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All sources NASA NTRS DOE OSTI

NASA NTRS1992

Acquisition and production of skilled behavior in dynamic decision-making tasks

Detailed summaries of two NASA-funded research projects are provided. The first project was an ecological task analysis of the Star Cruiser model. Star Cruiser is a psychological model designed to test a subject's level of cognitive activity. Ecological task analysis is used as a framework to predict the types of cognitive activity required to achieve productive behavior and to suggest how interfaces can be manipulated to alleviate certain types of cognitive demands. The second project is presented in the form of a thesis for the Masters Degree. The thesis discusses the modeling of decision-making through the use of neural network and genetic-algorithm machine learning technologies.

Kirlik, Alex↗

NASA NTRS1999

Sequential and coordinated action of phytochromes A and B during Arabidopsis stem growth revealed by kinetic analysis

Photoreceptor proteins of the phytochrome family mediate light-induced inhibition of stem (hypocotyl) elongation during the development of photoautotrophy in seedlings. Analyses of overt mutant phenotypes have established the importance of phytochromes A and B (phyA and phyB) in this developmental process, but kinetic information that would augment emerging molecular models of phytochrome signal transduction is absent. We have addressed this deficiency by genetically dissecting phytochrome-response kinetics, after having solved the technical issues that previously limited growth studies of small Arabidopsis seedlings. We show here, with resolution on the order of minutes, that phyA initiated hypocotyl growth inhibition upon the onset of continuous red light. This primary contribution of phyA began to decrease after 3 hr of irradiation, the same time at which immunochemically detectable phyA disappeared and an exclusively phyB-dependent phase of inhibition began. The sequential and coordinated actions of phyA and phyB in red light were not observed in far-red light, which inhibited growth persistently through an exclusively phyA-mediated pathway.

NASA Discipline Plant Biology↗

NASA NTRSDate not supplied

Investigating Biological Responses to Deep Space Radiation for Missions Beyond Low Earth Orbit (LEO) using Yeast

To enable long-term spaceflight missions and establish habitation on the Moon and Mars, we require a comprehensive understanding of the effects of chronic deep space radiation exposure on humans. BioSentinel is NASA’s first biological CubeSat to venture beyond Low Earth Orbit (LEO). It utilizes Saccharomyces cerevisiae (budding yeast) as a model organism to study biological responses to deep space radiation. Yeast share significant genetic homology with humans, including basic cellular metabolism and DNA repair mechanisms. In addition, unlike human cell cultures, yeast can survive the duration and constraints of a deep space mission. BioSentinel measures biological responses using an optical system and alamarBlue oxidation-reduction (redox) dye. Two strains of yeast are studied - a wild-type and a rad51 mutant strain that is deficient in DNA repair. Changes in metabolism and growth are monitored throughout the nominal 6-month mission. Preliminary tests indicate a significant change in the alamarBlue response to low-dose ionizing radiation (IR). Additionally, rad51 cells have shown an IR dose-dependent decrease in glucose uptake and accumulation of oxidized NADH (NAD+). These biomolecules are involved in reactions responsible for basic cell processes, including growth and development, signaling, and respiration. The current study expanded upon previous data by exposing yeast to deep space-relevant radiation. Glucose and NADH/NAD+ assays were conducted on yeast subjected to varying dosages of high-energy Fe-56 and simulated galactic cosmic rays (GCRs). The resulting data was analyzed using Excel and GraphPad Prism. A particular focus was to identify biomolecules resulting from aerobic respiration, which requires the presence of oxygen, or anaerobic processes. As long-term spaceflight missions draw near, it is increasingly important to characterize biological processes affected by the conditions of deep space. Studying biomolecular damage caused by deep space radiation may enable the development of engineering controls or biomedical therapeutics that mitigate health complications for future astronauts.

