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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 OSTI2022

RNA-guided nucleic acid modifying enzymes and methods of use thereof

The present disclosure provides CasY proteins, nucleic acids encoding the CasY proteins, and modified host cells comprising the CasY proteins and/or nucleic acids encoding same. CasY proteins are useful in a variety of applications, which are provided. The present disclosure provides CasY guide RNAs that bind to and provide sequence specificity to the CasY proteins, nucleic acids encoding the CasY guide RNAs, and modified host cells comprising the CasY guide RNAs and/or nucleic acids encoding same. CasY guide RNAs are useful in a variety of applications, which are provided. The present disclosure provides methods of identifying a CRISPR RNA-guided endonuclease.

Doudna, Jennifer A.↗
DOE OSTI2023

Method and apparatus for data protection in memory devices

An apparatus and method for efficiently transmitting data are described. A transmitter sends data to a receiver. An encoder of the transmitter divides a received first block of data into multiple sub-blocks. The encoder selects a portion of each sub-block to compare to one another. A portion in a particular sub-block has a same offset and a same size as other portions of other sub-blocks. If the encoder determines the multiple portions match one another, the encoder sends, to the receiver, a second block of data corresponding to the first block of data. The second block of data has a same size as a size of the received first block of data, and the second block of data includes security data from one of multiple error correction schemes. Therefore, the second block of data provides security without increasing an amount of data to transmit.

SeyedzadehDelcheh, SeyedMohammad↗
DOE OSTI2023

Translation control tool for plants

The present invention provides for a system comprising (a) first polynucleotide encoding a first promoter operatively linked to an open reading frame (ORF) encoding an endoribonuclease, or an enzymatic active fragment thereof, which cleaves a cognition sequence (cog), and (b) a second polynucleotide encoding a second promoter operatively linked to a nucleotide sequence encoding (i) a cog linked to a coding sequence of interest (COI), (ii) a cog linked to the 3′ end of a first COI and the 5′ end of a second COI, or (iii) a cog locating within a LS (or targeting sequence) linked to a COI.

Liang, Yan↗
DOE OSTI2025

Deformable phrase level attention: A flexible approach for improving AI based medical coding

Objective: Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel attention mechanism designed to enhance text classification models and ensure appropriate classification of medical concepts in unstructured electronic health records. Materials and Methods: We developed a deformable, phrase-level attention mechanism to identify important lexical word-level and contextual phrase-level information from clinical text documents. We evaluated conventional and transformer-based deep learning models that we extended with our attention mechanism on the extraction of critical cancer information (e.g., site, subsite, laterality, histology, behavior) from 629,908 electronic pathology reports and on the automated medical encoding of 52,722 hospital discharge summaries. Results: Transformer-based models with the deformable, phrase-level attention mechanism achieved the best performance on the extraction of critical cancer information from pathology reports. Conventional- and transformer-based models show similar or better performance than their baseline counterparts on the automated medical encoding of clinical documents. Discussion: The addition of phrase-level information allowed models extended with our proposed method to outperform standard word-level attention. Our method showed favorable properties for the real-world application in terms of model robustness and phenotyping. These results indicate that our method is promising for automated data harmonization for common data models. Conclusion: This work proposes a novel deformable, phrase-level attention mechanism that enhances text classification models in the extraction of medical concepts from clinical text documents. We demonstrate strong performances on two clinical text datasets and showcase real-world deployability of our method.

Automated medical encoding↗
DOE OSTI2020

Power System Event Classification and Localization Using a Convolutional Neural Network

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Crystal structures of OrfX1, OrfX2 and the OrfX1–OrfX3 complex from the orfX gene cluster of botulinum neurotoxin E1

Botulinum neurotoxins (BoNTs) are among the most lethal toxins known to humans, comprising seven established serotypes termed BoNT/A–G encoded in two types of gene clusters (ha and orfX) in BoNT-producing clostridia. The ha cluster encodes four non-toxic neurotoxin-associated proteins (NAPs) that assemble with BoNTs to protect and enhance their oral toxicity. However, the structure and function of the orfX-type NAPs remain largely unknown. Here, we report the crystal structures for OrfX1, OrfX2, and an OrfX1–OrfX3 complex, which are encoded in the orfX cluster of a BoNT/E1-producing Clostridium botulinum strain associated with human foodborne botulism. These structures lay the foundation for future studies on the potential roles of OrfX proteins in oral intoxication and pathogenesis of BoNTs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular structures reveal the origin of spectral variation in cryptophyte light harvesting antenna proteins

