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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

J-domain proteins interaction with neurodegenerative disease-related proteins

HSP70 chaperones, J-domain proteins (JDPs) and nucleotide exchange factors (NEF) form functional networks that have the ability to prevent and reverse the aggregation of proteins associated with neurodegenerative diseases. JDPs can interact with specific substrate proteins, hold them in a refolding-competent conformation and target them to specific HSP70 chaperones for remodeling. Thereby, JDPs select specific substrates and constitute an attractive target for pharmacological intervention of neurodegenerative diseases. This, under the condition that the exact mechanism of JDPs interaction with specific substrates is unveiled. In this review, we provide an overview of the structural and functional variety of JDPs that interact with neurodegenerative disease-associated proteins and we highlight those studies that identified specific residues, domains or regions of JDPs that are crucial for substrate binding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

An architectural approach to create self organizing control systems for practical autonomous robots

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

Greiner, Helen↗
DOE OSTI2019

Mapping Arbitrarily Sparse Two-body Interactions on One-dimensional Quantum Circuits

We consider an assignment problem arising in Fermionic-swap based mapping of the one-body and two-body interaction terms in simulating time evolution of a sparse second-quantized electronic structure Hamiltonian on a quantum computer. Relative efficiency of different swap networks depends on the relative costs of performing a swap layer and computing a Hamiltonian interaction term. Under the assumption that the interaction term cost dominates the computation, we develop an iterative algorithm that uses hypergraph optimal linear arrangement (HOLA) and partial distance-2 coloring to exploit arbitrary sparsity in the Hamiltonian for efficient computation using swap networks. Using a set of 122 problems from computational chemistry, we demonstrate performance improvements up to 100% relative to the state-of-the-art approaches for one-body terms and up to 86% utilization for two-body terms relative to a theoretical peak utilization. To the best of our knowledge, this is the first study to exploit arbitrary sparsity in orbital interactions for efficient computation on one-dimensional qubit connectivity layouts. The proposed algorithms form the basis for extension to map general k-body interactions that arise in other domains onto generalized qubit connectivity layouts.

quantum computing, Combinatorial Optimization, hyp↗
DOE OSTI2014

Co-localised Raman and force spectroscopy reveal the roles of hydrogen bonds and π-π interactions in defining the mechanical properties of diphenylalanine nano- and micro-tubes

An integrated atomic force and polarized Raman microscope were used to measure the elastic properties of individual diphenylalanine (FF) nano- and micro-tubes and to obtain quantitative information regarding the inter-molecular interactions that define their mechanical properties. For individual tubes, co-localised force spectroscopy and Raman spectroscopy measurements allowed the calculation of the Young's and shear moduli (25 ± 5 GPa and 0.28 ± 0.05 GPa, respectively) and the contribution of hydrogen bonding network to the Young's modulus (∼17.6 GPa). The π-π interactions between the phenyl rings, dominated by T-type arrangements, were estimated based on previously published X-ray data to only 0.20 GPa. These results provide experimental evidence obtained from individual FF tubes that the network of H-bonds dominates the elastic properties of the FF tubes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum

We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.

Zhang, Zeyu↗
NASA NTRS2012

Cognitive Network Modeling as a Basis for Characterizing Human Communication Dynamics and Belief Contagion in Technology Adoption

Societal level macro models of social behavior do not sufficiently capture nuances needed to adequately represent the dynamics of person-to-person interactions. Likewise, individual agent level micro models have limited scalability - even minute parameter changes can drastically affect a model's response characteristics. This work presents an approach that uses agent-based modeling to represent detailed intra- and inter-personal interactions, as well as a system dynamics model to integrate societal-level influences via reciprocating functions. A Cognitive Network Model (CNM) is proposed as a method of quantitatively characterizing cognitive mechanisms at the intra-individual level. To capture the rich dynamics of interpersonal communication for the propagation of beliefs and attitudes, a Socio-Cognitive Network Model (SCNM) is presented. The SCNM uses socio-cognitive tie strength to regulate how agents influence--and are influenced by--one another's beliefs during social interactions. We then present experimental results which support the use of this network analytical approach, and we discuss its applicability towards characterizing and understanding human information processing.

Hutto, Clayton↗
NASA NTRS1998

Experiments to Determine Whether Recursive Partitioning (CART) or an Artificial Neural Network Overcomes Theoretical Limitations of Cox Proportional Hazards Regression

New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.

