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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Modeling HCV cure after an ultra-short duration of therapy with direct acting agents

In cases of sustained-virological response (SVR or cure) after an ultra-short duration (≤ 27 days) of direct-acting antiviral (DAA)-based therapy, despite HCV being detected at end of treatment (EOT), have been reported. Established HCV mathematical models that predict the treatment duration required to achieve cure do not take into account the possibility that the infectivity of virus produced during treatment might be reduced. The aim of this study was to develop a new mathematical model that considers the fundamental and critical concept that HCV RNA in serum represents both infectious virus (V i ) and non-infectious virus (V ni ) in order to explain the observation of cure with ultrashort DAA therapy. Established HCV models were compared to the new mathematical model to retrospectively explain cure in 2 patients who achieved cure after 24 or 27 days of paritaprevir, ombitasvir, dasabuvir, ritonavir and ribavirin or sofosbuvir plus ribavirin, respectively. Fitting established models with measured longitudinal HCV viral loads indicated that in both cases, cure would not have been expected without an additional 3–6 weeks of therapy after the actual EOT. In contrast, the new model fits the observed outcome by considering that in addition to blocking V i and V ni production (ε~0.998), these DAA + ribavirin treatments further enhanced the ratio of V ni to V i , thus increasing the log (V ni /V i ) from 1 at pretreatment to 6 by EOT, which led to <1 infectious-virus particle in the extracellular body fluid (i.e., cure) prior to EOT. This new model can explain cure after short duration of DAA + ribavirin therapy by suggesting that a minimum 6-fold increase of log (V ni /V i ) results from drug-induced enhancement of the V ni /V i .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Correcting for telluric absorption: Methods, case studies, and release of the TelFit code

Ground-based astronomical spectra are contaminated by the Earth's atmosphere to varying degrees in all spectral regions. We present a Python code that can accurately fit a model to the telluric absorption spectrum present in astronomical data, with residuals of ∼3%-5% of the continuum for moderately strong lines. We demonstrate the quality of the correction by fitting the telluric spectrum in a nearly featureless A0V star, HIP 20264, as well as to a series of dwarf M star spectra near the 819 nm sodium doublet. We directly compare the results to an empirical telluric correction of HIP 20264 and find that our model-fitting procedure is at least as good and sometimes more accurate. The telluric correction code, which we make freely available to the astronomical community, can be used as a replacement for telluric standard star observations for many purposes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2014

The Rise and Fall of the Type Ib Supernova iPTF13bvn Not a Massive Wolf-Rayet Star

Context. We investigate iPTF13bvn, a core-collapse (CC) supernova (SN) in the nearby spiral galaxy NGC 5806. This object was discovered by the intermediate Palomar Transient Factory (iPTF) very close to the estimated explosion date and was classified as a stripped-envelope CC SN, likely of Type Ib. Furthermore, a possible progenitor detection in pre-explosion Hubble Space Telescope (HST) images was reported, making this the only SN Ib with such an identification. Based on the luminosity and color of the progenitor candidate, as well as on early-time spectra and photometry of the SN, it was argued that the progenitor candidate is consistent with a single, massive Wolf-Rayet (WR) star. Aims. We aim to confirm the progenitor detection, to robustly classify the SN using additional spectroscopy, and to investigate if our follow-up photometric and spectroscopic data on iPTF13bvn are consistent with a single-star WR progenitor scenario. Methods. We present a large set of observational data, consisting of multi-band light curves (UBVRI, g′r′i′z′) and optical spectra. We perform standard spectral line analysis to track the evolution of the SN ejecta. We also construct a bolometric light curve and perform hydrodynamical calculations to model this light curve to constrain the synthesized radioactive nickel mass and the total ejecta mass of the SN. Late-time photometry is analyzed to constrain the amount of oxygen. Furthermore, image registration of pre- and post-explosion HST images is performed. Results. Our HST astrometry confirms the location of the progenitor candidate of iPTF13bvn, and follow-up spectra securely classify this as a SN Ib. We use our hydrodynamical model to fit the observed bolometric light curve, estimating the total ejecta mass to be 1.9 solar mass and the radioactive nickel mass to be 0.05 solar mass. The model fit requires the nickel synthesized in the explosion to be highly mixed out in the ejecta. We also find that the late-time nebular r′-band luminosity is not consistent with predictions based on the expected oxygen nucleosynthesis in very massive stars. Conclusions. We find that our bolometric light curve of iPTF13bvn is not consistent with the previously proposed single massive WR-star progenitor scenario. The total ejecta mass and, in particular, the late-time oxygen emission are both significantly lower than what would be expected from a single WR progenitor with a main-sequence mass of at least 30 solar mass.

