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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Male Sterile2 Encodes a Plastid-Localized Fatty Acyl Carrier Protein Reductase Required for Pollen Exine Development in Arabidopsis

Male Sterile2 (MS2) is predicted to encode a fatty acid reductase required for pollen wall development in Arabidopsis (Arabidopsis thaliana). Transient expression of MS2 in tobacco (Nicotiana benthamiana) leaves resulted in the accumulation of significant levels of C16 and C18 fatty alcohols. Expression of MS2 fused with green fluorescent protein revealed that an amino-terminal transit peptide targets the MS2 to plastids. The plastidial localization of MS2 is biologically important because genetic complementation of MS2 in ms2 homozygous plants was dependent on the presence of its amino-terminal transit peptide or that of the Rubisco small subunit protein amino-terminal transit peptide. In addition, two domains, NAD(P)H-binding domain and sterile domain, conserved in MS2 and its homologs were also shown to be essential for MS2 function in pollen exine development by genetic complementation testing. Direct biochemical analysis revealed that purified recombinant MS2 enzyme is able to convert palmitoyl-Acyl Carrier Protein to the corresponding C16:0 alcohol with NAD(P)H as the preferred electron donor. Using optimized reaction conditions (i.e. at pH 6.0 and 30 C), MS2 exhibits a K{sub m} for 16:0-Acyl Carrier Protein of 23.3 {+-} 4.0 {mu}m, a V{sub max} of 38.3 {+-} 4.5 nmol mg{sup -1} min{sup -1}, and a catalytic efficiency/K{sub m} of 1,873 m{sup -1} s{sup -1}. Based on the high homology of MS2 to other characterized fatty acid reductases, it was surprising that MS2 showed no activity against palmitoyl- or other acyl-coenzyme A; however, this is consistent with its plastidial localization. In summary, genetic and biochemical evidence demonstrate an MS2-mediated conserved plastidial pathway for the production of fatty alcohols that are essential for pollen wall biosynthesis in Arabidopsis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Defective Pollen Wall 2 ( DPW2 ) Encodes an Acyl Transferase Required for Rice Pollen Development

Aliphatic and aromatic lipids are both essential structural components of the plant cuticle, an important interface between the plant and environment. Although cross links between aromatic and aliphatic or other moieties are known to be associated with the formation of leaf cutin and root and seed suberin, the contribution of aromatic lipids to the biosynthesis of anther cuticles and pollen walls remains elusive. In this study, we characterized the rice (Oryza sativa) male sterile mutant, defective pollen wall 2 (dpw2), which showed an abnormal anther cuticle, a defective pollen wall, and complete male sterility. Compared with the wild type, dpw2 anthers have increased amounts of cutin and waxes and decreased levels of lipidic and phenolic compounds. DPW2 encodes a cytoplasmically localized BAHD acyltransferase. In vitro assays demonstrated that recombinant DPW2 specifically transfers hydroxycinnamic acid moieties, using v-hydroxy fatty acids as acyl acceptors and hydroxycinnamoyl-CoAs as acyl donors. Thus, The cytoplasmic hydroxycinnamoyl-CoA:v-hydroxy fatty acid transferase DPW2 plays a fundamental role in male reproduction via the biosynthesis of key components of the anther cuticle and pollen wall.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
DOE OSTI2018

Modeling I/O Performance Variability Using Conditional Variational Auto Encoders

Storage system performance modeling is crucial for efficient use of heterogeneous shared resources on leadership class computers. Variability in application performance, particularly variability arising from concurrent applications sharing I/O resources, is a major hurdle in the development of accurate performance models. We adopt a deep learning approach based on conditional variational auto encoders (CVAE) for I/O performance modeling, and use it to quantify performance variability. We illustrate our approach using the data collected on Edison, a production supercomputing system at the National Energy Research Scientific Computing Center (NERSC). The CVAE approach is investigated by comparing it to a previously proposed sensitivity-based Gaussian process (GP) model. We find that the CVAE model performs slightly better than the GP model in cases where training and testing data come from different applications, since CVAE can inherently leverage the whole data from multiple applications whereas GP partitions the data and builds separate models for each partition. Hence, the CVAE offers an alternative modeling approach that does not need preprocessing; it has enough flexibility to handle data from a wide variety of applications without changing the inference approach.

