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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Engineered Production of Hydroxycinnamoyl Tyramine Conjugates Limits the Growth of the Pathogen Pseudomonas syringae in Arabidopsis

Hydroxycinnamoyl tyramine conjugates are phenolamides produced by plants in response to pathogen attack and biotic stresses. Their proposed mechanisms of action include cytotoxicity towards pathogens, cell wall reinforcement to restrict pathogen proliferation, and signaling activity to trigger general stress responses. Here, we engineered the production of the tyramine conjugates p-coumaroyltyramine (CT) and feruloyltyramine (FT) in Arabidopsis to gain insight into their mode of action. Co-expression of feedback-insensitive 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase and tyrosine decarboxylase increased tyramine content. Additional expression of tyramine hydroxycinnamoyltransferase led to de-novo production of CT and FT, which were found as soluble and cell-wall-bound forms. FT was associated with lignin in stems. The growth of pathogenic Pseudomonas syringae was reduced in rosettes of the Arabidopsis CT- and FT-producing lines compared to wild type. These lines also exhibited increased transpirational water loss in excised rosettes. Transcriptomic analysis of transgenic lines grown under normal conditions revealed alterations in the expression of genes associated with the biological circadian clock. These changes led to a reduction in flavonoids and an early flowering phenotype. Important changes in the expression of genes related to abiotic stress such as drought, cold, heat, and hypoxia potentially contribute to reduced growth of P. syringae in engineered Arabidopsis.

Phenolamides↗
DOE OSTI2020

A global analysis on the systemic effects of antibiotics in cultured fish and their potential human health risk: a review

Abstract The rearing of fish in intensive systems reduces their immunity leading to eruption of diseases, consequently prompting the use of antibiotics. Antibiotics are currently used in cultured fish globally to treat/prevent diseases. On the one hand, antibiotics used in aquaculture help to improve fish health by killing or inhibiting pathogenic bacteria proliferation. However, on the other hand, they cause multiple side effects. To date, information on systemic effects of antibiotics in cultured fish is scattered and their potential human health risk remains elusive. This review critically analyses and discusses the available information in the literature on the positive and negative effects of antibiotics in cultured fish and their potential human health risk via fish consumption. The review found 44 different antibiotics dominated by oxytetracycline were studied in various fish species worldwide. Antibiotics used in global aquaculture either decrease or increase fish growth and survival rates. They induce oxidative stress, which affect antioxidant and detoxification responses and cause host–microbiota dysbiosis. These changes compromise the fish immunity system, which ultimately lead to cellular damage. The toxicities generated inhibit the fish aerobic glycolysis, suppress lipogenesis and fatty acid β‐oxidation while increase energy demand by initiating gluconeogenesis and anaerobic glycolysis. Consumption of fish treated with antibiotics causes human health risk. Future studies are required on agents to restore dysfunctions induced by antibiotics in cultured fish, while attempts to limit their utilisation in aquaculture production are underway. [Correction added on 21 November 2020, after first online publication: The abstract has been amended to clarify the scope of the review.]

Limbu, Samwel Mchele↗
DOE OSTI2021

The UPR regulator IRE1 promotes balanced organ development by restricting TOR-dependent control of cellular differentiation in Arabidopsis

Proteostasis of the endoplasmic reticulum (ER) is controlled by sophisticated signaling pathways that are collectively called the unfolded protein response (UPR) and are initiated by specialized ER membrane-associated sensors. The evidence that complete loss-of-function mutations of the most conserved of the UPR sensors, inositol-requiring enzyme 1 (IRE1), dysregulates tissue growth and development in metazoans and plants raises the fundamental question as to how IRE1 is connected to organismal growth. To address this question, we interrogated the Arabidopsis primary root, an established model for organ development, using the tractable Arabidopsis IRE1 mutant ire1a ire1b, which has marked root development defects in the absence of exogenous stress. We demonstrate that IRE1 is required to reach maximum rates of cell elongation and root growth. We also established that in the actively growing ire1a ire1b mutant root tips the Target of Rapamycin (TOR) kinase, a widely conserved pro-growth regulator, is hyperactive, and that, unlike cell proliferation, the rate of cell differentiation is enhanced in ire1a ire1b in a TOR-dependent manner. By functionally connecting two essential growth regulators, these results underpin a novel and critical role of IRE1 in organ development and indicate that, as cells exit an undifferentiated state, IRE1 is required to monitor TOR activity to balance cell expansion and maturation during organ biogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2020

