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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Effect of Electroacupuncture at Wushu Acupoints of the Cardiopulmonary Meridian on the Autophagy in Rats with Acute Myocardial Ischemia

Wushu acupoints are the five acupoints distributed below the human elbow and knee joint. They are all located on the same meridian and divided into five categories: Jing, Ying, Shu, Jing, and He. It has been shown that electroacupuncture (EA) at Shenmen point of heart meridian can improve acute myocardial ischemia (AMI) early. However, it is still unclear if all the Wushu acupoints of the heart meridian can improve AMI. Hence, this study emphasizes Wushu acupoints of heart meridian, compares them with Wushu acupoints of lung meridian, and studies the therapeutic effect of EA at Wushu acupoints on AMI and its possible mechanism. It also discusses the specificity of the heart meridian to heart disease. The AMI model is established by ligation of the left anterior descending coronary artery. The detection methods like the physiological recorder, TTC staining, ELISA, and so forth were used to determine the ECG, myocardial infarct size, serum myocardial enzymes, and myocardial tissue-related protein expression in rats. The heart rate (HR) and ST segment along with creatine kinase (CK), creatine kinase isoenzymes (CK-MB), lactate dehydrogenase (LDH), and myocardial infarctions increased after the induction with AMI. Furthermore, the expressions of PINK1 and Parkin protein also showed an increase. However, EA at Wushu acupoints in the heart meridian can reverse the above changes, whereas EA at the lung meridian exhibits limited effect. It is depicted that the heart meridian has a relatively specific relationship with the heart in a diseased state.

Zhu, Chao↗
DOE OSTI2021

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm 2 ). Finally, the model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study

A novel coronavirus disease 2019 (COVID-19) has spread to all regions of the world. There is great uncertainty regarding how countries’ characteristics will affect the spread of the epidemic; to date, there are few studies that attempt to predict the spread of the epidemic in African countries. In this paper, we investigate the role of demographic patterns, urbanisation and comorbidities on the possible trajectories of COVID-19 in Ghana, Kenya and Senegal. We use an augmented deterministic Susceptible-Infected-Recovered model to predict the true spread of the disease, under the containment measures taken so far. We disaggregate the infected compartment into asymptomatic, mildly symptomatic and severely symptomatic to match observed clinical development of COVID-19. We also account for age structures, urbanisation and comorbidities (HIV, tuberculosis, anaemia). In our baseline model, we project that the peak of active cases will occur in July, subject to the effectiveness of policy measures. When accounting for the urbanisation, and factoring in comorbidities, the peak may occur between 2 June and 17 June (Ghana), 22 July and 29 August (Kenya) and, finally, 28 May and 15 June (Senegal). Successful containment policies could lead to lower rates of severe infections. While most cases will be mild, we project in the absence of policies further containing the spread, that between 0.78% and 1.03%, 0.61% and 1.22%, and 0.60% and 0.84% of individuals in Ghana, Kenya and Senegal, respectively, may develop severe symptoms at the time of the peak of the epidemic. Compared with Europe, Africa’s younger and rural population may modify the severity of the epidemic. The large youth population may lead to more infections but most of these infections will be asymptomatic or mild, and will probably go undetected. The higher prevalence of underlying conditions must be considered.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Cerebellar dysfunction in a mouse model of childhood-onset manganese-induced dystonia parkinsonism

