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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Lung health in era of climate change and dust storms

Highlights: • Dust storms provide a vital role on Earth. • The health impact of dust storms is principally determined by the particle size. • The immune system plays a vital role in dust exposure. • Association between dust and sarcoidosis has been proposed. • Prevention and reduction of particle exposure is of paramount importance. Dust storms are strong winds which lead to particle exposure over extensive areas. These storms influence air quality on both a local and global scale which lead to both short and long-term effects. The frequency of dust storms has been on the rise during the last decade. Forecasts suggest that their incidence will increase as a response to the effects of climate change and anthropogenic activities. Elderly people, young children, and individuals with chronic cardiopulmonary diseases are at the greatest risk for health effects of dust storms. A wide variety of infectious and non-infectious diseases have been associated with dust exposure. Influenza A virus, pulmonary coccidioidomycosis, bacterial pneumonia, and meningococcal meningitis are a few examples of dust-related infectious diseases. Among non-infectious diseases, chronic obstructive pulmonary disease, asthma, sarcoidosis and pulmonary fibrosis have been associated with dust contact. Here, we review two molecular mechanisms of dust induced lung disease for asthma and sarcoidosis. We can also then further understand the mechanisms by which dust particles disturb airway epithelial and immune cells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterization of partially observed epidemics through Bayesian inference: application to COVID-19

We demonstrate a Bayesian method for the "real-time'" characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients.The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted.The method also detected different disease dynamics when applied to specific region of New Mexico

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Predictive Skill of Deep Learning Models Trained on Limited Sequence Data

In this report we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, especially variants of recurrent neural networks (RNNs) have been studied for influenza forecasting, and have achieved higher forecasting skill compared to conventional models such as ARIMA models. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block temporal convolutional networks and simple neural attentive meta-learner for epidemiological forecasting and test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV)

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Modeling the impact of COVID-19 on air quality in southern California: implications for future control policies

Abstract. In response to the coronavirus disease of 2019 (COVID-19), California issued statewide stay-at-home orders, bringing about abrupt and dramatic reductions in air pollutant emissions. This crisis offers us an unprecedented opportunity to evaluate the effectiveness of emission reductions in terms of air quality. Here we use the Weather Research and Forecasting model with Chemistry (WRF-Chem) in combination with surface observations to study the impact of the COVID-19 lockdown measures on air quality in southern California. Based on activity level statistics and satellite observations, we estimate the sectoral emission changes during the lockdown. Due to the reduced emissions, the population-weighted concentrations of fine particulate matter (PM2.5) decrease by 15 % in southern California. The emission reductions contribute 68 % of the PM2.5 concentration decrease before and after the lockdown, while meteorology variations contribute the remaining 32 %. Among all chemical compositions, the PM2.5 concentration decrease due to emission reductions is dominated by nitrate and primary components. For O3 concentrations, the emission reductions cause a decrease in rural areas but an increase in urban areas; the increase can be offset by a 70 % emission reduction in anthropogenic volatile organic compounds (VOCs). These findings suggest that a strengthened control on primary PM2.5 emissions and a well-balanced control on nitrogen oxides and VOC emissions are needed to effectively and sustainably alleviate PM2.5 and O3 pollution in southern California.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Leveraging Flexible Smart Manufacturing to Accelerate Industrial Supply Chain Recovery

Any major crisis such as the latest coronavirus disease pandemic will have a monumental effect on the worldwide economy and on the international supply chain. Much of the global supply chain relies on China, Germany, and the United States for manufacturing and distribution; this fragile system is subject to failure. Climactic changes in demand and rapid shortages in necessities are the results of the pandemic’s disruption. The recovery forecast for the ongoing pandemic is uncertain and, therefore, any recovery effort will need to be adaptable to build the supply chain resiliency. A wide adoption of flexible smart automated technologies in the manufacturing sector are helping to build robust supply chains and assisting in recovery. Here, a brief discussion of these smart manufacturing technologies is presented, along with a list of potential supply chain issues and corresponding solutions. Such smart manufacturing technologies possess the potential to empower and revolutionize the traditional manufacturing environment by enhancing its resiliency and flexibility. These smart technologies will play a crucial role in accelerating the worldwide supply chain recovery in and after a pandemic.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

PRIME

SAND2021-0565 O PRIME is a modeling framework designed for the real-time characterization and forecasting of partially observed epidemics. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. Examples are provided using publicly available COVID-19 data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗
DOE OSTI2024

