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At least 1,495 records · Page 83

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NASA NTRS1976

Symposium on Space Industrialization, Huntsville, Ala., May 26, 27, 1976, Proceedings

Space habitats are considered, with attention given the evolution of space station systems, space station habitability, space settlement planning methodology, and orbital assembly. Various aspects of the Space Transportation System are discussed, including Shuttle booster/propulsion growth concept, advanced earth orbital transportation systems technology, single-stage-to-orbit vehicles and aeromaneuvering orbit transfer vehicles. Materials processing in space is examined, with emphasis on biological materials, metallurgical materials, the uses of space ultrahigh vacuum, and extraterrestrial mining and industrial processing. Solar space power is investigated, with attention given the potential of satellite solar power stations, thermal engine power satellites and microwave power transmission to earth. Individual items are announced in this issue.

Source record↗

NASA NTRS2017

Methods and Apparatus for Autonomous Robotic Control

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.

Versace, Massimiliano↗

NASA NTRS2018

Methods and Apparatus for Autonomous Robotic Control

Versace, Massimiliano↗

DOE OSTI2019

Voltage gated inter-cation selective ion channels from graphene nanopores

With the ability to selectively control ionic flux, biological protein ion channels perform a fundamental role in many physiological processes. For practical applications that require the functionality of a biological ion channel, graphene provides a promising solid-state alternative, due to its atomic thinness and mechanical strength. Here, we demonstrate that nanopores introduced into graphene membranes, as large as 50 nm in diameter, exhibit inter-cation selectivity with a ~20× preference for K + over divalent cations and can be modulated by an applied gate voltage. Liquid atomic force microscopy of the graphene devices reveals surface nanobubbles near the pore to be responsible for the observed selective behavior. Molecular dynamics simulations indicate that translocation of ions across the pore likely occurs via a thin water layer at the edge of the pore and the nanobubble. Our results reflect a significant improvement in the inter-cation selectivity displayed by a solid-state nanopore device and by utilizing the pores in a de-wetted state, offers a method to fabricate selective graphene membranes that does not rely on the fabrication of sub-nm pores.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2014

High Resolution Mapping of Soils and Landforms for the Desert Renewable Energy Conservation Plan (DRECP)

The Desert Renewable Energy Conservation Plan (DRECP), a major component of California's renewable energy planning efforts, is intended to provide effective protection and conservation of desert ecosystems, while allowing for the sensible development of renewable energy projects. This NASA mapping report was developed to support the DRECP and the Bureau of Land Management (BLM). We outline in this document remote sensing image processing methods to deliver new maps of biological soils crusts, sand dune movements, desert pavements, and sub-surface water sources across the DRECP area. We focused data processing first on the largely unmapped areas most likely to be used for energy developments, such as those within Renewable Energy Study Areas (RESA) and Solar Energy Zones (SEZs). We used imagery (multispectral and radar) mainly from the years 2009-2011.

Mapping↗

NASA NTRS1997

Lignin Formation and the Effects of Gravity: A New Approach

Two aspects of considerable importance in the enigmatic processes associated with lignification have made excellent progress. The first is that, even in a microgravity environment, compression wood formation, and hence altered lignin deposition, can be induced upon mechanically bending the stems of woody gymnosperms. It now needs to be established if an organism reorientating its woody stem tissue will generate this tissue in microgravity, in the absence of externally applied pressure. If it does not, then gravity has no effect on its formation, and instead it results from alterations in the stress gradient experienced by the organism impacted. The second area of progress involves establishing how the biochemical pathway to lignin is regulated, particularly with respect to selective monolignol biosynthesis. This is an important question since individual monomer deposition occurs in a temporally and spatially specific manner. In this regard, the elusive metabolic switch between E-p-coumaryl alcohol and E-coniferyl alcohol synthesis has been detected, the significance of which now needs to be defined at the enzyme and gene level. Switching between monolignol synthesis is important, since it is viewed to be a consequence of different perceptions by plants in the gravitational load experienced, and thus in the control of the type of lignification response. Additional experiments also revealed the rate-limiting processes involved in monolignol synthesis, and suggest that a biological system (involving metabolite concentrations, as well as enzymatic and gene (in)activation processes) is involved, rather than a single rate-limiting step.

Lewis, Norman G.↗

DOE OSTI2016

Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

Purpose: This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. Methods and Materials: We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breast by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. Results: For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Conclusions: Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

The Catabolism of Lignin-Derived p-Methoxylated Aromatic Compounds by Rhodococcus jostii RHA1

