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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

USSR Space Life Sciences Digest, issue 6

This is the sixth issue of NASA's USSR Space Life Sciences Digest. It contains abstracts of 54 papers recently published in Russian language periodicals and bound collections and of 10 new Soviet monographs. Selected abstracts are illustrated with figures and tables from the original. Additional features include a table of Soviet EVAs and information about English translations of Soviet materials available to readers. The topics covered in this issue have been identified as relevant to 26 areas of aerospace medicine and space biology. These areas are adaptation, biospherics, body fluids, botany, cardiovascular and respiratory systems, developmental biology, endocrinology, enzymology, exobiology, genetics, habitability and environment effects, health and medical treatment, hematology, human performance, immunology, life support systems, mathematical modeling, metabolism., microbiology, morphology and cytology, musculoskeletal system, neurophysiology, nutrition, perception, personnel selection, psychology, radiobiology, reproductive biology, and space medicine.

Hooke, L. R.↗
NASA NTRS1989

Statistical prediction with Kanerva's sparse distributed memory

A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.

Rogers, David↗
NASA NTRS2003

A Genetic Representation for Evolutionary Fault Recovery in Virtex FPGAs

Most evolutionary approaches to fault recovery in FPGAs focus on evolving alternative logic configurations as opposed to evolving the intra-cell routing. Since the majority of transistors in a typical FPGA are dedicated to interconnect, nearly 80% according to one estimate, evolutionary fault-recovery systems should benefit hy accommodating routing. In this paper, we propose an evolutionary fault-recovery system employing a genetic representation that takes into account both logic and routing configurations. Experiments were run using a software model of the Xilinx Virtex FPGA. We report that using four Virtex combinational logic blocks, we were able to evolve a 100% accurate quadrature decoder finite state machine in the presence of a stuck-at-zero fault.

Lohn, Jason↗
NASA NTRS2005

Evolutionary optimization of a Genetically Refined Truss

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This paper will present a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: Genetic Algorithms and Differential Evolution to successfully optimize a benchmark structural optimization problem. An non-traditional solution to the benchmark problem is presented in this paper, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.

Hull, Patrick V.↗
NASA NTRS2000

"Genetically Engineered" Nanoelectronics

The quantum mechanical functionality of nanoelectronic devices such as resonant tunneling diodes (RTDs), quantum well infrared-photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs) is enabled by material variations on an atomic scale. The design and optimization of such devices requires a fundamental understanding of electron transport in such dimensions. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool based on a fundamental non-equilibrium electron transport theory. NEW was combined with a parallelized genetic algorithm package (PGAPACK) to evolve structural and material parameters to match a desired set of experimental data. A numerical experiment that evolves structural variations such as layer widths and doping concentrations is performed to analyze an experimental current voltage characteristic. The genetic algorithm is found to drive the NEMO simulation parameters close to the experimentally prescribed layer thicknesses and doping profiles. With such a quantitative agreement between theory and experiment design synthesis can be performed.

Klimeck, Gerhard↗
NASA NTRS1977

Chemical and petrographic constraints on the origin of chondrules and inclusions in carbonaceous chondrites

Bulk chemical compositions of the various petrographic types of chondrules and inclusions in Type 3 carbonaceous chondrites (excluding those affected by metamorphism) have been determined by microprobe defocused-beam analysis. Inclusion compositions follow approximately the theoretical compositional trajectory for equlibrium condensation. Chondrules occurring in the same meteorites have higher silica contents and show only slight overlap with inclusion compositions. Dust fusion is apparently an inadequate mechanism for producing the wide chemical variations observed among chondrules. Impact-melting models require sampling of complex target rocks which are unknown as components of meteorites; this mechanism also demands efficient mechanical processing of chondrules before accretion. A genetic relationship between chondrules and inclusions in carbonaceous chondrites is suggested by the compositional continuum between these objects. A condensation sequence which dips into the liquid stability field at lower temperatures is advocated for the production of both inclusions and chondrules. Textural relationships between intergrown chondrules and inclusions support such a sequence. This model suggests that the assembled components (inclusions and chondrules) of carbonaceous chondrites are related by a common process.

