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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 NTRSDate not supplied

Evaluation of Initial and Mid-Term Air Traffic Procedures for Urban Air Mobility Operations

Urban air mobility (UAM) operations are expected to expand in scale over the next several years as novel aircraft types, including electric vertical takeoff and landing aircraft, are certified and begin operations. These new aircraft may increase safety, decrease noise, and lower operating costs compared with helicopters, allowing them to operate in ways existing aircraft do not. It is vital that these expanded operations are compatible with and do not disrupt existing operations or the air traffic management system. To study the ways in which scaled UAM operations can best integrate in the national airspace system, NASA and Joby Aviation partnered to conduct a high-fidelity air traffic controller-in-the-loop study. Building on air traffic procedures used to manage high tempo operations in other parts of the airspace, new procedures, routes, and communications protocols were developed and tested by retired controllers in NASA’s Future Flight Central tower simulation facility. In addition, new cooperative airspace constructs in the form of corridors were developed to understand their potential contributions to even greater scales of operation. The controllers managed traffic scenarios in the Dallas-Fort Worth and Dallas Love Field airports consisting of fleets of up to 100 UAM aircraft operating alongside traditional traffic . Metrics for air traffic controller workload, duration of communications, departure delays, and other measures of allowable aircraft throughput were collected. The analysis indicates that using today’s procedures for initial UAM operations under nominal conditions could enable up to 40 operations per hour to an airport’s central terminal area if that involved crossing a runway and up to 55operations per hour if reaching the central terminal did not involve crossing a runway. Operations at these tempos did not delay or otherwise interfere with simulated runway traffic and were rated acceptable by the air traffic controllers. The new corridor constructs dramatically lowered controller workload in certain circumstances, suggesting they may be effective in further increasing the allowable scale of operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Evaluation of Initial and Mid-Term Air Traffic Procedures for Urban Air Mobility

Urban air mobility (UAM) operations are expected to expand in scale over the next several years as novel aircraft types, including electric vertical takeoff and landing aircraft, are certified and begin operations. These new aircraft may increase safety, decrease noise, and lower operating costs compared with helicopters, allowing them to operate in ways existing aircraft do not. It is vital that these expanded operations are compatible with and do not disrupt existing operations or the air traffic management system. To study the ways in which scaled UAM operations can best integrate in the national airspace system, NASA and Joby Aviation partnered to conduct a high-fidelity air traffic controller-in-the-loop study. Building on air traffic procedures used to manage high tempo operations in other parts of the airspace, new procedures, routes, and communications protocols were developed and tested by retired controllers in NASA’s Future Flight Central tower simulation facility. In addition, new cooperative airspace constructs in the form of corridors were developed to understand their potential contributions to even greater scales of operation. The controllers managed traffic scenarios in the Dallas-Fort Worth and Dallas Love Field airports consisting of fleets of up to 100 UAM aircraft operating alongside traditional traffic. Metrics for air traffic controller workload, duration of communications, departure delays, and other measures of allowable aircraft throughput were collected. The analysis indicates that using today’s procedures for initial UAM operations under nominal conditions could enable up to 40 operations per hour to an airport’s central terminal area if that involved crossing a runway and up to 55 operations per hour if reaching the central terminal did not involve crossing a runway. Operations at these tempos did not delay or otherwise interfere with simulated runway traffic and were rated acceptable by the air traffic controllers. The new corridor constructs dramatically lowered controller workload in certain circumstances, suggesting they may be effective in further increasing the allowable scale of operations.

Advanced Air Mobility↗
NASA NTRS1975

Building a computer-aided design capability using a standard time share operating system

The paper describes how an integrated system of engineering computer programs can be built using a standard commercially available operating system. The discussion opens with an outline of the auxiliary functions that an operating system can perform for a team of engineers involved in a large and complex task. An example of a specific integrated system is provided to explain how the standard operating system features can be used to organize the programs into a simple and inexpensive but effective system. Applications to an aircraft structural design study are discussed to illustrate the use of an integrated system as a flexible and efficient engineering tool. The discussion concludes with an engineer's assessment of an operating system's capabilities and desirable improvements.

Sobieszczanski, J.↗
NASA NTRS1990

Integrated identification and robust control tuning for large space structures

System identification is studied for the explicit purpose of supporting modern H-infinity robust control design objectives. In the analysis, the true plant is not assumed to be in the identification model set. An integrated identification/robust control problem is posed in which the optimal solution guarantees the best robust performance relative to the system information contained in a given experimental data set. A numerical example demonstrating an approximate solution to the problem indicates the usefulness of the approach.

