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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A crucial role for dynamic expression of components encoding the negative arm of the circadian clock

In the Neurospora circadian system, the White Collar Complex (WCC) drives expression of the principal circadian negative arm component frequency ( frq ). FRQ interacts with FRH (FRQ-interacting RNA helicase) and CKI, forming a stable complex that represses its own expression by inhibiting WCC. In this study, a genetic screen identified a gene, designated as brd-8 , that encodes a conserved auxiliary subunit of the NuA4 histone acetylation complex. Loss of brd-8 reduces H4 acetylation and RNA polymerase (Pol) II occupancy at frq and other known circadian genes, and leads to a long circadian period, delayed phase, and defective overt circadian output at some temperatures. In addition to strongly associating with the NuA4 histone acetyltransferase complex, BRD-8 is also found complexed with the transcription elongation regulator BYE-1. Expression of brd-8, bye-1, histone h2a.z , and several NuA4 subunits is controlled by the circadian clock, indicating that the molecular clock both regulates the basic chromatin status and is regulated by changes in chromatin. Taken together, our data identify auxiliary elements of the fungal NuA4 complex having homology to mammalian components, which along with conventional NuA4 subunits, are required for timely and dynamic frq expression and thereby a normal and persistent circadian rhythm.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrated photonic encoder for low power and high-speed image processing

Abstract Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Encoding hierarchical 3D architecture through inverse design of programmable bonds

The ability to fabricate materials and devices at small scales by design has resulted in tremendous technological progress. However, the need for engineered three-dimensional (3D) nanoscale materials requires new strategies for organizing nanocomponents. Here we demonstrate an inverse design approach for the assembly of nanoparticles into hierarchically ordered 3D organizations using DNA voxels with directional, addressable bonds. By identifying intrinsic symmetries in repeating mesoscale structural motifs, we prescribe a set of voxels, termed a mesovoxel, that are assembled into target 3D crystals. The relationship between different degrees of encoded information used for voxel bonds and the fidelity of assembly is investigated using experimental and computational methods. We apply this assembly strategy to create periodic 3D nanoparticle ordered organizations, including structures with low-dimensional elements, helical motifs, a nanoscale analogue of a face-centred perovskite crystal and a distributed Bragg reflector based on a crystal with plasmonic and photonic length-scale regimes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Author Correction: Perspectives on ENCODE

In the original article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Network-level encoding of local neurotransmitters in cortical astrocytes

Abstract Astrocytes, the most abundant non-neuronal cell type in the mammalian brain, are crucial circuit components that respond to and modulate neuronal activity through calcium (Ca 2+ ) signalling 1–7 . Astrocyte Ca 2+ activity is highly heterogeneous and occurs across multiple spatiotemporal scales—from fast, subcellular activity 3,4 to slow, synchronized activity across connected astrocyte networks 8–10 —to influence many processes 5,7,11 . However, the inputs that drive astrocyte network dynamics remain unclear. Here we used ex vivo and in vivo two-photon astrocyte imaging while mimicking neuronal neurotransmitter inputs at multiple spatiotemporal scales. We find that brief, subcellular inputs of GABA and glutamate lead to widespread, long-lasting astrocyte Ca 2+ responses beyond an individual stimulated cell. Further, we find that a key subset of Ca 2+ activity—propagative activity—differentiates astrocyte network responses to these two main neurotransmitters, and may influence responses to future inputs. Together, our results demonstrate that local, transient neurotransmitter inputs are encoded by broad cortical astrocyte networks over a minutes-long time course, contributing to accumulating evidence that substantial astrocyte–neuron communication occurs across slow, network-level spatiotemporal scales 12–14 . These findings will enable future studies to investigate the link between specific astrocyte Ca 2+ activity and specific functional outputs, which could build a consistent framework for astrocytic modulation of neuronal activity.

