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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Modeling Measurement Error in Dose-Response Models of Community Annoyance to Low-Noise Supersonic Flight

The primary research goal of the forthcoming NASA Quesst mission community test campaign is to collect representative community response data in support of the development of supersonic overflight noise certification standards. Beginning in 2026, NASA will fly the novel X-59 demonstrator aircraft over select communities in United States in order to demonstrate the possibility of low-noise supersonic flight over land and to collect objective measurements and subjective data on the perceptual experience of this new noise source. It is believed that a regression of a binary perceptual response (‘highly annoyed’ or ‘not’) on estimated noise levels (doses, measured in decibels) will provide a useful dose-response relationship for regulators. However, as these estimated doses will be subject to measurement error, naïve estimators of regression coefficients are inconsistent and slopes may be subject to attenuation bias. In this presentation, I contrast functional modeling of measurement error via simulation extrapolation (SIMEX) with structural Bayesian measurement error models. These methods are applied to available data collected during two NASA risk reduction studies in California in 2011 and Texas in 2018. I’ll conclude noting that in the presence of nonnegligible measurement errors, probabilities of annoyance may be overpredicted for low noise levels and underpredicted for high noise levels, therefore, methods of correcting for measurement error will be necessary to improve the utility of the dose-response relationship for policy-making purposes.

simulation↗
NASA NTRSDate not supplied

Dose Error Correction Using Simulation Extrapolation for Community Noise Dose-Response Modeling

The objective of this work is to provide a framework to account and correct for dose error in dose-response modeling due to measurement uncertainty. Error in noise measurements, especially in the case of limited monitoring locations in a community, can lead to an attenuation or misestimation of parameters in dose-response models. This error can result in overpredicted annoyance at lower doses and underpredicted annoyance at higher doses. Simulated data in the present work are based on previous NASA community studies and incorporate a notional design for future studies with the X-59 aircraft. Several populations of different annoyance response sensitivities are included. Simulation extrapolation (SIMEX, Cook and Stefanski 1994) is used to correct for the dose error in a simple, fully pooled logistic regression. Results indicate the negative impact of attenuation is greatly diminished for all amounts of dose error considered, regardless of a population’s annoyance sensitivity. Therefore, SIMEX can help produce a more accurate dose-response relationship.

SIMEX↗
DOE OSTI2020

The Susceptibility of Juvenile American Shad to Rapid Decompression and Fluid Shear Exposure Associated with Simulated Hydroturbine Passage

Throughout many areas of their native range, American shad (Alosa sapidissima) and other Alosine populations are in decline. Though several conditions have influenced these declines, hydropower facilities have had significant negative effects on American shad populations. Hydropower facilities expose ocean-migrating American shad to physical stressors during passage through hydropower facilities, including strike, rapid decompression, and fluid shear. In this laboratory-based study, juvenile American shad were exposed separately to rapid decompression and fluid shear to determine their susceptibility to these stressors and develop dose–response models. These dose–response relationships can help guide the development and/or operation of hydropower turbines and facilities to reduce the negative effects to American shad. Relative to other species, juvenile American shad have a high susceptibility to both rapid decompression and fluid shear. Reducing or preventing exposure to these stressors at hydropower facilities may be a potential method to assist in the effort to restore American shad populations.

13 HYDRO ENERGY↗
NASA NTRSDate not supplied

The effect of modeling dose uncertainty on low-boom community noise dose-response curves

In logistic dose-response modeling, failing to account for uncertainty in estimated doses can cause an artificial flattening or attenuation of the slope of the summary curve. In Lee et al. [J. Acoust. Soc. Am. 147(4), pp. 2222-2234 (2020)], data from two NASA low-amplitude sonic boom community noise survey tests were modeled using a Bayesian multilevel logistic regression (MLR) statistical model that assumed there was no uncertainty in the noise dose estimates. However, in these community tests, the noise dose uncertainty was estimated by Page et al. [NASA/CR-2014-218180 and NASA/CR-2020-220589/Volume I] using a leave-one-out method. In the current work, a term was added to extend the Bayesian MLR model to account for the estimated noise dose uncertainty quantified in the Page et al. analyses. This uncertainty term was included in two ways, either as classical or as Berkson uncertainty, and yield similar results. When the uncertainty is accounted for in the Bayesian MLR model, the dose-response curves become 5-10% steeper, but the difference in the noise dose that elicits a 5% highly annoyed response is small (less than 1 dB). This result is encouraging for future X-59 community tests whose survey area will be sparsely populated with noise monitors.