Humans with pathogenic variants of the manganese (Mn) transporter gene SLC39A14 exhibit highly elevated brain Mn concentrations and childhood-onset dystonia-parkinsonism. Here we show that Slc39a14-knockout (KO) mice, a preclinical model of the disease with elevated Mn concentrations in the CB, express deficits in physiological tremor implicating cerebellar (CB) dysfunction. Imaging of intracellular Mn in Purkinje cells (PCs) using synchrotron-based X-ray fluorescence microscopy confirmed highly elevated Mn concentrations in the PCs of Slc39a14-KO mice. To determine biological pathways altered in the CB of Slc39a14-KO mice relative to wildtype (WT), we performed RNA sequencing and discovered significant upregulation of pathways and genes regulating immune response and cell death. To substantiate these findings, we performed quantitative autoradiography of the neuroinflammation biomarker Translocator Protein 18 kDa (TSPO) which was significantly increased in the CB of Slc39a14-KO mice relative to WT. The latter findings were confirmed via immunostaining with the microglial marker Iba-1, revealing widespread microglia activation and clustering in the CB cortex. Immunostaining for cleaved caspase-3 (cCASP3), a marker of apoptosis, showed increased number of PCs with positive immunolabeling for cCASP3 in Slc39a14-KO mice relative to WT. Degeneration of PCs was confirmed by Hematoxylin and Eosin (H&E) staining. Lastly, functional electrophysiological assessment of CB neurocircuitry revealed a marked decrease in firing rates of cerebellar nuclei (CN) neurons and increased variability of PC simple spikes firing. Collectively, these findings show, for the first time, Mn-induced PC degeneration and dysfunctional CB circuitry in Slc39a14-KO mice providing additional evidence for the pathological underpinnings of the dystonia-like movements, balance, and gait abnormalities in SLC39A14 mutation carriers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Gamma radiation sterilization of N95 respirators leads to decreased respirator performance

In response to personal protective equipment (PPE) shortages in the United States due to the Coronavirus Disease 2019, two models of N95 respirators were evaluated for reuse after gamma radiation sterilization. Gamma sterilization is attractive for PPE reuse because it can sterilize large quantities of material through hermetically sealed packaging, providing safety and logistic benefits. The Gamma Irradiation Facility at Sandia National Laboratories was used to irradiate N95 filtering facepiece respirators to a sterilization dose of 25 kGy(tissue). Aerosol particle filtration performance testing and electrostatic field measurements were used to determine the efficacy of the respirators after irradiation. Both respirator models exhibited statistically significant decreases in particle filtering efficiencies and electrostatic potential after irradiation. The largest decrease in capture efficiency was 40–50% and peaked near the 200 nm particle size. The key contribution of this effort is correlating the electrostatic potential change of individual filtration layer of the respirator with the decrease filtration efficiency after irradiation. This observation occurred in both variations of N95 respirator that we tested. Electrostatic potential measurement of the filtration layer is a key indicator for predicting filtration efficiency loss.

36 MATERIALS SCIENCE↗
DOE OSTI2021

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Small Particle Aerosol Exposure of African Green Monkeys to MERS-CoV as a Model for Highly Pathogenic Coronavirus Infection

Emerging coronaviruses are a global public health threat because of the potential for person-to-person transmission and high mortality rates. Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in 2012, causing lethal respiratory disease in $\gg$35% of cases. Primate models of coronavirus disease are needed to support development of therapeutics, but few models exist that recapitulate severe disease. For initial development of a MERS-CoV primate model, 12 African green monkeys were exposed to 10 3 , 10 4 , or 10 5 PFU target doses of aerosolized MERS-CoV. We observed a dose-dependent increase of respiratory disease signs, although all 12 monkeys survived for the 28-day duration of the study. This study describes dosedependent effects of MERS-CoV infection of primates and uses a route of infection with potential relevance to MERSCoV transmission. Aerosol exposure of African green monkeys might provide a platform approach for the development of primate models of novel coronavirus diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

In with the old, in with the new: machine learning for time to event biomedical research

The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer at diagnosis from the Department of Veterans Affairs (VA). Our model accounted for patient censoring. Harrell’s c-index for our model using only features available at the time of diagnosis was 0.757 95% confidence interval [0.756, 0.757]. Our results show that machine learning methods like XGBoost can be adapted to use accelerated failure time (AFT) with censoring to model long-term risk of disease progression. Furthermore, the long median survival justifies and requires censoring. Overall, we show that an existing machine learning approach can be used for AFT outcome modeling in prostate cancer, and more generally for other chronic diseases with long observation times.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Risk assessment of engineering diseases of embankment–bridge transition section for railway in permafrost regions

