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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications

Infectious disease modeling and forecasting have played a key role in helping assess and respond to epidemics and pandemics. Recent work has leveraged data on disease peak infection and peak hospital incidence to fit compartmental models for the purpose of forecasting and describing the dynamics of a disease outbreak. Incorporating these data can greatly stabilize a compartmental model fit on early observations, where slight perturbations in the data may lead to model fits that forecast wildly unrealistic peak infection. We introduce a new method for incorporating historic data on the value and time of peak incidence of hospitalization into the fit for a Susceptible-Infectious-Recovered (SIR) model by formulating the relationship between an SIR model’s starting parameters and peak incidence as a system of two equations that can be solved computationally. We demonstrate how to calculate SIR parameter estimates – which describe disease dynamics such as transmission and recovery rates – using this method, and determine that there is a noticeable loss in accuracy whenever prevalence data is misspecified as incidence data. To exhibit the modeling potential, we update the Dirichlet-Beta State Space modeling framework to use hospital incidence data, as this framework was previously formulated to incorporate only data on total infections. This approach is assessed for practicality in terms of accuracy and speed of computation via simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A review of antimicrobial implications for improving indoor air quality

The frequent outbreak of infectious respiratory diseases, such as the recent COVID-19 epidemic, raised the importance of indoor air quality. Removing microorganisms from indoor air is critical to improve indoor air quality. Numerous studies in recent years have been published on developing antimicrobial materials and technologies for antibacterial and antiviral applications. Further, this study critically reviews the recent antimicrobial advances for improving indoor air quality. This paper provides a comprehensive analysis of the antimicrobial mechanisms, development of materials, and deployment strategies, as well as a performance evaluation of the antimicrobial implication for indoor air quality. Furthermore, the challenges and opportunities of future research directions are also highlighted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A combination of cross-neutralizing antibodies synergizes to prevent SARS-CoV-2 and SARS-CoV pseudovirus infection

Coronaviruses have caused several human epidemics and pandemics including the ongoing coronavirus disease 2019 (COVID-19). Prophylactic vaccines and therapeutic antibodies have already shown striking effectiveness against COVID-19. Nevertheless, concerns remain about antigenic drift in SARS-CoV-2 as well as threats from other sarbecoviruses. Cross-neutralizing antibodies to SARS-related viruses provide opportunities to address such concerns. Here, we report on crystal structures of a cross-neutralizing antibody, CV38-142, in complex with the receptor-binding domains from SARS-CoV-2 and SARS-CoV. Recognition of the N343 glycosylation site and water-mediated interactions facilitate cross-reactivity of CV38-142 to SARS-related viruses, allowing the antibody to accommodate antigenic variation in these viruses. CV38-142 synergizes with other cross-neutralizing antibodies, notably COVA1-16, to enhance neutralization of SARS-CoV and SARS-CoV-2, including circulating variants of concern B.1.1.7 and B.1.351. Overall, this study provides valuable information for vaccine and therapeutic design to address current and future antigenic drift in SARS-CoV-2 and to protect against zoonotic SARS-related coronaviruses.

3D structure↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Structures of drug-specific monoclonal antibodies bound to opioids and nicotine reveal a common mode of binding

Opioid-related fatal overdoses have reached epidemic proportions. Because existing treatments for opioid use disorders offer limited long-term protection, accelerating the development of newer approaches is critical. Monoclonal antibodies (mAbs) are an emerging treatment strategy that targets and sequesters selected opioids in the bloodstream, reducing drug distribution across the blood-brain barrier, thus preventing or reversing opioid toxicity. We previously identified a series of murine mAbs with high affinity and selectivity for oxycodone, morphine, fentanyl, and nicotine. To determine their binding mechanism, we used X-ray crystallography to solve the structures of mAbs bound to their respective targets, to 2.2 Å resolution or higher. Structural analysis showed a critical convergent hydrogen bonding mode that is dependent on a glutamic acid residue in the mAbs’ heavy chain and a tertiary amine of the ligand. Further, characterizing drug-mAb complexes represents a significant step toward rational antibody engineering and future manufacturing activities to support clinical evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Silencing of RSPO1 mitigates obesity-related renal fibrosis in mice by deactivating Wnt/β-catenin pathway