In addition to their membrane-bound chlorophyll a/c light-harvesting antenna, the cryptophyte algae have evolved a unique phycobiliprotein antenna system located in the thylakoid lumen. The basic unit of this antenna consists of two copies of an αβ protomer where the α and β subunits scaffold different combinations of a limited number of linear tetrapyrrole chromophores. While the β subunit is highly conserved, encoded by a single plastid gene, the nuclear-encoded α subunits have evolved diversified multigene families. It is still unclear how this sequence diversity results in the spectral diversity of the mature proteins. By careful examination of three newly determined crystal structures in comparison with three previously obtained, we show how the α subunit amino acid sequences control chromophore conformations and hence spectral properties even when the chromophores are identical. Previously we have shown that α subunits control the quaternary structure of the mature αβ.αβ complex (either open or closed), however, each species appeared to only harbor a single quaternary form. Here we show that species of the Hemiselmis genus contain expressed α subunit genes that encode both distinct quaternary structures. Finally, we have discovered a common single-copy gene (expressed into protein) consisting of tandem copies of a small α subunit that could potentially scaffold pairs of light harvesting units. Together, our results show how the diversity of the multigene α subunit family produces a range of mature cryptophyte antenna proteins with differing spectral properties, and the potential for minor forms that could contribute to acclimation to varying light regimes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Predictability of Localized Plasmonic Responses in Nanoparticle Assemblies

Design of nanoscale structures with desired optical properties is a key task for nanophotonics. In this work, the correlative relationship between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks. In the im2spec network, the relationship between local particle geometries and local spectra is established via encoding the observed geometries to a small number of latent variables and subsequently decoding into plasmonic spectra; in the spec2im network, the relationship is reversed. Surprisingly, these reduced descriptions allow high-veracity predictions of local responses based on geometries for fixed compositions and surface chemical states. Analysis of the latent space distributions and the corresponding decoded and closest (in latent space) encoded images yields insight into the generative mechanisms of plasmonic interactions in the nanoparticle arrays. Ultimately, this approach creates a path toward determining configurations that yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures.

nanoparticle arrays↗
DOE OSTI2022

Dirac pairings, one-form symmetries and Seiberg-Witten geometries

The Coulomb phase of a quantum field theory, when present, illuminates the analysis of its line operators and one-form symmetries. For 4d $\mathcal{N}$ = 2 field theories the low energy physics of this phase is encoded in the special Kähler geometry of the moduli space of Coulomb vacua. We clarify how the information on the allowed line operator charges and one-form symmetries is encoded in the special Kähler structure. We point out the important difference between the lattice of charged states and the homology lattice of the abelian variety fibered over the moduli space, which, when principally polarized, is naturally identified with a choice of the lattice of mutually local line operators. This observation illuminates how the distinct S-duality orbits of global forms of $\mathcal{N}$ = 4 theories are encoded geometrically.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Islands in cosmology

A quantum extremal island suggests that a region of spacetime is encoded in the quantum state of another system, like the encoding of the black hole interior in Hawking radiation. We study conditions for islands to appear in general spacetimes, with or without black holes. They must violate Bekenstein’s area bound in a precise sense, and the boundary of an island must satisfy several other information-theoretic inequalities. These conditions combine to impose very strong restrictions, which we apply to cosmological models. We find several examples of islands in crunching universes. In particular, in the four-dimensional FRW cosmology with radiation and a negative cosmological constant, there is an island near the turning point when the geometry begins to recollapse. In a two-dimensional model of JT gravity in de Sitter spacetime, there are islands inside crunches that are encoded at future infinity or inside bubbles of Minkowski spacetime. Finally, we discuss simple tensor network toy models for islands in cosmology and black holes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Reynolds stress tensor measurements using magnetic resonance velocimetry: expansion of the dynamic measurement range and analysis of systematic measurement errors

