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NASA NTRS2009

Observations of Screw Dislocation Driven Growth and Faceting During CVD Homoepitaxy on 4H-SiC On-Axis Mesa Arrays

Previous studies of (0001) homoepitaxial growth carried out on arrays of small-area mesas etched into on-axis silicon-face 4H-SiC wafers have demonstrated that spiral growth emanating from at least one screw dislocation threading the mesa is necessary in order for a mesa to grow taller in the <0001> (c-axis vertical) direction while maintaining 4H stacking sequence [1]. However, even amongst mesas containing the screw dislocation step source necessary for vertical c-axis growth, we have observed striking differences in the height and faceting that evolve during prolonged homoepitaxial growths. This paper summarizes Atomic Force Microscopy (AFM), Electron Channeling Contrast Imaging (ECCI), Scanning Electron Microscopy (SEM), and optical microscopy observations of this phenomenon. These observations support our initially proposed model [2] that the observed large variation (for mesas where 3C-SiC nucleation has not occurred) is related to the lateral positioning of a screw dislocation step source within each etched mesa. When the screw dislocation step source is located close enough to the developing edge/sidewall facet of a mesa, the c-axis growth rate and facet angle are affected by the resulting interaction. In particular, the intersection (or near intersection) of the inward-sloping mesa sidewall facet with the screw dislocation appears to impede the rate at which the spiral provides new steps required for c-axis growth. Also, the inward slope of the sidewall facet during growth (relative to other sidewalls of the same mesa not near the screw dislocation) seems to be impeded by the screw dislocation. In contrast, mesas whose screw dislocations are centrally located grow vertically, but inward sloping sidewall facets shrink the area of the top (0001) growth surface almost to the point of vanishing.

Neudeck, Philip G.↗

NASA NTRS1973

Airborne Laser Polar Nephelometer

A polar nephelometer has been developed at NCAR to measure the angular variation of the intensity of light scattered by air molecules and particles. The system has been designed for airborne measurements using outside air ducted through a 5-cm diameter airflow tube; the sample volume is that which is common to the intersection of a collimated source beam and the detector field of view within the airflow tube. The source is a linearly polarized helium-neon laser beam. The optical system defines a collimated field-of-view (0.5deg half-angle) through a series of diaphragms located behind a I72-mm focal length objective lens. A photomultiplier tube is located immediately behind an aperture in the focal plane of the objective lens. The laser beam is mechanically chopped (on-off) at a rate of 5 Hz; a two-channel pulse counter, synchronized to the laser output, measures the photomultiplier pulse rate with the light beam both on and off. The difference in these measured pulse rates is directly proportional to the intensity of the scattered light from the volume common to the intersection of the laser beam and the detector field-of-view. Measurements can be made at scattering angles from 15deg to 165deg with reference to the direction of propagation of the light beam. Intermediate angles are obtained by selecting the angular increments desired between these extreme angles (any multiple of 0.1deg can be selected for the angular increment; 5deg is used in normal operation). Pulses provided by digital circuits control a stepping motor which sequentially rotates the detector by pre-selected angular increments. The synchronous photon-counting system automatically begins measurement of the scattered-light intensity immediately after the rotation to a new angle has been completed. The instrument has been flown on the NASA Convair 990 airborne laboratory to obtain data on the complex index of refraction of atmospheric aerosols. A particle impaction device is operated simultaneously to collect particles from the same airflow tube used to make the scattered-light measurements. A size distribution function is obtained by analysis of the particles collected by the impaction device. Calculated values of the angular variation of the scattered-light intensity are obtained by applying Mie scattering theory to the observed size distribution function and assuming different values of the complex index of refraction of the particles. The calculated values are then compared with data on the actual variation of the scattered-light intensity obtained with the polar nephelometer. The most probable value of the complex refractive index is that which provides the best fit between the experimental light scattering data and the parameters calculated from the observed size distribution function.

Grams, Gerald W.↗

NASA NTRS2016

Column Number Density Expressions Through M = 0 and M = 1 Point Source Plumes Along Any Straight Path

Analytical expressions for column number density (CND) are developed for optical line of sight paths through a variety of steady free molecule point source models including directionally-constrained effusion (Mach number M = 0) and flow from a sonic orifice (M = 1). Sonic orifice solutions are approximate, developed using a fair simulacrum fitted to the free molecule solution. Expressions are also developed for a spherically-symmetric thermal expansion (M = 0). CND solutions are found for the most general paths relative to these sources and briefly explored. It is determined that the maximum CND from a distant location through directed effusion and sonic orifice cases occurs along the path parallel to the source plane that intersects the plume axis. For the effusive case this value is exactly twice the CND found along the ray originating from that point of intersection and extending to infinity along the plume's axis. For sonic plumes this ratio is reduced to about 4/3. For high Mach number cases the maximum CND will be found along the axial centerline path. Keywords: column number density, plume flows, outgassing, free molecule flow.