I/O perfornance variability↗
DOE OSTI2019

Time-Encoded Gamma-Ray Imaging Using a 3-D Position-Sensitive CdZnTe Detector Array

The gamma-ray response of thick pixelated CdZnTe detectors varies spatially but is relatively constant as a function of time. Detector irregularities arise from flaws in the material, electrode fabrication, and gaps between the detectors in an array. These nonuniformities generate artifacts in coded aperture images that can obscure a source of interest. This article presents the theory, design, and demonstration of a 3-D CdZnTe-based time-encoded imager. Experimental results show the gamma-ray images of point-like and extended 57 Co and HEU-metal sources formed using a spatially nonuniform 3-D CdZnTe detector array and moving coded aperture mask. Image signal-to-noise ratio (SNR) for a point source was within 10% of its statistical limit at 250k counts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

42 ENGINEERING↗
DOE OSTI2020

PHOTO‐SENSITIVE LEAF ROLLING 1 encodes a polygalacturonase that modifies cell wall structure and drought tolerance in rice

Summary The biosynthesis and modification of cell wall composition and structure are controlled by hundreds of enzymes and have a direct consequence on plant growth and development. However, the majority of these enzymes has not been functionally characterised. Rice mutants with leaf‐rolling phenotypes were screened in a field. Phenotypic analysis under controlled conditions was performed for the selected mutant and the relevant gene was identified by map‐based cloning. Cell wall composition was analysed by glycome profiling assay. We identified a photo‐sensitive leaf rolling 1 ( psl1 ) mutant with ‘napping’ (midday depression of photosynthesis) phenotype and reduced growth. The PSL1 gene encodes a cell wall‐localised polygalacturonase (PG), a pectin‐degrading enzyme. psl1 with a 260‐bp deletion in its gene displayed leaf rolling in response to high light intensity and/or low humidity. Biochemical assays revealed PG activity of recombinant PSL1 protein. Significant modifications to cell wall composition in the psl1 mutant compared with the wild‐type plants were identified. Such modifications enhanced drought tolerance of the mutant plants by reducing water loss under osmotic stress and drought conditions. Taken together, PSL1 functions as a PG that modifies cell wall biosynthesis, plant development and drought tolerance in rice.

Zhang, Guangheng↗
DOE OSTI2025

Amino acid substrate specificities and tissue expression profiles of the nine CYP79A encoding genes in Sorghum bicolor

Cytochrome P450s of the CYP79 family catalyze two N-hydroxylation reactions, converting a selected number of amino acids into the corresponding oximes. The sorghum genome (Sorghum bicolor) harbours nine CYP79A encoding genes, and here sequence comparisons of the CYP79As along with their substrate recognition sites (SRSs) are provided. The substrate specificity of previously uncharacterized CYP79As was investigated by transient expression in Nicotiana benthamiana and subsequent transformation of the oximes formed into the corresponding stable oxime glucosides catalyzed by endogenous UDPG-glucosyltransferases (UGTs). CYP79A61 uses phenylalanine as a substrate, whereas CYP79A91, CYP79A93, and CYP79A95 use valine and isoleucine as substrates, with CYP79A93 showing the ability also to use phenylalanine. CYP79A94 uses isoleucine as a substrate. Analysis of 249 sorghum transcriptomes from two different sorghum cultivars showed the expression levels and tissue-specific expression of the CYP79As. CYP79A1 is the committed gene in dhurrin formation and was the highest expressed gene in most tissues/organs. CYP79A61 was primarily expressed in fully developed leaf blades and leaf sheaths. CYP79A91 and CYP79A92 were expressed mainly in roots >200 cm below ground, while CYP79A93 and CYP79A94 were most highly expressed in the leaf collar and leaf sheath, respectively. Here, the possible signalling effects of the oximes and their metabolites produced in different sorghum tissues are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2014