Kyra Keenan↗

NASA NTRS2001

Animal models for osteoporosis

Animal models will continue to be important tools in the quest to understand the contribution of specific genes to establishment of peak bone mass and optimal bone architecture, as well as the genetic basis for a predisposition toward accelerated bone loss in the presence of co-morbidity factors such as estrogen deficiency. Existing animal models will continue to be useful for modeling changes in bone metabolism and architecture induced by well-defined local and systemic factors. However, there is a critical unfulfilled need to develop and validate better animal models to allow fruitful investigation of the interaction of the multitude of factors which precipitate senile osteoporosis. Well characterized and validated animal models that can be recommended for investigation of the etiology, prevention and treatment of several forms of osteoporosis have been listed in Table 1. Also listed are models which are provisionally recommended. These latter models have potential but are inadequately characterized, deviate significantly from the human response, require careful choice of strain or age, or are not practical for most investigators to adopt. It cannot be stressed strongly enough that the enormous potential of laboratory animals as models for osteoporosis can only be realized if great care is taken in the choice of an appropriate species, age, experimental design, and measurements. Poor choices will results in misinterpretation of results which ultimately can bring harm to patients who suffer from osteoporosis by delaying advancement of knowledge.

NASA Discipline Regulatory Physiology↗

NASA NTRSDate not supplied

The Pheno-Evo Model: Evolution of Microbial Phenotypic Diversity in 2D Space

Biologists appreciate microbes for their simplicity and predictability: we assume that a population of genetically identical cells in a uniform environment will all behave the same way. While this assumption is often useful, it is also often wrong. Not only might microbes in a clonal population act differently from one another, the differences may be categorical (growing v. non-growing; toxin-sensitive v. tolerant), and this diversity might be an evolved trait conferring increased fitness on the population. How does such phenotypic heterogeneity evolve? And how does a population find the optimal distribution of phenotypes for a given environment? Efforts at modeling microbial phenotypic heterogeneity often focus on populations with two discrete phenotypic types; phenotypes in continuous distributions remain poorly explored. To address this gap and to explore the role of spatial relationships, we use agent-based modeling to simulate a phenotypically diverse population of microbial cells evolving in the presence of periodic toxic stress. Cells on patches in a 2D grid may degrade toxin, suffer damage from toxin, switch phenotype, and reproduce. An individual's phenotype its toxin degradation rate-- for which there is a tradeoff with reproduction-- and the genotype encodes the distribution of phenotypic values in the population. The rate of toxin diffusion determines how individuals influence their neighbors' environments. We examine the effects of toxin concentration, diffusion rate, and environmental predictability on the survival success of populations with different phenotype distributions, and populations' evolutionary trajectories when phenotype distribution is allowed to evolve. We conduct all simulations on the platform NetLogo, which provides a friendly interface allowing users of any experience level to tweak parameters and run their own simulations. We have also created custom tools in R for analyzing and visualizing the results of multiple runs.

microbial evolution↗

NASA NTRS2006

Comparison of a Convected Helmholtz and Euler Model for Impedance Eduction in Flow

Impedances educed from a well-tested convected Helmholtz model are compared to that of a recently developed linearized Euler model using two ceramic test liners under the assumed conditions or uniform flow and a plane wave source. The convected Helmholtz model is restricted to uniform mean flow whereas the linearized Euler model can account for the effect or the shear layer. Test data to educe the impedance is acquired from measurements obtained in the NASA Langley Research Center Grazing Incidence Tube for mean flow Mach numbers ranging from 0.0 to 0.5 and source frequencies ranging from 0.5 kHz to 3.0 kHz. The unknown impedance of the liner b educed by judiciously choo~ingth e impedance via an optimization method to match the measured acoustic pressure on the wall opposite the test liner. Results are presented on four spatial grids using three different optimization methods (contour deformation, Davidon-Fletcher Powell, and the Genetic Algorithm). All three optimization methods converge to the same impedance when used with the same model and to nearly identical impedances when used on different models. h anomaly was observed only at 0.5 kHz for high mean flow speeds. The anomaly is likely due to the use of measured data in a flow regime where shear layer effects are important but are neglected in the math models. Consistency between the impedances educed using the two models provides confidence that the linearized Euler model is ready For application to more realistic flows, such as those containing shear layers.

Watson, Willie R.↗

NASA NTRS1995

Acoustic design of rotor blades using a genetic algorithm

A genetic algorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that genetic algorithm successfully searches for "good" designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.

