Results for “sampling variability” — Page 49

NASA NTRS2017

Recent Radiation Test Results for Trench Power MOSFETs

Single-event effect (SEE) radiation test results are presented for various trench-gate power MOSFETs. The heavy-ion response of the first (and only) radiation-hardened trench-gate power MOSFET is evaluated: the manufacturer SEE response curve is verified and importantly, no localized dosing effects are measured, distinguishing it from other, non-hardened trench-gate power MOSFETs. Evaluations are made of n-type commercial and both n- and p-type automotive grade trench-gate device using ions comparable to of those on the low linear energy transfer (LET) side of the iron knee of the galactic cosmic ray spectrum, to explore suitability of these parts for missions with higher risk tolerance and shorter duration, such as CubeSats. Part-to-part variability of SEE threshold suggests testing with larger sample sizes and applying more aggressive derating to avoid on-orbit failures. The n-type devices yielded expected localized dosing effects including when irradiated in an unbiased (0-V) configuration, adding to the challenge of inserting these parts into space flight missions.

Schottky Diodes↗

NASA NTRSDate not supplied

Robust Path-following Controller for Multirotor Vehicles in Uncertain Wind Conditions

This paper presents simulations of L1 adaptive controller on a multirotor vehicle for compensation of convective winds. There are increasing efforts to utilize unmanned vehicles for fighting wildfires, but such vehicles need to be able to compensate for convective updrafts and other uncertainties (e.g., wind shear and turbulence) caused by the wildfire. Other application domains, such as urban air mobility, also must account for convective or uncertain winds present in urban microclimates. Geometric controllers have great tracking performance properties in environments without uncertainties, but the tracking performance degrades when wind disturbances and external loads are present in the environment. Introducing L1 adaptive control into the controller architecture compensates for these wind disturbances and reduces the tracking error caused by such disturbances. The controller architecture is implemented on a small quadrotor. The simulations demonstrate the advantages of the L1 adaptive augmentation for a sample figure-eight trajectory while encountering unknown variable wind conditions with an external load.

John L Bullock↗

NASA NTRSDate not supplied

Robust Path-following Controller for Multirotor Vehicles in Uncertain Wind Conditions

This paper presents simulations of L1 adaptive controller on a multirotor vehicle for compensation of convective winds. There are increasing efforts to utilize unmanned vehicles for fighting wildfires, but such vehicles need to be able to compensate for convective updrafts and other uncertainties (e.g., wind shear and turbulence) caused by the wildfire. Other application domains, such as urban air mobility, also must account for convective or uncertain winds present in urban microclimates. Geometric controllers have great tracking performance properties in environments without uncertainties, but the tracking performance degrades when wind disturbances and external loads are present in the environment. Introducing L1 adaptive control into the controller architecture compensates for these wind disturbances and reduces the tracking error caused by such disturbances. The controller architecture is implemented on a small quadrotor. The simulations demonstrate the advantages of the L1 adaptive augmentation for a sample figure-eight trajectory while encountering unknown variable wind conditions with an external load.

John Bullock↗

DOE OSTI2017

High-Resolution Characterization of Comingled Contaminants in the Deep Vadose Zone, Hanford Site, Washington - 17387

