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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

The Effects of Aging on Indices of Oxidative Stress and Apoptosis in the Female Fischer 344/Nnia X Brown Norway/BiNia Rat Heart

Oxidative-nitrosative stress may play a role in age-associated cardiovascular disease as implied by recent studies.However, limited research has been conducted using aged female rodent models. In this study, we examined hearts obtained from 6-, 26-, and 30-month old female Fischer 344/Nnia x Brown Norway/BiNia (F344xBN) rats in order to examine how aging affects levels of cardiac oxidative-nitrosative stress and apoptosis. Oxidative (superoxide anion and 4-HNE) and nitrosative (protein nitrosylation) stress markers were increased 180 ± 17 %, 110 ± 3 %, and 14 ± 2 %, respectively in 30-month hearts compared to the hearts of 6-month female rats. Coincident with these changes in oxidative-nitrosative stress, aging was also found to be associated with increases in the number of Tdt-mediated dUTP nick labeling (TUNEL)-positive cardiomyocytes, alterations in the Bax/Bcl-2 ratio, and elevated cleavage of caspase-3. Regression analysis demonstrates significant correlation in the age-associated changes markers of oxidative–nitrosative stress with changes in apoptotic signaling. The findings from this descriptive study imply that age-associated increases in mitochondrial-mediated apoptosis may be associated with the increase in oxidative-nitrosative stress in the aging F344xBN female heart.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Health Care Usage During the COVID-19 Pandemic and the Adoption of Telemedicine: Retrospective Study of Chronic Disease Cohorts

Background: The COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic. Objective: We studied the impact of the COVID-19 pandemic on in-person health care usage and telehealth adoption across chronic diseases to understand differences in telehealth adoption across disease cohorts and patient demographics (such as the Social Vulnerability Index [SVI]). Methods: We conducted a retrospective cohort study of 6 different disease cohorts (anxiety: n=67,578; depression: n=45,570; diabetes: n=81,885; kidney failure: n=29,284; heart failure: n=21,152; and cancer: n=35,460). We used summary statistics to characterize changes in usage and regression analysis to study how patient characteristics relate to in-person health care and telehealth adoption and usage during the first 12 months of the pandemic. Results: We observed a reduction in in-person health care usage across disease cohorts (ranging from 10% to 24%). For most diseases we study, telehealth appointments offset the reduction in in-person visits. Furthermore, for anxiety and depression, the increase in telehealth usage exceeds the reduction in in-person visits (by up to 5%). We observed that younger patients and men have higher telehealth usage after accounting for other covariates. Patients from higher SVI areas are less likely to use telehealth; however, if they do, they have a higher number of telehealth visits, after accounting for other covariates. Conclusions: The COVID-19 pandemic affected health care usage across diseases, and the role of telehealth in replacing in-person visits varies by disease cohort. Understanding these differences can inform current practices and provides opportunities to further guide modalities of in-person and telehealth visits. Critically, further study is needed to understand barriers to telehealth service usage for patients in higher SVI areas. A better understanding of the role of social determinants of health may lead to more support for patients and help individual health care providers improve access to care for patients with chronic conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability

The solar industry in the US typically uses a credit score such as the FICO score as an indicator of consumer utility payment performance and credit worthiness to approve customers for new solar installations. Using data on over 800,000 utility payment performance and over 5,000 demographic variables, we compare machine learning and econometric models to predict the probability of default to credit-score cutoffs. Here, we compare these models across a variety of measures, including how they affect consumers of different socio-economic backgrounds and profitability. We find that a traditional regression analysis using a small number of variables specific to utility repayment performance greatly increases accuracy and LMI inclusivity relative to FICO score, and that using machine learning techniques further enhances model performance. Relative to FICO, the machine learning model increases the number of low-to-moderate income consumers approved for community solar by 1.1% to 4.2% depending on the stringency used for evaluating potential customers, while decreasing the default rate by 1.4 to 1.9 percentage points. Using electricity utility repayment as a proxy for solar installation repayment, shifting from a FICO score cutoff to the machine learning model increases profits by 34% to 1882% depending on the stringency used for evaluating potential customers.

Davuluri, Sruthi↗
DOE OSTI2023

Iodide uptake by forest soils is principally related to the activity of extracellular oxidases

129 I is a nuclear fission decay product of concern because of its long half-life (16 Ma) and propensity to bioaccumulate. Microorganisms impact iodine mobility in soil systems by promoting iodination (covalent binding) of soil organic matter through processes that are not fully understood. Here, we examined iodide uptake by soils collected at two depths (0–10 and 10–20 cm) from 5 deciduous and coniferous forests in Japan and the United States. Autoclaved soils, and soils amended with an enzyme inhibitor (sodium azide) or an antibacterial agent (bronopol), bound significantly less 125 I tracer (93%, 81%, 61% decrease, respectively) than the untreated control soils, confirming a microbial role in soil iodide uptake. Correlation analyses identified the strongest significant correlation between 125 I uptake and three explanatory variables, actinobacteria soil biomass ( p = 6.04E-04, 1.35E-02 for Kendall-Tau and regression analysis, respectively), soil nitrogen content ( p = 4.86E-04, 4.24E-03), and soil oxidase enzyme activity at pH 7.0 using the substrate L-DOPA ( p = 2.83E-03, 4.33E-04) and at pH 5.5 using the ABTS ( p = 5.09E-03, 3.14E-03). Together, the results suggest that extracellular oxidases, primarily of bacterial origin, are the primary catalyst for soil iodination in aerobic, surface soils of deciduous and coniferous forests, and that soil N content may be indicative of the availability of binding sites for reactive iodine species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China

