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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Characterization and modification of enzymes in the 2-ketoisovalerate biosynthesis pathway of Ralstonia eutropha H16

2-Ketoisovalerate is an important cellular intermediate for the synthesis of branched-chain amino acids as well as other important molecules, such as pantothenate, coenzyme A, and glucosinolate. This ketoacid can also serve as a precursor molecule for the production of biofuels, pharmaceutical agents, and flavor agents in engineered organisms, such as the betaproteobacterium Ralstonia eutropha. The biosynthesis of 2-ketoisovalerate from pyruvate is carried out by three enzymes: acetohydroxyacid synthase (AHAS, encoded by ilvBH), acetohydroxyacid isomeroreductase (AHAIR, encoded by ilvC), and dihydroxyacid dehydratase (DHAD, encoded by ilvD). In this study, enzymatic activities and kinetic parameters were determined for each of the three R. eutropha enzymes as heterologously purified proteins. AHAS, which serves as a gatekeeper for the biosynthesis of all three branched-chain amino acids, demonstrated the tightest regulation through feedback inhibition by l-valine (IC50 = 1.2 mM), l-isoleucine (IC50 = 2.3 mM), and l-leucine (IC50 = 5.4 mM). Intermediates in the valine biosynthesis pathway also exhibit feedback inhibitory control of the AHAS enzyme. In addition, AHAS has a very weak affinity for pyruvate (K-M = 10.5 mu M) and is highly selective towards 2-ketobutyrate (R = 140) as a second substrate. AHAIR and DHAD are also inhibited by the branched-chain amino acids, although to a lesser extent when compared to AHAS. Experimental evolution and rational site-directed mutagenesis revealed mutants of the regulatory subunit of AHAS (IlvH) (N11S, T34I, A36V, T104S, N11F, G14E, and N29H), which, when reconstituted with wild-type IlvB, lead to AHAS having reduced valine, leucine, and isoleucine sensitivity. The study of the kinetics and inhibition mechanisms of R. eutropha AHAS, AHAIR, and DHAD has shed light on interactions between these enzymes and the products they produce; it, therefore, can be used to engineer R. eutropha strains with optimal production of 2-ketoisovalerate for value-added materials.

Lu, JN↗
DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗
DOE OSTI2023

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗
DOE OSTI2017

A Mobile Element in mutS Drives Hypermutation in a Marine Vibrio

ABSTRACT Bacteria face a trade-off between genetic fidelity, which reduces deleterious mistakes in the genome, and genetic innovation, which allows organisms to adapt. Evidence suggests that many bacteria balance this trade-off by modulating their mutation rates, but few mechanisms have been described for such modulation. Following experimental evolution and whole-genome resequencing of the marine bacterium Vibrio splendidus 12B01, we discovered one such mechanism, which allows this bacterium to switch to an elevated mutation rate. This switch is driven by the excision of a mobile element residing in mutS , which encodes a DNA mismatch repair protein. When integrated within the bacterial genome, the mobile element provides independent promoter and translation start sequences for mutS —different from the bacterium’s original mutS promoter region—which allow the bacterium to make a functional mutS gene product. Excision of this mobile element rejoins the mutS gene with host promoter and translation start sequences but leaves a 2-bp deletion in the mutS sequence, resulting in a frameshift and a hypermutator phenotype. We further identified hundreds of clinical and environmental bacteria across Betaproteobacteria and Gammaproteobacteria that possess putative mobile elements within the same amino acid motif in mutS . In a subset of these bacteria, we detected excision of the element but not a frameshift mutation; the mobile elements leave an intact mutS coding sequence after excision. Our findings reveal a novel mechanism by which one bacterium alters its mutation rate and hint at a possible evolutionary role for mobile elements within mutS in other bacteria. IMPORTANCE DNA mutations are a double-edged sword. Most mutations are harmful; they can scramble precise genetic sequences honed over thousands of generations. However, in rare cases, mutations also produce beneficial new traits that allow populations to adapt to changing environments. Recent evidence suggests that some bacteria balance this trade-off by altering their mutation rates to suit their environment. To date, however, we know of few mechanisms that allow bacteria to change their mutation rates. We describe one such mechanism, driven by the action of a mobile element, in the marine bacterium Vibrio splendidus 12B01. We also found similar mobile genetic sequences in the mutS genes of many different bacteria, including clinical and agricultural pathogens. These mobile elements might play an as yet unknown role in the evolution of these important bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Final Report for Project "A high-throughput pipeline for mapping inter-species interactions and metabolic synergy relevant to next-generation biofuel production"

