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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS1967

Prediction of radiation damage effects in transistors

Quantative relationships between radiation dosage to transistors and resultant damage are established. Calculation of these dose levels is based on high energy particle population data and analysis of the shielding effect provided by the enclosures surrounding a given transistor.

Source record↗
NASA NTRS1980

The effect of radiation pressure on the restricted three-body problem

The classical restricted three-body problem is extended to account for the effects of radiation pressure and the Poynting-Robertson effect on a particle with a surface-to-volume relation such that radiation pressure forces are nonnegligible moving in the gravitational field of two larger, orbiting bodies. The positions of the L4 and L5 Lagrangian points are found as functions of the ratio of radiational to gravitational forces. The inclusion of the Poynting-Robertson effect, however, is found to render the L4 and L5 points unstable on a time scale which is long compared to the rotation period of the two massive bodies. The results imply that space colonies with solar facing adjustable sails may be able to remain stationary at any heliocentric distance between a planet and the sun, and may provide a mechanism for producing asymmetries in the interplanetary dust cloud.

Schuerman, D. W.↗
DOE OSTI2024

Effects of radiative heat loss on extinction limits of counterflow premixed ammonia-air flames

The effect of radiative heat loss on counterflow premixed ammonia-air flames has been investigated as a function of equivalence ratio and system pressure. Non-adiabatic counterflow premixed flame simulations incorporating detailed chemistry, transport, and radiative heat transfer in the optically thin limit have been employed to elucidate important underlying physics and extinction characteristics. Similar to methane-air flame systems, non-adiabatic counterflow premixed computations show that the combined effects of positive flame stretch, sub-unity Lewis number, and radiative heat loss lead to the extension of the lean flammability limit for ammonia-air flames, revealing a C-shaped curve near the lean flammability limit that exhibits both radiation-induced and stretch-induced extinction states. Here, the computations compare favorably with the experimentally determined extinction stretch rate values for the stretch-induced extinction states over a wide range of equivalence ratios at different pressures up to 5 atm. A novel feature sensitivity analysis has also been developed to highlight important sensitive reactions for the stretch- and radiation-induced extinction states. Furthermore, the controlling chemistry at the dual-extinction states and at varying pressures are compared and discussed.

10 SYNTHETIC FUELS↗
NASA NTRS1975

Effects of radiation on charge-coupled devices

The effects of 1 MeV electron irradiation upon the performance of two phase, polysilicon aluminum gate CCDs are reported. Both n- and p-surface channel and n-buried channel devices are investigated using 64- and 128-stage line arrays. Characteristics measured as a function of radiation dose include: Transfer inefficiency, threshold voltage, field effect mobility, interface state density, full well signal level and dark current. Surface channel devices are found to degrade considerably at less than 10 to the 5th power rads (Si) due to the large increase in fast interface state density caused by radiation. Buried channel devices maintain efficient operation to the highest dose levels used.

Carnes, J. E.↗
DOE OSTI2021

Bias-dependent displacement damage effects in a silicon avalanche photodiode

Radiation effects studies on a commercial beveled-edge avalanche photodiode (APD) showed that applying reverse bias to the device during irradiation enhanced the severity of apparent damage. Proton microbeam irradiations were made using a proton beam energy of 2 MeV and fluence ranging from 2.0 × 10 10 to 5.1 × 10 12 cm -2 . Charge collection measurements using the ion beam induced charge (IBIC) technique showed that relative losses increased by up to an order of magnitude when the reverse bias applied during irradiation increased from 50 to 1500 V. The presence of reverse bias also led to equivalent losses in charge collection that would only be seen in irradiations with an order of magnitude higher fluence in unbiased APDs. The results demonstrate that bias-enhanced irradiation damage is insufficiently understood and must be accounted for in characterizations of radiation effects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2017

Effect of Radiation and Endurance on Pulsed Programming of Commercial NAND Flash Memory

Independent and coupled effects of radiation and endurance on commercial NAND Flash memory is studied using pulsed programming enabled by interrupting the programming command at the chip-level. The effects of total ionization dose (TID) and endurance stress increase and decrease, respectively, the number of pulses needed to program an entire page. The coupled effect of endurance stress and TID appear to exhibit linear correlation. A neutral electron/ hole trap model is evaluated and correlates well with our results.

Narasimham, Avyaya Jayanthi↗
DOE OSTI2025

Improved representation of black carbon mixing structures suggests stronger direct radiative heating

Black carbon significantly influences the Earth system because of its strong solar radiation absorption. However, its direct radiative effect remains poorly understood in current climate models, partly because current climate models oversimplify the diverse structures formed when black carbon mixes with other atmospheric components. Here we show that incorporating more realistic, multi-mixing-structure representations of black carbon increases the direct radiative effect. We find that aged black carbon particles, with thicker coatings and higher embedded fractions, enhance the direct radiative effect more efficiently. Using machine learning alongside the Community Earth System Model, we show that the direct radiative effect at the top of the atmosphere in regions with heavy black carbon pollution is 31.6% greater when multi-mixing structures are considered. These findings highlight the importance of modeling complex mixing structures of particle-resolved black carbon to accurately capture their warming impacts on global atmosphere, particularly in highly polluted regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Space Environments & Effects

The space radiation environment can lead to extremely harsh operating conditions for spacecraft electronic systems. A hardness assurance methodology must be followed to assure that the space radiation environment does not compromise the functionality and performance of space-based systems during the mission lifetime. The methodology includes a definition of the radiation environment, assessment of the radiation sensitivity of parts, worst-case analysis of the impact of radiation effects, and part acceptance decisions which are likely to include mitigation measures.

Space radiation spacecraft mitigation space-based ↗
NASA NTRS1976

Effects of radiation pressure on the equipotential surfaces in X-ray binaries

Equipotential surfaces incorporating the effect of radiation pressure were computed for the X-ray binaries Cen X-3, Cyg X-1 = HDE 226868, Vela XR-1 = 3U 0900-40 = HD 77581, and 3U 1700-37 = HD 153919. The topology of the equipotential surfaces is significantly affected by radiation pressure. In particular, the so-called critical Roche (Jacobian) lobes, the traditional figure 8's, do not exist. The effects of these results on modeling X-ray binaries are discussed.

Kondo, Y.↗
NASA NTRS2003

Characterization of a novel epigenetic effect of ionizing radiation: the death-inducing effect

The detrimental effects associated with exposure to ionizing radiation have long been thought to result from the direct targeting of the nucleus leading to DNA damage; however, the emergence of concepts such as radiation-induced genomic instability and bystander effects have challenged this dogma. After cellular exposure to ionizing radiation, we have isolated a number of clones of Chinese hamster-human hybrid GM10115 cells that demonstrate genomic instability as measured by chromosomal destabilization. These clones show dynamic and persistent generation of chromosomal rearrangements multiple generations after the original insult. We hypothesize that these unstable clones maintain this delayed instability phenotype by secreting factors into the culture medium. To test this hypothesis we transferred filtered medium from unstable cells to unirradiated GM10115 cells. No GM10115 cells were able to survive this medium. This phenomenon by which GM10115 cells die when cultured in medium from chromosomally unstable GM10115 clones is the death-inducing effect. Medium transfer experiments indicate that a factor or factors is/are secreted by unstable cells within 8 h of growth in fresh medium and result in cell killing within 24 h. These factors are stable at ambient temperature but do not survive heating or freezing, and are biologically active when diluted with fresh medium. We present the initial description and characterization of the death-inducing effect. This novel epigenetic effect of radiation has implications for radiation risk assessment and for health risks associated with radiation exposure.

Non-NASA Center↗