massive↗
DOE OSTI2018

Bayesian characterization and forward propagation of the uncertainty in thermodynamic models for redox active materials

Demonstrating the thermodynamic efficiency of hydrogen conversion processes using various materials is a critical step in developing new technologies for storing concentrated solar energy, and is largely accomplished by using a thermodynamic model derived from experimental data. A main goal of this project is to calculate the uncertainty of the thermodynamic efficiency by calculating the uncertainty of the components that feed into the efficiency. Many different models and data sets were used to test the workflow. First, the models were fit to the data using a Bayesian Inference and a method called Markov Chain Monte Carlo (MCMC), which found the maximum a priori parameters, and a posterior probability distribution of the parameters. Next, the different models were compared to each other using model evidence values. It was found that for cleaner data sets, overfitting had not yet been reached, and the most complicated model was ideal, but on the noisier data sets, the less complex models were favored because the more complicated models resulted in overfitting. Next, forward propagation was used to calculate the enthalpy change and its associated uncertainty. A few variations on the models were tried, such as fitting in a different variable, producing negligible or negative effects on the fits of the models. Thus, the original models were used. A sensitivity analysis was performed, and used to calculate the model error. On the cleaner data sets, there was very minimal experimental noise, and thus, all resulting error was from the model. With consideration of the model error, the models fit the data very well, and the simpler model had a high model error, as expected. All these components will then be used to calculate the thermodynamic efficiency of the different materials.

14 SOLAR ENERGY↗
NASA NTRS2010

Atmospheric and Fundamental Parameters of Stars in Hubble's Next Generation Spectral Library

Hubble's Next Generation Spectral Library (NGSL) consists of R approximately 1000 spectra of 374 stars of assorted temperature, gravity, and metallicity. We are presently working to determine the atmospheric and fundamental parameters of the stars from the NGSL spectra themselves via full-spectrum fitting of model spectra to the observed (extinction-corrected) spectrum over the full wavelength range, 0.2-1.0 micron. We use two grids of model spectra for this purpose: the very low-resolution spectral grid from Castelli-Kurucz (2004), and the grid from MARCS (2008). Both the observed spectrum and the MARCS spectra are first degraded in resolution to match the very low resolution of the Castelli-Kurucz models, so that our fitting technique is the same for both model grids. We will present our preliminary results with a comparison with those from the Sloan/Segue Stellar Parameter Pipeline, ELODIE, and MILES, etc.

Heap, Sally↗
DOE OSTI2025

HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data

High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm’s behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl .

Gorstein, Evan↗
NASA NTRS2010

Data Validation in the Kepler Science Operations Center Pipeline

We present an overview of the Data Validation (DV) software component and its context within the Kepler ScienceOperations Center (SOC) pipeline and overall Kepler Science mission. The SOC pipeline performs a transiting planetsearch on the corrected light curves for over 150,000 targets across the focal plane array. We discuss the DV strategy forautomated validation of Threshold Crossing Events (TCEs) generated in the transiting planet search. For each TCE, atransiting planet model is fitted to the target light curve. A multiple planet search is conducted by repeating the transitingplanet search on the residual light curve after the model flux has been removed; if an additional detection occurs, aplanet model is fitted to the new TCE. A suite of automated tests are performed after all planet candidates have beenidentified. We describe a centroid motion test to determine the significance of the motion of the target photocenterduring transit and to estimate the coordinates of the transit source within the photometric aperture; a series of eclipsingbinary discrimination tests on the parameters of the planet model fits to all transits and the sequences of odd and eventransits; and a statistical bootstrap to assess the likelihood that the TCE would have been generated purely by chancegiven the target light curve with all transits removed.

photometry↗
DOE OSTI2026

Improving neutrino-nuclei interaction models: Recommendations and case studies on Peelle’s Pertinent Puzzle

Improving the modeling of neutrino-nuclei interactions using data-driven methods is crucial for high-precision neutrino oscillation experiments. This paper investigates Peelle’s Pertinent Puzzle (PPP) in the context of neutrino measurements, a longstanding challenge to fitting theoretical models to experimental data. Inconsistencies in data-model comparisons hinder efforts to enhance the accuracy and reliability of model predictions. We analyze various sources contributing to these inconsistencies and propose strategies to address them, supported by practical case studies. We advocate for incorporating model fitting exercises as a standard practice in cross section publications to enhance the robustness of results. We use a common analysis framework to explore PPP-related challenges with MicroBooNE and T2K data in an unified manner. Our findings offer valuable insights for improving the accuracy and reliability of neutrino-nuclei interaction models, particularly by systematically tuning models using data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2010