The goal of our project was to implement a pipeline for the systematic, computationally-driven study and optimization of microbial interactions and their effect on lignocellulose degradation and biofuel production. We specifically sought to design and construct artificial microbial consortia that could collectively degrade lignocellulose from plant biomass, and produce precursors of energy-rich biofuels. This project fits into the bigger picture goal of helping identify a sustainable strategy for the production of energy-rich biofuels that would satisfy the existing energy constraints and demand of our society. Based on the observation that complex natural microbial communities tend to be metabolically efficient and ecologically robust, we pursued the study of a microbial system in which the desired engineering function is achieved through division of labor across multiple microbial species. Our approach was aimed at bypassing the complexity of natural communities by establishing a rational approach to design small synthetic microbial consortia. Towards this goal, we combined multiple approaches, including computer modeling of ecosystem-level microbial metabolism, mass spectrometry of metabolites, genetic engineering, and experimental evolution. The microbial production of biofuels from lignocellulose is a complex, multi-step process. Microbial consortia are an ideal approach to consolidated bioprocessing: a community of microorganisms performs a wide variety of functions more efficiently and is more resilient to environmental perturbations than a microbial monoculture. Each organism we chose for this project addresses a specific challenge: lignin degradation (Pseudomonas putida); (hemi)cellulose degradation (Cellulomonas fimi); lignin degradation product demethoxylation (Methylobacterium spp); generation of biofuel lipid precursors (Yarrowia lipolytica). These organisms are genetically tractable, aerobic, and have been used in biotechnological applications. Throughout the project, we have used mass spectrometry to characterize and measure the metabolic inputs and outputs of each of these consortium members, providing valuable information for model refinement, and enabling the establishment of metabolism-mediated interactions. In addition to lignocellulose degradation, we have started addressing the challenge of removing metabolites (e.g. formaldehyde) produced by the demethoxylation of lignin monomers, which can otherwise inhibit microbial growth due to their toxicity. On the computational side, we have implemented genome-scale models for all consortium members, based on KBase reconstructions and literature curation, and we studied small consortia and their properties. Overall, our project has identified a complex landscape of interactions types and metabolic processes relevant to community-level functions, illustrating the challenges and opportunities of microbial community engineering for the transformation of biomass into bioproducts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The Hopkins Microbiology Course

For 10 years, (2008-2018), DOE supported the four-week, intensive summer course in Microbiology at the Hopkins Marine Station. This course builds upon the inquiry-driven teaching tradition introduced by C. B. van Niel in his renowned Hopkins Microbiology Course taught in the 1950s and 1960s. The distinguishing aspect of this Hopkins Microbiology Course is the unique integration and application of conceptual ideas and experimentation of microbial physiology, ecology, population genetics and experimental evolution in a cohesive study of the environment. In traditional University curricula, courses in microbial physiology, genetics, ecology and evolution are taught distinctly from each other often competitively and without much cross-referencing. Each field has its own theories, thinking, and vocabulary, but the synthesis and assimilation of these clearly related aspects of microbial life is often lacking. This Hopkins Microbiology Course is not bound to adhere to such a strict curriculum. Therefore, we have assembled an international expert team specializing in physiology, ecology, and evolution to develop a coherent picture of how to understand microbe’s function in nature and the principles of what constitutes Microbial Diversity, how it is generated, and how it is maintained.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2021

Adaptive Phenotypic Plasticity Stabilizes Evolution in Fluctuating Environments

Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Sequencing of MHC-Adapted Viral Lines Reveals Complex Recombinational Exchanges With Endogenous Retroviruses Leading to High-Frequency Variants

Experimental evolution (serial passage) of Friend virus complex (FVC) in mice demonstrates phenotypic adaptation to specific host major histocompatibility complex (MHC) genotypes. These evolved viral lines show increased fitness and virulence in their host-genotype-of-passage, but display fitness and virulence tradeoffs when infecting unfamiliar host MHC genotypes. Here, we deep sequence these viral lines in an attempt to discover the genetic basis of FVC adaptation. The principal prediction for genotype-specific adaptation is that unique mutations would rise to high frequency in viral lines adapted to each host MHC genotype. This prediction was not supported by our sequencing data as most observed high-frequency variants were present in each of our independently evolved viral lines. However, using a multi-variate approach to measure divergence between viral populations, we show that populations of replicate evolved viral lines from the same MHC congenic mouse strain were more similar to one another than to lines derived from different MHC congenic mouse strains, suggesting that MHC genotype does predictably act on viral evolution in our model. Sequence analysis also revealed rampant recombination with endogenous murine leukemia virus sequences (EnMuLVs) that are encoded within the BALB/c mouse genome. The highest frequency variants in all six lines contained a 12 bp insertion from a recombinant EnMuLV source, suggesting such recombinants were either being favored by selection or were contained in a recombinational hotspot. Interestingly, they did not reach fixation, as if they are low fitness. The amount of background mutations linked to FVC/EnMuLV variable sites indicated that FVC/EnMuLV recombinants had not reached mutation selection equilibrium and thus, that EnMuLV sequences are likely continuously introgressing into the replicating viral population. These discoveries raise the question: is the expression of EnMuLV sequences in mouse splenocytes that permit recombination with exogenous FVC a pathogen or host adaptation?