X-59↗
DOE OSTI2021

A Primer on Dose-Response Data Modeling in Radiation Therapy

An overview of common approaches used to assess a dose response for radiation therapy–associated endpoints is presented, using lung toxicity data sets analyzed as a part of the High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic effort as an example. Each component presented (eg, data-driven analysis, dose-response analysis, and calculating uncertainties on model prediction) is addressed using established approaches. Specifically, the maximum likelihood method was used to calculate best parameter values of the commonly used logistic model, the profile-likelihood to calculate confidence intervals on model parameters, and the likelihood ratio to determine whether the observed data fit is statistically significant. The bootstrap method was used to calculate confidence intervals for model predictions. Correlated behavior of model parameters and implication for interpreting dose response are discussed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2022

Simulation and Application of Bayesian Dose Uncertainty Modeling for Low-Boom Community Noise Surveys

In dose-response modeling, failing to account for dose uncertainty can cause artificial flattening of the estimated slope of the dose-response curve. Previous analyses of NASA sonic boom community noise survey data utilized a Bayesian multilevel logistic regression model, which did not account for dose uncertainty. The current work extends the model to account for either classical or Berkson dose uncertainty. The extended model is applied to two simulated dose-response datasets to illustrate conditions under which the dose uncertainty term does and does not correct for the artificial flattening introduced by dose uncertainty. Finally, the extended model is applied to two previous NASA sonic boom community noise surveys. The resulting dose-response curve slope for the average participant is 5 to 10% steeper, but the difference in the noise dose that elicits a 5% highly annoyed response is small (less than 1 dB). The difference remains insignificant when producing population summary dose-response curves. Commentary is included on applicability to future X-59 low-boom community noise survey data modeling and analysis.

X-59↗
NASA NTRS2014

Induction of Chromosomal Aberrations at Fluences of Less Than One HZE Particle per Cell Nucleus

The assumption of a linear dose response used to describe the biological effects of high LET radiation is fundamental in radiation protection methodologies. We investigated the dose response for chromosomal aberrations for exposures corresponding to less than one particle traversal per cell nucleus by high energy and charge (HZE) nuclei. Human fibroblast and lymphocyte cells where irradiated with several low doses of <0.1 Gy, and several higher doses of up to 1 Gy with O (77 keV/ (long-s)m), Si (99 keV/ (long-s)m), Fe (175 keV/ (long-s)m), Fe (195 keV/ (long-s)m) or Fe (240 keV/ (long-s)m) particles. Chromosomal aberrations at first mitosis were scored using fluorescence in situ hybridization (FISH) with chromosome specific paints for chromosomes 1, 2 and 4 and DAPI staining of background chromosomes. Non-linear regression models were used to evaluate possible linear and non-linear dose response models based on these data. Dose responses for simple exchanges for human fibroblast irradiated under confluent culture conditions were best fit by non-linear models motivated by a non-targeted effect (NTE). Best fits for the dose response data for human lymphocytes irradiated in blood tubes were a NTE model for O and a linear response model fit best for Si and Fe particles. Additional evidence for NTE were found in low dose experiments measuring gamma-H2AX foci, a marker of double strand breaks (DSB), and split-dose experiments with human fibroblasts. Our results suggest that simple exchanges in normal human fibroblasts have an important NTE contribution at low particle fluence. The current and prior experimental studies provide important evidence against the linear dose response assumption used in radiation protection for HZE particles and other high LET radiation at the relevant range of low doses.

Hada, Megumi↗
NASA NTRS2023

Multilevel Logistic Regression with Random Slope for Community Annoyance Survey Data

This paper documents recent dose-response modeling work at NASA in anticipation of follow-on work by a contactor. Specifically, this paper compares the results of a Bayesian MLR model with a fixed slope to one with a random slope using WSPR and QSF18 data. Previously reported dose-response modeling efforts of WSPR and QSF18 data have used a MLR model with a fixed slope term. A random slope may more accurately depict the dose-response relationship of individuals in the efforts to produce a population summary dose-response curve. Results of a fixed versus random slope model with WSPR and QSF18 data indicate minimal difference between the modeling methods. The simpler fixed slope model is preferable for these data, but these results do not preclude consideration of a random slope term in modeling efforts of future X-59 community test data.