The contaminated deep vadose zone in the Central Plateau of the Hanford Site in Washington state presents a unique challenge to remediation. The Comprehensive Environmental Response, Compensation, and Liability Act, commonly known as the CERCLA project responsible for addressing these deep contaminants is designated as the 200-Deep Vadose Zone Operable Unit (DV-1 project). The vadose zone is 55-82 meters (180-270 feet) thick and is contaminated with both radiological and chemical contaminants from multiple historical waste streams. The mobile contaminants from this waste are still present in the deep vadose zone and pose a potential threat to the groundwater. Characterizing the nature and extent of the comingled mobile contaminants is an important step before assessing possible remedial alternatives. One factor controlling the migration and distribution of subsurface contaminants is the lithologic heterogeneity of the vadose zone sediments. A sampling plan for the DV-1 project includes continuous sediment core collection and analysis to identify key lithologic factors (i.e., small-scale bedding and structural features) that act as barriers, preferential pathways or conduits for mobile contaminants migrating through the vadose zone. Over six hundred meters (2,000 feet) of intact sediment core from 26 boreholes were collected in 1.5 meters (5 feet) long, 10 centimeter (4-inch) diameter segments. These intact sediment samples provide significant improvement in the level of detail and understanding of mechanisms and features controlling contaminant distribution that is not possible with traditional homogenized drill cuttings or conventional split-spoon samples. Detailed geological descriptions and photographs of the intact core are correlated to high-resolution geophysical logs which aid in the development of a comprehensive contaminant distribution profile of the borehole. These lithologic interpretations are used to define specific depth intervals for sampling based on changes in moisture, lithology, and elevated gamma radiation. The selected samples are analyzed for contaminants of concern, geochemical parameters, and hydraulic properties. Significant lithologic features observed in the intact cores include sharp contact boundaries to graded bedding, iron-oxide and carbonate cementation, silty laminations, clastic dike intrusions, and soft sediment deformation. Preliminary geologic logs integrated with the sample analysis indicate features that correlate to the variable distribution of contaminants both laterally and vertically. Integration of measured hydraulic parameters to lithologic units in individual borehole profiles provide key inputs for more precise geo-framework and vadose zone flux models supporting the DV-1 project. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1981

Rapid X-ray variability in the Seyfert galaxy NGC 6814

In contrast with the Tennant et al (1981) sample of over 30 active galactic nuclei, HEAO 1 A-2 high time resolution X-ray observations of the X-ray emitting Seyfert 1 galaxy NGC 6814 show strong X-ray variability on time scales of less than 3 hr. On a time scale of 90 min, the mean flux varied by a factor of about 2.5, and a characteristic time for variability of 100 + 60 or - 25 sec is obtained by autocorrelation analysis. There is no indication of spectral variability with an upper limit on a change in the power law spectral index of less than about 0.37, for a factor 2 change in intensity. The constraints of such rapid variability on a range of X-ray source mechanisms is discussed.

Tennant, A. F.↗

NASA NTRS2019

The Acquisition, Containment, and Curation of Mars Samples on Earth

The Astromaterials Acquisition and Curation Office at NASA Johnson Space Center (henceforth AACO) is responsible for receiving and curating all of NASA’s extraterrestrial samples, current and future (as per NASA Policy Directive (NPD) 7100.10E “Curation of Extraterrestrial Materials”). As such, the AACO coordinates sample capture, containment, and transportation to the curation facility as well as documents, preserves, prepares, and distributes all of the samples within NASA’s astromaterial collections for research, education, and public outreach. Since the lunar rock and soil samples returned during the Apollo Program, NASA’s first Class V Restricted Earth Return Missions, the AACO curates six other astromaterials collections. Lessons learned from each collection and respective missions (e.g. Apollo, Genesis, Stardust) as well as advancements in science and technology have informed the AACO’s plan for acquiring and curating Martian samples. Given the nature of the collection, a mobile and modular facility is recommended. The two broad requirements a Mars sample facility must maintain are: 1) the ability to contain the samples to protect the public from exposure of an “unknown unknown” biological agent and 2) ensure the scientific integrity of the samples are maintained (while maximizing scientific outcome). Although Apollo samples were eventually deemed safe and released to the scientific community for evaluation, there is no guarantee that this will be the case for Martian samples. Therefore, the facility in which the samples will be contained and investigated must be modular and able to accommodate an array of instrumentation that could be highly variable depending on the initial scientific outcomes. Furthermore, in order to facilitate proper sample capture and containment upon landing as well as sample distribution to other laboratories with proper containment, a mobile facility is a valuable investment.