Kattan, Michael W.↗
DOE OSTI2014

Regulation of FANCD2 and FANCI monoubiquitination by their interaction and by DNA

FANCD2 and FANCI function together in the Fanconi anemia network of deoxyribonucleic acid (DNA) crosslink repair. These proteins form the dimeric ID2 complex that binds DNA and becomes monoubiquitinated upon exposure of cells to DNA crosslinking agents. The monoubiquitinated ID2 complex is thought to facilitate DNA repair via recruitment of specific nucleases, translesion DNA polymerases and the homologous recombination machinery. Using the ubiquitin conjugating enzyme (E2) UBE2T and ubiquitin ligase (E3) FANCL, monoubiquitination of human FANCD2 and FANCI was examined. The ID2 complex is a poor substrate for monoubiquitination, consistent with the published crystal structure showing the solvent inaccessibility of the target lysines. Importantly, FANCD2 monoubiquitination within the ID2 complex is strongly stimulated by duplex or branched DNA, but unstructured singlestranded DNA or chromatinized DNA is ineffective. Interaction of FANCL with the ID2 complex is indispensable for its E3 ligase efficacy. Interestingly, mutations in FANCI that impair its DNA binding activity compromise DNA-stimulated FANCD2 monoubiquitination. Moreover, we demonstrate that in the absence of FANCD2, DNA also stimulates FANCI monoubiquitination, but in a FANCL-independent manner. These results implicate the role of a proper DNA ligand in FANCD2 and FANCI monoubiquitination, and reveal regulatory mechanisms that are dependent on protein–protein and protein–DNA interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Neural-network quantum states for ultra-cold Fermi gases

Abstract Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2011

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: Linking systems biology with vaccine development

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhanced approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic *sipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1,2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗
NASA NTRS1993

Bandwidth characteristics of multimedia data traffic on a local area network

Limited spacecraft communication links call for users to investigate the potential use of video compression and multimedia technologies to optimize bandwidth allocations. The objective was to determine the transmission characteristics of multimedia data - motion video, text or bitmap graphics, and files transmitted independently and simultaneously over an ethernet local area network. Commercial desktop video teleconferencing hardware and software and Intel's proprietary Digital Video Interactive (DVI) video compression algorithm were used, and typical task scenarios were selected. The transmission time, packet size, number of packets, and network utilization of the data were recorded. Each data type - compressed motion video, text and/or bitmapped graphics, and a compressed image file - was first transmitted independently and its characteristics recorded. The results showed that an average bandwidth of 7.4 kilobits per second (kbps) was used to transmit graphics; an average bandwidth of 86.8 kbps was used to transmit an 18.9-kilobyte (kB) image file; a bandwidth of 728.9 kbps was used to transmit compressed motion video at 15 frames per second (fps); and a bandwidth of 75.9 kbps was used to transmit compressed motion video at 1.5 fps. Average packet sizes were 933 bytes for graphics, 498.5 bytes for the image file, 345.8 bytes for motion video at 15 fps, and 341.9 bytes for motion video at 1.5 fps. Simultaneous transmission of multimedia data types was also characterized. The multimedia packets used transmission bandwidths of 341.4 kbps and 105.8kbps. Bandwidth utilization varied according to the frame rate (frames per second) setting for the transmission of motion video. Packet size did not vary significantly between the data types. When these characteristics are applied to Space Station Freedom (SSF), the packet sizes fall within the maximum specified by the Consultative Committee for Space Data Systems (CCSDS). The uplink of imagery to SSF may be performed at minimal frame rates and/or within seconds of delay, depending on the user's allocated bandwidth. Further research to identify the acceptable delay interval and its impact on human performance is required. Additional studies in network performance using various video compression algorithms and integrated multimedia techniques are needed to determine the optimal design approach for utilizing SSF's data communications system.