Data Validation in the Kepler Science Operations Center Pipeline

We present an overview of the Data Validation (DV) software component and its context within the Kepler Science Operations Center (SOC) pipeline and overall Kepler Science mission. The SOC pipeline performs a transiting planet search on the corrected light curves for over 150,000 targets across the focal plane array. We discuss the DV strategy for automated validation of Threshold Crossing Events (TCEs) generated in the transiting planet search. For each TCE, a transiting planet model is fitted to the target light curve. A multiple planet search is conducted by repeating the transiting planet search on the residual light curve after the model flux has been removed; if an additional detection occurs, a planet model is fitted to the new TCE. A suite of automated tests are performed after all planet candidates have been identified. We describe a centroid motion test to determine the significance of the motion of the target photocenter during transit and to estimate the coordinates of the transit source within the photometric aperture; a series of eclipsing binary discrimination tests on the parameters of the planet model fits to all transits and the sequences of odd and even transits; and a statistical bootstrap to assess the likelihood that the TCE would have been generated purely by chance given the target light curve with all transits removed. Keywords: photometry, data validation, Kepler, Earth-size planets

Wu, Hayley↗
DOE OSTI2016

TU-D-207B-02: Delta-Radiomics: The Prognostic Value of Therapy-Induced Changes in Radiomics Features for Stage III Non-Small Cell Lung Cancer Patients

Purpose: To determine how radiomics features change during radiation therapy and whether those changes (delta-radiomics features) can improve prognostic models built with clinical factors. Methods: 62 radiomics features, including histogram, co-occurrence, run-length, gray-tone difference, and shape features, were calculated from pretreatment and weekly intra-treatment CTs for 107 stage III NSCLC patients (5–9 images per patient). Image preprocessing for each feature was determined using the set of pretreatment images: bit-depth resample and/or a smoothing filter were tested for their impact on volume-correlation and significance of each feature in univariate cox regression models to maximize their information content. Next, the optimized features were calculated from the intratreatment images and tested in linear mixed-effects models to determine which features changed significantly with dose-fraction. The slopes in these significant features were defined as delta-radiomics features. To test their prognostic potential multivariate cox regression models were fitted, first using only clinical features and then clinical+delta-radiomics features for overall-survival, local-recurrence, and distant-metastases. Leave-one-out cross validation was used for model-fitting and patient predictions. Concordance indices(c-index) and p-values for the log-rank test with patients stratified at the median were calculated. Results: Approximately one-half of the 62 optimized features required no preprocessing, one-fourth required smoothing, and one-fourth required smoothing and resampling. From these, 54 changed significantly during treatment. For overall-survival, the c-index improved from 0.52 for clinical factors alone to 0.62 for clinical+delta-radiomics features. For distant-metastases, the c-index improved from 0.53 to 0.58, while for local-recurrence it did not improve. Patient stratification significantly improved (p-value<0.05) for overallsurvival and distant-metastases when delta-radiomics features were included. The delta-radiomics versions of autocorrelation, kurtosis, and compactness were selected most frequently in leave-one-out iterations. Conclusion: Weekly changes in radiomics features can potentially be used to evaluate treatment response and predict patient outcomes. High-risk patients could be recommended for dose escalation or consolidation chemotherapy. This project was funded in part by grants from the National Cancer Institute (NCI) and the Cancer Prevention Research Institute of Texas (CPRIT).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Incidence and predictors of tuberculosis among children receiving antiretroviral therapy in the Wolaita Zone: A retrospective cohort study