PRIME - A Software Toolkit for the Characterization of Partially Observed Epidemics in a Bayesian Framework

PRIME is a modeling framework designed for the “real-time’” characterization and forecasting of partially observed epidemics. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. We include examples using publicly available COVID-19 data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The human naive B cell repertoire contains distinct subclasses for a germline-targeting HIV-1 vaccine immunogen

Traditional vaccine development to prevent some of the worst current pandemic diseases has been unsuccessful so far. Germline-targeting immunogens have potential to prime protective antibodies (Abs) via more targeted immune responses. Success of germline-targeting vaccines in humans will depend on the composition of the human naive B cell repertoire, including the frequencies and affinities of epitope-specific B cells. However, the human naive B cell repertoire remains largely undefined. Assessment of antigen-specific human naive B cells among hundreds of millions of B cells from multiple donors may be used as pre–phase 1 ex vivo human testing to potentially forecast B cell and Ab responses to new vaccine designs. VRC01 is an HIV broadly neutralizing Ab (bnAb) against the envelope CD4-binding site (CD4bs). In this work, we characterized naive human B cells recognizing eOD-GT8, a germline-targeting HIV-1 vaccine candidate immunogen designed to prime VRC01-class Abs. Several distinct subclasses of VRC01-class naive B cells were identified, sharing sequence characteristics with inferred precursors of known bnAbs VRC01, VRC23, PCIN63, and N6. Multiple naive B cell clones exactly matched mature VRC01-class bnAb L-CDR3 sequences. Non–VRC01-class B cells were also characterized, revealing recurrent public light chain sequences. Unexpectedly, we also identified naive B cells related to the IOMA-class CD4bs bnAb. These different subclasses within the human repertoire had strong initial affinities (KD) to the immunogen, up to 13 nM, and represent encouraging indications that multiple independent pathways may exist for vaccine-elicited VRC01-class bnAb development in most individuals. The frequencies of these distinct eOD-GT8 B cell specificities give insights into antigen-specific compositional features of the human naive B cell repertoire and provide actionable information for vaccine design and advancement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Integrated Resource Supply-Demand-Routing Model for the COVID-19 Crisis

As part of the Department of Energy response to the novel coronavirus disease (COVID-19) pandemic of 2020, a modeling effort was sponsored by the DOE Office of Science. Through this effort, an integrated planning framework was developed whose capabilities were demonstrated with the combination of a treatment resource demand model and an optimization model for routing supplies. This report documents this framework and models, and an application involving ventilator demands and supplies in the continental United States. The goal of this application is to test the feasibility of implementing nationwide ventilator sharing in response to the COVID-19 crisis. Multiple scenarios were run using different combinations of forecasted and observed patient streams, and it is demonstrated that using a "worst-case forecast for planning may be preferable to best mitigate supply-demand risks in an uncertain future. There is also a brief discussion of model uncertainty and its implications for the results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2019

Leveraging latest computer science tools to advance nuclear cardiology

Nuclear cardiology has unique advantages compared to other modalities, since the image analysis is already much more automated compared to what is currently clinically performed for CT, MR, or echocardiography imaging. The diverse image and clinical data available to assess coronary disease function, perfusion, flow, and associated CT data provide new opportunities, but logistically these additional assessments increase the overall complexity of SPECT/PET reporting, necessitating additional expertise and time. The advances in artificial intelligence software can be leveraged to obtain comprehensive risk predictions and diagnoses from all available data. They will allow nuclear cardiology to retain competitive edge compared to other modalities and improve its overall clinical utility. These tools will enhance diagnosis and risk prediction beyond what is possible by subjective visual analysis and mental integration of data by physicians.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

EBSD seed LDRD project: Does Corona Virus – 2019 (COVID-19) and Seasonal Flu have similar meteorology and air quality controls driving their spread?