Mcsween, H. Y., Jr.↗
NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRS2000

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Dhaeseleer, Patrik↗
NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to outbred mice data

Accurately quantifying the differences in radiation quality between space and terrestrial environments is important for predicting health risks for astronauts.Recently, Edmundson et al. 2020[1] provided valuable new results from out-bred mice linking tumor induction and genetic background aer exposure to low and high-LET radiation. The goal of the current study is to more rigorously estimate a relative biological effectiveness (RBE) factor by leveraging the solid tumor data from Edmundson et al. 2020. Excess relative risk (ERR) models and excess absolute risk (EAR) models were fit using Poisson regression similar to the models that the Radiation Effects Research Foundation uses to fit atomic bomb survivor data. Linear ERR and EAR slopes were simulated usingBayesian analyses, and RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope using the full posterior distribution.

Lori J. Chappell↗
NASA NTRS1999

Centrosome and microtubule instability in aging Drosophila cells

Several cytoskeletal changes are associated with aging which includes alterations in muscle structure leading to muscular atrophy, and weakening of the microtubule network which affects cellular secretion and maintenance of cell shape. Weakening of the microtubule network during meiosis in aging oocytes can result in aneuploidy or trisomic zygotes with increasing maternal age. Imbalances of cytoskeletal organization can lead to disease such as Alzheimer's, muscular disorders, and cancer. Because many cytoskeletal diseases are related to age we investigated the effects of aging on microtubule organization in cell cultures of the Drosophila cell model system (Schneider S-1 and Kc23 cell lines). This cell model is increasingly being used as an alternative system to mammalian cell cultures. Drosophila cells are amenable to genetic manipulations and can be used to identify and manipulate genes which are involved in the aging processes. Immunofluorescence, scanning, and transmission electron microscopy were employed for the analysis of microtubule organizing centers (centrosomes) and microtubules at various times after subculturing cells in fresh medium. Our results reveal that centrosomes and the microtubule network becomes significantly affected in aging cells after 5 days of subculture. At 5-14 days of subculture, 1% abnormal out of 3% mitoses were noted which were clearly distinguishable from freshly subcultured control cells in which 3% of cells undergo normal mitosis with bipolar configurations. Microtubules are also affected in the midbody during cell division. The midbody in aging cells becomes up to 10 times longer when compared with midbodies in freshly subcultured cells. During interphase, microtubules are often disrupted and disorganized, which may indicate improper function related to transport of cell organelles along microtubules. These results are likely to help explain some cytoskeletal disorders and diseases related to aging.

Non-NASA Center↗
NASA NTRS2005

Launch Vehicle Propulsion Design with Multiple Selection Criteria

The approach and techniques described herein define an optimization and evaluation approach for a liquid hydrogen/liquid oxygen single-stage-to-orbit system. The method uses Monte Carlo simulations, genetic algorithm solvers, a propulsion thermo-chemical code, power series regression curves for historical data, and statistical models in order to optimize a vehicle system. The system, including parameters for engine chamber pressure, area ratio, and oxidizer/fuel ratio, was modeled and optimized to determine the best design for seven separate design weight and cost cases by varying design and technology parameters. Significant model results show that a 53% increase in Design, Development, Test and Evaluation cost results in a 67% reduction in Gross Liftoff Weight. Other key findings show the sensitivity of propulsion parameters, technology factors, and cost factors and how these parameters differ when cost and weight are optimized separately. Each of the three key propulsion parameters; chamber pressure, area ratio, and oxidizer/fuel ratio, are optimized in the seven design cases and results are plotted to show impacts to engine mass and overall vehicle mass.

Shelton, Joey D.↗
NASA NTRS2019

On the Use of Optimization Techniques for Turbulence Model Calibration

This article discusses the use of numerical optimization procedures to aid in the calibration of turbulence model coefficients. Such methods would increase the rigor and repeatability of the calibration procedure by requiring clearly defined and objective optimization metrics, and could be used to identify unique combinations of coefficient values for specific flow problems. The approach is applied to the re-calibration of an explicit algebraic Reynolds stress model for the incompressible planar mixing layer using the Nelder-Mead simplex algorithm and a micro-genetic algorithm with minimally imposed constraints. Three composite fitness functions, each based upon the error in the mixing layer growth rate and the normal and shear components of the Reynolds stresses, are investigated. The results demonstrate a significant improvement in the target objectives through the adjustment of three pressure-strain coefficients. Adjustments of additional coefficients provide little further benefit. Issues regarding the effectiveness of the fitness functions and the efficiency of the optimization algorithms are also discussed.