Highlights: • Renal RSPO1 expression was increased in mice fed on high-fat diet for 12 weeks. • Silencing RSPO1 remitted kidney dysfunction and renal fibrosis in obesity mice. • Silencing RSPO1 inhibited LGR4 expression and Wnt/β-catenin pathway activation. • RSPO1/LGR4 axis was involved in obesity-related renal fibrosis. Obesity, a global epidemic, is one of the critical causes of chronic kidney disease (CKD). R-spondin1 (RSPO1) possessing the potential to activate Wnt/β-catenin pathway was reported to be elevated in circulation of obesity objects. However, the function of RSPO1 and the latent mechanism in obesity-related CKD are still left to be revealed. In the present study, renal RSPO1 expression was increased in mice fed on high-fat diet (HFD) for 12 weeks. Lentivirus-mediated RSPO1 knockdown partly recovered obesity-related metabolic symptoms, while distinctly remitted kidney dysfunction and renal fibrosis in obesity mice. In vitro, recombinant RSPO1 was found to elevate leucine-rich repeat-containing G protein coupled receptor 4 (LGR4) expression, promote Wnt/β-catenin signaling pathway activation, facilitate epithelial-mesenchymal transition (EMT) and increase collagen deposition in HK2 renal tubular cells. Such pro-fibrotic effect of RSPO1 was diminished by LGR4 siRNA in HK2 cells. In summary, we demonstrate that RSPO1/LGR4 axis is involved in obesity-related renal fibrosis at least through activating Wnt/β-catenin signaling pathway, providing a potential therapeutic target for this disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Proliferation of Faulty Materials Data Analysis in the Literature

As a group of subject matter experts in X-ray photoelectron spectroscopy (XPS), we write this letter to raise awareness of the epidemic of poor and incorrect materials data analysis in the Literature. This is a chronic and growing problem with very undesirable consequences. This issue may contribute to what has been identified as a “reproducibility crisis”1,2 that was the subject of a recent U.S. National Academies study. We are using XPS as one example of what we observe to be a systemic issue.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fraction of Free-Base Nicotine in Simulated Vaping Aerosol Particles Determined by X-ray Spectroscopies

A new generation of electronic cigarettes is exacerbating the youth vaping epidemic by incorporating additives that increase the acidity of generated aerosols, which facilitate uptake of high nicotine levels. We need to better understand the chemical speciation of vaping aerosols to assess the impact of acidification. Here we used X-ray photoelectron spectroscopy (XPS) and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy to probe the acid-base equilibria of nicotine in hydrated vaping aerosols. Here we show that, unlike the behavior observed in bulk water, nicotine in the core of aqueous particles was partially protonated when the pH of the nebulized solution was 10.4, with a fraction of free-base nicotine (α FB ) of 0.34. Nicotine was further protonated by acidification with equimolar addition of benzoic acid (α FB = 0.17 at pH 6.2). By contrast, the degree of nicotine protonation at the particle surface was significantly lower, with 0.72 < α FB < 0.80 in the same pH range. The presence of propylene glycol and glycerol completely eliminated protonation of nicotine at the surface (α FB = 1) while not affecting significantly its acid-base equilibrium in the particle core. These results provide a better understanding of the role of acidifying additives in vaping aerosols, supporting public health policy interventions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Nicotine Monitoring with a Wearable Sweat Band

The tobacco epidemic is a public health threat that has taken a heavy toll of lives around the globe each year. Smoking affects both the smokers and those who are exposed to secondhand smoke, and careful tracking of exposure can be key to mitigating the potential hazards. For smokers, the variation of chemical compositions between commercial cigarettes has led to ambiguity in estimating the health risks, both for active smokers and others involuntarily exposed to tobacco smoke and byproducts. In this regard, sweat possesses an attractive opportunity to monitor smoke exposure due to sweat's abundance in biomolecules and its great accessibility. In this paper, we present a wearable sweat band to monitor nicotine, a prominent ingredient in cigarettes, as a viable way to quantitatively assess a wearer's exposure to smoking. Both smokers and normal subjects are tested to demonstrate the use of this device for smoke-related health monitoring. Our results exhibit confirmable and elevated nicotine levels in sweat for subjects inhaling cigarette smoke. This continuous and personalized sweat sensing device is leverage to monitor smoke pollution for a potentially broad population.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Forecasting influenza activity using machine-learned mobility map