Science & Technology - Other Topics↗
DOE OSTI2021

Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models

Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder–decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are ED models a general tool for random noise removal, but their application to low signal-to-noise data can enhance the data’s quantitative usage since they are able to learn the functional form of the signal. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from two-time correlation functions, containing statistical noise and dynamic heterogeneities. Strategies for optimizing the models’ performance and their applicability limits are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Structure-guided functional studies of plasmid-encoded dihydrofolate reductases reveal a common mechanism of trimethoprim resistance in Gram-negative pathogens

Two plasmid-encoded dihydrofolate reductase (DHFR) isoforms, DfrA1 and DfrA5, that give rise to high levels of resistance in Gram-negative bacteria were structurally and biochemically characterized to reveal the mechanism of TMP resistance and to support phylogenic groupings for drug development against antibiotic resistant pathogens. Preliminary screening of novel antifolates revealed related chemotypes that showed high levels of inhibitory potency against Escherichia coli chromosomal DHFR (EcDHFR), DfrA1, and DfrA5. Kinetics and biophysical analysis, coupled with crystal structures of trimethoprim bound to EcDHFR, DfrA1 and DfrA5, and two propargyl-linked antifolates (PLA) complexed with EcDHFR, DfrA1 and DfrA5, were determined to define structural features of the substrate binding pocket and guide synthesis of pan-DHFR inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rational design of a genetically encoded NMR zinc sensor

Elucidating the biochemical roles of the essential metal ion, Zn 2+ , motivates detection strategies that are sensitive, selective, quantitative, and minimally invasive in living systems. Fluorescent probes have identified Zn 2+ in cells but complementary approaches employing nuclear magnetic resonance (NMR) are lacking. Recent studies of maltose binding protein (MBP) using ultrasensitive 129 Xe NMR spectroscopy identified a switchable salt bridge which causes slow xenon exchange and elicits strong hyperpolarized 129 Xe chemical exchange saturation transfer (hyper-CEST) NMR contrast. To engineer the first genetically encoded, NMR-active sensor for Zn 2+ , we converted the MBP salt bridge into a Zn 2+ binding site, while preserving the specific xenon binding cavity. The zinc sensor (ZS) at only 1 μM achieved ‘turn-on’ detection of Zn 2+ with pronounced hyper-CEST contrast. This made it possible to determine different Zn 2+ levels in a biological fluid via hyper-CEST. ZS was responsive to low-micromolar Zn 2+ , only modestly responsive to Cu 2+ , and nonresponsive to other biologically important metal ions, according to hyper-CEST NMR spectroscopy and isothermal titration calorimetry (ITC). Protein X-ray crystallography confirmed the identity of the bound Zn 2+ ion using anomalous scattering: Zn 2+ was coordinated with two histidine side chains and three water molecules. Penta-coordinate Zn 2+ forms a hydrogen-bond-mediated gate that controls the Xe exchange rate. Metal ion binding affinity, 129 Xe NMR chemical shift, and exchange rate are tunable parameters via protein engineering, which highlights the potential to develop proteins as selective metal ion sensors for NMR spectroscopy and imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Crystal structure of a guanine nucleotide exchange factor encoded by the scrub typhus pathogen Orientia tsutsugamushi

Rho family GTPases regulate an array of cellular processes and are often modulated by pathogens to promote infection. In this work, we identify a cryptic guanine nucleotide exchange factor (GEF) domain in the OtDUB protein encoded by the pathogenic bacterium Orientia tsutsugamushi. A proteomics-based OtDUB interaction screen identified numerous potential host interactors, including the Rho GTPases Rac1 and Cdc42. We discovered a domain in OtDUB with Rac1/Cdc42 GEF activity (OtDUB GEF ), with higher activity toward Rac1 in vitro. While this GEF bears no obvious sequence similarity to known GEFs, crystal structures of OtDUB GEF alone (3.0 Å) and complexed with Rac1 (1.7 Å) reveal striking convergent evolution, with a unique topology, on a V-shaped bacterial GEF fold shared with other bacterial GEF domains. Structure-guided mutational analyses identified residues critical for activity and a mechanism for nucleotide displacement. Ectopic expression of OtDUB activates Rac1 preferentially in cells, and expression of the OtDUBGEF alone alters cell morphology. Cumulatively, this work reveals a bacterial GEF within the multifunctional OtDUB that co-opts host Rac1 signaling to induce changes in cytoskeletal structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dual-rail encoding with superconducting cavities