V. L. Wells↗

NASA NTRS2000

Mechanisms of the early phases of plant gravitropism

Gravitropism is directed growth of a plant or plant organ in response to gravity and can be divided into the following temporal sequence: perception, transduction, and response. This article is a review of the research on the early events of gravitropism (i.e., phenomena associated with the perception and transduction phases). The two major hypotheses for graviperception are the protoplast-pressure and starch-statolith models. While most researchers support the concept of statoliths, there are suggestions that plants have multiple mechanisms of perception. Evidence supports the hypothesis that the actin cytoskeleton is involved in graviperception/transduction, but the details of these mechanisms remain elusive. A number of recent developments, such as increased use of the molecular genetic approach, magnetophoresis, and laser ablation, have facilitated research in graviperception and have allowed for refinement of the current models. In addition, the entire continuum of acceleration forces from hypo- to hyper-gravity have been useful in studying perception mechanisms. Future interdisciplinary molecular approaches and the availability of sophisticated laboratories on the International Space Station should help to develop new insights into mechanisms of gravitropism in plants.

NASA Discipline Plant Biology↗

NASA NTRS2003

Role of genetic background in induced instability

Genomic instability is effectively induced by ionizing radiation. Recently, evidence has accumulated supporting a relationship between genetic background and the radiation-induced genomic instability phenotype. This is possibly due to alterations in proteins responsible for maintenance of genomic integrity or altered oxidative metabolism. Studies in human cell lines, human primary cells, and mouse models have been performed predominantly using high linear energy transfer (LET) radiation, or high doses of low LET radiation. The interplay between genetics, radiation response, and genomic instability has not been fully determined at low doses of low LET radiation. However, recent studies using low doses of low LET radiation suggest that the relationship between genetic background and radiation-induced genomic instability may be more complicated than these same relationships at high LET or high doses of low LET radiation. The complexity of this relationship at low doses of low LET radiation suggests that more of the population may be at risk than previously recognized and may have implications for radiation risk assessment.

NASA Discipline Radiation Health↗

NASA NTRS1991

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Peck, Charles C.↗

NASA NTRS2000

Optimization of Microelectronic Devices for Sensor Applications

The NASA/JPL goal to reduce payload in future space missions while increasing mission capability demands miniaturization of active and passive sensors, analytical instruments and communication systems among others. Currently, typical system requirements include the detection of particular spectral lines, associated data processing, and communication of the acquired data to other systems. Advances in lithography and deposition methods result in more advanced devices for space application, while the sub-micron resolution currently available opens a vast design space. Though an experimental exploration of this widening design space-searching for optimized performance by repeated fabrication efforts-is unfeasible, it does motivate the development of reliable software design tools. These tools necessitate models based on fundamental physics and mathematics of the device to accurately model effects such as diffraction and scattering in opto-electronic devices, or bandstructure and scattering in heterostructure devices. The software tools must have convenient turn-around times and interfaces that allow effective usage. The first issue is addressed by the application of high-performance computers and the second by the development of graphical user interfaces driven by properly developed data structures. These tools can then be integrated into an optimization environment, and with the available memory capacity and computational speed of high performance parallel platforms, simulation of optimized components can proceed. In this paper, specific applications of the electromagnetic modeling of infrared filtering, as well as heterostructure device design will be presented using genetic algorithm global optimization methods.

Cwik, Tom↗

NASA NTRS2005

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Source record↗

NASA NTRS1996

Optimization of Operations Resources via Discrete Event Simulation Modeling

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

Joshi, B.↗

NASA NTRS2019

Evaluating Effects of Altered Gravity on the Nervous System Using D. melanogaster

