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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Structural basis of influenza virus fusion inhibition by the antiviral drug Arbidol

The broad-spectrum antiviral drug Arbidol shows efficacy against influenza viruses by targeting the hemagglutinin (HA) fusion machinery. However, the structural basis of the mechanism underlying fusion inhibition by Arbidol has remained obscure, thereby hindering its further development as a specific and optimized influenza therapeutic. We determined crystal structures of Arbidol in complex with influenza virus HA from pandemic 1968 H3N2 and recent 2013 H7N9 viruses. Arbidol binds in a hydrophobic cavity in the HA trimer stem at the interface between two protomers. This cavity is distal to the conserved epitope targeted by broadly neutralizing stem antibodies and is ~16 Å from the fusion peptide. Arbidol primarily makes hydrophobic interactions with the binding site but also induces some conformational rearrangements to form a network of inter- and intraprotomer salt bridges. By functioning as molecular glue, Arbidol stabilizes the prefusion conformation of HA that inhibits the large conformational rearrangements associated with membrane fusion in the low pH of the endosome. Here, this unique binding mode compared with the small-molecule inhibitors of other class I fusion proteins enhances our understanding of how small molecules can function as fusion inhibitors and guides the development of broad-spectrum therapeutics against influenza virus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗
DOE OSTI2018

Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling

A wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Reversible self-assembly of superstructured networks

Soft structures in nature, such as protein assemblies, can organize reversibly into functional and often hierarchical architectures through noncovalent interactions. Molecularly encoding this dynamic capability in synthetic materials has remained an elusive goal. We report on hydrogels of peptide-DNA conjugates and peptides that organize into superstructures of intertwined filaments that disassemble upon the addition of molecules or changes in charge density. Experiments and simulations demonstrate that this response requires large-scale spatial redistribution of molecules directed by strong noncovalent interactions among them. Simulations also suggest that the chemically reversible structures can only occur within a limited range of supramolecular cohesive energies. Storage moduli of the hydrogels change reversibly as superstructures form and disappear, as does the phenotype of neural cells in contact with these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Structural Basis for Cooperative Binding of EBNA1 to the Epstein-Barr Virus Dyad Symmetry Minimal Origin of Replication

Epstein-Barr virus is associated with several human malignancies, including nasopharyngeal carcinoma, gastric cancer, and lymphoma. Latently infected cells carry a circularized EBV episome where the origin of replication (oriP) is comprised of two elements: the family of repeats (FR) and dyad symmetry (DS). The viral protein Epstein-Barr virus (EBV) nuclear antigen 1 (EBNA1) binds to FR and DS to promote EBV episome maintenance and DNA replication during latent infection in proliferating cells. EBNA1 binding to the DS constitutes a minimal origin of DNA replication. Here we report the crystal structure of two EBNA1 DNA-binding domain dimers bound to a DS half-site. This structure shows that the DNA is smoothly bent, allowing for stabilizing interactions between the dimers. The dimer-dimer interface requires an intricate hydrogen bonding network involving residues R491 and D581. When this interface is disrupted, we note loss of stable dimer-dimer complex formation on the DNA, compromised oriP-containing plasmid replication in cells, and impaired recruitment of the MCM3 complex to the oriP. Surface conservation analysis reveals that these residues are part of a larger conserved surface that may be critical for recruitment of replication machinery to the oriP. Our results reveal a new region of EBNA1 critical for its activity and one that may be exploited by targeted small molecules to treat EBV-associated disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Systems Level Engineering of Plant Cell Wall Biosynthesis to Improve Biofuel Feedstock Quality

Our new regulatory model of cell wall biosynthesis proposes original network architecture with several newly incorporated components. The mapped set of protein-DNA interactions will serve as a foundation for 1) understanding the regulation of a complex and integral plant component and 2) the manipulation of crop species for biofuel and biotechnology purposes. This study revealed interesting and novel aspects of grass growth and development and further enforce the importance of a grass model system. By functionally characterizing a suite of genes, we have begun to improve the sparse model for transcription regulation of biomass accumulation in grasses. In the process, we have advanced methodology and brachy molecular genetic tools that will serve as valuable community resource.