Emergent strategies to valorize lignin, an abundant but underutilized aromatic biopolymer, include tandem processes that integrate chemical depolymerization and biological catalysis. To date, aromatic monomers from C-O bond cleavage of lignin have been converted to bioproducts, but the presence of recalcitrant C-C bonds in lignin limits the product yield. A promising chemocatalytic strategy that overcomes this limitation involves phenol methyl protection and autoxidation. Incorporating this into a tandem process requires microbial cell factories able to transform the p-methoxylated products in the resulting methylated lignin stream. In this study, we assessed the ability of Rhodococcus jostii RHA1 to catabolize the major aromatic products in a methylated lignin stream and elucidated the pathways responsible for this catabolism. RHA1 grew on a methylated pine lignin stream, catabolizing the major aromatic monomers: p-methoxybenzoate (p-MBA), veratrate, and veratraldehyde. Bioinformatic analyses suggested that a cytochrome P450, PbdA, and its cognate reductase, PbdB, are involved in p-MBA catabolism. Gene deletion studies established that both pbdA and pbdB are essential for growth on p-MBA and several derivatives. Furthermore, a deletion mutant of a candidate p-hydroxybenzoate (p-HBA) hydroxylase, ..delta..pobA, did not grow on p-HBA. Veratraldehyde and veratrate catabolism required both vanillin dehydrogenase (Vdh) and vanillate O-demethylase (VanAB), revealing previously unknown roles of these enzymes. Finally, a ..delta..pcaL strain grew on neither p-MBA nor veratrate, indicating they are catabolized through the ..beta..-ketoadipate pathway. This study expands our understanding of the bacterial catabolism of aromatic compounds and facilitates the development of biocatalysts for lignin valorization.

aromatic biopolymers↗

DOE OSTI2021

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

The ability to recreate synaptic functionalities in synthetic circuit elements is essential for neuromorphic computing systems that seek to emulate the cognitive powers of the brain with comparable efficiency and density. To date, silicon-based three-terminal transistors and two-terminal memristors have been widely used in neuromorphic circuits, in large part due to their ability to co-locate information processing and memory. Yet these devices cannot achieve the interconnectivity and complexity of the brain because they are power-hungry, fail to mimic key synaptic functionalities, and suffer from high noise and high switching voltages. To overcome these limitations, we have developed and characterized a biomolecular memristor that mimics the composition, structure, and switching characteristics of biological synapses. Here, we describe the process of assembling and characterizing biomolecular memristors consisting of a 5 nm-thick lipid bilayer formed between lipid-functionalized water droplets in oil and doped with voltage-activated alamethicin peptides. While similar assembly protocols have been used to investigate biophysical properties of droplet-supported lipid membranes and membrane-bound ion channels, this article focuses on key modifications of the droplet interface bilayer method essential for achieving consistent memristor performance. Specifically, we describe the liposome preparation process and the incorporation of alamethicin peptides in lipid bilayer membranes, and the appropriate concentrations of each constituent as well as their impact on the overall response of the memristors. As a result, we also detail the characterization process of biomolecular memristors, including measurement and analysis of memristive current-voltage relationships obtained via cyclic voltammetry, as well as short-term plasticity and learning in response to step-wise voltage pulse trains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Influence of the Turing instability on the motion of domain boundaries

Turing's theory of pattern formation has provided crucial insights into the behavior of various biological, geographical, and chemical systems over the last few decades. Existing studies have focused on moving-boundary Turing systems for which the motion of the boundary is prescribed by an external agent. In this paper, we present an extension of this theory to a class of systems in which the front motion is governed by the physical processes that occur within the domain. Biological systems exhibiting apically dominant growth and corrosion of metals and alloys highlight some of the noteworthy examples of such systems. In this study, we characterize the nature of interaction between the moving front and the Turing-instability for both an activator-inhibitor and an activator-substrate model. Behavioral regimes of periodic, as well as nonperiodic (nonconstant), growth rates are obtained. Furthermore, the trends in the first show striking similarities with the cyclic-boundary-kinetics observed in experimental systems. In general, a stationary, periodic structure is also left behind the moving front. If the periodicity of the boundary kinetics agrees with the allowed range of the stable-periodic solutions, the pattern formed tends to persist. Lastly, it evolves to a nearby energy-minimum either by peak-splitting, peak-decay, or by settling down to a spatially homogeneous state.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Towards the ionizing radiation induced bond dissociation mechanism in oxygen, water, guanine and DNA fragmentation: a density functional theory simulation

The radiation-induced damages in bio-molecules are ubiquitous processes in radiotherapy and radio-biology, and critical to space projects. In this study, we present a precise quantification of the fragmentation mechanisms of deoxyribonucleic acid (DNA) and the molecules surrounding DNA such as oxygen and water under non-equilibrium conditions using the first-principle calculations based on density functional theory (DFT). Our results reveal the structural stability of DNA bases and backbone that withstand up to a combined threshold of charge and hydrogen abstraction owing to simultaneously direct and indirect ionization processes. We show the hydrogen contents of the molecules significantly control the stability in the presence of radiation. This study provides comprehensive information on the impact of the direct and indirect induced bond dissociations and DNA damage and introduces a systematic methodology for fine-tuning the input parameters necessary for the large-scale Monte Carlo simulations of radio-biological responses and mitigation of detrimental effects of ionizing radiation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Producing glucose 6-phosphate from cellulosic biomass: Structural insights into levoglucosan bioconversion

