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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map.

stereocilia↗
DOE OSTI2021

Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses

If viral strains are sufficiently similar in their immunodominant epitopes, then populations of cross-reactive T cells may be boosted by exposure to one strain and provide protection against infection by another at a later date. This type of pre-existing immunity may be important in the adaptive immune response to influenza and to coronaviruses. Patterns of recognition of epitopes by T cell clonotypes (a set of cells sharing the same T cell receptor) are represented as edges on a bipartite network. We describe different methods of constructing bipartite networks that exhibit cross-reactivity, and the dynamics of the T cell repertoire in conditions of homeostasis, infection and re-infection. Cross-reactivity may arise simply by chance, or because immunodominant epitopes of different strains are structurally similar. We introduce a circular space of epitopes, so that T cell cross-reactivity is a quantitative measure of the overlap between clonotypes that recognize similar (that is, close in epitope space) epitopes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

R&D100 Finalist: Neuromorphic Cyber Microscope

The Neuromorphic Cyber Microscope provides security analysts with unprecedented visibility of their network, computer and storage assets. This processor is the world's first practical implementation of neuromorphic technology to a major computer science mission. Working with Lewis Rhodes Labs, engineers at Sandia National Laboratories have created a device that is orders of magnitude faster at analyzing data to identify cyber-attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Data analysis for valve leak detection of nuclear power plant safety critical components - 336

A nuclear plant consists of several safety-related components. These safety-related components are tested periodically in order to verify their functionality when they are called upon to perform. The data acquired during periodic testing provides information about possible degradation of the equipment or device under test. This research is focused on leak monitoring techniques to provide diagnostic information on the determination of valve aging and degradation. Several commercially available valve diagnostic-monitoring methods are evaluated, especially for the techniques based on measurements of acoustic emission, and accelerometer. The check valve is one of typical components being extensively used in safety systems of nuclear power plants. The failures of check valves have resulted in significant maintenance efforts, on occasion, have resulted in water hammer, over-pressurization of low-pressure systems, and damage to flow system components. In this paper, acoustic emission signals due to leak from check valves with artificial leakage and worn parts are investigated both analytically and experimentally. The results of advanced signal processing and de-noising techniques also have been identified. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Identification of the threshold security applied for fuel under reactivity-initiated conditions by self-organizing maps and neural networks - 14386

In recent times, we have 439 reactors distributed in 31 countries that give about 377 GW, covering around 12% of world capacity of energy. Currently, it accounts for an estimated 40 serious accidents reported in nuclear power plants. In this article, turns out the behavior of fuel rod in light-water reactors under accident conditions. Besides, we investigate deeply about reactivity-initiated accidents, by introducing Nordheim-Fuchs model as state equations. The early research showed that pre-irradiated fuels and afterward subjected to a rapid pulse of energy. It adopts the key items necessary to check the threshold of failure. These items are few limits as burning the fuel; the value applied to the peak of energy, pulse width, oxidation layer, and cladding. The kinetic equations of nuclear reactors modeled as a non-linear system. In this way, it can apply the paradigm of artificial intelligence using the self-organized maps. Where the goal is to find the cluster of failure for irradiated fuels until 75 GWd/MTU. The second model based on statistical is the logistic regression theory. Hence, the tests carried out and compared against the results found by neural networks for classification of clusters. It proofs that both approaches present capacity to find the flaws accurately of about 80% of cases. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning

An understanding of a parallel application’s communication behavior is useful for a range of activities including debugging and optimization, job scheduling, target system selection, and system design. Because it can be challenging to understand communication behavior, especially for those who lack expertise or who are not familiar with the application, I and two colleagues recently developed an automated, search-based approach for recognizing and parameterizing application communication behavior using a library of common communication patterns. This initial approach was effective for characterizing the behavior of many workloads, but I identified some combinations of communication patterns for which the method was inefficient or would fail. In this paper, I discuss one such troublesome pattern combination and propose modifications to the recognition method to handle it. Specifically, I propose an alternative approach that uses communication graphs instead of traditional communication matrices to improve recognition accuracy for collective communication operations, and that uses a non-greedy recognition technique to avoid search space dead-ends that trap the original greedy recognition approach. My modified approach uses aggressive search space pruning and heuristics to control the potential for state explosion caused by its non-greedy pattern recognition method. I demonstrate the improved recognition accuracy and pruning efficacy of the modified approach using several synthetic and real-world communication pattern combinations.

