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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to outbred mice data

Accurately quantifying the differences in radiation quality between space and terrestrial environments is important for predicting health risks for astronauts.Recently, Edmundson et al. 2020[1] provided valuable new results from out-bred mice linking tumor induction and genetic background aer exposure to low and high-LET radiation. The goal of the current study is to more rigorously estimate a relative biological effectiveness (RBE) factor by leveraging the solid tumor data from Edmundson et al. 2020. Excess relative risk (ERR) models and excess absolute risk (EAR) models were fit using Poisson regression similar to the models that the Radiation Effects Research Foundation uses to fit atomic bomb survivor data. Linear ERR and EAR slopes were simulated usingBayesian analyses, and RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope using the full posterior distribution.

Lori J. Chappell↗
NASA NTRSDate not supplied

Watching Without Seeing a Tool to Surveil Astronaut Health Outcomes While Maintaining Astronaut Medical Privacy

BACKGROUND The Privacy Act of 1974 regulates the use a nd disclosure of personally identifiable information by US Federal agencies. The Act applies to biographical, financial, a nd other identity-linked information, a s well a s personal health information (PHI). As such, the use of astronaut PHI is limited to authorized personnel for preapproved uses, with data reporting often limited to aggregated information about groups. These limitations on the use a nd reporting of astronaut PHI complicates surveillance efforts, wherein epidemiologists a t the National Aeronautics and Space Administration (NASA)monitor the incidence of targeted health conditions in the astronaut population, or to discover emerging trends of aging and disease. Stratification on one or more covariates –particularly time-period, sex, a nd mission participation –can lead to extremely small datasets such that the reporting of results is potentially attributable to individuals. An additional challenge is the small size of the astronaut population, both in terms of numbers of individuals a s well a s in terms of density of exposure time. Such small datasets yield volatile rate estimates that are difficult to interpret. To a id the epidemiological surveillance efforts, a surveillance tool is required that can (a) satisfy the need for rapid computation of condition-specific incidence and mortality rates; (b) improve the statistical estimates of these estimated rates; and (c) maintain astronaut privacy. Here we describe a nd demonstrate such a tool. METHODS We devised a system that models incidence a nd mortality rates rather than calculating them directly. This ha s the advantage of using all the available data to derive the estimates, lea ding to rates that a re not attributable to any one individual, a nd a re a s numerically stable a s they can be given the extremely limited data. The system models disease endpoints using a Poisson regression model with exposure density (measured in person-years) a s a n offset term. By doing so the model is estimating event counts per person-year, equivalent to modeling the rates directly. It uses a standard (pre-specified)set of covariates; the system does not engage in “model-building” as model parsimony is not the goa l. Instead, it is explicitly recognized that if a covariate is not statistically significant a nd not a confounder then it will likely have very little effect on the estimate of the incidence a nd mortality rates. Users are able to specify the disease endpoint of interest and the covariates over which they would like to stratify. The system then uses the resulting model to compute the estimated rates for the user-chosen configuration of variables as visualizes those either over an age range within a specified time-period, or over time for astronauts with a specified age range. RESULTS The first iteration of the tool computes incidence a nd mortality rates for cardiovascular conditions and cancers. Code ha s been developed to retrieve the appropriate data from the IMPALA analysis platform, compute the models for incidence a nd mortality, a nd then use those models to generate the corresponding rate curves. A companion graphical user interface allows the user to specify the curves and visualize the results. CONCLUSIONS It is important to note that the rapid surveillance tool described here is neither meant to be a definitive assessment of the incidence or mortality of any particular disease or condition in the astronaut population, nor is it meant to be used for research purposes. Rather, it is meant as an early indicator that in-depth investigation may be warranted. By automating a repetitive process and leveraging carefully curated astronaut health outcomes, the tool makes possible a rapid “first look” into known areas of concern, and, if used judiciously, may surface new areas of concern for long-term astronaut health. This work is supported in part by the Translational Research Institute for Space Health (TRISH) through NASA Cooperative Agreement NNX16AO69A.