Metabolic multistability and hysteresis in a model aerobe-anaerobe microbiome community

Major changes in the microbiome are associated with health and disease. Some microbiome states persist despite seemingly unfavorable conditions, such as the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wound infections or small intestinal bacterial overgrowth. Mechanisms underlying transitions into and persistence of these states remain unclear. Using two microbial taxa relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory to show that multistability and hysteresis (MSH) is a mechanism describing the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify changes in metabolism and gene expression that lead to MSH and associated multi-stable states. In such systems, conceptual causation-correlation connections break and MSH must be used for analysis. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of stability of microbiome states and transitions between states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Stiffness anisotropy coordinates supracellular contractility driving long-range myotube-ECM alignment

The ability of cells to organize into tissues with proper structure and function requires the effective coordination of proliferation, migration, polarization, and differentiation across length scales. Skeletal muscle is innately anisotropic; however, few biomaterials can emulate mechanical anisotropy to determine its influence on tissue patterning without introducing confounding topography. Here, we demonstrate that substrate stiffness anisotropy coordinates contractility-driven collective cellular dynamics resulting in C2C12 myotube alignment over millimeter-scale distances. When cultured on mechanically anisotropic liquid crystalline polymer networks (LCNs) lacking topography, C2C12 myoblasts collectively polarize in the stiffest direction. Cellular coordination is amplified through reciprocal cell-ECM dynamics that emerge during fusion, driving global myotube-ECM ordering. Conversely, myotube alignment was restricted to small local domains with no directional preference on mechanically isotropic LCNs of the same chemical formulation. These findings provide valuable insights for designing biomaterials that mimic anisotropic microenvironments and underscore the importance of stiffness anisotropy in orchestrating tissue morphogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A selective WDR5 degrader inhibits acute myeloid leukemia in patient-derived mouse models

Interactions between WD40 repeat domain protein 5 (WDR5) and its various partners such as mixed lineage leukemia (MLL) and c-MYC are essential for sustaining oncogenesis in human cancers. However, inhibitors that block protein-protein interactions (PPIs) between WDR5 and its binding partners exhibit modest cancer cell killing effects and lack in vivo efficacy. Here, we present pharmacological degradation of WDR5 as a promising therapeutic strategy for treating WDR5-dependent tumors and report two high-resolution crystal structures of WDR5-degrader-E3 ligase ternary complexes. We identified an effective WDR5 degrader via structure-based design and demonstrated its in vitro and in vivo antitumor activities. On the basis of the crystal structure of an initial WDR5 degrader in complex with WDR5 and the E3 ligase von Hippel–Lindau (VHL), we designed a WDR5 degrader, MS67, and demonstrated the high cooperativity of MS67 binding to WDR5 and VHL by another ternary complex structure and biophysical characterization. MS67 potently and selectively depleted WDR5 and was more effective than WDR5 PPI inhibitors in suppressing transcription of WDR5-regulated genes, decreasing the chromatin-bound fraction of MLL complex components and c-MYC, and inhibiting the proliferation of cancer cells. In addition, MS67 suppressed malignant growth of MLL-rearranged acute myeloid leukemia patient cells in vitro and in vivo and was well tolerated in vivo. Collectively, our results demonstrate that structure-based design can be an effective strategy to identify highly active degraders and suggest that pharmacological degradation of WDR5 might be a promising treatment for WDR5-dependent cancers.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Tailoring a Global Iron Regulon to a Uropathogen