Roth, Philip↗
DOE OSTI2023

Deep learning Hamiltonians from disordered image data in quantum materials

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder—a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of V⁢O 2 . We then apply our trained convolutional neural network architecture to new optical microscope images of a different V⁢O 2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. Finally, we also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Auto-associative nanoelectronic neural network

In this paper, an auto-associative neural network using single-electron tunneling (SET) devices is proposed and simulated at low temperature. The nanoelectronic auto-associative network is able to converge to a stable state, previously stored during training. The recognition of the pattern involves decreasing the energy of the input state until it achieves a point of local minimum energy, which corresponds to one of the stored patterns.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Comparison of measurement and simulation of ATLAS cavern radiation background

Sixteen Medipix2 pixel detector based (MPX) devices were operated at various positions within the ATLAS detector and cavern continuously from early 2008 up to 2013. In addition to photons, each MPX detector is capable to detect charged particles, and neutrons as it is covered with a mask of converter materials dividing its area into regions sensitive to thermal or fast neutrons. The MPX detector network was effectively used for real-time measurements of the spectral characteristics and composition of complex radiation fields in ATLAS. This article reports comparison of the results of measurements performed with MPX detectors during the LHC operation period in 2010 and 2011 with Monte Carlo simulations results from the FLUGG and GCALOR codes. For the purpose of this comparison, the MPX detectors were operated in tracking mode with low threshold (8–10 keV) allowing one to distinguish among particle categories based on the recognition of track patterns left by the particles in the MPX sensitive layer. The comparison of measurements with simulations shows that the agreement between measured and simulated data is satisfactory in most cases within a factor of two.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Protocadherin cis -dimer architecture and recognition unit diversity

Clustered protocadherins (Pcdhs) mediate numerous neural patterning functions, including neuronal self-recognition and non–self-discrimination to direct self-avoidance among vertebrate neurons. Individual neurons stochastically express a subset of Pcdh isoforms, which assemble to form a stochastic repertoire of cis -dimers. We describe herein the structure of a PcdhγB7 cis -homodimer, which includes the membrane-proximal extracellular cadherin domains EC5 and EC6. The structure is asymmetric with one molecule contributing interface surface from both EC5 and EC6, and the other only from EC6. Structural and sequence analyses suggest that all Pcdh isoforms will dimerize through this interface. Site-directed mutants at this interface interfere with both Pcdh cis -dimerization and cell surface transport. The structure explains the known restrictions of cis -interactions of some Pcdh isoforms, including α-Pcdhs, which cannot form homodimers. The asymmetry of the interface approximately doubles the size of the recognition repertoire, and restrictions on cis -interactions among Pcdh isoforms define the limits of the Pcdh recognition unit repertoire.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

cGAS-like receptors sense RNA and control 3'2'-cGAMP signalling in Drosophila

Cyclic GMP–AMP synthase (cGAS) is a cytosolic DNA sensor that produces the second messenger cG[2'–5']pA[3'–5']p (2'3'-cGAMP) and controls activation of innate immunity in mammalian cells. Animal genomes typically encode multiple proteins with predicted homology to cGAS, but the function of these uncharacterized enzymes is unknown. Here we show that cGAS-like receptors (cGLRs) are innate immune sensors that are capable of recognizing divergent molecular patterns and catalysing synthesis of distinct nucleotide second messenger signals. Crystal structures of human and insect cGLRs reveal a nucleotidyltransferase signalling core shared with cGAS and a diversified primary ligand-binding surface modified with notable insertions and deletions. We demonstrate that surface remodelling of cGLRs enables altered ligand specificity and used a forward biochemical screen to identify cGLR1 as a double-stranded RNA sensor in the model organism Drosophila melanogaster. We show that RNA recognition activates Drosophila cGLR1 to synthesize the novel product cG[3'–5']pA[2'–5']p (3'2'-cGAMP). A crystal structure of Drosophila stimulator of interferon genes (dSTING) in complex with 3'2'-cGAMP explains selective isomer recognition, and 3'2'-cGAMP induces an enhanced antiviral state in vivo that protects from viral infection. Similar to radiation of Toll-like receptors in pathogen immunity, our results establish cGLRs as a diverse family of metazoan pattern recognition receptors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning

Abstract Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.

Hwang, Gyujoon↗
DOE OSTI2019

Caveat regarding CMS Merit-based Incentive Payment Systems incidental adrenal nodule measure

Current Medicare MIP measures encourage radiologists not to recommend follow-up for ≤ 1 cm adrenal nodules. However, a radiologist may be the first to discover a small, subclinical pheochromocytoma. As such, recognition of the enhancement pattern of pheochromocytoma is important to ensure detection and properly guide management, which begins with clinical and laboratory assessment for elevated catecholamines.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

CHERENCUBE: Concept definition and implementation challenges of a Cherenkov-based detector block for PET