Venting↗

NASA NTRS2016

Column Number Density Expressions Through M = 0 and M = 1 Point Source Plumes Along Any Straight Path

Analytical expressions for column number density (CND) are developed for optical line of sight paths through a variety of steady free molecule point source models including directionally-constrained effusion (Mach number M = 0) and flow from a sonic orifice (M 1). Sonic orifice solutions are approximate, developed using a fair simulacrum fitted to the free molecule solution. Expressions are also developed for a spherically-symmetric thermal expansion (M = 0). CND solutions are found for the most general paths relative to these sources and briefly explored. It is determined that the maximum CND from a distant location through directed effusion and sonic orifice cases occurs along the path parallel to the source plane that intersects the plume axis. For the effusive case this value is exactly twice the CND found along the ray originating from that point of intersection and extending to infinity along the plumes axis. For sonic plumes this ratio is reduced to about 43. For high Mach number cases the maximum CND will be found along the axial centerline path.

Column Density↗

NASA NTRS2017

Prediction and Warning of Transported Turbulence in Long-Haul Aircraft Operations

An aviation flight planning system is used for predicting and warning for intersection of flight paths with transported meteorological disturbances, such as transported turbulence and related phenomena. Sensed data and transmitted data provide real time and forecast data related to meteorological conditions. Data modelling transported meteorological disturbances are applied to the received transmitted data and the sensed data to use the data modelling transported meteorological disturbances to correlate the sensed data and received transmitted data. The correlation is used to identify transported meteorological disturbances source characteristics, and identify predicted transported meteorological disturbances trajectories from source to intersection with flight path in space and time. The correlated data are provided to a visualization system that projects coordinates of a point of interest (POI) in a selected point of view (POV) to displays the flight track and the predicted transported meteorological disturbances warnings for the flight crew.

Shipley, Scott T.↗

NASA NTRS2017

Column Number Density Expressions Through M = 0 and M = 1 Point Source Plumes Along Any Straight Path

Providers of payloads carried aboard the International Space Station must conduct analyses to demonstrate that any planned gaseous venting events generate no more than a certain level of material that may interfere with optical measurements from other experiments or payloads located nearby. This requirement is expressed in terms of a maximum column number density (CND). Depending on the level of rarefaction, such venting may be characterized by effusion for low flow rates, or by a sonic distribution at higher levels. Since the relative locations of other sensitive payloads are often unknown because they may refer to future projects, this requirement becomes a search for the maximum CND along any path.In another application, certain astronomical observations make use of CND to estimate light attenuation from a distant star through gaseous plumes, such as the Fermi Bubbles emanating from the vicinity of the black hole at the center of our Milky Way galaxy, in order to infer the amount of material being expelled via those plumes.This paper presents analytical CND expressions developed for general straight paths based upon a free molecule point source model for steady effusive flow and for a distribution fitted to model flows from a sonic orifice. Among other things, in this Mach number range it is demonstrated that the maximum CND from a distant location occurs along the path parallel to the source plane that intersects the plume axis. For effusive flows this value is exactly twice the CND found along the ray originating from that point of intersection and extending to infinity along the plumes axis. For sonic plumes this ratio is reduced to about 43.

Venting↗

NASA NTRS2014

Ice Surface Morphology and Flow on Malaspina Glacier, Alaska: Implications for Regional Tectonics in the Saint Elias Orogen

The Saint Elias Mountains in southern Alaska are located at a structural syntaxis where the coastal thrust and fold belt of the Fairweather plate boundary intersects thrust faults and folds generated by collision of the Yakutat Terrane. The axial trace of this syntaxis extends southeastward out of the Saint Elias Mountains and beneath Malaspina Glacier where it is hidden from view and cannot be mapped using conventional methods. Here we examine the surface morphology and flow patterns of Malaspina Glacier to infer characteristics of the bedrock topography and organization of the syntaxis. Faults and folds beneath the eastern part of the glacier trend northwest and reflect dextral transpression near the terminus of the Fairweather fault system. Those beneath the western part of the glacier trend northeast and accommodate folding and thrust faulting during collision and accretion of the Yakutat Terrane. Mapping the location and geometry of the structural syntaxis provides important constraints on spatial variations in seismicity, fault kinematics, and crustal shortening beneath Malaspina Glacier, as well as the position of the collisional deformation front within the Yakutat Terrane. We also speculate that the geometrical complexity of intersecting faults within the syntaxis formed a barrier to rupture propagation during two regional Mw 8.1earthquakes in September 1899.