Tuberculosis is the leading cause of morbidity and mortality among children living with the human immunodeficiency virus (HIV), mainly in sub-Saharan Africa, including Ethiopia. Tuberculosis remains a significant health concern for HIV-positive children in Ethiopia. There is a paucity of data on the incidence and predictors of tuberculosis among children living with HIV on antiretroviral therapy in the Wolaita zone. Hence, this study aimed to assess the incidence and predictors of tuberculosis among children living with HIV on antiretroviral therapy in the Wolaita zone between January 2010 to December 2020. A retrospective cohort study was conducted among 389 children receiving antiretroviral therapy in Wolaita zone health facilities between January 2010 to December 2020. The checklist was adapted from the standardized antiretroviral treatment (ART) follow-up form currently used by the institutions’ ART clinics. The Kaplan-Meier survival function and Log-rank were used to estimate the survival for each categorical variable to compare the survival between different exposure groups. Both bivariable and multivariable parametric survival Gompertz models were fitted to identify predictors of tuberculosis among HIV-positive children. The association was summarized using an adjusted hazard ratio (AHR), and statistical significance was declared at 95% CI and p-value < 0.05. The goodness of the model fit was assessed using a Cox-Snell residual plot. The incidence rate of tuberculosis among children living with HIV was 3.5 (95% CI 2.7–4.5) per 100 child years. World Health Organization clinical stage III or IV (AHR = 2.31, 95% CI [1.26, 4.22]), hemoglobin level <10 g/dL (AHR = 2.87, 95% CI [1.51, 5.45]), fair or poor ART adherence (AHR = 4.4, 95% CI[2.18, 9.05]), underweight (AHR = 2.55, 95% CI [1.45, 4.51]), age >10 years (AHR = 3.62; 95% CI [1.29, 10.0]), and cotrimoxazole preventive therapy (AHR = 0.23; 95% CI [0.08, 0.60]) were among the independent predictors of TB occurrence. The incidence of tuberculosis among children on ART was high. HIV-positive children presenting with advanced disease staging (III and IV), anemia, “fair” and “poor” ART adherence, underweight, age above ten years, and not receiving cotrimoxazole preventive therapy were at higher risk of TB. Therefore, counseling on ART adherence, early diagnosis, and prompt treatment of anemia and malnutrition are recommended to avert tuberculosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Cultural Artifact Detection in Long Wave Infrared Imagery.

Detection of cultural artifacts from airborne remotely sensed data is an important task in the context of on-site inspections. Airborne artifact detection can reduce the size of the search area the ground based inspection team must visit, thereby improving the efficiency of the inspection process. This report details two algorithms for detection of cultural artifacts in aerial long wave infrared imagery. The first algorithm creates an explicit model for cultural artifacts, and finds data that fits the model. The second algorithm creates a model of the background and finds data that does not fit the model. Both algorithms are applied to orthomosaic imagery generated as part of the MSFE13 data collection campaign under the spectral technology evaluation project.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

WE-AB-207B-10: On Spinal Nerve Toxicity from Single-Session SAbR in Pigs and the Translation of Small Animal NTCP Models

Purpose: The incidence of peripheral neuropathy has risen with increased utilization of SAbR. There is no consensus regarding the dose-tolerance of the peripheral nervous system. In 2015, we commenced an investigation to test the hypotheses that single-session irradiation to the pig spinal nerves exhibit a similar dose-tolerance as that of the spinal cord and that a dose-length effect exists. This work evaluates the direct application of small animal NTCP models to both large animal spinal cord and preliminary peripheral nerve data. Methods: To date, 16 of 25 Yucatan minipigs have received single-session SAbR to a 1.5cm length and 4 of 25 have received irradiation to a 0.5cm length of left-sided C6-C8 spinal nerves. Toxicity related gait change has been observed in 13 animals (9 from the long length group and 4 from the short). This preliminary data is overlaid on several dose-response models which have been fit to rodent spinal cord tolerance experiments. Model parameters define a toxicity profile between a completely serial or parallel behaving organ. Adequacy of model application, including how length effects are handled, to published minipig spinal cord dose-response data and to preliminary peripheral nerve response data was evaluated through residual analysis. Results: No rodent-derived dose-response models were directly applicable to all pig data for the different lengths irradiated. Several models fit the long-length irradiated spinal cord data well, with the more serial-like models fitting best. Preliminary data on the short-length irradiation suggests no length effect exists, disproving our hypothesis. Conclusion: Direct application of small-animal NTCP models to pig data suggests dose-length effect predictions from small animal data may not translate clinically. However, the small animal models used have not considered dose heterogeneity and it is expected that including the low-to-mid dose levels in the penumbral region will improve this match. This work was funded by the Cancer Prevention Research Institute of Texas (CPRIT).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%–94% of the variance in aneuploid strains’ growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

genic load↗
DOE OSTI2023

Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps

Abstract Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity often necessitate labor-intensive manual model building and fitting into cryo-EM maps. In this work, we designed a protein model-building workflow, which combines a deep-learning cryo-EM map feature enhancement tool, CryoFEM (Cryo-EM Feature Enhancement Model) and AlphaFold. A benchmark test using 36 cryo-EM maps shows that CryoFEM achieves state-of-the-art performance in optimizing the Fourier Shell Correlations between the maps and the ground truth models. Furthermore, in a subset of 17 datasets where the initial AlphaFold predictions are less accurate, the workflow significantly improves their model accuracy. Our work demonstrates that the integration of modern deep learning image enhancement and AlphaFold may lead to automated model building and fitting for the atomistic interpretation of cryo-EM maps.

59 BASIC BIOLOGICAL SCIENCES↗