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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

The Application of Advanced Cultivation Techniques in the Long Term Maintenance of Space Flight Plant Biological Systems

The development of the International Space Station (ISS) presents extensive opportunities for the implementation of long duration space life sciences studies. Continued attention has been placed in the development of plant growth chamber facilities capable of supporting the cultivation of plants in space flight microgravity conditions. The success of these facilities is largely dependent on their capacity to support the various growth requirements of test plant species. The cultivation requirements for higher plant species are generally complex, requiring specific levels of illumination, temperature, humidity, water, nutrients, and gas composition in order to achieve normal physiological growth and development. The supply of water, nutrients, and oxygen to the plant root system is a factor, which has proven to be particularly challenging in a microgravity space flight environment. The resolution of this issue is particularly important for the more intensive crop cultivation of plants envisaged in Nasa's advanced life support initiative. BioServe Space Technologies is a NASA, Research Partnership Center (RPC) at the University of Colorado, Boulder. BioServe has designed and operated various space flight plant habitat systems, and placed specific emphasis on the development and enhanced performance of subsystem components such as water and nutrient delivery, illumination, gas exchange and atmosphere control, temperature and humidity control. The further development and application of these subsystems to next generation habitats is of significant benefit and contribution towards the development of both the Space Plant biology and the Advanced Life Support Programs. The cooperative agreement between NASA Ames Research center and BioServe was established to support the further implementation of advanced cultivation techniques and protocols to plant habitat systems being coordinated at NASA Ames Research Center. Emphasis was placed on the implementation of passive-based water and nutrient support systems and techniques, which can be used to minimize demands on power, mass, and operational complexity in space flight studies. This effort has direct application to the development of next-generation space flight plant chambers such as the Plant Research Unit (PRU). Work was also directed at the development of in-flight plant preservation techniques and protocols consistent with the interest in applying recent developments in gene chip micro array technologies. Cultivation technologies and protocols were evaluated in a 55 day space flight plant growth study, conducted on the ISS, mission 9A (10/7/02 - 12/7/02).

Heyenga, A. G.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2006

The 'Biologically-Inspired Computing' Column

The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

Hinchey, Mike↗
NASA NTRS2022

The hierarchical organization of autocatalytic reaction networks and its relevance to origin of life

Abiogenesis has long been suspected to require that chemical reaction networks contain multiple autocatalytic cores, but little is known about what features of these networks allow for the gradual accretion of complexity. To identify realistic scenarios for the emergence of life-like properties, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical “seeds”. We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks and biological ecosystems. Applying our algorithms to both an abiotic reaction network and a biochemical one, each driven by a set of simple food chemicals, we detect SDASs that are organized as trophic tiers, of which the higher tier can be seeded by relatively simple chemicals if the lower tier is already activated. This indicates that sequential activation of trophically organized SDASs by seed chemicals that are not much more complex than what already exist could be a mechanism of gradual complexification from relatively simple abiotic reactions to more complex life-like systems. Interestingly, in both reaction networks, higher-tier SDASs include chemicals that might alter emergent features of chemical systems and could serve as early targets of selection. Our analysis provides computational tools for analyzing very large chemical/biochemical reaction networks and suggests new approaches to studying abiogenesis in the lab.

Zhen Peng↗
NASA NTRS1999

Transposable elements as a molecular evolutionary force

This essay addresses the paradoxes of the complex and highly redundant genomes. The central theses developed are that: (1) the distinctive feature of complex genomes is the existence of epigenetic mechanisms that permit extremely high levels of both tandem and dispersed redundancy; (2) the special contribution of transposable elements is to modularize the genome; and (3) the labilizing forces of recombination and transposition are just barely contained, giving a dynamic genetic system of ever increasing complexity that verges on the chaotic.