ice velocities↗

NASA NTRS2014

A Novel Application of TMA to Converging Runway Operations in a Simulated NextGen Environment

In 2013, the Airspace Operations Laboratory at NASA Ames Research Center conducted a human-in-the-loop simulation that examined the feasibility of applying a number of Next Generation Air Transportation System (NextGen) solutions to complex arrival operations in and around the New York metroplex. The delivery of arrivals to Newark Liberty International Airport (EWR) was the focus of this simulation, which involved extending the Terminal Sequencing and Spacing (TSS) scheduling capability to precisely schedule arrivals to intersecting runways 22 Left and 11. An important enabler for the concept was the availability of a dependent runway scheduler that was able to coordinate arrival times between aircraft landing on intersecting runways. At the time of the study, there was no functionality within the TSS scheduler to automatically create the dependent runway schedules. Instead, a Traffic Management Coordinator (TMC) manually created a de-conflicted schedule, which allowed for the concept to be tested as well as provided valuable insight into the tool requirements for a dependent runway scheduler. Throughout the course of preparations for the simulation, the individual serving as the TMC developed a number of strategies and procedures for manually adjusting the Scheduled Time of Arrival (STA) of the EWR arrivals in order to ensure that adequate spacing was provided between runway 22L and 11 arrival pairs. This paper describes the strategies and procedures that were developed and details how they were successfully applied during the simulation. Results will also be presented that shed additional light on exactly how the schedules were manipulated and their impact on delivery performance and safety. Ideas for additional TSS enhancements and next steps, based on participant feedback, will also be presented.

Homola, Jeffrey↗

NASA NTRS2014

Development of a Route Crossing Tool for Shared Airspace Environments

In current-day Terminal Radar Approach Control (TRACON) operations, departure and arrival controllers maintain separate and dedicated airspace for their respective traffic flows. Although this practice has obvious safety features, it also leads to inefficiencies; for example, departure aircraft may be routinely capped beneath arrival airspace. With the right decision-support and coordination tools, departures could continue to climb through arrival airspace when sufficient gaps exist. Previous studies of ‘shared airspace’ have examined pre-arranged coordination procedures, as well as tools that gave feedback to the controllers on where gaps between arrivals were located and whether the departure aircraft could be scheduled to fly through those gaps [1, 2, 3, 4]. Since then, the Route Crossing Tool (RCT) has been developed to allow controllers to assess multiple pre-defined route options at points where the arrivals and departures cross, thereby increasing the possibility of climbing a departure through an arrival gap.The RCT aids in ensuring lateral separation between departure and arrival aircraft that pass through the same altitude. Since the RCT can be applied tactically, it can enable aircraft to fly through arrival flows even if these aircraft depart outside scheduled times. The RCT makes use of a set of predefined parallel departure routes crossing the arrival flow at equidistant intersecting points on the arrival route. The RCT uses the Estimated Time of Arrival (ETA) of the departure aircraft at each intersecting point to calculate the lateral separation with the neighboring arrivals when it crosses that point; this information is graphically displayed to the controller. Additionally, the RCT incorporates forecast winds in its ETA predictions.Multiple prototypes of the RCT have been iteratively developed with feedback from Subject Matter Experts (SMEs). This paper presents the final design, the design process, and lessons learned. Initial results from a simulation suggest that the tool was successful in helping controllers to safely climb more aircraft. Controller feedback on the tool was also positive.

shared airspace↗

NASA NTRSDate not supplied

Vulcan Reuse

This is an exciting time in the space launch market. We have providers developing various reuse concepts to help drive continued cost reductions in access to space. At the same time visions of a future marketplace in space are emerging that have energized the community to move toward economically viable space-based ventures. This paper explores the intersection of these two developments. Specifically, we focus on the underlying technologies developed for ULA’s Sensible, Modular, Autonomous Return Technology (SMART) approach to reuse and the intersection with needs in the cislunar marketplace. ULA has been working with NASA Langley Research Center on maturing their Hypersonic Inflatable Aerodynamic Decelerator (HIAD) technology into the Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) technology demonstration mission scheduled to fly with the JPSS-2 Launch in 2022, and have honed in on a 6m diameter HIAD for that application. These efforts, under the NASA Space Act Agreements (SAA), have helped identify elements requiring further development for SMART Reuse, which could also benefit other potential mission infusion opportunities. This has proved to be a very fruitful collaboration as these aspects have evolved. The paper further delves into applications across commercial space and NASA’s Space Exploration mandate, in addition to the reusable launch vehicle applications.