09 BIOMASS FUELS↗
DOE OSTI2023

Bioimaging Science Program Principal Investigator Meetings Proceedings (2021)

As part of the 2021 Biological Systems Science Division (BSSD) Principal Investigator (PI) Meeting, the Bioimaging Science program (BSP), within BSSD's Biomolecular Characterization and Imaging Science portfolio, held its annual PI meeting virtually February 22–23. BSP's mission is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. BSP convenes annual PI meetings to bring together its contributing investigators to review progress and current state-of-the-art bioimaging research. Holding the BSP meeting as part of the broader BSSD PI meeting allowed researchers to interact with the extended Genomic Science program community. This convergence provided a platform for networking and exchange of ideas, helping to forge new multidisciplinary collaborations among investigators from the two sister programs. An important highlight of the BSP meeting was the keynote presentation “Enhancing Fluorescence Microscopy with Computation” by Dr. Hari Shroff of the NIH National Institute of Biomedical Imaging and Bioengineering. All the BSP PIs made presentations describing their research focus and progress, and these were followed by roundtable discussions of each project. The meeting’s proceedings provide an outline of the program’s current state and potential future directions and opportunities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Ca II H(2v) and K(2v) cell grains

The bright Ca II H(2v) and K(2v) grains, which are intermittently present in the interiors of network cells in quiet-sun areas, should provide important diagnostics of the dynamical interaction between the quiet photosphere and the chromosphere above it, but their nature has so far eluded identification. The extensive observational literature on these grains and on related phenomena is here reviewed, and various contradictions are resolved. It is concluded that the grains are a hydrodynamical phenomenon in which magnetic fields do not play a major role. The grains are due to interference between a pervasive standing oscillation and an 8 Mm horizontal wavelength in the chromosphere, and the wave trains of the evanescent p-mode interference pattern in the upper photosphere.

Rutten, Robert J.↗
NASA NTRS2002

Solving Problems With SINDA/FLUINT

SINDA/FLUINT, the NASA standard software system for thermohydraulic analysis, provides computational simulation of interacting thermal and fluid effects in designs modeled as heat transfer and fluid flow networks. The product saves time and money by making the user's design process faster and easier, and allowing the user to gain a better understanding of complex systems. The code is completely extensible, allowing the user to choose the features, accuracy and approximation levels, and outputs. Users can also add their own customizations as needed to handle unique design tasks or to automate repetitive tasks. Applications for SINDA/FLUINT include the pharmaceutical, petrochemical, biomedical, electronics, and energy industries. The system has been used to simulate nuclear reactors, windshield wipers, and human windpipes. In the automotive industry, it simulates the transient liquid/vapor flows within air conditioning systems.

Source record↗
NASA NTRS2006

NASA Tech Briefs, June 2006

Topics covered include: Magnetic-Field-Response Measurement-Acquisition System; Platform for Testing Robotic Vehicles on Simulated Terrain; Interferometer for Low-Uncertainty Vector Metrology; Rayleigh Scattering for Measuring Flow in a Nozzle Testing Facility; "Virtual Feel" Capaciflectors; FETs Based on Doped Polyaniline/Polyethylene Oxide Fibers; Miniature Housings for Electronics With Standard Interfaces; Integrated Modeling Environment; Modified Recursive Hierarchical Segmentation of Data; Sizing Structures and Predicting Weight of a Spacecraft; Stress Testing of Data-Communication Networks; Framework for Flexible Security in Group Communications; Software for Collaborative Use of Large Interactive Displays; Microsphere Insulation Panels; Single-Wall Carbon Nanotube Anodes for Lithium Cells; Tantalum-Based Ceramics for Refractory Composites; Integral Flexure Mounts for Metal Mirrors for Cryogenic Use; Templates for Fabricating Nanowire/Nanoconduit- Based Devices; Measuring Vapors To Monitor the State of Cure of a Resin; Partial-Vacuum-Gasketed Electrochemical Corrosion Cell; Theodolite Ring Lights; Integrating Terrain Maps Into a Reactive Navigation Strategy; Reducing Centroid Error Through Model-Based Noise Reduction; Adaptive Modeling Language and Its Derivatives; Stable Satellite Orbits for Global Coverage of the Moon; and Low-Cost Propellant Launch From a Tethered Balloon