Effects of reflector and crystal surface on the performance of a depth-encoding PET detector with dual-ended readout

Purpose: Depth encoding detectors are required to improve the spatial resolution and spatial resolution uniformity of small animal positron emission tomography (PET) scanners, as well as dedicated breast and brain scanners. Depth of interaction (DOI) can be measured by using dual-ended readout of lutetium oxyorthosilicate (LSO) scintillator arrays with position-sensitive avalanche photodiodes. Inter-crystal reflectors and crystal surface treatments play important roles in determining the performance of dual-ended detectors. In this paper, the authors evaluated five LSO arrays made with three different intercrystal reflectors and with either polished or unpolished crystal surfaces. Methods: The crystal size in all arrays was 1.5 mm, which is typical of the detector size used in small animal and dedicated breast scanners. The LSO arrays were measured with dual-ended readout and were compared in terms of flood histogram, energy resolution, and DOI resolution performance. Results: The four arrays using enhanced specular reflector (ESR) and Toray reflector provided similar quality flood histograms and the array using Crystal Wrap reflector gave the worst flood histogram. The two arrays using ESR reflector provided the best energy resolution and the array using Crystal Wrap reflector yielded the worst energy resolution. All arrays except the polished ESR array provided good DOI resolution ranging from 1.9 mm to 2.9 mm. DOI resolution improved as the gradient in light collection efficiency with depth (GLCED) increased. The geometric mean energies were also calculated for these dual-ended readout detectors as an alternative to the conventional summed total energy. It was shown that the geometric mean energy is advantageous in that it provides more uniform photopeak amplitude at different depths for arrays with high GLCED, and is beneficial in event selection by allowing a fixed energy window independent of depth. A new method of DOI calculation that improved the linearity of DOI ratio vs depth and simplifies the DOI calibration procedure also was developed and tested. Conclusions: The results of these studies provide useful guidance in selecting the proper reflectors and crystal surface treatments when LSO arrays are used for high-resolution PET applications in small animal scanners or dedicated breast and brain scanners.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Mitochondria-encoded genes contribute to evolution of heat and cold tolerance in yeast

Genetic analysis of phenotypic differences between species is typically limited to interfertile species. Here, we conducted a genome-wide noncomplementation screen to identify genes that contribute to a major difference in thermal growth profile between two reproductively isolated yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum . The screen identified only a single nuclear-encoded gene with a moderate effect on heat tolerance, but, on the other hand, revealed a large effect of mitochondrial DNA (mitotype) on both heat and cold tolerance. Recombinant mitotypes indicate that multiple genes contribute to thermal divergence, and we show that protein divergence in COX1 affects both heat and cold tolerance. Our conclusions point to the yeast mitochondrial genome as an evolutionary hotspot for thermal divergence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A bistable and reconfigurable molecular system with encodable bonds

Molecular systems with ability to controllably transform between different conformations play pivotal roles in regulating biochemical functions. Here, we report the design of a bistable DNA origami four-way junction (DOJ) molecular system that adopts two distinct stable conformations with controllable reconfigurability by using conformation-controlled base stacking. Exquisite control over DOJ’s conformation and transformation is realized by programming the stacking bonds (quasi–blunt-ends) within the junction to induce prescribed coaxial stacking of neighboring junction arms. A specific DOJ conformation may be achieved by encoding the stacking bonds with binary stacking sequences based on thermodynamic calculations. Dynamic transformations of DOJ between various conformations are achieved by using specific environmental and molecular stimulations to reprogram the stacking codes. This work provides a useful platform for constructing self-assembled DNA nanostructures and nanomachines and insights for future design of artificial molecular systems with increasing complexity and reconfigurability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Polymeric peptide pigments with sequence-encoded properties