Yam, Y.↗
NASA NTRS1985

Fabrication and evaluation of superplastically formed/weld-brazed corrugated compression panels with beaded webs

A study was made to investigate the feasibility of superplastically forming corrugated panels with beaded webs and to demonstrate the structural integrity of these panels by testing. The test panels in the study consist of superplastically formed titanium alloy Ti-6Al-4V half-hat elements that are joined by weld-brazing to titanium alloy Ti-6Al-4V caps to form either single-corrugation compression panels or multiple-corrugation compression panels. Stretching and subsequent thinning of the titanium sheet during superplastic forming is reduced by approximately 35 percent with a shallow half-hat die concept instead of a deep die concept and results in a more uniform thickness across the beaded webs. The complete panels are tested in end compression at room temperature and the results compared with analysis. The heavily loaded panels failed at loads approaching the yield strength of the titanium material. At maximum load, the caps wrinkled locally accompanied with separation of the weld-braze joint in the wrinkle. None of the panels tested, however, failed catastrophically in the weld-braze joint. Experimental test results are in good agreement with structural analysis of the panels.

Royster, D. M.↗
NASA NTRS1996

Aerodynamic Characteristics of Two Waverider-Derived Hypersonic Cruise Configurations

An evaluation was made on the effects of integrating the required aircraft components with hypersonic high-lift configurations known as waveriders to create hypersonic cruise vehicles. Previous studies suggest that waveriders offer advantages in aerodynamic performance and propulsion/airframe integration (PAI) characteristics over conventional non-waverider hypersonic shapes. A wind-tunnel model was developed that integrates vehicle components, including canopies, engine components, and control surfaces, with two pure waverider shapes, both conical-flow-derived waveriders for a design Mach number of 4.0. Experimental data and limited computational fluid dynamics (CFD) solutions were obtained over a Mach number range of 1.6 to 4.63. The experimental data show the component build-up effects and the aerodynamic characteristics of the fully integrated configurations, including control surface effectiveness. The aerodynamic performance of the fully integrated configurations is not comparable to that of the pure waverider shapes, but is comparable to previously tested hypersonic models. Both configurations exhibit good lateral-directional stability characteristics.

Cockrell, Charles E., Jr.↗
NASA NTRS2003

Modeling of Depth Cue Integration in Manual Control Tasks

Psychophysical research has demonstrated that human observers utilize a variety of visual cues to form a perception of three-dimensional depth. However, most of these studies have utilized a passive judgement paradigm, and failed to consider depth-cue integration as a dynamic and task-specific process. In the current study, we developed and experimentally validated a model of manual control of depth that examines how two potential cues (stereo disparity and relative size) are utilized in both first- and second-order active depth control tasks. We found that stereo disparity plays the dominate role for determining depth position, while relative size dominates perception of depth velocity. Stereo disparity also plays a reduced role when made less salient (i.e., when viewing distance is increased). Manual control models predict that position information is sufficient for first-order control tasks, while velocity information is required to perform a second-order control task. Thus, the rules for depth-cue integration in active control tasks are dependent on both task demands and cue quality.

Sweet, Barbara T.↗
NASA NTRS2024

Assessing the Impact of Climate Change on Atmospheric Rivers: A Modeling Perspective

Atmospheric rivers (ARs) play a crucial role in the poleward transport of water vapor, and the AR-associated precipitation is a critical component of global water supplies, making it critical that we understand how ARs may change in the future. To approach this issue, integrations of the NASA Goddard Institute for Space Studies global climate model ModelE version 2.1 (GISSE2.1) are employed. Multiple configurations of the model simulating different climates are analyzed: (1) the last-glacial maximum; (2) present day; (3) the end of the 21st century. The thermodynamic and dynamic components of changes to AR frequency are analyzed using a decomposition method. This method utilizes differences in distinct AR seasonal climatology frequencies derived from various vertically integrated water vapor transport (IVT) thresholds to resolve AR frequency into its components. Global mean state changes in poleward AR frequency for different climates are dominated by precipitable water vapor (PWV) changes. A set of idealized cold and warm climates in which present day sea surface temperatures are uniformly changed are considered for a targeted analysis of the south Pacific Ocean basin. For this analysis, frequency and distribution of AR events in the model runs are analyzed by comparing them to changes in the jet stream as well as the Eulerian storm tracks and low-level baroclinicity. Latitudinal shifts in the ARs in the south Pacific Ocean basin using our integrations are not as tightly coupled to these two storm-related climatological metrics in the midlatitudes but fare better on the poleward side of the storm tracks.

Atmospheric rivers↗
NASA NTRS1986

Vertical averages of rheology of the continental lithosphere Relation to thin sheet parameters

The calculations of the vertically-integrated strengths of the lithosphere and the Agrand number are described. The crustal and mantle contributions to the vertically-integrated strength, which is approximated from the brittle layer, and the depth and stress difference at the brittle-ductile transtion, is examined. The effective power laws for vertically-integrated rheology, which are dependent on the thermal stability of the lithosphere and the depth to the brittle-ductile transition in the crust, are studied. The relationship between the Agrand number and vertically-integrated strengths of the lithosphere and the thermal regime of the uppermost mantle and the stress state in the upper crust is analyzed.