Seasonal influenza and Influenza like Illnesses (ILI) pose a serious public health risk and in turn affect the economy. Various factors affect ILI cases and mortality, including the pathogen and its interaction with the host, as well as environmental and socioeconomic factors such as meteorology, household structure, air pollution, urbanization, and population. Despite the growing number of studies on influenza and ILI, challenges remain in forecasting the timing of seasonal onset, outbreak patterns, and key factors affecting transmission. In particular, the impacts of meteorology and air quality on ILI have been challenging to understand, with linear regression studies focused on different geographic regions producing contradictory results. For example, influenza seasonality has been associated with cold-dry conditions in temperate mid-latitudes, but with humid-rainy conditions in tropical climates. These apparently contradictory results imply that the relationships between influenza cases and atmospheric variables may be too complex to be captured by linear regression models. In this seed project, we analyze meteorology and air quality variables from numerical models to determine which atmospheric variables are most helpful in predicting weekly changes in recorded flu cases. In contrast to most previous studies that relied on linear regression analysis to predict the timing of the flu onset or peak, we employ a robust machine learning algorithm to evaluate the contribution of atmospheric variables to weekly changes in recorded ILI cases. These results may also be relevant to the spread of other respiratory illnesses such as Corona Virus Disease – 2019 (COVID-19).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy , pleiotropy , complexity , dynamics , ambiguity , circularity , personalized outcomes , and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

In silico pharmacogenetic approach: The natalizumab case study

Natalizumab is a humanized monoclonal antibody to α{sub 4}β{sub 1} integrin and is approved for the treatment of Multiple Sclerosis. In patients there is a great variation in drug response and there is much evidence that genetic contributors play an important role in defining an individual's susceptibility. Natalizumab binds to α{sub 4}-residues Gln-152, Lys-201, Lys256, and these seem to be essential for its activity. Studies on a range of species in disease model have showed a loss of reactivity when any one of those three residues were different to human. Based on these animal studies, we thought that the single nucleotide polymorphism in the ITGA4 human gene causing a lysine to arginine transversion at amino acid position 256 require further investigations in the context of individual drug susceptibility. So, the aim of our study was to investigate the association between this genetic polymorphism and the resistance to natalizumab. We had applied molecular dynamics simulation to study the possible conformational changes induced by Lys256Arg transversion on the overall structure of integrin and we have analyzed the binding affinities of natalizumab in the non-mutated and mutated structures through HINT score. We found that this SNP does not affect the VLA4-natalizumab interaction. Instead, the binding affinities are slightly higher in the mutated complex than in the wild-type. We reported one of the first work in which MD simulation was applied in the pharmacogenetic context, and this approach is rapid and cost effective, since a population survey is carried out only after the positive prediction of simulation. - Highlights: • Untraditional computational approach to discover drugs mechanisms of action (MOA) in pharmacogenetic • Molecular dynamics to predict protein-monoclonal antibody complexes for multiple sclerosis • Computational prediction of mutation behavior to determine or not a human population survey.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

How Advances in Imaging Will Affect Precision Radiation Oncology

Radiation oncology is 1 of the most structured disciplines in medicine. It is of a highly technical nature with reliance on robotic systems to deliver intervention, engagement of diverse expertise, and early adoption of digital approaches to optimize and execute the application of this highly effective cancer treatment. As a localized intervention, the dependence on sensitive, specific, and accurate imaging to define the extent of disease, its heterogeneity, and adjacency to normal tissues directly affects the therapeutic ratio. Image-based in vivo temporal monitoring of the response to treatment enables adaptation and further affects the therapeutic ratio. Thus, more precise intervention will enable fractionation schedules that better interoperate with advances such as immunotherapy. In the data set–rich era that promises precision and personalized medicine, the radiation oncology field will integrate these new data into highly protocoled pathways of care that begin with multimodality prediction and enable patient-specific adaptation of therapy based on quantitative measures of the individual's dose–volume temporal trajectory and midtherapy predictions of response. In addition to advancements in computed tomography imaging, emerging technologies, such as ultra-high-field magnetic resonance and molecular imaging will bring new information to the design of treatments. Next-generation image guided radiation therapy systems will inject high specificity and sensitivity data and stimulate adaptive replanning. In addition, a myriad of pre- and peritherapeutic markers derived from advances in molecular pathology (eg, tumor genomics), automated and comprehensive imaging analytics (eg, radiomics, tumor microenvironment), and many other emerging biomarkers (eg, circulating tumor cell assays) will need to be integrated to maximize the benefit of radiation therapy for an individual patient. We present a perspective on the promise and challenges of fully exploiting imaging data in the pursuit of personalized radiation therapy, drawing from the presentations and broader discussions at the 2016 American Society of Therapeutic Radiation Oncology–National Cancer Institute workshop on Precision Medicine in Radiation Oncology (Bethesda, MD).

62 RADIOLOGY AND NUCLEAR MEDICINE↗