Melanins are a family of heterogeneous polymeric pigments that provide ultraviolet (UV) light protection, structural support, coloration, and free radical scavenging. Formed by oxidative oligomerization of catecholic small molecules, the physical properties of melanins are influenced by covalent and noncovalent disorder. We report the use of tyrosine-containing tripeptides as tunable precursors for polymeric pigments. In these structures, phenols are presented in a (supra-)molecular context dictated by the positions of the amino acids in the peptide sequence. Oxidative polymerization can be tuned in a sequence-dependent manner, resulting in peptide sequence–encoded properties such as UV absorbance, morphology, coloration, and electrochemical properties over a considerable range. Short peptides have low barriers to application and can be easily scaled, suggesting near-term applications in cosmetics and biomedicine.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Marker Exchange Mutagenesis of mxaF , Encoding the Large Subunit of the Mxa Methanol Dehydrogenase, in Methylosinus trichosporium OB3b

Here, methanotrophs have remarkable redundancy in multiple steps of the central pathway of methane oxidation to carbon dioxide. For example, it has been known for over 30 years that two forms of methane monooxygenase, responsible for oxidizing methane to methanol, exist in methanotrophs, i.e., soluble methane monooxygenase (sMMO) and particulate methane monooxygenase (pMMO), and that expression of these two forms is controlled by the availability of copper. Specifically, sMMO expression occurs in the absence of copper, while pMMO expression increases with increasing copper concentrations. More recently, it was discovered that multiple forms of methanol dehydrogenase (MeDH), Mxa MeDH and Xox MeDH, also exist in methanotrophs and that the expression of these alternative forms is regulated by the availability of cerium. That is, expression of Xox MeDH increases in the presence of cerium, while Mxa MeDH expression decreases in the presence of cerium. As it had been earlier concluded that pMMO and Mxa MeDH form a supercomplex in which electrons from Mxa MeDH are back donated to pMMO to drive the initial oxidation of methane, we speculated that Mxa MeDH could be rendered inactive through marker-exchange mutagenesis but growth on methane could still be possible if cerium was added to increase the expression of Xox MeDH under sMMO-expressing conditions. Here we report that mxaF, encoding the large subunit of Mxa MeDH, could indeed be knocked out in Methylosinus trichosporium OB3b, yet growth on methane was still possible, so long as cerium was added. Interestingly, growth of this mutant occurred in both the presence and the absence of copper, suggesting that Xox MeDH can replace Mxa MeDH regardless of the form of MMO expressed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Structure of a Vaccine-Induced, Germline-Encoded Human Antibody Defines a Neutralizing Epitope on the SARS-CoV-2 Spike N-Terminal Domain

Structural characterization of infection- and vaccination-elicited antibodies in complex with antigen provides insight into the evolutionary arms race between the host and the pathogen and informs rational vaccine immunogen design. We isolated a germ line-encoded monoclonal antibody (mAb) from plasmablasts activated upon mRNA vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and determined its structure in complex with the spike glycoprotein by electron cryomicroscopy (cryo-EM). We show that the mAb engages a previously uncharacterized neutralizing epitope on the spike N-terminal domain (NTD). The high-resolution structure reveals details of the intermolecular interactions and shows that the mAb inserts its heavy complementarity-determining region 3 (HCDR3) loop into a hydrophobic NTD cavity previously shown to bind a heme metabolite, biliverdin. We demonstrate direct competition with biliverdin and that, because of the conserved nature of the epitope, the mAb maintains binding to viral variants B.1.1.7 (alpha), B.1.351 (beta), B.1.617.2 (delta), and B.1.1.529 (omicron). Our study describes a novel conserved epitope on the NTD that is readily targeted by vaccine-induced antibody responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Quantum Key Distribution with Nonbinary Phase–Time Encoding That Admits an Exact Proof of Secrecy

A quantum key distribution (QKD) protocol with nonbinary encoding is proposed that admits an exact proof of secrecy. The protocol provides higher rate of key generation compared with the BB84 protocol and, moreover, has a simple experimental implementation without active elements on the receiver side, that does not require polarization matching at the output of the communication line.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗
DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied 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 the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this 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. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