This study presents magnetic resonance velocimetry (MRV) Reynolds Stress measurements in a periodic hill channel with a hill Reynolds number of Re = 29,500. The velocity encoding scheme is based on the ICOSA6 method with six icosahedral encoding directions and multiple encoding values are measured to increase the dynamic range. The full Reynolds stress tensor is obtained from a voxel-wise three-dimensional Gaussian fit using the magnitude data of all acquisitions. The MRV results are compared to a wall-resolved large eddy simulation and laser Doppler velocimetry measurements conducted in the same channel. It is shown that the MRV Reynolds stress data have excellent precision and agree qualitatively with the reference data. However, there are apparent systematic deviations. One of the most prominent error contributions is the signal attenuation caused by higher orders of motion, which leads to an overestimation of the turbulence level. Another fundamental error is identified in the assumption that the turbulence is Gaussian distributed. With the presented reconstruction technique, the MRV data are fitted to a statistical model, and depending on the examined flow setup, the Gaussian model can lead to considerable errors. Possible ways of how to reduce all identified errors are presented. In summary, this technique enables Reynolds stress tensor measurements in complex internal flows with high dynamic range and excellent precision. However, several issues need to be resolved to make the turbulence quantification more accurate.

42 ENGINEERING↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗
DOE OSTI2024

Chlamydomonas cells transition through distinct Fe nutrition stages within 48 h of transfer to Fe-free medium

Low iron (Fe) bioavailability can limit the biosynthesis of Fe-containing proteins, which are especially abundant in photosynthetic organisms, thus negatively affecting global primary productivity. Understanding cellular coping mechanisms under Fe limitation is therefore of great interest. For this paper, we surveyed the temporal responses of Chlamydomonas ( Chlamydomonas reinhardtii ) cells transitioning from an Fe-rich to an Fe-free medium to document their short and long-term adjustments. While slower growth, chlorosis and lower photosynthetic parameters are evident only after one or more days in Fe-free medium, the abundance of some transcripts, such as those for genes encoding transporters and enzymes involved in Fe assimilation, change within minutes, before changes in intracellular Fe content are noticeable, suggestive of a sensitive mechanism for sensing Fe. Promoter reporter constructs indicate a transcriptional component to this immediate primary response. With acetate provided as a source of reduced carbon, transcripts encoding respiratory components are maintained relative to transcripts encoding components of photosynthesis and tetrapyrrole biosynthesis, indicating metabolic prioritization of respiration over photosynthesis. In contrast to the loss of chlorophyll, carotenoid content is maintained under Fe limitation despite a decrease in the transcripts for carotenoid biosynthesis genes, indicating carotenoid stability. These changes occur more slowly, only after the intracellular Fe quota responds, indicating a phased response in Chlamydomonas, involving both primary and secondary responses during acclimation to poor Fe nutrition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep convolutional autoencoders as generic feature extractors in seismological applications

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms), and phase picking. These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only outperform the baseline under certain conditions, such as when the target problems require features that are similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.

58 GEOSCIENCES↗
DOE OSTI2026

AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting

Accurate reservoir inflow forecasting is crucial for effective water resource management, yet most machine learning models focus on single-reservoir prediction and overlook spatial dependencies among hydrologically connected reservoirs. Here, we propose AGFormer (Adaptive Graph-Informed Transformer), an end-to-end framework that integrates adaptive graph learning with temporal sequence modeling for multi-reservoir inflow forecasting. A shared encoder and graph attention mechanism generate reservoir-specific embeddings, which are then processed by the Transformer-based encoder–decoder for multi-step inflow forecasting. We also introduce a pretraining paradigm to learn robust temporal embeddings from misaligned historical records. Evaluated on 30 reservoirs in the Upper Colorado River Basin, AGFormer achieves superior seven-day-ahead forecasts, with NSE > 0.75 for 20 reservoirs—outperforming Encoder–Decoder LSTM, GCN+LSTM, and Transformer baselines. Adaptive graph learning captures dynamic inter-reservoir dependencies, and feature attribution aligns with snowmelt-driven hydrology. Incorporating forecasted meteorological inputs further enhances accuracy, demonstrating AGFormer’s potential to support reservoir management under dynamic hydrological conditions.