Review↗
NASA NTRS2001

Adjustable Autonomy Testbed

The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

Malin, Jane T.↗
NASA NTRS2016

Expanding NASA and Roscosmos Scientific Collaboration on the International Space Station

The International Space Station (ISS) is a world-class laboratory orbiting in space. NASA and Roscosmos have developed a strong relationship through the ISS Program Partnership, working together and with the other ISS Partners for more than twenty years. Since 2013, based on a framework agreement between the Program Managers, NASA and Roscosmos are building a joint program of collaborative research on ISS. This international collaboration is developed and implemented in phases. Initially, members of the ISS Program Science Forum from NASA and TsNIIMash (representing Roscosmos) identified the first set of NASA experiments that could be implemented in the "near term". The experiments represented the research categories of Technology Demonstration, Microbiology, and Education. Through these experiments, the teams from the "program" and "operations" communities learned to work together to identify collaboration opportunities, establish agreements, and jointly plan and execute the experiments. The first joint scientific activity on ISS occurred in January 2014, and implementation of these joint experiments continues through present ISS operations. NASA and TsNIIMash have proceeded to develop "medium term" collaborations, where scientists join together to improve already-proposed experiments. A major success is the joint One-Year Mission on ISS, with astronaut Scott Kelly and cosmonaut Mikhail Kornienko, who returned from ISS in March, 2016. The teams from the NASA Human Research Program and the RAS Institute for Biomedical Problems built on their considerable experience to design joint experiments, learn to work with each other's protocols and processes, and share medical and research data. New collaborations are being developed between American and Russian scientists in complex fluids, robotics, rodent research and space biology, and additional human research. Collaborations are also being developed in Earth Remote Sensing, where scientists will share data from imaging systems mounted on ISS as well as other orbiting spacecraft to improve our understanding of the Earth and its climate. NASA and Roscosmos continue to encourage international scientific cooperation and expanded use of the ISS Laboratory. "Long-term", larger collaborations will achieve scientific objectives that no single national science team or agency can achieve on its own. The joint accomplishments achieved so far have paved the way for a stronger international scientific community and improved results and benefits from ISS.

Hasbrook, Pete↗
NASA NTRS2001

Searching for Organics, Fossils, and Biology on Mars

One of the goals of Astrobiology is to understand life on a fundamental level. All life on Earth is constructed from the same basic biochemical building blocks consisting of 20 amino acids with left handed symmetry, five nucleotides, a few sugars of right handed symmetry and some lipids. Using the metaphor of computers this is equivalent to saying that all life shares the same hardware. Beyond hardware similarity, it is now known that all life has fundamentally the same software. The genetic code of life is common to all organisms. Some have argued that the "hammer of evolution is heavy" and life anywhere is likely to be composed of identical biochemical and genetic patterns. However, in a system as complex as biochemistry it is likely that there are numerous local optima and the details of the optimum found by evolutionary selection on another world would likely depend on the initial conditions and random developments in the early biological history on that world. To address these fundamental questions in Astrobiology we need a second example of life: a second genesis.

McKay, Christopher P.↗
NASA NTRS2010

Mapping and Quantification of Vascular Branching in Plants, Animals and Humans by VESGEN Software

Humans face daunting challenges in the successful exploration and colonization of space, including adverse alterations in gravity and radiation. The Earth-determined biology of humans, animals and plants is significantly modified in such extraterrestrial environments. One physiological requirement shared by humans with larger plants and animals is a complex, highly branching vascular system that is dynamically responsive to cellular metabolism, immunological protection and specialized cellular/tissue function. The VESsel GENeration (VESGEN) Analysis has been developed as a mature beta version, pre-release research software for mapping and quantification of the fractal-based complexity of vascular branching. Alterations in vascular branching pattern can provide informative read-outs of altered vascular regulation. Originally developed for biomedical applications in angiogenesis, VESGEN 2D has provided novel insights into the cytokine, transgenic and therapeutic regulation of angiogenesis, lymphangiogenesis and other microvascular remodeling phenomena. Vascular trees, networks and tree-network composites are mapped and quantified. Applications include disease progression from clinical ophthalmic images of the human retina; experimental regulation of vascular remodeling in the mouse retina; avian and mouse coronary vasculature, and other experimental models in vivo. We envision that altered branching in the leaves of plants studied on ISS such as Arabidopsis thaliana cans also be analyzed.

