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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Spontaneous Inward Opening of the Dopamine Transporter Is Triggered by PIP 2 -Regulated Dynamics of the N-Terminus

We present the dynamic mechanism of concerted motions in a full-length molecular model of the human dopamine transporter (hDAT), a member of the neurotransmitter/sodium symporter (NSS) family, involved in state-to-state transitions underlying function. The findings result from an analysis of unbiased atomistic molecular dynamics simulation trajectories (totaling >14 μs) of the hDAT molecule immersed in lipid membrane environments with or without phosphatidylinositol 4,5-biphosphate (PIP2) lipids. The N-terminal region of hDAT (N-term) is shown to have an essential mechanistic role in correlated rearrangements of specific structural motifs relevant to state-to-state transitions in the hDAT. The mechanism involves PIP2-mediated electrostatic interactions between the N-term and the intracellular loops of the transporter molecule. Quantitative analyses of collective motions in the trajectories reveal that these interactions correlate with the inward-opening dynamics of hDAT and are allosterically coupled to the known functional sites of the transporter. The observed large-scale motions are enabled by specific reconfiguration of the network of ionic interactions at the intracellular end of the protein. The isomerization to the inward-facing state in hDAT is accompanied by concomitant movements in the extracellular vestibule and results in the release of an Na+ ion from the Na2 site and destabilization of the substrate dopamine in the primary substrate binding S1 site. The dynamic mechanism emerging from the findings highlights the involvement of the PIP2-regulated interactions between the N-term and the intracellular loop 4 in the functionally relevant conformational transitions that are also similar to those found to underlie state-to-state transitions in the leucine transporter (LeuT), a prototypical bacterial homologue of the NSS.

Biochemistry & Molecular Biology↗
NASA NTRS1993

A constrained joint source/channel coder design and vector quantization of nonstationary sources

The emergence of broadband ISDN as the network for the future brings with it the promise of integration of all proposed services in a flexible environment. In order to achieve this flexibility, asynchronous transfer mode (ATM) has been proposed as the transfer technique. During this period a study was conducted on the bridging of network transmission performance and video coding. The successful transmission of variable bit rate video over ATM networks relies on the interaction between the video coding algorithm and the ATM networks. Two aspects of networks that determine the efficiency of video transmission are the resource allocation algorithm and the congestion control algorithm. These are explained in this report. Vector quantization (VQ) is one of the more popular compression techniques to appear in the last twenty years. Numerous compression techniques, which incorporate VQ, have been proposed. While the LBG VQ provides excellent compression, there are also several drawbacks to the use of the LBG quantizers including search complexity and memory requirements, and a mismatch between the codebook and the inputs. The latter mainly stems from the fact that the VQ is generally designed for a specific rate and a specific class of inputs. In this work, an adaptive technique is proposed for vector quantization of images and video sequences. This technique is an extension of the recursively indexed scalar quantization (RISQ) algorithm.

Sayood, Khalid↗
DOE OSTI2012

Inferring Correlation Networks from Genomic Survey Data

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Nuclei with Up to $$\varvec{A=6}$$ Nucleons with Artificial Neural Network Wave Functions

The ground-breaking works of Weinberg have opened the way to calculations of atomic nuclei that are based on systematically improvable Hamiltonians. Solving the associated many-body Schrödinger equation involves non-trivial difficulties, due to the non-perturbative nature and strong spin-isospin dependence of nuclear interactions. Artificial neural networks have proven to be able to compactly represent the wave functions of nuclei with up to $A=4$ nucleons. In this work, we extend this approach to $^6$Li and $^6$He nuclei, using as input a leading-order pionless effective field theory Hamiltonian. We successfully benchmark their binding energies, point-nucleon densities, and radii with the highly-accurate hyperspherical harmonics method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2003

Social roles and the evolution of networks in extreme and isolated environments

This article reports on the evolution of network structure as it relates to formal and informal social roles in well-bounded, isolated groups. Research was conducted at the Amundsen-Scott South Pole Station. Data were collected on crewmembers' networks of social interaction over each of three winter-over periods, when the station is completely isolated. In addition, data were collected on the informal roles played by crewmembers (e.g., instrumental leadership, expressive leadership). The study found that globally coherent networks in winter-over groups were associated with group consensus on the presence of critically important informal social roles (e.g., expressive leadership) where global coherence is the extent to which a network forms a single group composed of a unitary core and periphery as opposed to being factionalized into two or more subgroups. Conversely, the evolution of multiple subgroups was associated with the absence of consensus on critical informal social roles, above all the critically important role of instrumental leader.