R J Reynolds↗
NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to three combined HZE-irradiated mouse datasets

Radiation health risks are predominantly derived from low linear energy transfer (LET) terrestrial exposures; however, space radiation includes exposure to high-LET and high-charge, high-energy (HZE) particles. Accurately quantifying the differences in radiation quality between the space and terrestrial radiation environments is important for assessing and predicting health risks for astronauts. Weil et al. 2009 and 2014 used two different inbred mouse strains to study differences in hepatocellular carcinoma (HCC) tumorigenesis after exposures to low- and high- LET radiation. More recently, Edmundson et al. 2020 provided valuable new tumor data in outbred mice that were exposed to low- and high-LET radiation. The present study aims to rigorously investigate a relative biological effectiveness (RBE) factor by leveraging the HCC tumor data from Weil et al. 2009, Weil et al. 2014, and Edmundson et al. 2020. The three experiments were similarly designed, allowing the raw data to be combined into a pooled dataset to estimate excess relative risk (ERR) and excess absolute risk (EAR) models using Bayesian Poisson regression. These effect estimates from the pooled data provide greater power to calculate a data driven RBE. Extensive sensitivity analyses test the robustness of RBE estimates to various model assumptions. The following questions will be explored through the sensitivity analyses: • Is the shape of the dose response different for low-LET radiation and HZE radiation, indicating that RBE is a function of dose? • Does attained age modify the effect estimates differently for low-LET radiation and HZE radiation, indicating RBE is a function of attained age? • Are the effect estimates and RBE estimates different for inbred mouse strains and outbred mouse strains? • Do assumptions about differences in ERR models and EAR models change the estimated RBE? Additional studies would be needed to validate the findings from these exploratory analyses.

Lori J. Chappell↗
NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to three combined HZE-irradiated mouse datasets

Radiation health risks are predominantly derived from low linear energy transfer (LET) terrestrial exposures; however, space radiation includes exposure to high-LET and high-charge, high-energy (HZE) particles. Accurately quantifying the differences in radiation quality between the space and terrestrial radiation environments is important for assessing and predicting health risks for astronauts. Weil et al. 2009 and 2014 used two different inbred mouse strains to study differences in hepatocellular carcinoma (HCC) tumorigenesis after exposures to low- and high- LET radiation[1-2]. More recently, Edmondson et al. 2020 provided valuable new tumor data in outbred mice that were exposed to low- and high-LET radiation[3]. The present study aims to rigorously investigate a relative biological effectiveness (RBE) factor by leveraging the HCC tumor data from the three datasets[1-3]. The three experiments were similarly designed, allowing the raw data to be combined into a pooled dataset to estimate excess relative risk (ERR) and excess absolute risk (EAR) models using Bayesian Poisson regression.

Lori J. Chappell↗
NASA NTRSDate not supplied

Evaluating the Feasibility of Data Simulation for Radiation Research: Estimating RBE Values for HZE Radiation

As the drive for deep space exploration intensifies, a comprehensive understanding of the health effects of radiation exposure becomes paramount to the future of human space flight. However, epidemiological data for radiation exposure, particularly to high-energy (HZE) ions, is limited, partially due to the financial and logistical costs of radiation studies. As an alternative, this study aims to assess the viability of data simulation strategies to accurately model potential study parameters prior to utilizing laboratory conditions. This study estimates a relative biological effectiveness (RBE) factor based on the solid tumor data for outbred mice provided by Edmundson et al. 2020. Excess relative risk (ERR) models for HZE-Fe ions and gamma radiation were estimated using Poisson regression with Weibull models to represent the background solid tumor hazard without radiation. RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope. The parameters from these models were then applied to simulate iterations of 300 datasets across HZE-Fe doses of 0.05, 0.2, 0.4, and 0.75 Gy; gamma radiation doses of 0.75, 2, and 3 Gy, as well as an unirradiated control group. Sample size per dose varied from 100-500 mice across simulations (800-4000 mice total per dataset). 1500 datasets were generated total (300 each for sample sizes 800, 1600, 3200, and 4000). For each dataset, ERR per radiation type and RBE for HZE-Fe were calculated. The RBE from the Edmondson data was calculated to be 5.55. The RBEs from the simulated data converged around this value across the increasing sample sizes. For n = 4000, the mean RBE across the 300 datasets was 5.71 (95% CI: 5.47-5.95). These results suggest that RBEs calculated from simulated data are credible. Based on this exploration, data simulation is a viable method of testing radiation studies. It provides a method of testing study assumptions and refining research questions prior to embarking on costly laboratory experiments.

Sneha Mehta↗
DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Analyzing count data with measurement error

In this article, we analyze observed count data such as the number of defects in a steel product where the observed counts are the true counts measured with errors. We account for the measurement error by using a measurement error model based on a latent lognormal (LLN) distribution. We consider making inference about a single population (e.g., from samples of a production lot) and a regression model (e.g., from runs of a designed experiment), where the measurement system properties are known, that is, the parameters of the LLN distribution are known. Then, we consider simultaneous inference for the single population and regression model as well as the measurement system. We demonstrate the proposed methodology with both simulated and real observed counts.