Pathogenicity islands and plasmids bear genes for pathogenesis of various Escherichia coli pathotypes. Although there is a basic understanding of the contribution of these virulence factors to disease, less is known about variation in regulatory networks in determining disease phenotypes. Here, we dissected a regulatory network directed by the conserved iron homeostasis regulator, ferric uptake regulator (Fur), in uropathogenic E. coli (UPEC) strain CFT073. Comparing anaerobic genome-scale Fur DNA binding with Fur-dependent transcript expression and protein levels of the uropathogen to that of commensal E. coli K-12 strain MG1655 showed that the Fur regulon of the core genome is conserved but also includes genes within the pathogenicity/genetic islands. Unexpectedly, regulons indicative of amino acid limitation and the general stress response were also indirectly activated in the uropathogen fur mutant, suggesting that induction of the Fur regulon increases amino acid demand. Using RpoS levels as a proxy, addition of amino acids mitigated the stress. In addition, iron chelation increased RpoS to the same levels as in the fur mutant. The increased amino acid demand of the fur mutant or iron chelated cells was exacerbated by aerobic conditions, which could be partly explained by the O 2 -dependent synthesis of the siderophore aerobactin, encoded by an operon within a pathogenicity island. Taken together, these data suggest that in the iron-poor environment of the urinary tract, amino acid availability could play a role in the proliferation of this uropathogen, particularly if there is sufficient O 2 to produce aerobactin. IMPORTANCE Host iron restriction is a common mechanism for limiting the growth of pathogens. We compared the regulatory network controlled by Fur in uropathogenic E. coli (UPEC) to that of nonpathogenic E. coli K-12 to uncover strategies that pathogenic bacteria use to overcome iron limitation. Although iron homeostasis functions were regulated by Fur in the uropathogen as expected, a surprising finding was the activation of the stringent and general stress responses in the uropathogen fur mutant, which was rescued by amino acid addition. This coordinated global response could be important in controlling growth and survival under nutrient-limiting conditions and during transitions from the nutrient-rich environment of the lower gastrointestinal (GI) tract to the more restrictive environment of the urinary tract. The coupling of the response of iron limitation to increased demand for amino acids could be a critical attribute that sets UPEC apart from other E. coli pathotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hybridization capture sequencing for Vibrio spp. and associated virulence factors

ABSTRACT Proliferation ofVibriospp. in aquatic ecosystems is associated with climate change and, concomitantly, increased incidence of vibriosis. They are autochthonous to aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing (HCS) was employed to profile low-abundanceVibriospp. in environmental samples. The HCS panel targeted a family of molecular chaperones (CPN60) specific to 69Vibriospp. and 162Vibrio-specific virulence factors. This approach was evaluated in parallel with traditional whole-community shotgun sequencing in a metagenomic analysis of water and oyster samples collected from the Chesapeake Bay. In addition,Vibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples were subjected to whole-genome sequencing to determine the genetic characteristics of pathogenicVibriospp. circulating in an aquatic environment. HCS, employed to determine the incidence and characterization of specificVibriospp., yielded significantly greater metagenomic insight, notably a variety of otherVibriospp., including detection ofVibrio cholerae,Vibrio fluvialis, andVibrio aestuarianus, in addition toVibrio parahaemolyticusandVibrio vulnificus, and also important virulence factors not detectable using traditional molecular methods. Thus, pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood. It is concluded that environmental surveillance should include HCS, a valuable tool for the detection and characterization of pathogenic agents in aquatic ecosystems, notably vibrios. IMPORTANCE The increasing prevalence of pathogenicVibriospp. in aquatic ecosystems, driven by climate change, is closely linked to a rise in cholera and vibriosis cases, emphasizing the need for improved environmental surveillance. Vibrios are naturally occurring in aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing was employed to profile low-abundanceVibriospp. in metagenomic samples, namely water and oysters collected from the Chesapeake Bay. This approach was evaluated in parallel with traditional whole-community shotgun sequencing and whole-genome sequencing ofVibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples. Results suggest pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood, when multiple methods are considered for environmental surveillance.