Harrington, Andrea D.↗

NASA NTRS1997

Extending ROSAT Light Curves of Ecliptic Pole AGN Formation and Galaxy Evolution

In collaboration with UCLA graduate student Fred Baganoff, Professor Malkan has obtained the longest continuous light curves ever available for a large sample (# = 60) of active galactic nuclei. This was accomplished by using the ROSATAII-Sky Survey, which covered the ecliptic pole regions once every 9O-minute orbit. Using this Astrophysics Data Processing grant from NASA, we extended these light curves by combining the RASS data with pointed observations over the next several years of operation of the ROSAT PSPC. This lengthens the baselines of about half of the light curves from a few months up to a few years. The proportion of AGN showing variability increases substantially with this improvement. In fact most AGN in this representative sample are now shown to be significantly variable in the X-rays. We are also able to say something about the amplitudes of variability on timescales from days to years, with more detail than previously has been possible. We have also identified some dependence of the X-ray variability properties on a) the luminosity of the AGN; and b) The presence of a "Blazar" nucleus. By extending the ROSAT light curves, we are also able to learn more about the correlation of X-ray and optical emission on longer time-scales. It appears to be very weak, at best.

Malkan, Matthew A.↗

DOE OSTI2018

Tribological Behavior of Electrochemically Etched AISI 316 Stainless Steel with a Textured Surface

In this work, AISI 316 stainless steel (316 SS) was electrochemically etched in four NaCl solutions (10, 15, 20, and 25 wt.%) to form textured surfaces. Surface morphologies, three-dimensional surface topographies, and surface profiles of the textured 316 SS samples were obtained by scanning electron microscopy (SEM) and laser scanning confocal microscope (LSCM). Sliding friction tests of the textured 316 SS samples were conducted in air and under grease lubrication. The results suggested that the variable degrees of microtextured surfaces after electrochemical etching in 10, 15, and 20 wt.% NaCl solutions. The 316 SS samples etched in 15 and 20 wt.% NaCl solutions showed more promising tribological performance than other samples, as reflected by their friction coefficients, mass loss per unit distance traveled (mg/m), and wear morphologies.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Extreme Variability Quasars from the Sloan Digital Sky Survey and the Dark Energy Survey

Here, we perform a systematic search for long-term extreme variability quasars (EVQs) in the overlapping Sloan Digital Sky Survey (SDSS) and 3-Year Dark Energy Survey (DES) imaging, which provide light curves spanning more than 15 years. We also identified 1000 EVQs with a maximum g band magnitude change of more than 1 mag over this period, about 10% of all quasars searched. The EVQs have L bol 10 45 - 10 47 erg s -1 and L=L Edd 0:01-1. Accounting for selection effects, we estimate an intrinsic EVQ fraction of 30-50% among all g.22 quasars over a baseline of 15 years. These EVQs are good candidates for so-called “changing-look quasars”, where a spectral transition between the two types of quasars (broad-line and narrow-line) is observed between the dim and bright states. We performed detailed multi-wavelength, spectral and variability analyses for the EVQs and compared to their parent quasar sample. We found that EVQs are distinct from a control sample of quasars matched in redshift and optical luminosity: (1) their UV broad emission lines have larger equivalent widths; (2) their Eddington ratios are systematically lower; and (3) they are more variable on all timescales. The intrinsic difference in quasar properties for EVQs suggest that internal processes associated with accretion are the main driver for the observed extreme long-term variability. In spit of their different properties, EVQs seem to be in the tail of a continuous distribution of quasar properties, rather than standing out as a distinct population. We speculate that EVQs are normal quasars accreting at relatively low accretion rates, where the accretion flow is more likely to experience instabilities that drive the factor of few changes in flux on multi-year timescales.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

Approaches of comparison for clear-sky radiative fluxes from general circulation models with Earth Radiation Budget Experiment data

In order to compare the clear-sky greenhouse effect and cloud-radiative forcing from general circulation models with Earth Radiation Budget Experiments (ERBE) data, it is necessary to calculate the general circulation model (GCM) clear-sky radiative fluxes in a way consistent with ERBE. This study discusses problems associated with the available methods for clear-sky radiative flux computations in GCMs and proposes a new approach, which uses a statistical relationship between the grid cloud cover and the availability of ERBE clear-sky measurement, established from ERBE pixel data, to sample the model radiative fluxes. Calculations with version 2 of the National Center for Atmospheric Research (NCAR) Community Climate Model using observed sea surface temperature (SST) show good agreement of clear-sky sampling from the proposed method with ERBE sampling. It is also shown that large improvements are achieved in the spatial variability of the model clear-sky radiative fluxes over ocean, with reference to ERBE, by using the new clear-sky sampling method.