NASA Program Biomedical Research and Countermeasur↗
DOE OSTI2016

Multimodal probing of oxygen and water interaction with metallic and semiconducting carbon nanotube networks under ultraviolet irradiation

In this study, carbon nanotube (CNT) networks composed of semiconducting single wall nanotubes (s-SWNTs), metallic single wall nanotubes (m-SWNTs), and multiwall nanotubes (MWNTs) were exposed to O 2 and H 2 O vapor in the dark and under UV irradiation. Changes in film resistance and mass were measured in situ . In the dark, resistance of metallic nanotube networks increases in the presence of O 2 and H 2 O, whereas resistance of s-SWNT networks decreases. We find that UV irradiation increases the sensitivity of CNT networks to O 2 and H 2 O by more than an order of magnitude. Under UV irradiation, the resistance of metallic nanotube networks decreases in the presence of O 2 and H 2 O likely through the generation of free charge carriers. UV irradiation increases the gas/vapor sensitivity of s-SWNT networks by nearly a factor of 2 compared to metallic nanotube networks. Networks of s-SWNTs show evidence of delamination from the gold-plated QCM crystal, possibly due to preferential adsorption of O 2 and H 2 O on gold.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. Here, we make the method available to the scientific community to speed biological research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

We report that the ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learning (ML) techniques provide a possible avenue for the efficient prediction of atomic partial charges. Modern ML advances in the prediction of molecular energies [i.e., the hierarchical interacting particle neural network (HIP-NN)] has provided the necessary model framework and architecture to predict transferable, extensible, and conformationally dynamic atomic partial charges based on reference density functional theory (DFT) simulations. Utilizing HIP-NN, we show that ML charge prediction can be highly accurate over a wide range of molecules (both small and large) across a variety of charge partitioning schemes such as the Hirshfeld, CM5, MSK, and NBO methods. To demonstrate transferability and size extensibility, we compare ML results with reference DFT calculations on the COMP6 benchmark, achieving errors of 0.004e – (elementary charge). This is remarkable since this benchmark contains two proteins that are multiple times larger than the largest molecules in the training set. An application of our atomic charge predictions on nonequilibrium geometries is the generation of IR spectra for organic molecules from dynamical trajectories on a variety of organic molecules, which show good agreement with calculated IR spectra with reference method. Critically, HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations. Lastly, these combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

CCM2-CCM3 interaction stabilizes their protein expression and permits endothelial network formation

Mutations in the essential adaptor proteins CCM2 or CCM3 lead to cerebral cavernous malformations (CCM), vascular lesions that most frequently occur in the brain and are strongly associated with hemorrhagic stroke, seizures, and other neurological disorders. CCM2 binds CCM3, but the molecular basis of this interaction, and its functional significance, have not been elucidated. Here, we used x-ray crystallography and structure-guided mutagenesis to show that an α-helical LD-like motif within CCM2 binds the highly conserved “HP1” pocket of the CCM3 focal adhesion targeting (FAT) homology domain. By knocking down CCM2 or CCM3 and rescuing with binding-deficient mutants, we establish that CCM2–CCM3 interactions protect CCM2 and CCM3 proteins from proteasomal degradation and show that both CCM2 and CCM3 are required for normal endothelial cell network formation. However, CCM3 expression in the absence of CCM2 is sufficient to support normal cell growth, revealing complex-independent roles for CCM3.