Adaptive graph learning↗
DOE OSTI2022

Mitochondrial respiratory chain protein co-regulation in the human brain

Mitochondrial respiratory chain (RC) function requires the stoichiometric interaction among dozens of proteins but their co-regulation has not been defined in the human brain. Here, using quantitative proteomics across three independent cohorts we systematically characterized the co-regulation patterns of mitochondrial RC proteins in the human dorsolateral prefrontal cortex (DLPFC). Whereas the abundance of RC protein subunits that physically assemble into stable complexes were correlated, indicating their co-regulation, RC assembly factors exhibited modest co-regulation. Within complex I, nuclear DNA-encoded subunits exhibited >2.5-times higher co-regulation than mitochondrial (mt)DNA-encoded subunits. Moreover, mtDNA copy number was unrelated to mtDNA-encoded subunits abundance, suggesting that mtDNA content is not limiting. Alzheimer's disease (AD) brains exhibited reduced abundance of complex I RC subunits, an effect largely driven by a 2–4% overall lower mitochondrial protein content. These findings provide foundational knowledge to identify molecular mechanisms contributing to age- and disease-related erosion of mitochondrial function in the human brain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An efficient explicit implementation of a near-optimal quantum algorithm for simulating linear dissipative differential equations

We propose an efficient block-encoding technique for the implementation of the Linear Combination of Hamiltonian Simulations (LCHS) for simulating dissipative initial-value problems. This algorithm approximates a target nonunitary operator as a weighted sum of Hamiltonian evolutions, thereby emulating a dissipative problem by mixing various time scales. We introduce an efficient encoding of the LCHS into a quantum circuit based on a simple coordinate transformation that turns the dependence on the summation index into a trigonometric function. Classically, this method is equivalent to the use of a highly accurate Fejér-Clenshaw-Curtis quadrature formula. Quantumly, this significantly simplifies block-encoding of a dissipative problem and allows one to perform an exponential number of Hamiltonian simulations by a single Quantum Signal Processing (QSP) circuit. The resulting LCHS circuit has high success probability and the selector scales logarithmically with the number of terms in the LCHS sum and linearly with time. Careful analysis of error convergence proves that this method is more efficient than other LCHS circuits that have recently appeared in the literature. We verify the quantum circuit and its scaling by simulating it on a digital emulator of fault-tolerant quantum computers and, as a test problem, solve the advection-diffusion equation. The proposed algorithm can be used for simulating a wide class of nonunitary initial-value problems including the Liouville equation with added dissipation and linear embeddings of nonlinear systems, such as the Koopman-von Neumann and Carleman embeddings.

Novikau, I [Lawrence Livermore National Laboratory↗
DOE OSTI2024

Harnessing Fermentation May Enhance the Performance of Biological Sulfate-Reducing Bioreactors

Biological sulfate reduction (BSR) represents a promising strategy for bioremediation of sulfate-rich waste streams, yet the impact of metabolic interactions on performance is largely unexplored. Here, genome-resolved metagenomics was used to characterize 17 microbial communities in reactors treating synthetic sulfate-contaminated solutions. Reactors were supplemented with lactate or acetate and a small amount of fermentable substrate. Of the 163 genomes representing all the abundant bacteria, 130 encode 321 NiFe and FeFe hydrogenases and all genomes of the 22 sulfate-reducing microorganisms (SRM) encode genes for H 2 uptake. We observed lactate oxidation solely in the first packed bed reactor zone, with propionate and acetate oxidation in the middle and predominantly acetate oxidation in the effluent zone. The energetics of these reactions are very different, yet sulfate reduction kinetics were unaffected by the type of electron donor available. We hypothesize that the comparable rates, despite the typically slow growth of SRM on acetate, are a result of the consumption of H 2 generated by fermentation. This is supported by the sustained performance of a predominantly acetate-supplemented stirred tank reactor dominated by diverse fermentative bacteria encoding FeFe hydrogenase genes and SRM capable of acetate and hydrogen consumption and CO 2 assimilation. Thus, addition of fermentable substrates to stimulate syntrophic relationships may improve the performance of BSR reactors supplemented with inexpensive acetate.

59 BASIC BIOLOGICAL SCIENCES↗