Backgrounds The diagnostic delay of tuberculosis (TB) contributes to further transmission and impedes the implementation of the End TB Strategy. Therefore, we aimed to describe the characteristics of patient delay, health system delay, and total delay among TB patients in Shanghai, identify areas at high risk for delay, and explore the potential factors of long delay at individual and spatial levels. Method The study included TB patients among migrants and residents in Shanghai between January 2010 and December 2018. Patient and health system delays exceeding 14 days and total delays exceeding 28 days were defined as long delays. Time trends of long delays were evaluated by Joinpoint regression. Multivariable logistic regression analysis was employed to analyze influencing factors of long delays. Spatial analysis of delays was conducted using ArcGIS, and the hierarchical Bayesian spatial model was utilized to explore associated spatial factors. Results Overall, 61,050 TB patients were notified during the study period. Median patient, health system, and total delays were 12 days (IQR: 3–26), 9 days (IQR: 4–18), and 27 days (IQR: 15–43), respectively. Migrants, females, older adults, symptomatic visits to TB-designated facilities, and pathogen-positive were associated with longer patient delays, while pathogen-negative, active case findings and symptomatic visits to non-TB-designated facilities were associated with long health system delays (LHD). Spatial analysis revealed Chongming Island was a hotspot for patient delay, while western areas of Shanghai, with a high proportion of internal migrants and industrial parks, were at high risk for LHD. The application of rapid molecular diagnostic methods was associated with reduced health system delays. Conclusion Despite a relatively shorter diagnostic delay of TB than in the other regions in China, there was vital social-demographic and spatial heterogeneity in the occurrence of long delays in Shanghai. While the active case finding and rapid molecular diagnosis reduced the delay, novel targeted interventions are still required to address the challenges of TB diagnosis among both migrants and residents in this urban setting.

Sun, Ruoyao↗
DOE OSTI2024

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Negative Medium-Voltage Direct Current Discharges in Air under Simulated Sub-Atmospheric Pressures for All-Electric Aircraft

The increase in the global temperature due to greenhouse gas emissions is a major concern to the world. To achieve the goal of zero emissions by 2050 in the USA the practical realization of all-electric vehicles, particularly all-electric aircraft (AEA), is important. For the design of electrical power systems (EPSs) in all-electric aircraft, a bipolar medium-voltage direct current (MVDC) system of ±5 kV is being investigated. However, several challenges manifest when using such voltages in a low-pressure environment. One of the main challenges is the partial discharge (PD) behavior of the insulation. It is important to study the PD behavior of the insulation by simulating the aviation environment in the lab. This work aimed to study the partial discharge behavior of air under a negative DC voltage in a needle-to-plane electrode geometry by simulating the aviation pressures in the lab. The partial discharge inception voltage (PDIV) and the breakdown voltage (BDV) show an obvious pressure-dependent variation. Regression analysis was performed to better understand the relationship between the PDIV and pressures. Plots were drawn for the average discharge current at each voltage step until breakdown. This paper’s findings can provide valuable insight into the design of EPS for an AEA. To the best of our knowledge, such a study has not been carried out to date.

air discharge↗
DOE OSTI2021

Effects of Low Dose Space Radiation Exposures on the Splenic Metabolome

Future space missions will include a return to the Moon and long duration deep space roundtrip missions to Mars. Leaving the protection that Low Earth Orbit provides will unavoidably expose astronauts to higher cumulative doses of space radiation, in addition to other stressors, e.g., microgravity. Immune regulation is known to be impacted by both radiation and spaceflight and it remains to be seen whether prolonged effects that will be encountered in deep space can have an adverse impact on health. In this study, we investigated the effects in the overall metabolism of three different low dose radiation exposures (γ-rays, 16O, and 56Fe) in spleens from male C57BL/6 mice at 1, 2, and 4 months after exposure. Forty metabolites were identified with significant enrichment in purine metabolism, tricarboxylic acid cycle, fatty acids, acylcarnitines, and amino acids. Early perturbations were more prominent in the γ irradiated samples, while later responses shifted towards more prominent responses in groups with high energy particle irradiations. Regression analysis showed a positive correlation of the abundance of identified fatty acids with time and a negative association with γ-rays, while the degradation pathway of purines was positively associated with time. Taken together, there is a strong suggestion of mitochondrial implication and the possibility of long-term effects on DNA repair and nucleotide pools following radiation exposure.

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

An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO 2 , ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm 2 for the ARMP and 0.26 g/cm 2 for the AERONET datasets, with bias of 0.072 g/cm 2 and 0.096 g/cm 2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Corn response to climate stress detected with satellite-based NDVI time series

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmental factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Bias corrections of GOSAT SWIR XCO 2 and XCH 4 with TCCON data and their evaluation using aircraft measurement data

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO 2 (XCO 2 ) and CH 4 (XCH 4 ) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO 2 and XCH 4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO 2 /XCH 4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

54 ENVIRONMENTAL SCIENCES↗