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Structural basis for CSPG4 as a receptor for TcdB and a therapeutic target in Clostridioides difficile infection

C. difficile is a major cause of antibiotic-associated gastrointestinal infections. Two C. difficile exotoxins (TcdA and TcdB) are major virulence factors associated with these infections, and chondroitin sulfate proteoglycan 4 (CSPG4) is a potential receptor for TcdB, but its pathophysiological relevance and the molecular details that govern recognition remain unknown. Here, we determine the cryo-EM structure of a TcdB–CSPG4 complex, revealing a unique binding site spatially composed of multiple discontinuous regions across TcdB. Mutations that selectively disrupt CSPG4 binding reduce TcdB toxicity in mice, while CSPG4-knockout mice show reduced damage to colonic tissues during C. difficile infections. We further show that bezlotoxumab, the only FDA approved anti-TcdB antibody, blocks CSPG4 binding via an allosteric mechanism, but it displays low neutralizing potency on many TcdB variants from epidemic hypervirulent strains due to sequence variations in its epitopes. In contrast, a CSPG4-mimicking decoy neutralizes major TcdB variants, suggesting a strategy to develop broad-spectrum therapeutics against TcdB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The pesticide chlorpyrifos promotes obesity by inhibiting diet-induced thermogenesis in brown adipose tissue

Obesity results from a caloric imbalance between energy intake, absorption and expenditure. In both rodents and humans, diet-induced thermogenesis contributes to energy expenditure and involves the activation of brown adipose tissue (BAT). We hypothesize that environmental toxicants commonly used as food additives or pesticides might reduce BAT thermogenesis through suppression of uncoupling protein 1 (UCP1) and this may contribute to the development of obesity. Using a step-wise screening approach, we discover that the organophosphate insecticide chlorpyrifos suppresses UCP1 and mitochondrial respiration in BAT at concentrations as low as 1 pM. In mice housed at thermoneutrality and fed a high-fat diet, chlorpyrifos impairs BAT mitochondrial function and diet-induced thermogenesis, promoting greater obesity, non-alcoholic fatty liver disease (NAFLD) and insulin resistance. This is associated with reductions in cAMP; activation of p38MAPK and AMPK; protein kinases critical for maintaining UCP1 and mitophagy, respectively in BAT. These data indicate that the commonly used pesticide chlorpyrifos, suppresses diet-induced thermogenesis and the activation of BAT, suggesting its use may contribute to the obesity epidemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High impact of bacterial predation on cyanobacteria in soil biocrusts

Diverse bacteria lead a life as pathogens or predators of other bacteria in many environments. However, their impact on emerging ecological processes in natural settings remains to be assessed. Here we describe a novel type of obligate, intracellular predatory bacterium of widespread distribution that preys on soil cyanobacteria in biocrusts. The predator, Candidatus Cyanoraptor togatus, causes localized, cm-sized epidemics that are visible to the naked eye, obliterates cyanobacterial net primary productivity, and severely impacts crucial biocrust properties like nitrogen cycling, dust trapping and moisture retention. The combined effects of high localized morbidity and areal incidence result in decreases approaching 10% of biocrust productivity at the ecosystem scale. Our findings show that bacterial predation can be an important loss factor shaping not only the structure but also the function of microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A single nanobody neutralizes multiple epochally evolving human noroviruses by modulating capsid plasticity

Acute gastroenteritis caused by human noroviruses (HuNoVs) is a significant global health and economic burden and is without licensed vaccines or antiviral drugs. The GII.4 HuNoV causes most epidemics worldwide. This virus undergoes epochal evolution with periodic emergence of variants with new antigenic profiles and altered specificity for histo-blood group antigens (HBGA), the determinants of cell attachment and susceptibility, hampering the development of immunotherapeutics. Here, we show that a llama-derived nanobody M4 neutralizes multiple GII.4 variants with high potency in human intestinal enteroids. The crystal structure of M4 complexed with the protruding domain of the GII.4 capsid protein VP1 revealed a conserved epitope, away from the HBGA binding site, fully accessible only when VP1 transitions to a “raised” conformation in the capsid. Together with dynamic light scattering and electron microscopy of the GII.4 VLPs, our studies suggest a mechanism in which M4 accesses the epitope by altering the conformational dynamics of the capsid and triggering its disassembly to neutralize GII.4 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