Yoder, Dennis A.↗
NASA NTRSDate not supplied

Use of Tardigrade Proteins for Enhancing Cellular Stress Tolerance on the Moon

With sights set on returning a human presence to the Moon and plans to explore even farther beyond, it is imperative we understand the impact of deep space radiation, partial gravity, and the lunar surface on biology. The need for countermeasures to protect future astronauts from the unique conditions of space is ever-pressing and an assessment of bioproduction capabilities in space is a crucial step for sustaining long-term missions. The Lunar Explorer Instrument for space biology Applications (LEIA) aims to investigate the cell’s response to the lunar environment and test strategies for enhancing cellular tolerance on the Moon, utilizing the budding yeast Saccharomyces cerevisiae as a biosensor. S. cerevisiae serves as an excellent model organism due to its widespread use as an analog for human cells, well-characterized set of genetic tools, and ability to survive desiccation. However, these cells must remain desiccated for up to one year prior to launch and the radiation-sensitive strains designed for LEIA will likely be more vulnerable to desiccation effects. In this work, we introduce tardigrade proteins known for their protective mechanisms into potential LEIA strains to test genetic strategies for improving desiccation and radiation tolerance. Tardigrades are extremotolerant animals well-known for their ability to survive in harsh environments. Notably, they can survive extreme desiccation and radiation due to the upregulation of intrinsically disordered proteins (IDPs). It has been shown that integration of these tardigrade IDPs, such as cytosolic abundant heat-soluble (CAHS) proteins, into yeast can confer increased desiccation tolerance in synergy with trehalose. Our ongoing testing and assessment of bioengineered radiation and desiccation tolerance will help us gain insight into optimizing strain design and selection for protecting LEIA’s biological payload as well as genetic engineering strategies for enhancing bioproduction potential for future missions beyond low Earth orbit.

Genetics↗
NASA NTRSDate not supplied

Use of Tardigrade Proteins for Enhancing Cellular Stress Tolerance on the Moon

With sights set on returning a human presence to the Moon and plans to explore even farther beyond, it is imperative we understand the impact of deep space radiation, partial gravity, and the lunar surface on biology. The need for countermeasures to protect future astronauts from the unique conditions of space is ever-pressing and an assessment of bioproduction capabilities in space is a crucial step for sustaining long-term missions. The Lunar Explorer Instrument for space biology Applications (LEIA) aims to investigate the cell’s response to the lunar environment and test strategies for enhancing cellular tolerance on the Moon, utilizing the budding yeast Saccharomyces cerevisiae as a biosensor. S. cerevisiae serves as an excellent model organism due to its widespread use as an analog for human cells, well-characterized set of genetic tools, and ability to survive desiccation. However, these cells must remain desiccated for up to one year prior to launch and the radiation-sensitive strains designed for LEIA will likely be more vulnerable to desiccation effects. In this work, we introduce tardigrade proteins known for their protective mechanisms into potential LEIA strains to test genetic strategies for improving desiccation and radiation tolerance. Tardigrades are extremotolerant animals well-known for their ability to survive in harsh environments. Notably, they can survive extreme desiccation and radiation due to the upregulation of intrinsically disordered proteins (IDPs). It has been shown that integration of these tardigrade IDPs, such as cytosolic abundant heat-soluble (CAHS) proteins, into yeast can confer increased desiccation tolerance in synergy with trehalose. Our ongoing testing and assessment of bioengineered radiation and desiccation tolerance will help us gain insight into optimizing strain design and selection for protecting LEIA’s biological payload as well as genetic engineering strategies for enhancing bioproduction potential for future missions beyond low Earth orbit.

Genetics↗
NASA NTRS1995

Satellite cell proliferation in adult skeletal muscle

Novel methods of retroviral-mediated gene transfer for the in vivo corporation and stable expression of eukaryotic or prokaryotic foreign genes in tissues of living animals is described. More specifically, methods of incorporating foreign genes into mitotically active cells are disclosed. The constitutive and stable expression of E. coli .beta.-galactosidase gene under the promoter control of the Moloney murine leukemia virus long terminal repeat is employed as a particularly preferred embodiment, by way of example, establishes the model upon which the incorporation of a foreign gene into a mitotically-active living eukaryotic tissue is based. Use of the described methods in therapeutic treatments for genetic diseases, such as those muscular degenerative diseases, is also presented. In muscle tissue, the described processes result in genetically-altered satellite cells which proliferate daughter myoblasts which preferentially fuse to form a single undamaged muscle fiber replacing damaged muscle tissue in a treated animal. The retroviral vector, by way of example, includes a dystrophin gene construct for use in treating muscular dystrophy. The present invention also comprises an experimental model utilizable in the study of the physiological regulation of skeletal muscle gene expression in intact animals.

Booth, Frank W.↗