dose-response↗
NASA NTRS2010

Non-Targeted Effects and the Dose Response for Heavy Ion Tumorigenesis

BACKGROUND: There is no human epidemiology data available to estimate the heavy ion cancer risks experienced by astronauts in space. Studies of tumor induction in mice are a necessary step to estimate risks to astronauts. Previous experimental data can be better utilized to model dose response for heavy ion tumorigenesis and plan future low dose studies. DOSE RESPONSE MODELS: The Harderian Gland data of Alpen et al.[1-3] was re-analyzed [4] using non-linear least square regression. The data set measured the induction of Harderian gland tumors in mice by high-energy protons, helium, neon, iron, niobium and lanthanum with LET s ranging from 0.4 to 950 keV/micron. We were able to strengthen the individual ion models by combining data for all ions into a model that relates both radiation dose and LET for the ion to tumor prevalence. We compared models based on Targeted Effects (TE) to one motivated by Non-targeted Effects (NTE) that included a bystander term that increased tumor induction at low doses non-linearly. When comparing fitted models to the experimental data, we considered the adjusted R2, the Akaike Information Criteria (AIC), and the Bayesian Information Criteria (BIC) to test for Goodness of fit.In the adjusted R2test, the model with the highest R2values provides a better fit to the available data. In the AIC and BIC tests, the model with the smaller values of the summary value provides the better fit. The non-linear NTE models fit the combined data better than the TE models that are linear at low doses. We evaluated the differences in the relative biological effectiveness (RBE) and found the NTE model provides a higher RBE at low dose compared to the TE model. POWER ANALYSIS: The final NTE model estimates were used to simulate example data to consider the design of new experiments to detect NTE at low dose for validation. Power and sample sizes were calculated for a variety of radiation qualities including some not considered in the Harderian Gland data set and with different background tumor incidences. We considered different experimental designs with varying number of doses and varying low doses dependant on the LET of the radiation. The optimal design to detect a NTE for an individual ion had 4 doses equally spaced below a maximal dose where bending due to cell sterilization was < 2%. For example at 100 keV/micron we would irradiate at 0.03 Gy, 0.065 Gy, 0.13 Gy, and 0.26 Gy and require 850 mice including a control dose for a sensitivity to detect NTE with 80% power. Sample sizes could be improved by combining ions similar to the methods used with the Harderian Gland data.

Chappelli, Lori J.↗
NASA NTRS2013

Estimation of the Dose and Dose Rate Effectiveness Factor

Current models to estimate radiation risk use the Life Span Study (LSS) cohort that received high doses and high dose rates of radiation. Transferring risks from these high dose rates to the low doses and dose rates received by astronauts in space is a source of uncertainty in our risk calculations. The solid cancer models recommended by BEIR VII [1], UNSCEAR [2], and Preston et al [3] is fitted adequately by a linear dose response model, which implies that low doses and dose rates would be estimated the same as high doses and dose rates. However animal and cell experiments imply there should be curvature in the dose response curve for tumor induction. Furthermore animal experiments that directly compare acute to chronic exposures show lower increases in tumor induction than acute exposures. A dose and dose rate effectiveness factor (DDREF) has been estimated and applied to transfer risks from the high doses and dose rates of the LSS cohort to low doses and dose rates such as from missions in space. The BEIR VII committee [1] combined DDREF estimates using the LSS cohort and animal experiments using Bayesian methods for their recommendation for a DDREF value of 1.5 with uncertainty. We reexamined the animal data considered by BEIR VII and included more animal data and human chromosome aberration data to improve the estimate for DDREF. Several experiments chosen by BEIR VII were deemed inappropriate for application to human risk models of solid cancer risk. Animal tumor experiments performed by Ullrich et al [4], Alpen et al [5], and Grahn et al [6] were analyzed to estimate the DDREF. Human chromosome aberration experiments performed on a sample of astronauts within NASA were also available to estimate the DDREF. The LSS cohort results reported by BEIR VII were combined with the new radiobiology results using Bayesian methods.

Chappell, L.↗
DOE OSTI2023

PNNL Superfund Research Program Analytics (srpAnalytics Data Release v.1.0)

The OSU/PNNL superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. The PNNL Superfund have implemented a dose-response modelling pipeline to calculate benchmark dose parameters that enable the comparison of potency across chemicals and phenotypes. Our portal provides public access to this dataset and an interactive web site designed to enable exploration and re-use of this data by the scientific community at http://srp.pnnl.gov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

PNNL Superfund Research Program Analytics (srpAnalytics Data Release v.1.0)

The OSU/PNNL superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. The PNNL Superfund have implemented a dose-response modelling pipeline to calculate benchmark dose parameters that enable the comparison of potency across chemicals and phenotypes. Our portal provides public access to this dataset and an interactive web site designed to enable exploration and re-use of this data by the scientific community at http://srp.pnnl.gov.