Microbiology↗
DOE OSTI2023

A previously uncharacterized divisome-associated lipoprotein, DalA, is needed for normal cell division in Rhodobacterales

ABSTRACT The bacterial cell envelope is a key subcellular compartment with important roles in antibiotic resistance, nutrient acquisition, and cell morphology. We seek to gain a better understanding of proteins that contribute to the function of the cell envelope in Alphaproteobacteria . Using Rhodobacter sphaeroides , we show that a previously uncharacterized protein, RSP_1200, is an outer membrane (OM) lipoprotein that non-covalently binds peptidoglycan (PG). Using a fluorescently tagged version of this protein, we find that RSP_1200 undergoes a dynamic repositioning during the cell cycle and is enriched at the septum during cell division. We show that the position of RSP_1200 mirrors the location of FtsZ rings, leading us to propose that RSP_1200 is a newly identified component of the R. sphaeroides ’ divisome. Additional support for this hypothesis includes the co-precipitation of RSP_1200 with FtsZ, the Pal protein, and several predicted PG L,D-transpeptidases. We also find that a ∆ RSP_1200 mutation leads to defects in cell division, sensitivity to PG-active antibiotics, and results in the formation of OM protrusions at the septum during cell division. Based on these results, we propose to name RSP_1200 DalA (for division-associated lipoprotein A) and postulate that DalA serves as a scaffold to position or modulate the activity of PG transpeptidases that are needed to form envelope invaginations during cell division. We find that DalA homologs are present in members of the Rhodobacterales order within Alphaproteobacteria . Therefore, we propose that further analysis of this and related proteins will increase our understanding of the macromolecular machinery and proteins that participate in cell division in Gram-negative bacteria. IMPORTANCE Multi-protein complexes of the bacterial cell envelope orchestrate key processes like growth, division, biofilm formation, antimicrobial resistance, and production of valuable compounds. The subunits of these protein complexes are well studied in some bacteria, and differences in their composition and function are linked to variations in cell envelope composition, shape, and proliferation. However, some envelope protein complex subunits have no known homologs across the bacterial phylogeny. We find that Rhodobacter sphaeroides RSP_1200 is a newly identified lipoprotein (DalA) and that loss of this protein causes defects in cell division and changes the sensitivity to compounds, affecting cell envelope synthesis and function. We find that DalA forms a complex with proteins needed for cell division, binds the cell envelope polymer peptidoglycan, and colocalizes with enzymes involved in the assembly of this macromolecule. The analysis of DalA provides new information on the cell division machinery in this and possibly other Alphaproteobacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

BONCAT-Live for isolation and cultivation of active environmental bacteria

In diverse environments, microbes drive a myriad of processes, from geochemical and nutrient cycling to interspecies interactions, including associations with plants and animals. Their physiological state is dynamic and impacted by abiotic and biotic conditions, responding to environmental fluctuations by changes in cellular metabolism, according to their genetic potential. Molecular, cellular, and genomic approaches can identify and measure microbial responses and adaptation to environmental changes in native communities. However, isolating individual microbial cells that respond to specific changes for cultivation has been difficult. To address this, we implemented a novel bacterial isolation approach (BONCAT-Live) by integrating bio-orthogonal non-canonical amino acid tagging (BONCAT) in diverse native communities, with isolation and cultivation of cells responding to specific stimuli, at different time scales. In frozen Arctic permafrost samples, we identified and isolated dormant bacteria that become active after thawing under native or nutrient-enriched conditions. From the Populus tree rhizosphere, we isolated strains that thrive under high concentrations of root exudates that act as defense compounds and nutrients. In the human microbiome, we identified and isolated bacteria that rapidly proliferated when exposed to metabolites provided by the host or other co-occurring microbes. Further characterization of isolated bacterial strains will provide opportunities for in-depth determination of how these microbes adapt to changes in their environments, individually and as part of model communities.

Analytical Methods↗
DOE OSTI2021

Design Considerations for GPU-based Mixed Integer Programming on Parallel Computing Platforms

Mixed Integer Programming (MIP) is a powerful abstraction in combinatorial optimization that finds real-life application across many significant sectors. The recent proliferation of graphical processing unit (GPU)-based accelerated computing architectures in large-scale parallel computing or supercomputing presents new opportunities as well as challenges in the advancement of MIP solver technology to effectively use the new accelerated computing platforms and scale to large parallel systems. Here, we recount the conventional processor-based strategies and focus on configurations where the most promising intersection lies between parallel MIP solver approaches and the specific strengths of accelerated parallel platforms. We note that the best potential lies in solving problems whose individual matrix sizes (of the linear program relaxation) fit entirely within one accelerator's memory and whose branch-and-bound (or branch-and-cut) trees cannot be fully contained within a small number of computational nodes. Additionally, we identify ideal features of computational linear algebra support on GPU accelerators that would help advance this direction of scalable parallel solution of MIP problems on GPU-based accelerated computing architectures.