Source record↗
NASA NTRS2014

A Self-Stabilizing Hybrid-Fault Tolerant Synchronization Protocol

In this report we present a strategy for solving the Byzantine general problem for self-stabilizing a fully connected network from an arbitrary state and in the presence of any number of faults with various severities including any number of arbitrary (Byzantine) faulty nodes. Our solution applies to realizable systems, while allowing for differences in the network elements, provided that the number of arbitrary faults is not more than a third of the network size. The only constraint on the behavior of a node is that the interactions with other nodes are restricted to defined links and interfaces. Our solution does not rely on assumptions about the initial state of the system and no central clock nor centrally generated signal, pulse, or message is used. Nodes are anonymous, i.e., they do not have unique identities. We also present a mechanical verification of a proposed protocol. A bounded model of the protocol is verified using the Symbolic Model Verifier (SMV). The model checking effort is focused on verifying correctness of the bounded model of the protocol as well as confirming claims of determinism and linear convergence with respect to the self-stabilization period. We believe that our proposed solution solves the general case of the clock synchronization problem.

Malekpour, Mahyar R.↗
NASA NTRS2015

A Self-Stabilizing Hybrid Fault-Tolerant Synchronization Protocol

This paper presents a strategy for solving the Byzantine general problem for self-stabilizing a fully connected network from an arbitrary state and in the presence of any number of faults with various severities including any number of arbitrary (Byzantine) faulty nodes. The strategy consists of two parts: first, converting Byzantine faults into symmetric faults, and second, using a proven symmetric-fault tolerant algorithm to solve the general case of the problem. A protocol (algorithm) is also present that tolerates symmetric faults, provided that there are more good nodes than faulty ones. The solution applies to realizable systems, while allowing for differences in the network elements, provided that the number of arbitrary faults is not more than a third of the network size. The only constraint on the behavior of a node is that the interactions with other nodes are restricted to defined links and interfaces. The solution does not rely on assumptions about the initial state of the system and no central clock nor centrally generated signal, pulse, or message is used. Nodes are anonymous, i.e., they do not have unique identities. A mechanical verification of a proposed protocol is also present. A bounded model of the protocol is verified using the Symbolic Model Verifier (SMV). The model checking effort is focused on verifying correctness of the bounded model of the protocol as well as confirming claims of determinism and linear convergence with respect to the self-stabilization period.

Malekpour, Mahyar R.↗
NASA NTRSDate not supplied

NASA's Space Health Impacts for the NASA Experience (SHINE) Training Program – Space Radiation Curriculum

In February 2023, the Space Radiation Element of the NASA Human Research Program initiated a virtual, annual space radiation curriculum. The Space Health Impacts for the NASA Experience (SHINE) Space Radiation Didactic Curriculum aims to educate participants not only in the scientific aspects of space radiation but also in the agency’s risk management strategies. SHINE Space Radiation Didactic Curriculum combined weekly seminars by speakers from NASA, other government agencies, academia, and industry, with networking sessions designed to foster collaboration between the competitively selected participants as well as interactions with NASA scientists and HRP funded investigators. The inaugural course ran weekly from February 2023 to August 2023 and was comprised of lectures, less formal coffee hours, and office hours. Each two-hour seminar sessions hosted 1-3 presentations on topics which ranged from the space radiation environment to health effects and countermeasure to granting opportunities. All lectures were recorded and will be published on the THREE (The Health Risks of Extraterrestrial Environments) website for public access (https://three.jsc.nasa.gov/). As a course requirement, participants developed individual or collaborative beam time proposals for real, proposed, or potential experiments at the NASA Space Radiation Laboratory (NSRL) at the Brookhaven National Laboratory. For the 2023 course, 25 participants were selected from a total of 59 applicants. Selected participants were citizens from 8 countries and comprised 5 graduate students, 4 postdocs, 11 scientists and 5 professors/medical doctors. Participants had a wide range of expertise including molecular and cellular biology, microbiology, botany, physiology, engineering, physics, aerospace medicine, planetary science, biostatistics, and modeling. The second annual SHINE training program is scheduled from February to August 2024. In addition, a separate SHINE Space Radiation Practicum session will be held in Fall 2024 at the NSRL. The SHINE Space Radiation Practicum is a unique opportunity that has not been available to the public since the closure of the NASA Space Radiation Summer School in 2017 and will allow a small cohort of participants competitively selected in Fall 2023 to gain hands-on radiation experience.