Abstract The embankment–bridge transition section (EBTS) is one of the zones where railway diseases occur frequently in permafrost regions. Disease risk assessment of EBTSs can provide guidance for maintenance. In this study, considering the engineering geological conditions, climate characteristics, and embankment structure types along the Qinghai–Tibet Railway (QTR) as well as based on the disease inventory of the QTR from 2010 to 2019, the logistic regression (LR), support vector machine (SVM), and combination‐weight‐based gay relation analysis (GRA) were used for disease risk assessment of the EBTSs along the QTR in permafrost regions. The results indicate that the LR and SVM models have a better capability for EBTS disease prediction than the GRA model, and the SVM model can select more disease samples in relatively larger regions than the LR model. Based on the SVM and LR models, the risk level of EBTSs is divided into four classes: low‐ (29.9%), moderate‐ (39.6%), high‐ (22.1%), and very high (8.4%) risk. Finally, we selected 272 EBTSs in high‐ and very‐high‐risk classes for key observation during the maintenance of the QTR in permafrost regions. This study provides a reference for the risk assessment of railways built in permafrost regions using data‐driven methods.

Zhang, Saize↗
DOE OSTI2020

Predicting Future Disease Burden in a Rapidly Changing Climate

The interplay of a rapidly changing climate and infectious disease occurrence is emerging as a critical topic, requiring investigation of possible direct, as well as indirect, connections between disease processes and climate-related variation and phenomena. First, we introduce and overview three infectious disease exemplars (dengue, influenza, valley fever) representing different transmission classes (insect-vectored, human-to-human, environmentally-transmitted) to illuminate the complex and significant interplay between climate disease processes, as well as to motivate discussion of how Sandia can transform the field, and change our understanding of climate-driven infectious disease spread. We also review state-of-the-art epidemiological and climate modeling approaches, together with data analytics and machine learning methods, potentially relevant to climate and infectious disease studies. We synthesize the modeling and disease exemplars information, suggesting initial avenues for research and development (R&D) in this area, and propose potential sponsors for this work. Whether directly or indirectly, it is certain that a rapidly changing climate will alter global disease burden. The trajectory of climate change is an important control on this burden, from local, to regional and global scales. The efforts proposed herein respond to the National Research Councils call for the creation of a multidisciplinary institute that would address critical aspects of these interlocking, cascading crises.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Chicago Social Interaction Model (ChiSIM)

ChiSIM is a framework for implementing agent-based models that simulate the mixing of a synthetic population. In a ChiSIM based model, each agent, that is, each person in the simulated population, resides in a place (a household, dormitory or retirement home/long term care facility, for example), and moves among other places such as workplaces, homes, clinics, and community resources. Agents typically move between places according to their domain specific activity profile, such that each agent has a profile that determines at what times throughout the day they occupy a particular location. Once in a place, an agent mixes with other agents in some model or domain-specific way. For example, an agent may expose other agents to a disease in an epidemiological model.

Ozik, Jonathan↗
DOE OSTI2023

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Differential contagiousness of respiratory disease across the United States

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, $R_0$. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific $R_0$ values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for $R_0$ range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data, sometimes coupled with a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Computational epidemiological tools for pandemic analysis, understanding, and response

This suite of software tools is being developed to enhance and analyze computational epidemiological models that incorporate realistic disease dynamics and human behavior, with the goal of supporting epidemic and pandemic response. Specifically, the tools enable data analysis, feature extraction, data synthesis, machine learning model development, and prediction of key public health outcomes, such as cases, hospitalizations, deaths, and behavioral responses, for airborne infectious diseases like COVID-19 and influenza.

Butts, David↗