Perumalla, Kalyan↗
DOE OSTI2021

Conflation of Geospatial POI Data and Ground-level Imagery via Link Prediction on Joint Semantic Graph

With the proliferation of smartphone cameras and social networks, we have rich, multi-modal data about points of interest (POIs) - like cultural landmarks, institutions, businesses, etc. - within a given areas of interest (AOI) (e.g., a county, city or a neighborhood) available to us. Data conflation across multiple modalities of data sources is one of the key challenges in maintaining a geographical information system (GIS) which accumulate data about POIs. Given POI data from nine different sources, and ground-level geo-tagged and scene-captioned images from two different image hosting platforms, in this work we explore the application of graph neural networks (GNNs) to perform data conflation, while leveraging a natural graph structure evident in geospatial data. The preliminary results demonstrate the capacity of a GNN operation to learn distributions of entity (POIs and images) features, coupled with topological structure of entity's local neighborhood in a semantic nearest neighbor graph, in order to predict links between a pair of entities.

Gurav, Rutuja↗
DOE OSTI2023

Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut

Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unweighted MaxCut problems by leveraging similarities in the objective landscape among different problem instances. However, we show that the more general weighted MaxCut problem has significantly modified objective landscapes, with a proliferation of poor local optima. Our main contribution is a simple rescaling scheme that overcomes these deleterious effects of weights. Here we show that for a given QAOA depth, a single “typical” vector of QAOA parameters can be successfully transferred to weighted MaxCut instances. This transfer leads to a median decrease in the approximation ratio of only 2.0 percentage points relative to a considerably more expensive direct optimization on a dataset of 34,701 instances with up to 20 nodes and multiple weight distributions. This decrease can be reduced to 1.2 percentage points at the cost of only 10 additional QAOA circuit evaluations with parameters sampled from a pretrained metadistribution, or the transferred parameters can be used as a starting point for a single local optimization run to obtain approximation ratios equivalent to those achieved by exhaustive optimization in 96.35% of our cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Extensions to the SENSEI In situ Framework for Heterogeneous Architectures

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.

Loring, Burlen↗
DOE OSTI2023

Tournament-Based Pretraining to Accelerate Federated Learning

Advances in hardware, proliferation of compute at the edge, and data creation at unprecedented scales have made federated learning (FL) necessary for the next leap forward in pervasive machine learning. For privacy and network reasons, large volumes of data remain stranded on endpoints located in geographically austere (or at least austere network-wise) locations. However, challenges exist to the effective use of these data. To solve the system and functional level challenges, we present an three novel variants of a serverless federated learning framework. We also present tournament-based pretraining, which we demonstrate significantly improves model performance in some experiments. Overall, these extensions to FL and our novel training method enable greater focus on science rather than ML development.

Baughman, Matt↗
DOE OSTI2025

A Digital Twin of Scalable Quantum Clouds

Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the limit of classical systems. The rapid development of quantum processors has led to the proliferation of cloud-based quantum computing services offered by platforms such as IBM, Google, and Amazon. These platforms introduce unique challenges in resource allocation, job scheduling, and multi-device orchestration as quantum workloads become increasingly complex. In this work, we present a digital twin of quantum cloud infrastructures: a framework designed to model and simulate the behavior of real quantum cloud systems. Developed in Python using the SimPy discrete-event simulation library, the framework replicates key aspects of quantum cloud environments, including detailed quantum device modeling, job lifecycle management, and job fidelity. It incorporates noise-aware fidelity estimation, making it the first of its kind to simulate superconducting gate-based quantum cloud systems at an administrative level with job fidelity. We present use cases as proof of concept, demonstrating that our quantum cloud simulation framework can act as a digital twin of a quantum cloud and support the modeling and implementation of practical systems.

Luo, Waylon [Kent State University]↗