The design of quantum hardware that reduces and mitigates errors is essential for practical quantum error correction (QEC) and useful quantum computation. To this end, we introduce the circuit-Quantum Electrodynamics (QED) dual-rail qubit in which our physical qubit is encoded in the single-photon subspace, { | 01 〉 , | 10 〉 } , of two superconducting microwave cavities. The dominant photon loss errors can be detected and converted into erasure errors, which are in general much easier to correct. In contrast to linear optics, a circuit-QED implementation of the dual-rail code offers unique capabilities. Using just one additional transmon ancilla per dual-rail qubit, we describe how to perform a gate-based set of universal operations that includes state preparation, logical readout, and parametrizable single and two-qubit gates. Moreover, first-order hardware errors in the cavities and the transmon can be detected and converted to erasure errors in all operations, leaving background Pauli errors that are orders of magnitude smaller. Hence, the dual-rail cavity qubit exhibits a favorable hierarchy of error rates and is expected to perform well below the relevant QEC thresholds with today’s coherence times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cyanobacteria from marine oxygen-deficient zones encode both form I and form II Rubiscos

Cyanobacteria are highly abundant in the marine photic zone and primary drivers of the conversion of inorganic carbon into biomass. To date, all studied cyanobacterial lineages encode carbon fixation machinery relying upon form I Rubiscos within a CO 2 -concentrating carboxysome. Here, we report that the uncultivated anoxic marine zone (AMZ) IB lineage ofProchlorococcusfrom pelagic oxygen-deficient zones (ODZs) harbors both form I and form II Rubiscos, the latter of which are typically noncarboxysomal and possess biochemical properties tuned toward low-oxygen environments. We demonstrate that these cyanobacterial form II enzymes are functional in vitro and were likely acquired from proteobacteria. Metagenomic analysis reveals that AMZ IB are essentially restricted to ODZs in the Eastern Pacific, suggesting that form II acquisition may confer an advantage under low-O 2 conditions. AMZ IB populations express both forms of Rubisco in situ, with the highest form II expression at depths where oxygen and light are low, possibly as a mechanism to increase the efficiency of photoautotrophy under energy limitation. Our findings expand the diversity of carbon fixation configurations in the microbial world and may have implications for carbon sequestration in natural and engineered systems.

Science & Technology - Other Topics↗
DOE OSTI2025

State-dependent motion of a genetically encoded fluorescent biosensor

Genetically encoded biosensors can measure biochemical properties such as small-molecule concentrations with single-cell resolution, even in vivo. Despite their utility, these sensors are “black boxes”: Very little is known about the structures of their low- and high-fluorescence states or what features are required to transition between them. We used LiLac, a lactate biosensor with a quantitative fluorescence-lifetime readout, as a model system to address these questions. X-ray crystal structures and engineered high-affinity metal bridges demonstrate that LiLac exhibits a large interdomain twist motion that pulls the fluorescent protein away from a “sealed,” high-lifetime state in the absence of lactate to a “cracked,” low-lifetime state in its presence. Understanding the structures and dynamics of LiLac will help to think about and engineer other fluorescent biosensors.

Rosen, Paul C. (ORCID:000000017414454X)↗
DOE OSTI2023

Insights into the transcriptional regulation of poorly characterized alcohol acetyltransferase-encoding genes (HgAATs) shed light into the production of acetate esters in the wine yeast Hanseniaspora guilliermondii

Abstract Hanseniaspora guilliermondii is a well-recognized producer of acetate esters associated with fruity and floral aromas. The molecular mechanisms underneath this production or the environmental factors modulating it remain unknown. Herein, we found that, unlike Saccharomyces cerevisiae, H. guilliermondii over-produces acetate esters and higher alcohols at low carbon-to-assimilable nitrogen (C:N) ratios, with the highest titers being obtained in the amino acid-enriched medium YPD. The evidences gathered support a model in which the strict preference of H. guilliermondii for amino acids as nitrogen sources results in a channeling of keto-acids obtained after transamination to higher alcohols and acetate esters. This higher production was accompanied by higher expression of the four HgAATs, genes, recently proposed to encode alcohol acetyl transferases. In silico analyses of these HgAat’s reveal that they harbor conserved AATs motifs, albeit radical substitutions were identified that might result in different kinetic properties. Close homologues of HgAat2, HgAat3, and HgAat4 were only found in members of Hanseniaspora genus and phylogenetic reconstruction shows that these constitute a distinct family of Aat’s. These results advance the exploration of H. guilliermondii as a bio-flavoring agent providing important insights to guide future strategies for strain engineering and media manipulation that can enhance production of aromatic volatiles.