John G Reed↗

NASA NTRSDate not supplied

Shock-Layer Radiation Insights Available Through Flowfield Property Binning

This paper introduces a method for assessing similarity between available shock-layer radiation measurements and the radiative heating to a vehicle of interest. This method will be referred to as the flowfield property binning (FPB) approach. The challenge in developing this approach is that radiometer measurements from a surface normal line of sight, observed radiation measurements of the entire shock-layer from a distance, or measured shock-tube nonequilibrium profiles are notably different geometrically than the radiative flux to a vehicle surface. Therefore, the relative contributions of various regions of the shock-layer to the measurement, and how these relative contributions compare with the radiative heating to a vehicle of interest, are unclear. By tracking the radiation contribution from each flowfield location, and then grouping this contribution based on two local flowfield properties, these geometric differences are removed. This provide a two dimensional group, or bin, distribution that represents the radiation environment in the same format for various types of radiation simulations, such as observed radiation or radiative flux to a vehicle surface. In the limits of optically-thin radiation transport and thermochemical equilibrium flow, the choice of pressure and temperature as the binning flowfield properties is shown to provide a bin distribution that uniquely defines the radiation simulation. This concept is found to be sufficient for establishing similarity between two cases even when the optically-thin radiation transport and thermochemical equilibrium assumptions are not satisfied. A simple metric of comparison between bin distributions from different cases is provided by the histogram intersection algorithm. This algorithm results in a histogram intersection factor (H) between 0 and 1, which represents the fraction of the radiation that is similar between the two cases. Therefore, if H evaluated between bin distributions for two completely different radiation problems is near 1, then they represent essentially the same radiation problem. This means that they depend on the same weighting of various radiation modeling parameters evaluated at the same pressure and temperature. If one of these cases agrees well with a radiation measurement, then the matching bin distributions provide justification for applying this experimental validation to the other case. Even for H values as low as 0.2, the measurement case could be considered relevant if there are no other useful measurements. This approach allows observed radiation measurements to directly inform radiative heating simulations, which allows NASA’s past and future investments in observed radiation measurements to directly inform the radiative heating uncertainty or margins for future vehicle design. This FPB approach is also used to determine shock-tube conditions for informing backshell radiative heating simulations, where the normal shock velocity and Lagrangian time from the shock is used for the binning variables instead of pressure and temperature.

Shock Layer Radiation↗

NASA NTRS2021

Using GPUs and the Parameterization Method for Rapid Search and Refinement of Connections between Tori in Periodically Perturbed Planar Circular Restricted 3-Body Problems

When the planar circular restricted 3-body problem is periodically perturbed, most unstable periodic orbits become invariant tori. However, 2D Poincare ́ sections no longer work to find their manifolds’ intersections; new methods are needed. In this study, we first review a method of restricting the intersection search to only certain manifold subsets. We then implement this search using Julia and OpenCL, representing the manifolds as triangular meshes and gaining a 30x speedup using GPUs. We finally show how to use manifold parametrizations to refine the ap- proximate connections found in the mesh search. We demonstrate the tools on the planar elliptic RTBP.

de la Llave, Rafael↗

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), has typically limited machine learning (ML) in space studies and further study of 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 (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗

NASA NTRS2022

Science CONOPS for Application of Sport Mission Data to Study Large (~1000KM) Ionospheric Plasma Depletions

The Scintillation Prediction Observations Research Task (SPORT) mission is a single 6U CubeSat space weather satellite planned for an October 2022 launch into an ISS-like orbit. The primary purpose of the SPORT mission is to determine the longitudinal effects on equatorial plasma bubble (EPB) growth resulting from the offset dipole magnetic field of the Earth. By combining field and plasma measurements from SPORT with other low-altitude (i.e., alt < 1000 km) spacecraft, it is possible to investigate large-scale (> 1000 km) EPB structures. The types of investigation made possible by measurements from SPORT and other contemporaneous missions include 1) dynamics of depleted magnetic flux tubes; 2) dynamics of field-aligned EPB expansion versus propagation speed; 3) EPB vertical extent; and 4) EPB temporal evolution. To support these investigation types, the respective modes of conjunctions are: 1) simultaneous intersection of a magnetic flux tube; 2) intersection of magnetic flux tube separated in time; 3) Simultaneous Latitude/Longitude position conjunction; and 4) Non-simultaneous latitude/longitude position conjunction. This paper will summarize the SPORT satellite and data used for Science CONOPS to accomplish these objectives.

Ionospheres↗

NASA NTRSDate not supplied

Science Conops for Application of Sport Mission Data to Study Large (~1000KN) Ionospheric Plasma Depletions

Scintillation↗

NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

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 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↗