Zhang, M. H.↗

DOE OSTI2022

CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Abstract. Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes (i.e., phenomena) such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.

58 GEOSCIENCES↗

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 OSTI2019

A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E

Abstract. Mass–dimension (m–D) relationships determining bulkmicrophysical properties such as total water content (TWC) and radarreflectivity factor (Z) from particle size distributions are used in bothnumerical models and remote sensing retrievals. The a and b coefficientsrepresenting m=aD b relationships, however, can vary significantlydepending on meteorological conditions, particle habits, the definition ofparticle maximum dimension, the probes used to obtain the data, techniquesused to process the cloud probe data, and other unknown reasons. Thus,considering a range of a,b coefficients may be more applicable for use innumerical models and remote sensing retrievals. Microphysical data collectedby two-dimensional optical array probes (OAPs) installed on the University ofNorth Dakota (UND) Citation aircraft during the Mid-latitude Continental ConvectiveClouds Experiment (MC3E) were used in conjunction with TWC data from aNevzorov probe and ground-based S-band radar data to determine a and busing a technique that minimizes the chi-square difference between the TWC andZ derived from the OAPs and those directly measured by a TWC probe andradar. All a and b values within a specified tolerance were regarded as equallyplausible solutions. Of the 16 near-constant-temperature flight legs analyzedduring the 25 April, 20 May, and 23 May 2011 events, the derived surfaces ofsolutions on the first 2 days where the aircraft-sampled stratiform cloudhad a larger range in a and b for lower temperature environments thatcorrespond to less variability in N(D), TWC, and Z for a flight leg.Because different regions of the storm were sampled on 23 May, differences inthe variability in N(D), TWC, and Z influenced the distribution ofchi-square values in the (a,b) phase space and the specified tolerance in away that yielded 2.8 times fewer plausible solutions compared to the flightlegs on the other dates. These findings show the importance of representingthe variability in a,b coefficients for numerical modeling and remotesensing studies, rather than assuming fixed values, as well as the need tofurther explore how these surfaces depend on environmental conditions inclouds containing ice hydrometeors.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

Multifrequency observations of blazars. II - The variability of the 1 micron to 2 mm continuum

The results of monitoring, over a period of two years, a sample of 12 blazars, in 11 wave bands between 1 micron and 2 mm are presented. All sources exhibit some variability, both in flux density and spectral shape. The infrared variability is consistent with repeated injections of reaccelerations of electrons, which subsequently suffer radiative losses. For OJ 287, 3C 279, and 3C 345, a decay and steepening of the infrared spectrum occurred as the submillimeter turnover evolved to lower frequencies, consistent with expansion of the emitting region. The peak flux, however, increased during this evolution. This behavior is inconsistent with most models for compact extragalactic sources, but it is naturally explained by shock waves traveling in an adiabatically expanding relativistic jet (Marscher and Gear, 1985).

Gear, W. K.↗

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 NTRS1992

Long-term variability in low-mass X-ray binaries - A study using data from Vela 5B

A complete sample of low-mass X-ray binaries in uncrowded fields obtained from Vela 5B is studied here for periodic and aperiodic time variability on time scales of more than one day. The strong 176-day periodicity in X1820 - 303 is confirmed, and evidence is found for a significant 77-day period in Cyg X-2 and for variability on time scales of less than about 200 days in Cyg X-3. Limits are set on the possible amplitude of candidate periodicities in the literature for Ser X-1 and X0614 + 091. The evidence reported here suggests that while long-term cyclic variability is common among high-mass binaries, it is a rare occurrence in low-mass systems.

Smale, Alan P.↗