OSU-PNNL Superfund Research Program Center SRP↗
DOE OSTI2023

The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes

The OSU/PNNL Superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons (PAHs) at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. Toward this end, we have linked over 150 PAHs found at Superfund sites to the effect of these same chemicals in zebrafish, creating a rich dataset that links environmental exposure to biological response. To quantify this response, we have implemented a dose-response modelling pipeline to calculate benchmark dose parameters which enable potency comparison across over 500 chemicals and 10 zebrafish-specific phenotypes. Our portal provides public access to this dataset via an interactive web site designed to support exploration and re-use of these data by the scientific community at http://srp.pnnl.gov.

Gosline, Sara JC↗
NASA NTRS2022

The Importance of Internal Climate Variability in Climate Impact Projections

Uncertainty in climate projections is driven by three components: scenario uncertainty, intermodel uncertainty, and internal variability. Although socioeconomic climate impact studies increasingly take into account the first two components, little attention has been paid to the role of internal variability, although underestimating this uncertainty may lead to underestimating the socioeconomic costs of climate change. Using large ensembles from seven coupled general circulation models with a total of 414 model runs, we partition the climate uncertainty in classic dose–response models relating county-level corn yield, mortality, and per-capita gross domestic product to temperature in the continental United States. The partitioning of uncertainty depends on the time frame of projection, the impact model, and the geographic region. Internal variability represents more than 50% of the total climate uncertainty in certain projections, including mortality projections for the early 21st century, although its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early-century and midcentury projections, projections in regions with high internal variability such as the Upper Midwest United States, and impacts driven by nonlinear relationships.

climate impacts↗
DOE OSTI2021

MACCS Theory Manual

This report describes the models of the MACCS computer code as presented in MACCS Version 3.10.0. The purpose of MACCS is to simulate the impact of severe accidents at nuclear power plants on the surrounding environment. MACCS has been developed by Sandia National Laboratories for the U.S. Nuclear Regulatory Commission. From a given release of radioactive material into the atmosphere, MACCS estimates the extent and magnitude of radiological contamination, offsite doses, protective actions, socioeconomic impacts and costs, and health effects. Since the weather at the time of an accident is not predictable, MACCS supports various sampling options to run a representative set of simulations to evaluate weather variability. MACCS simulates atmospheric transport with a straight-line Gaussian plume segment model. From the estimated air and ground concentrations, MACCS models dose projections through several dose exposure pathways. These exposures can be offset by protective actions during the emergency response and long-term recovery of the accident. MACCS users directly specify the evacuation and sheltering area, while other protective actions (e.g., relocation, farmland restrictions, decontamination) are based on user-specified dose or concentration limits. While protective actions help reduce dose accumulation, they also cause social and economic impacts. MACCS models the extent of displaced individuals and land contamination, and the cost of offsite property damage, economic disruptions, and various accident expenditures caused by protective actions. Finally, from the dose accumulation, MACCS estimates early and stochastic health effects according to dose-response models. The purpose of consequence analyses is to be able to understand and estimate the impact of nuclear accidents. Consequence analysis is an essential tool to inform determinations of adequate protection of the public, to understand nuclear power hazards, to measure the value of regulations, and to help us appreciate the importance of nuclear safety. As such, MACCS has a variety of regulatory uses including environmental analyses (10 CFR 51.53, 52.47), regulatory cost-benefit analyses, backfit analyses (10 CFR 50.109), consequence analysis studies such as SOARCA (NUREG-1935), Level 3 PRA studies, and risk-informing of emergency planning (10 CFR 50 App. E and 50.47). This report updates the previous MACCS theory manual (NUREG/CR-4691 Vol. 2; Chanin, Sprung, Ritchie, & Jow, 1990) and accompanies the MACCS User's Guide (SAND-2021-1588) that describes the use and input requirements of the graphical user interface of MACCS known as WinMACCS. The MACCS User's Guide is also a reference guide that describes data input file formats, describes various software components in the MACCS code suite, and provides a set of example tutorials for running WinMACCS. Also, soon to be published is a MACCS input parameter guidance report (NUREG/CR-7270) that provides technical bases for commonly used MACCS input values. This page left blank

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2010

Non-Targeted Effects and the Dose Response for Heavy Ion Tumorigenesis

There is no human epidemiology data available to estimate the heavy ion cancer risks experienced by astronauts in space. Studies of tumor induction in mice are a necessary step to estimate risks to astronauts. Previous experimental data can be better utilized to model dose response for heavy ion tumorigenesis and plan future low dose studies.

Chappell, Lori J.↗