Chuang, Shery L.↗
DOE OSTI2022

Low-abundance populations distinguish microbiome performance in plant cell wall deconstruction

Abstract Background Plant cell walls are interwoven structures recalcitrant to degradation. Native and adapted microbiomes can be particularly effective at plant cell wall deconstruction. Although most understanding of biological cell wall deconstruction has been obtained from isolates, cultivated microbiomes that break down cell walls have emerged as new sources for biotechnologically relevant microbes and enzymes. These microbiomes provide a unique resource to identify key interacting functional microbial groups and to guide the design of specialized synthetic microbial communities. Results To establish a system assessing comparative microbiome performance, parallel microbiomes were cultivated on sorghum ( Sorghum bicolor L. Moench) from compost inocula. Biomass loss and biochemical assays indicated that these microbiomes diverged in their ability to deconstruct biomass. Network reconstructions from gene expression dynamics identified key groups and potential interactions within the adapted sorghum-degrading communities, including Actinotalea , Filomicrobium , and Gemmatimonadetes populations. Functional analysis demonstrated that the microbiomes proceeded through successive stages that are linked to enzymes that deconstruct plant cell wall polymers. The combination of network and functional analysis highlighted the importance of cellulose-degrading Actinobacteria in differentiating the performance of these microbiomes. Conclusions The two-tier cultivation of compost-derived microbiomes on sorghum led to the establishment of microbiomes for which community structure and performance could be assessed. The work reinforces the observation that subtle differences in community composition and the genomic content of strains may lead to significant differences in community performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A physics-constrained neural network for multiphase flows

The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations. The multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is next implemented to correct the predicted order parameters. This enforces the predicted order parameters to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by another NN with the same network structure, but the conservation of momentum is included in the loss function to shrink the parameter space. The proposed PCNN for MPFs sequentially performs (NN)-(MCBOM)-(NN), which avoids nonphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.

Mechanics↗
DOE OSTI2013

A series of novel gallium and indium borates constructed from [B{sub 5}O{sub 8}(OH){sub 2}]{sup 3-} clusters and metal complex linkers

Four novel gallium and indium borates, [M(en){sub 2}][B{sub 5}O{sub 8}(OH){sub 2}]{center_dot}H{sub 2}O (M=Ga (1), In (2); en=ethylenediamine), [In(dap){sub 2}][B{sub 5}O{sub 8}(OH){sub 2}]{center_dot}H{sub 2}O (3, dap=1,2-diaminopropane), [In(dien)][B{sub 5}O{sub 8}(OH){sub 2}] (4, dien=diethylenetriamine), have been hydrothermally synthesized and characterized by IR spectra, elemental analyses, powder X-ray diffraction, single crystal X-ray diffraction, and thermogravimetric analyses. Compounds 1, 2 and 3 are isostructural and consist of one-dimensional (1-D) chains built up from [B{sub 5}O{sub 8}(OH){sub 2}]{sup 3-} clusters and Ga-/In-complex linkers. The structure of compound 4 features a 1-D chain containing 8-member rings built by [B{sub 5}O{sub 8}(OH){sub 2}]{sup 3-} clusters and {l_brace}In{sub 2}O{sub 4}{r_brace} dimeric cluster linkers which have never observed in the structural chemistry of borates to date. The H-bonding interactions between adjacent chains lead to 3-D supramolecular network structure. - Graphical abstract: Four novel gallium and indium borates with one-dimensional structures built by B{sub 5}O{sub 8}(OH){sub 2}{sup 3+} clusters and metal complex linkers have been successfully synthesized under hydrothermal conditions. Highlights: Black-Right-Pointing-Pointer Four new Ga/In borates have been hydrothermally made. Black-Right-Pointing-Pointer The helical/ribbon-like chains made of B{sub 5}O{sub 8}(OH){sub 2}{sup 3+} clusters and Ga-/In-complex linkers. Black-Right-Pointing-Pointer The chains form novel 3-D supermolecule framework via H-bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

A Lattice Boltzmann Fictitious Domain Method for Modeling Red Blood Cell Deformation and Multiple-Cell Hydrodynamic Interactions in Flow

To model red blood cell (RBC) deformation in flow, the recently developed LBM-DLM/FD method ([Shi and Lim, 2007)29], derived from the lattice Boltzmann method and the distributed Lagrange multiplier/fictitious domain methodthe fictitious domain method, is extended to employ the mesoscopic network model for simulations of red blood cell deformation. The flow is simulated by the lattice Boltzmann method with an external force, while the network model is used for modeling red blood cell deformation and the fluid-RBC interaction is enforced by the Lagrange multiplier. To validate parameters of the RBC network model, sThe stretching numerical tests on both coarse and fine meshes are performed and compared with the corresponding experimental data to validate the parameters of the RBC network model. In addition, RBC deformation in pipe flow and in shear flow is simulated, revealing the capacity of the current method for modeling RBC deformation in various flows.

Shi, Xing↗