42 ENGINEERING↗
DOE OSTI2022

Analysis of overlapping count data

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Additionally, proofs of the paper’s results are also included in the supplemental material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

How much have adverse occupational health outcomes among construction workers improved over time? Evidence from 25 years of medical screening

Background Construction workers have always had a high risk of occupational illnesses. We used 25 years of data from a medical screening program serving older construction workers to determine how much health outcomes have improved over the past 60 years. Methods We investigated changes in relative risk for chest radiographs consistent with pneumoconiosis, COPD by spirometry, lung cancer mortality, and audiometry-assessed hearing impairment among workers participating in a medical screening program. Results were stratified by decade of first construction employment: before 1960, 1960–1969, 1970–1979, 1980–1989, and after 1990. Poisson and Cox regression analyses assessed relative risk by decade adjusted for age, sex, smoking, and years of construction trade work. Results Subjects were 94% male and, on average, 60 years old with 25 years of construction work. When compared to workers employed before 1960, those first employed after 1990 experienced the following reductions in model-adjusted relative risks: chronic obstructive pulmonary disease, 32%; all pneumoconiosis, 68%; parenchymal abnormalities, 35%; pleural abnormalities, 71%; hearing impairment, 20%; and lung cancer mortality, 48%. Risks started to decline in the 1960s with greatest reductions among workers first employed after 1970. Conclusions This study demonstrates the positive impact that adoption of occupational health protections have had over the past 60 years. The greatest risk reductions were observed for outcomes with strong regulatory and legal incentives to reduce exposures and associated risks, such as those associated with inhalation hazards (asbestos and silica), while lowest improvement was for hearing impairment, for which little regulatory enforcement and few prevention incentives have been adopted.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1984

Long-wavelength Magnetic and Gravity Anomaly Correlations of Africa and Europe

Preliminary MAGSAT scalar magnetic anomaly data were compiled for comparison with long-wavelength-pass filtered free-air gravity anomalies and regional heat-flow and tectonic data. To facilitate the correlation analysis at satellite elevations over a spherical-Earth, equivalent point source inversion was used to differentially reduce the magnetic satellite anomalies to the radial pole at 350 km elevation, and to upward continue the first radial derivative of the free-air gravity anomalies. Correlation patterns between these regional geopotential anomaly fields are quantitatively established by moving window linear regression based on Poisson's theorem. Prominent correlations include direct correspondences for the Baltic Shield, where both anomalies are negative, and the central Mediterranean and Zaire Basin where both anomalies are positive. Inverse relationships are generally common over the Precambrian Shield in northwest Africa, the Basins and Shields in southern Africa, and the Alpine Orogenic Belt. Inverse correlations also presist over the North Sea Rifts, the Benue Rift, and more generally over the East African Rifts. The results of this quantitative correlation analysis support the general inverse relationships of gravity and magnetic anomalies observed for North American continental terrain which may be broadly related to magnetic crustal thickness variations.

Vonfrese, R. R. B.↗
NASA NTRS1985

Long-wavelength magnetic and gravity anomaly correlations on Africa and Europe

Preliminary MAGSAT scalar magnetic anomaly data were compiled for comparison with long-wavelength-pass filtered free-air gravity anomalies and regional heat-flow and tectonic data. To facilitate the correlation analysis at satellite elevations over a spherical-Earth, equivalent point source inversion was used to differentially reduce the magnetic satellite anomalies to the radial pole at 350 km elevation, and to upward continue the first radial derivative of the free-air gravity anomalies. Correlation patterns between these regional geopotential anomaly fields are quantitatively established by moving window linear regression based on Poisson's theorem. Prominent correlations include direct correspondences for the Baltic shield, where both anomalies are negative, and the central Mediterranean and Zaire Basin where both anomalies are positive. Inverse relationships are generally common over the Precambrian Shield in northwest Africa, the Basins and Shields in southern Africa, and the Alpine Orogenic Belt. Inverse correlations also presist over the North Sea Rifts, the Benue Rift, and more generally over the East African Rifts. The results of this quantitative correlation analysis support the general inverse relationships of gravity and magnetic anomalies observed for North American continental terrain which may be broadly related to magnetic crustal thickness variations.