Purpose: A new concept for a depth-of-interaction (DOI) capable time-of-flight (TOF) PET detector is defined, based only on the detection of Cherenkov photons. The proposed “CHERENCUBE” consists of a cubic Cherenkov radiator with position-sensitive photodetectors covering each crystal face. By means of the spatial distribution of the detected photons and their time of arrival, the point of interaction of the gamma-ray in the crystal can be determined. This study analyzes through theoretical calculations and Monte Carlo simulations the potential advantages of the concept toward reaching a Cherenkov-only detector for TOF-PET with DOI capability. Furthermore, an algorithm for the DOI estimation is presented and the requirements for a practical implementation of the proposed concept are defined. Methods: The Monte Carlo simulations consisted of a cubic crystal with one photodetector coupled to each one of the faces of the cube. The sensitive area of the detector matched exactly the crystal size, which was varied in 1 mm steps between 1 × 1 × 1 mm{sup 3} and 10 × 10 × 10 mm{sup 3}. For each size, five independent simulations of ten thousand 511 keV gamma-rays were triggered at a fixed distance of 10 mm. The crystal chosen was PbWO{sub 4}. Its scintillation properties were simulated, but only Cherenkov photons were analyzed. Photodetectors were simulated having perfect photodetection efficiency and infinite time resolution. For every generated particle, the analysis considered its creation process, parent and daughter particles, energy, origin coordinates, trajectory, and time and position of detection. The DOI determination is based on the distribution of the emission time of all photons per event. These values are calculated as a function of the coordinates of detection and origin for every photon. The common origin is estimated by finding the distribution with the most similar emission time-points. Results: Detection efficiency increases with crystal size from 8.2% (1 × 1 × 1 mm{sup 3}) to 58.6% (10 × 10 × 10 mm{sup 3}) and decreases applying a photon detection threshold of 5/10/20 photons to 6.3%/4.3%/0.7% and 49.3%/30.4%/2.8%, respectively. The detection rate in the six photodetectors is uniform due to the nearly isotropic cone emission. Most cones originated after a photoelectric effect interaction, with two dominating peaks for the kinetic energy of the electron at 422.99 and 441.47 keV. The detection distance between same-event photons defines the spatial resolution of the detector required for individual photon recognition, with 20% of the detected photons having their closest neighbor within a distance of 5% of the length of the cube. Same-event photons are detected within a time window whose width is determined by the crystal size, with values of 30 and 150 ps for a 1 × 1 × 1 mm{sup 3} and a 10 × 10 × 10 mm{sup 3} cube, respectively. The DOI reconstruction has an accuracy of approximately 23% of the length of the cube, with an average value of 2.2 mm for a 10 × 10 × 10 mm{sup 3} CHERENCUBE. Conclusions: The proposed concept requires a detector with high photodetection efficiency. The structure of the sensitive surface of the detector should be a two dimensional array of microcells, able to provide individual detection coordinates and time stamps. The microcell size determines the ability to recognize individual photons, influencing detection efficiency. The 3D DOI recognition relies on the accuracy of the time stamps and detection coordinates, without the need for a recognition of the projected patterns of photons. The refractive index of the material defines a detector intrinsic energy-based rejection of scattered PET events at the cost of reduced sensitivity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Molecular association model of PPARα and its new specific and efficient ligand, pemafibrate: Structural basis for SPPARMα

Highlights: • Computationally constructed the structure of hPPARα in a complex with pemafibrate. • Structural basis for potent effects of pemafibrate predicted by in silico FMO method. • Computational results were confirmed by in vitro luciferase assay analysis. • Unlike fibrates, pemafibrate binds efficiently that makes it as a novel SPPARMα. Peroxisome proliferator-activated receptor-α (PPARα) is a ligand-activated transcription factor involved in the regulation of lipid homeostasis and improves hypertriglyceridemia. Pemafibrate is a novel selective PPARα modulator (SPPARMα) that activates PPARα transcriptional activity. Here, we computationally constructed the structure of the human PPARα in a complex with pemafibrate, along with that of hPPARα complexed with the classical fenofibrate, and studied their interactions quantitatively by using the first-principles calculations-based fragment molecular orbital (FMO) method. Comprehensive structural and protein-ligand binding elucidation along with the in vitro luciferase analysis let us to identify pemafibrate as a novel SPPARMα. Unlike known fibrate ligands, which bind only with the arm I of the Y-shaped ligand binding pocket, the Y-shaped pemafibrate binds to the entire cavity region. This lock and key nature causes enhanced induced fit in pemafibrate-ligated PPARα. Importantly, this selective modulator allosterically changes PPARα conformation to form a brand-new interface, which in turn binds to PPARα co-activator, PGC-1α, resulting in the full activation of PPARα. The structural basis for the potent effects of pemafibrate on PPARα transcriptional activity predicted by the in silico FMO methods was confirmed by in vitro luciferase assay for mutants. The unique binding mode of pemafibrate reveals a new pattern of nuclear receptor ligand recognition and suggests a novel basis for ligand design, offering cues for improving the binding affinity and selectivity of ligand for better clinical consequences. The findings explain the high affinity and efficacy of pemafibrate, which is expected to be in the clinical use soon.

60 APPLIED LIFE SCIENCES↗