A comprehensive understanding of the effects of spaceflight and altered gravity on human physiology is necessary for continued human space exploration and long-term space habitation. The oxidative stress response has been identified in astronauts exposed to short- and long-term space missions that are exposed to the multitude of stress factors of spaceflight, including altered gravity and radiation exposure. Reactive oxygen species (ROS) are byproducts of homeostatic cellular metabolism, yet when overproduced the oxidative stress response ensues, rendering molecules destructive causing cell death and inflammation. Controlling aberrant ROS production is necessary to prevent pathological consequences, in particular within the nervous system, since neurons are extremely sensitive overexpressed ROS insults. We hypothesize that exposure to altered gravity triggers the oxidative stress response, leading to impairments in the nervous system. In this study, we used a well-established spaceflight model organism, Drosophila melanogaster, to assess altered gravity associated changes in the nervous system using a ground-based hypergravity model. Acute hypergravity resulted in an induction of oxidative stress-related genes with an increase in reactive oxygen species (ROS) in fly brains (p<0.001). Also, qPCR analysis shows that parkin gene expression is significantly reduced in these fly brains(p<0.05). Additionally, chronic hypergravity resulted in depressed locomotor phenotype in these flies (p<0.05) in conjunction to decreased dopaminergic neuron counts (p<0.0001) and increased apoptosis in these fly brains (p<0.0001). Further, assessment of neurological changes, including the neuronal architecture, synaptic integrity and genetic regulation caused by hypergravity conditions were noted. Overall, our results validate chronic hypergravity simulation as a behavioral model to study spaceflight effects, and oxidative stress pathway as a potential avenue for countermeasure development for astronauts undergoing short- and long-term missions and for neurodegenerative research on Earth.

Mhatre, Siddhita↗

NASA NTRS2001

Actuator Placement Via Genetic Algorithm for Aircraft Morphing

This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of Genetic Algorithm (GA) approaches that could be used to solve an actuator placement problem by treating this as a discrete optimization problem. In these efforts, the actuators are assumed to be "smart" devices that change the aerodynamic shape of an aircraft wing to alter the flow past the wing, and, as a result, provide aerodynamic moments that could provide flight control. The earlier work investigated issued for the problem statement, developed the appropriate actuator modeling, recognized the importance of symmetry for this problem, modified the aerodynamic analysis routine for more efficient use with the genetic algorithm, and began a problem size study to measure the impact of increasing problem complexity. The research discussed in this final summary further investigated the problem statement to provide a "combined moment" problem statement to simultaneously address roll, pitch and yaw. Investigations of problem size using this new problem statement provided insight into performance of the GA as the number of possible actuator locations increased. Where previous investigations utilized a simple wing model to develop the GA approach for actuator placement, this research culminated with application of the GA approach to a high-altitude unmanned aerial vehicle concept to demonstrate that the approach is valid for an aircraft configuration.

Crossley, William A.↗

NASA NTRS2024

Applications of Remote Sensing for Land Use Planning Scenarios with Suitability Analysis

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate growing population and manage natural resources. Suitability analysis modeling is a widely used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends and land surface temperature (LST) from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current land use planning practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

remote sensing time series↗

NASA NTRS2005

Drosophila melanogaster--the model organism of choice for the complex biology of multi-cellular organisms

Drosophila melanogaster has been intensely studied for almost 100 years. The sophisticated array of genetic and molecular tools that have evolved for analysis of gene function in this organism are unique. Further, Drosophila is a complex multi-cellular organism in which many aspects of development and behavior parallel those in human beings. These combined advantages have permitted research in Drosophila to make seminal contributions to the understanding of fundamental biological processes and ensure that Drosophila will continue to provide unique insights in the genomic era. An overview of the genetic methodologies available in Drosophila is given here, together with examples of outstanding recent contributions of Drosophila to our understanding of cell and organismal biology. The growing contribution of Drosophila to our knowledge of gravity-related responses is addressed.

Review, Tutorial↗

NASA NTRS2005

Mouse infection models for space flight immunology

Several immunological processes can be affected by space flight. However, there is little evidence to suggest that flight-induced immunological deficits lead to illness. Therefore, one of our goals has been to define models to examine host resistance during space flight. Our working hypothesis is that space flight crews will come from a heterogeneous population; the immune response gene make-up will be quite varied. It is unknown how much the immune response gene variation contributes to the potential threat from infectious organisms, allergic responses or other long term health problems (e.g. cancer). This article details recent efforts of the Kansas State University gravitational immunology group to assess how population heterogeneity impacts host health, either in laboratory experimental situations and/or using the skeletal unloading model of space-flight stress. This paper details our use of several mouse strains with several different genotypes. In particular, mice with varying MHCII allotypes and mice on the C57BL background with different genetic defects have been particularly useful tools with which to study infections by Staphylococcus aureus, Salmonella typhimurium, Pasteurella pneumotropica and Ehrlichia chaffeensis. We propose that some of these experimental challenge models will be useful to assess the effects of space flight on host resistance to infection.

Review↗