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2024

Evolution and interplay of lithium metal interphase components revealed by experimental and theoretical studies

ABSTRACT: Lithium metal batteries (LMB) have high energy densities and are crucial for clean energy solutions. The characterization of lithium metal interphase is fundamentally and practically important but technically challenging. Taking advantage of synchrotron x-ray which has the unique capability of analyzing crystalline/amorphous phases quantitative-ly with statistical significance, we study the composition and dynamics of LMB interphase for a newly developed im-portant LMB electrolyte that is based on fluorinated ether. Pair distribution function analysis revealed the sequential role of anion and solvent in interphase formation during cycling. The relative ratio between Li2O and LiF first increases and then decreases during cycling, suggesting suppressed Li2O formation in both initial and long extended cycles. Theoretical studies revealed that in initial cycles, this is due to the energy barrier in many-electron transfer. In long extended cycles, the anion decomposition product Li2O encourages solvent decomposition by facilitating solvent adsorption on Li2O which is followed by concurrent depletion of both. This work highlights the important role of Li2O in transitioning from anion-derived interphase to a solvent-derived one.

Tan, Sha↗
DOE OSTI2021

Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression

The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Density and magnetic fluctuations in type III-ELM pedestal evolution in JET: experimental and numerical characterization

Density and magnetic fluctuation measurements in low-β type-III ELM discharges are obtained in the Joint European Torus (JET). They are observed during the inter-ELM pedestal evolution, after the LH transition phase, at about 60–70 kHz. Density fluctuations are measured with a correlation reflectometer system installed on the low-field side and they are localized at the pedestal top. Magnetic fluctuations with a spatial scale $k_y\rho_i\sim 0.1$ are measured through a high resolution coil array. The main features and the relations with local plasma parameters are presented. The nature of these fluctuations is discussed along with linear gyrokinetic simulations. Ion temperature gradient (ITG) modes are the dominant instabilities in the frequency range of interest. In terms of radial localization, typical oscillation frequency and qualitative relation with the possible linear drive, ITG modes are consistent with the experimental density fluctuations measurements. Micro-tearing modes (MTMs), found unstable with a lower growth rate, appear a possible explanation for magnetic fluctuations in terms of typical wavenumbers and direction of propagation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

3D Experimental Measurements of Evolution of Force Chains in Natural Silica Sand

The mechanisms of force transmission in granular materials is a classic physics problem that has been addressed since the 19th century, when Heinrich Rudolf Hertz investigated the interaction between two similar objects that were in contact under compression. However, the study of force transmission mechanisms in assemblies of more particles has proven to be a formidable problem due to the complex nature of granular materials. In recent years, synchrotron microcomputed tomography (SMT) and three-dimensional X-ray diffraction microscopy (3DXRD) have been employed to study the mechanics of granular materials experimentally. Combining SMT and 3DXRD offers unique three-dimensional (3D) experimental measurements of the internal structure, kinematics (such as rotation and translation), and lattice strains of individual sand particles. In this paper, in situ SMT and 3DXRD scans were acquired at multiple load steps for a specimen composed of 2,705 natural Ottawa sand particles that were subjected to one-dimensional (1D) confined compression. An algorithm was developed to combine SMT images and 3DXRD lattice strain measurements and used to characterize the constitutive behavior of sand particles. The results were used to identify the crystal structure and the evolution of the stresses and lattice strains of individual sand particles. Another algorithm was developed to characterize the force structures within the specimen. Force structures were identified, and their properties (such as length) and evolution through the experiment were examined. The contact number of particles is a particle-scale property that affects the mechanics of granular materials. The effect of the contact number of the sand particles on the onset and evolution of the force structures was also investigated and discussed.

3D x-ray diffraction microscopy↗
DOE OSTI2017

Experimental recreation of the evolution of lignin-degrading enzymes from the Jurassic to date

Floudas et al. (Science 336: 1715) established that lignin-degrading fungi appeared at the end of Carboniferous period associated with the production of the first ligninolytic peroxidases. Here, the subsequent evolution of these enzymes in Polyporales, where most wood-rotting fungi are included, is experimentally recreated using genomic information.

Ancestral sequence reconstruction↗