Draheim, Kyle M.↗
DOE OSTI2015

Characterizing Dynamics of Anion/Pi Interactions through Molecular Dynamics Simulations

Proteins are not static structures, but undergo dynamical variations at room temperature that can lead to changes in the number and strength of different non-covalent interactions. The negatively charged (Asp/Glu) and aromatic (Phe/Trp/Tyr) amino acid groups in proteins can form anion/pi interactions, that have been shown to have a distance and angle dependency. This study characterizes the dynamic stability of these interactions in RmlC (PDB-1EP0), a homodimeric epimerase showing a cluster of six anion/pi pairs in crystal structure, as a function of time using Molecular Dynamics (MD) simulations. The dynamic variations found in these interactions correspond to a potential of mean force (PMF) that exhibits a relatively wide range of distances (~4.5 to~8.5Å) and angles (0 to~45deg) sampled by these anion/pi pairs. The corresponding PMF indicates an associated free energy change that can be more stabilizing (up to -5kcal/mol) than the ab initio-calculated interaction energy for the pairs in the crystal structure (-2.2kcal/mol). Possible anion/pi pairs and triplets (anion/anion/pi or anion/pi/pi) not seen in the crystal structure are also observed in other conformations sampled by the protein- both at the dimer interface and active site of the protein, forming an extensive network of anion/pi interactions covering most of the protein structure. Initial site-directed mutagenesis (SDM) experiments targeting one of these pairs, Asp84-Phe112, show that the single Phe/Met mutation decreases the stability of the protein compared to the wild type. Together these results suggest that anion-pi interactions can play an important role in maintaining both the structural stability and function of the proteins.

Kapoor, Karan↗
DOE OSTI2011

A general model of resource production and exchange in systems of interdependent specialists.

Infrastructures are networks of dynamically interacting systems designed for the flow of information, energy, and materials. Under certain circumstances, disturbances from a targeted attack or natural disasters can cause cascading failures within and between infrastructures that result in significant service losses and long recovery times. Reliable interdependency models that can capture such multi-network cascading do not exist. The research reported here has extended Sandia's infrastructure modeling capabilities by: (1) addressing interdependencies among networks, (2) incorporating adaptive behavioral models into the network models, and (3) providing mechanisms for evaluating vulnerability to targeted attack and unforeseen disruptions. We have applied these capabilities to evaluate the robustness of various systems, and to identify factors that control the scale and duration of disruption. This capability lays the foundation for developing advanced system security solutions that encompass both external shocks and internal dynamics.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2012

A Human View Model for Socio-Technical Interactions

The Human View was developed as an additional architectural viewpoint to focus on the human part of a system. The Human View can be used to collect and organize data in order to understand how human operators interact and impact the other elements of a system. This framework can also be used to develop a model to describe how humans interact with each other in network enabled systems. These socio-technical interactions form the foundation of the emerging area of Human Interoperability. Human Interoperability strives to understand the relationships required between human operators that impact collaboration across networked environments, including the effect of belonging to different organizations. By applying organizational relationship concepts from network theory to the Human View elements, and aligning these relationships with a model developed to identify layers of coalition interoperability, the conditions for different levels for Human Interoperability for network enabled systems can be identified. These requirements can then be captured in the Human View products to improve the overall network enabled system.

Handley, Holly A.↗
DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations Version 2.0

This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Lightweight and effective tensor sensitivity for atomistic neural networks

Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here, we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 $\frac{\text{kcal}}{\text{mol}}$ for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Integrated multi-omic characterizations of the synapse reveal RNA processing factors and ubiquitin ligases associated with neurodevelopmental disorders

The molecular composition of the excitatory synapse is incompletely defined due to its dynamic nature across developmental stages and neuronal populations. To address this gap, we apply proteomic mass spectrometry to characterize the synapse in multiple biological models including the fetal human brain and hiPSC-derived neurons. To prioritize the identified proteins, we develop an orthogonal multi-omic screen of genomic, transcriptomic, interactomic, and structural data. This data-driven framework identifies proteins with key molecular features intrinsic to the synapse, including characteristic patterns of biophysical interactions and cross-tissue expression. The multi-omic analysis captures synaptic proteins across developmental stages and experimental systems, including 493 synaptic candidates supported by proteomics. We further investigate three such proteins that are associated with neurodevelopmental disorders – the CUL3 E3 ubiquitin ligase, the DDX3X and YBX1 nucleic-acid binding proteins – by mapping their networks of physically interacting synapse proteins or transcripts. Our study demonstrates the potential of an integrated multi-omic approach to systematically and more comprehensively resolve the synaptic architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A.

Phalak, Poonam↗