Parsons-Wingerter, Patricia A.↗
NASA NTRS2010

Mapping and Quantification of Vascular Branching in Plants, Animals and Humans by VESGEN Software

Humans face daunting challenges in the successful exploration and colonization of space, including adverse alterations in gravity and radiation. The Earth-determined biology of plants, animals and humans is significantly modified in such extraterrestrial environments. One physiological requirement shared by larger plants and animals with humans is a complex, highly branching vascular system that is dynamically responsive to cellular metabolism, immunological protection and specialized cellular/tissue function. VESsel GENeration (VESGEN) Analysis has been developed as a mature beta version, pre-release research software for mapping and quantification of the fractal-based complexity of vascular branching. Alterations in vascular branching pattern can provide informative read-outs of altered vascular regulation. Originally developed for biomedical applications in angiogenesis, VESGEN 2D has provided novel insights into the cytokine, transgenic and therapeutic regulation of angiogenesis, lymphangiogenesis and other microvascular remodeling phenomena. Vascular trees, networks and tree-network composites are mapped and quantified. Applications include disease progression from clinical ophthalmic images of the human retina; experimental regulation of vascular remodeling in the mouse retina; avian and mouse coronary vasculature, and other experimental models in vivo. We envision that altered branching in the leaves of plants studied on ISS such as Arabidopsis thaliana cans also be analyzed.

Parsons-Wingerter, P. A.↗
NASA NTRS2022

Design of synthetic collagens that assemble into supramolecular banded fibers as a functional biomaterial testbed

Collagens are the most abundant proteins of the extracellular matrix, and the hierarchical folding and supramolecular assembly of collagens into banded fibers is essential for mediating cell-matrix interactions and tissue mechanics. Collagen extracted from animal tissues is a valuable commodity, but suffers from safety and purity issues, limiting its biomaterials applications. Synthetic collagen biomaterials could address these issues, but their construction requires molecular-level control of folding and supramolecular assembly into ordered banded fibers, comparable to those of natural collagens. Here, we show an innovative class of banded fiber-forming synthetic collagens that recapitulate the morphology and some biological properties of natural collagens. The synthetic collagens comprise a functional-driver module that is flanked by adhesive modules that effectively promote their supramolecular assembly. Multiscale simulations support a plausible molecular-level mechanism of supramolecular assembly, allowing precise design of banded fiber morphology. We also experimentally demonstrate that synthetic fibers stimulate osteoblast differentiation at levels comparable to natural collagen. This work thus deepens understanding of collagen biology and disease by providing a ready source of safe, functional biomaterials that bridge the current gap between the simplicity of peptide biophysical models and the complexity of in vivo animal systems.

Jinyuan Hu↗
NASA NTRS1980

Galactic-scale civilization

Evolutionary arguments are presented in favor of the existence of civilization on a galactic scale. Patterns of physical, chemical, biological, social and cultural evolution leading to increasing levels of complexity are pointed out and explained thermodynamically in terms of the maximization of free energy dissipation in the environment of the organized system. The possibility of the evolution of a global and then a galactic human civilization is considered, and probabilities that the galaxy is presently in its colonization state and that life could have evolved to its present state on earth are discussed. Fermi's paradox of the absence of extraterrestrials in light of the probability of their existence is noted, and a variety of possible explanations is indicated. Finally, it is argued that although mankind may be the first occurrence of intelligence in the galaxy, it is unjustified to presume that this is so.

Kuiper, T. B. H.↗
NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗
NASA NTRS2017

Vascular Patterning Analysis by VESGEN 2D/3D with Bioinformatics: Updates for Rodent Tissues

Fractally branching vascular systems are a complex physiological requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants. Vascular trees, networks, and tree-network composites are therefore mapped and quantified by the VESsel GENeration Analysis (VESGEN) software according to weighted physiological vascular rules that include vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport depends upon a complex distributed system of highly regulated laminar flow. VESGEN has elucidated changes in vascular patterning resulting from inflammatory, developmental and other signaling pathways within numerous tissues of major model organisms important for Space Biology, especially for rodents. Important early stage regenerative opportunities have been identified by VESGEN vascular analysis for visual impairments in the human retina, and is currently being used for research into astronaut visual and ocular disorders associated with long duration missions. The VESGEN 2D software is a mature, automated, widely published capability for which beta testing and public release by NASA is planned for the upcoming year. Early-stage capabilities for VESGEN 3D analysis are under development for the rodent retina and intestine as prototype tissues. A prototype VESGEN 2D Bioinformatics software capability has also been developed to associate phenotypic changes in molecular expression with vascular structure and function. By new VESGEN bioinformatic innovations, expression patterns of the genetic, transcriptional, protein and other markers for regulatory molecules such as vascular endothelial growth factor (VEGF) and their receptors, often indicators of tissue oxygenation status, are co-localized with alterations in vascular pattern. Biomarkers are therefore mapped and quantified as information dimensions directly correlated with the spatial dimensions of a vascular pattern. Further important technology innovations by NASA include substantial image segmentation advances for more automated binary extraction of the grayscale vascular patterns, together with informative associated image quality assessments. Vascular mapping and quantification capabilities for the rodent retina and intestine are illustrated for VESGEN 2D, along with technology status reports on VESGEN 3D and Bioinformatic capabilities. Research partially supported by Ames Center Innovation Awards.

