains, both around only 0.1% of the baseline average annual employment in the city over that period. The analysis also indicates that lower-income households are relatively more affected than others by the scenarios. Overall, even in the most negatively impactful case, the economic output and employment effects are quite small when taken in the context of the overall size of the regional economy and the large reduction in GHGs.

economic impact modeling↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