The most abundant carbohydrate product of cellulosic biomass pyrolysis is the anhydrosugar levoglucosan (1,6-anhydro-β-d-glucopyranose), which can be converted to glucose 6-phosphate by levoglucosan kinase (LGK). In addition to the canonical kinase phosphotransfer reaction, the conversion requires cleavage of the 1,6-anhydro ring to allow ATP-dependent phosphorylation of the sugar O6 atom. Using x-ray crystallography, we show that LGK binds two magnesium ions in the active site that are additionally coordinated with the nucleotide and water molecules to result in ideal octahedral coordination. To further verify the metal binding sites, we co-crystallized LGK in the presence of manganese instead of magnesium and solved the structure de novo using the anomalous signal from four manganese atoms in the dimeric structure. The first metal is required for catalysis, whereas our work suggests that the second is either required or significantly promotes the catalytic rate. Although the enzyme binds its sugar substrate in a similar orientation to the structurally related 1,6-anhydro-N-acetylmuramic acid kinase (AnmK), it forms markedly fewer bonding interactions with the substrate. In this orientation, the sugar is in an optimal position to couple phosphorylation with ring cleavage. We also observed a second alternate binding orientation for levoglucosan, and in these structures, ADP was found to bind with lower affinity. These combined observations provide an explanation for the high Km of LGK for levoglucosan. Furthermore, greater knowledge of the factors that contribute to the catalytic efficiency of LGK can be used to improve applications of this enzyme for levoglucosan-derived biofuel production.

09 BIOMASS FUELS↗

NASA NTRS2014

Improved Algorithms for Accurate Retrieval of UV - Visible Diffuse Attenuation Coefficients in Optically Complex, Inshore Waters

Photochemical processes driven by high-energy ultraviolet radiation (UVR) in inshore, estuarine, and coastal waters play an important role in global bio geochemical cycles and biological systems. A key to modeling photochemical processes in these optically complex waters is an accurate description of the vertical distribution of UVR in the water column which can be obtained using the diffuse attenuation coefficients of down welling irradiance (Kd()). The Sea UV Sea UVc algorithms (Fichot et al., 2008) can accurately retrieve Kd ( 320, 340, 380,412, 443 and 490 nm) in oceanic and coastal waters using multispectral remote sensing reflectances (Rrs(), Sea WiFS bands). However, SeaUVSeaUVc algorithms are currently not optimized for use in optically complex, inshore waters, where they tend to severely underestimate Kd(). Here, a new training data set of optical properties collected in optically complex, inshore waters was used to re-parameterize the published SeaUVSeaUVc algorithms, resulting in improved Kd() retrievals for turbid, estuarine waters. Although the updated SeaUVSeaUVc algorithms perform best in optically complex waters, the published SeaUVSeaUVc models still perform well in most coastal and oceanic waters. Therefore, we propose a composite set of SeaUVSeaUVc algorithms, optimized for Kd() retrieval in almost all marine systems, ranging from oceanic to inshore waters. The composite algorithm set can retrieve Kd from ocean color with good accuracy across this wide range of water types (e.g., within 13 mean relative error for Kd(340)). A validation step using three independent, in situ data sets indicates that the composite SeaUVSeaUVc can generate accurate Kd values from 320 490 nm using satellite imagery on a global scale. Taking advantage of the inherent benefits of our statistical methods, we pooled the validation data with the training set, obtaining an optimized composite model for estimating Kd() in UV wavelengths for almost all marine waters. This optimized composite set of SeaUVSeaUVc algorithms will provide the optical community with improved ability to quantify the role of solar UV radiation in photochemical and photobiological processes in the ocean.

UV↗

NASA NTRS1999

Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

Williams Colin P.↗

DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Biomimetic model-based advanced control strategy integrated with multi-agent optimization for nonlinear chemical processes

Here, a novel framework is proposed for integrating biomimetic-based advanced control and multi-agent optimization approaches for nonlinear chemical process applications. In particular, a Biologically-Inspired Optimal Control Strategy, denoted as BIO-CS, is combined with multi-agent optimization (MAO) techniques to provide optimal solutions for dynamic systems. In this combined framework, the BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents’ local interactions. Also, the MAO uses the capabilities of heuristic-based optimization techniques by sharing process information to obtain optimal operating setpoints for the controller considering an overall process objective. The applicability of the proposed method is demonstrated using a nonlinear, multivariable, process model of a fermentation system. Specifically, the optimal operating points are computed by the MAO implementation for setpoint tracking, trajectory tracking and plant-model mismatch scenarios for BIO-CS application. Results of the developed framework are compared to a gradient-based Sequential Quadratic Programming (SQP) technique and a classical proportional-integral (PI) controller in terms of optimization and control studies, respectively. As an additional contribution, BIO-CS is also cast as a model predictive controller (MPC) for the first time and compared to the agent-based BIO-CS approach in terms of computational time and tracking error. Closed-loop control results show up to 46% improvement in tracking performance during transient for the multi-agent BIO-CS when compared to BIO-CS as MPC for additional computational expense. The obtained results illustrate the capabilities of this novel integrated framework including BIO-CS as MPC to achieve desired nonlinear system performance for various scenarios.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