Sonder, L. J.↗
NASA NTRS2015

Integrated Assessment of Climate Change Impacts on Maize Farms and Farm Household Incomes in South India: A Case Study from Tamil Nadu: Chapter - 9

South India is characterized by a wide variety of landscapes, soils and climatic zones. It is comprised of tropical, semi-arid, humid-moist, and high-altitude environments, which support a diversity of agricultural systems. Our study focused on the state of Tamil Nadu, which is characterized by a generally tropical climate, and receive rainfall during both the southwest monsoon season (SWM, June to September) and the northeast monsoon (NEM, September to December). Agriculture continues to be an important sector in the state economy, as more than 56 of the people depend on agriculture and allied sectors for their livelihood. Analysis of land-use patterns in Tamil Nadu reveals that in the past decade there has been a reduction in net sown area and current fallow, while the share of cultivable wastelands has increased. The area under cereals, pulses, and oilseeds had marginally declined, although area under commercial crops like turmeric, sugar-cane, banana, fruits, and vegetables has shown an increasing trend. The production performance of major crops like cereals, pulses, and oilseeds has not shown any significant increase. Demand and supply gap of important crops in Tamil Nadu for the year 2010 indicates that the state is lagging far behind in the production of various crops.

agriculture↗
NASA NTRS2017

Things That Scientists Don't Understand About NASA Spaceflight Research

So you want to conduct human spaceflight research aboard the International Space Station (ISS)? Once your spaceflight research aboard the ISS is proposal is funded.... the real work begins. Because resources are so limited for ISS research, it is necessary to maximize the work being done, while at the same time, minimizing the resources spent. Astronauts may be presented with over 30 human research experiments and select, on average approximately 15 in which to participate. In order to conduct this many studies, ISSMP uses the study requirements provided by the principle investigator to integrate all of this work into the astronauts' complement. The most important thing for investigators to convey to the ISSMP team is their RESEARCH REQUIREMENTS. Requirements are captured in the Experiment document. This document is the official record of how, what, where and when data will be collected. One common mistake that investigators make is not taking this document seriously, but when push comes to shove, if a research requirement is not in this document....it will not get done. The research requirements are then integrated to form a complement of research for each astronaut. What do we mean by integration? Many experiments have overlapping requirements; blood draws, behavioral surveys, heart rate measurement. Where possible, these measures are combined to reduce redundancy and save crew time. Investigators can access these data via data sharing agreements. More examples of how ISS research is integrated will be presented. There are additional limitations commonly associated with human spaceflight research that will also be discussed. Large/heavy hardware, invasive procedures, and toxic reagents are extremely difficult to implement on the ISS. There are strict limits placed on the amount of blood that can be drawn from crew members during (and immediately after) spaceflight. These limits are based on 30-day rolling accumulations. We have recently had to start restricting studies due to this limit. The NASA Human Research Program (HRP) provides extensive support, via ISSMP, to help investigators cope with all of the intricacies of conducting human spaceflight research. This presentation will help you take the best advantage of that support.

Platts, S. H.↗
NASA NTRS1994

Assessment of velocity/trajectory measurement technologies during a particle capture event

Since the early 1960s, the means to measure the time of flight (TOF) of dust grain within a mechanical detection array has existed, first in the laboratory and then in space experiments. Laboratory hypervelocity dust particle accelerators have used electrostatic detection of charge on accelerated particles for TOF and particle mass detections. These laboratory studies have led to the development of ultra-thin-film sensors that have been used for TOF measurements in dust particle space experiments. The prototypes for such devices were ultra-thin-film capacitors that were used in the OGO series of satellites. The main goal of the experimental work to be described is the development of the capability to determine the velocity vector or trajectory of a dust grain traversing an integrated dust detection array. The results of these studies have shown that the capability of detecting the charge liberated by hypervelocity dust grains with diameters in the micrometer range can be detected. Based on these results, detection systems have been designed to provide a precise analysis of the physical and dynamic properties of micrometer and submicrometer dust grains, namely the design verification unit (DVU). Through unique combinations of in situ detection systems, direct measurements of particle surface charge, velocity, momentum, kinetic energy, and trajectory have been achieved. From these measurements, the remaining physical parameters of mass, size, and density can be determined.

Tanner, William G.↗
NASA NTRS1995

Integrating Reliability Analysis with a Performance Tool

A large number of commercial simulation tools support performance oriented studies of complex computer and communication systems. Reliability of these systems, when desired, must be obtained by remodeling the system in a different tool. This has obvious drawbacks: (1) substantial extra effort is required to create the reliability model; (2) through modeling error the reliability model may not reflect precisely the same system as the performance model; (3) as the performance model evolves one must continuously reevaluate the validity of assumptions made in that model. In this paper we describe an approach, and a tool that implements this approach, for integrating a reliability analysis engine into a production quality simulation based performance modeling tool, and for modeling within such an integrated tool. The integrated tool allows one to use the same modeling formalisms to conduct both performance and reliability studies. We describe how the reliability analysis engine is integrated into the performance tool, describe the extensions made to the performance tool to support the reliability analysis, and consider the tool's performance.

Nicol, David M.↗