Vonfrese, R. R. B.↗
DOE OSTI2022

Foodborne illness outbreaks linked to unpasteurised milk and relationship to changes in state laws – United States, 1998–2018

Consumption of unpasteurised milk in the United States has presented a public health challenge for decades because of the increased risk of pathogen transmission causing illness outbreaks. We analysed Foodborne Disease Outbreak Surveillance System data to characterise unpasteurised milk outbreaks. Using Poisson and negative binomial regression, we compared the number of outbreaks and outbreak-associated illnesses between jurisdictions grouped by legal status of unpasteurised milk sale based on a May 2019 survey of state laws. During 2013–2018, 75 outbreaks with 675 illnesses occurred that were linked to unpasteurised milk; of these, 325 illnesses (48%) were among people aged 0–19 years. Of 74 single-state outbreaks, 58 (78%) occurred in states where the sale of unpasteurised milk was expressly allowed. Compared with jurisdictions where retail sales were prohibited (n = 24), those where sales were expressly allowed (n = 27) were estimated to have 3.2 (95% CI 1.4–7.6) times greater number of outbreaks; of these, jurisdictions where sale was allowed in retail stores (n = 14) had 3.6 (95% CI 1.3–9.6) times greater number of outbreaks compared with those where sale was allowed on-farm only (n = 13). This study supports findings of previously published reports indicating that state laws resulting in increased availability of unpasteurised milk are associated with more outbreak-associated illnesses and outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Risk Ratio and Risk Difference Estimation in Case-cohort Studies

Background: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. Methods: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. Results: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. Conclusion: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Quantile regression-enriched event modeling framework for dropout analysis in high-temperature superconductor manufacturing

High-temperature superconductor (HTS) tapes have shown promising characteristics of high critical current, which are prerequisites for applications in high-field magnets. Due to the unstable growth conditions in the HTS manufacturing process, however, the frequent occurrences of dropouts in the critical current impede the consistent performance of HTS tapes. To manufacture HTS tapes with large scale, high yield, and uniform performance, it is essential to develop novel data analysis approaches for modeling the dropouts and identifying the related important process parameters. Conventional methods for modeling recurrent events, such as the point process, require the extraction of events from quality measurements. As the critical current is a continuous process, it may not comprehensively represent the drop patterns by transforming the time-series measurements into a set of events. Here, to solve this issue, we develop a novel quantile regression-enriched event modeling (QREM) framework that integrates the non-homogeneous Poisson process for modeling the occurrence of dropouts and the quantile regression for capturing the drop patterns. By incorporating the feature selection and regularization, the proposed framework identifies a set of significant process parameters that can potentially cause the dropouts of HTS tapes. The proposed method is tested on real HTS tapes produced using an advanced manufacturing process, successfully identifying important parameters that influence dropout events including the substrate temperature and voltage. The results demonstrate that the proposed QREM method outperforms the standard point process in predicting the occurrence of dropouts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]↗
NASA NTRS1977

Utility of correlation techniques in gravity and magnetic interpretation

Internal correspondence uses Poisson's Theorem in a moving-window linear regression analysis between the anomalous first vertical derivative of gravity and total magnetic field reduced to the pole. The regression parameters provide critical information on source characteristics. The correlation coefficient indicates the strength of the relation between magnetics and gravity. Slope value gives delta j/delta sigma estimates of the anomalous source. The intercept furnishes information on anomaly interference. Cluster analysis consists of the classification of subsets of data into groups of similarity based on correlation of selected characteristics of the anomalies. Model studies are used to illustrate implementation and interpretation procedures of these methods, particularly internal correspondence. Analysis of the results of applying these methods to data from the midcontinent and a transcontinental profile shows they can be useful in identifying crustal provinces, providing information on horizontal and vertical variations of physical properties over province size zones, validating long wavelength anomalies, and isolating geomagnetic field removal problems.

Chandler, V. W.↗
NASA NTRS1995

Inverse sequential procedures for the monitoring of time series

When one or more new values are added to a developing time series, they change its descriptive parameters (mean, variance, trend, coherence). A 'change index (CI)' is developed as a quantitative indicator that the changed parameters remain compatible with the existing 'base' data. CI formulate are derived, in terms of normalized likelihood ratios, for small samples from Poisson, Gaussian, and Chi-Square distributions, and for regression coefficients measuring linear or exponential trends. A substantial parameter change creates a rapid or abrupt CI decrease which persists when the length of the bases is changed. Except for a special Gaussian case, the CI has no simple explicit regions for tests of hypotheses. However, its design ensures that the series sampled need not conform strictly to the distribution form assumed for the parameter estimates. The use of the CI is illustrated with both constructed and observed data samples, processed with a Fortran code 'Sequitor'.

Radok, Uwe↗