SHINE↗
DOE OSTI2021

Climate warming enhances microbial network complexity and stability

Unravelling the relationships between network complexity and stability under changing climate is a challenging topic in theoretical ecology that remains understudied in the field of microbial ecology. Here, we examined the effects of long-term experimental warming on the complexity and stability of molecular ecological networks in grassland soil microbial communities. Warming significantly increased network complexity, including network size, connectivity, connectance, average clustering coefficient, relative modularity and number of keystone species, as compared with the ambient control. Molecular ecological networks under warming became significantly more robust, with network stability strongly correlated with network complexity, supporting the central ecological belief that complexity begets stability. Furthermore, warming significantly strengthened the relationships of network structure to community functional potentials and key ecosystem functioning. Furthermore, these results indicate that preserving microbial ‘interactions’ is critical for ecosystem management and for projecting ecological consequences of future climate warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Dynamic Networks Experiment FY18 - Quality Control Focus Area Efforts

The availability of data whose quality is fittingly labeled is of tantamount importance to signal detection and all other downstream signal processing operations. Without identification, the goals of low yield nuclear monitoring cannot reach their full potential. A fundamental question that remains is the knowledge of the capability gaps that prevent us from using open network data. With these new channels, we will need better understanding of the frequency, type, severity, and impact of artifacts when compared to conventional networks. Moreover, beyond a simple quality label, thresholds for acceptable use of the data will need to be established, likely on a use case by use case basis. Finally, orders of magnitude more data with an at-best static analyst pool size means that quality artifacts must be identified with as little human interaction as possible. Automated methodologies must be developed to account for the scale of the problem when ad hoc networks are candidates for processing. At the same time that “data wealth” provides more opportunity for signal exploitation, the introduction of ad hoc networks introduces an increased risk quality issues in both sensor value and metadata issues. The artifacts shown below are examples of some sensor-based quality issues that impair downstream processing. The machine learning model used in this project was able to identify the artifacts that produce these spurious correlations, significantly improving analyst productivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Role of Cooperative Interactions Among Surfaces, Solvents, and Reactive Intermediates on Catalysis at Liquid–Solid Interfaces (Final Report DE-SC0020224)

This project established quantitative links between inner‑sphere chemistry (active metal identity, coordination, and zeolite topology) and outer‑sphere organization (solvent identity, hydrogen‑bond networks, and pore condensation) that govern rates, activation barriers, and selectivities for alkene epoxidation and epoxide ring‑opening at solid–liquid and quasi‑liquid–solid interfaces. We deconvoluted contributions from covalent interactions at active sites and noncovalent, solvent‑mediated interactions within pores by pairing well‑defined metal substituted zeolites with controlled solvent environments. We then mapped those contributions onto measurable kinetics (ΔH‡, ΔS‡), adsorption thermodynamics (ITC), and in situ spectroscopy. The transferrable outcomes include a set of design rules that include the following understandings. First, tune silanol ((SiOH)x) density and pore topology to organize solvent networks that selectively stabilize transition states. Second, exploit activity‑coefficient‑normalized rates and adsorption– barrier correlations to diagnose when solvent reorganization rather than surface chemistry limits performance. Third, use partial pore condensation (e.g., acetonitrile, water but also generalizable to other solvents) to elicit liquid‑like stabilization effects even in nominally vapor‑phase reactors. Collectively, these results provide strategies to increase epoxidation rates, improve oxidant utilization (i.e., selectivities), and steer regioselectivity in zeolite‑based catalytic processes relevant to sustainable oxidation chemistry. These outcomes should be transferable to other classes of reactions that proceed in microporous materials and under confinement provided by organized solvents (e.g., electrochemical double layers).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