Parsons-Wingerter, P.↗
NASA NTRS2011

Step-by-Step Simulation of Radiation of Radiation Chemistry Using Green Functions for Diffusion-Influenced Reactions

The irradiation of biological systems leads to the formation of radiolytic species such as H(raised dot), (raised dot)OH, H2, H2O2, e(sup -)(sub aq), etc.[1]. These species react with neighboring molecules, which result in damage in biological molecules such as DNA. Radiation chemistry is there for every important to understand the radiobiological consequences of radiation[2]. In this work, we discuss an approach based on the exact Green Functions for diffusion-influenced reactions which may be used to simulate radiation chemistry and eventually extended to study more complex systems, including DNA.

Plante, Ianik↗
NASA NTRSDate not supplied

The Light Microscopy Module A Facility Overview-History and Science

We will share a brief history and provide a science overview of the many accomplishments of the Light Microscopy Module (LMM), a microscope that has been operating in the microgravity environment of the International Space Station (ISS) since 2010. It will be removed in October 2021. It was initially outfitted for the Constrained Vapor Bubble – Wickless Heat Pipe experiment. It was later reconfigured and upgraded many times to support a broad portfolio of Physical science and Biological experiments including: Complex Fluids, Colloids, Macro-Molecular Biophysics, Protein Crystals, and Plant Biology. The LMM has enabled many significant studies and discoveries and has seen its fair share of pleasant surprises. These include recording order arising out of disorder, e.g., systems of colloids that were glasses on Earth crystallizing to form large defect-free crystals, nematic ordering of elliptical colloids, and colloidal-polymer systems crystallizing. The LMM science teams have also seen how to improve product stabilizers once the effects of sedimentation on Earth were removed; they were able to see which tagged genes express in plants when gravity is removed, telling them which genes are essential for growing plants in space; they’ve tested protein crystal growth models; checked models for creating bijel electrodes that turn batteries into fast-charging supercapacitors; tested many forms of colloidal self-assembly: including those using depletion attraction, magnetic fields, and critical Casimir forces; they’ve observed explosive bubble nucleation in a wickless heat pipe, and much more. This work has been supported by the NASA Biological and Physical Sciences (BPS) Division and the ISS Program Office, Johnson Space Center Code OZ and Code OB, the Center for the Advancement of Science in Space (CASIS) / ISS National Lab, EPSCoR, and ISS international partners from ESA – the Netherlands and Italy, CSA, JAXA and S. Korea, and by Space Act Agreements with Procter and Gamble (P&G).

Colloids↗
NASA NTRS1995

Fractals in biology and medicine

Our purpose is to describe some recent progress in applying fractal concepts to systems of relevance to biology and medicine. We review several biological systems characterized by fractal geometry, with a particular focus on the long-range power-law correlations found recently in DNA sequences containing noncoding material. Furthermore, we discuss the finding that the exponent alpha quantifying these long-range correlations ("fractal complexity") is smaller for coding than for noncoding sequences. We also discuss the application of fractal scaling analysis to the dynamics of heartbeat regulation, and report the recent finding that the normal heart is characterized by long-range "anticorrelations" which are absent in the diseased heart.

Review↗
NASA NTRS1986

Carbon dioxide evolution rate as a method to monitor and control an aerobic biological waste treatment system

An experimental system was developed to study the microbial growth kinetic of an undefined mixed culture in an erobic biological waste treatment process. The experimental results were used to develop a mathematical model that can predict the performance of a bioreactor. The bioreactor will be used to regeneratively treat waste material which is expected to be generated during a long term manned space mission. Since the presence of insoluble particles in the chemically undefined complex media made estimating biomass very difficult in the real system, a clean system was devised to study the microbial growth from the soluble substrate.

Lee, S. S.↗