Seixas, Isabel↗
DOE OSTI2020

Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)–based ranking for concept normalization

Concept normalization, the task of linking phrases in text to concepts in an ontology, is useful for many downstream tasks including relation extraction, information retrieval, etc. We present a generate-and-rank concept normalization system based on our participation in the 2019 National NLP Clinical Challenges Shared Task Track 3 Concept Normalization. The shared task provided 13 609 concept mentions drawn from 100 discharge summaries. We first design a sieve-based system that uses Lucene indices over the training data, Unified Medical Language System (UMLS) preferred terms, and UMLS synonyms to generate a list of possible concepts for each mention. We then design a listwise classifier based on the BERT (Bidirectional Encoder Representations from Transformers) neural network to rank the candidate concepts, integrating UMLS semantic types through a regularizer. Our generate-and-rank system was third of 33 in the competition, outperforming the candidate generator alone (81.66% vs 79.44%) and the previous state of the art (76.35%). During postevaluation, the model’s accuracy was increased to 83.56% via improvements to how training data are generated from UMLS and incorporation of our UMLS semantic type regularizer. Analysis of the model shows that prioritizing UMLS preferred terms yields better performance, that the UMLS semantic type regularizer results in qualitatively better concept predictions, and that the model performs well even on concepts not seen during training. Our generate-and-rank framework for UMLS concept normalization integrates key UMLS features like preferred terms and semantic types with a neural network–based ranking model to accurately link phrases in text to UMLS concepts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

A variational encoder–decoder approach to precise spectroscopic age estimation for large Galactic surveys

Constraints on the formation and evolution of the Milky Way Galaxy require multidimensional measurements of kinematics, abundances, and ages for a large population of stars. Ages for luminous giants, which can be seen to large distances, are an essential component of studies of the Milky Way, but they are traditionally very difficult to estimate precisely for a large data set and often require careful analysis on a star-by-star basis in asteroseismology. Because spectra are easier to obtain for large samples, being able to determine precise ages from spectra allows for large age samples to be constructed, but spectroscopic ages are often imprecise and contaminated by abundance correlations. Here we present an application of a variational encoder–decoder on cross-domain astronomical data to solve these issues. The model is trained on pairs of observations from APOGEE and Kepler of the same star in order to reduce the dimensionality of the APOGEE spectra in a latent space while removing abundance information. The low dimensional latent representation of these spectra can then be trained to predict age with just ∼1000 precise seismic ages. We demonstrate that this model produces more precise spectroscopic ages (∼ 22 per cent overall, ∼ 11 per cent for red-clump stars) than previous data-driven spectroscopic ages while being less contaminated by abundance information (in particular, our ages do not depend on [α/M]). We create a public age catalogue for the APOGEE DR17 data set and use it to map the age distribution and the age-[Fe/H]-[α/M] distribution across the radial range of the Galactic disc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Random circuit block-encoded matrix and a proposal of quantum LINPACK benchmark

The LINPACK benchmark reports the performance of a computer for solving a system of linear equations with dense random matrices. Although this task was not designed with a real application directly in mind, the LINPACK benchmark has been used to define the list of TOP500 supercomputers since the debut of the list in 1993. We propose that a similar benchmark, called the quantum LINPACK benchmark, could be used to measure the whole machine performance of quantum computers. The success of the quantum LINPACK benchmark should be viewed as the minimal requirement for a quantum computer to perform a useful task of solving linear algebra problems, such as linear systems of equations. We propose an input model called the Random Circuit Block-Encoded Matrix (RACBEM), which is a proper generalization of a dense random matrix in the quantum setting. The RACBEM model is efficient to be implemented on a quantum computer and can be designed to optimally adapt to any given quantum architecture, with relying on a black-box quantum compiler. Besides solving linear systems, the RACBEM model can be used to perform a variety of linear algebra tasks relevant to many physical applications, such as computing spectral measures, time series generated by a Hamiltonian simulation, and thermal averages of the energy. We implement these linear algebra operations on IBM Q quantum devices as well as quantum virtual machines, and demonstrate their performance in solving scientific computing problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Exact block encoding of imaginary time evolution with universal quantum neural networks

We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.

97 MATHEMATICS AND COMPUTING↗