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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

MFA-DVR: direct volume rendering of MFA models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. Here, in this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Typically, 13 C flux analysis relies on assumptions of both metabolic and isotopic steady state. If metabolism is steady but isotope labeling is not allowed to fully equilibrate, isotopically nonstationary metabolic flux analysis (INST-MFA) can be used to estimate fluxes. This requires solution of differential equations that describe the time-dependent labeling of network metabolites, while iteratively adjusting the flux and pool size parameters to match the transient labeling measurements. INST-MFA holds a number of unique advantages over approaches that rely solely upon steady-state isotope enrichments. First, INST-MFA can be applied to estimate fluxes in autotrophic systems, which consume only single-carbon substrates. Second, INST-MFA is ideally suited to systems that label slowly due to the presence of large intermediate pools or pathway bottlenecks. Finally, INST-MFA provides increased measurement sensitivity to estimate reversible exchange fluxes and metabolite pool sizes, which represents a potential framework for integrating metabolomic analysis with 13 C flux analysis. This review highlights the unique capabilities of INST-MFA, describes newly available software tools that automate INST-MFA calculations, presents several practical examples of recent INST-MFA applications, and discusses the technical challenges that lie ahead.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

I-mfa domain proteins specifically interact with HTLV-1 Tax and repress its transactivating functions

The I-mfa domain proteins HIC (also known as MDFIC) and I-mfa (also known as MDFI) are candidate tumor suppressor genes that are involved in cellular and viral transcriptional regulation. Here, we show that HIC and I-mfa directly interact with human T-cell leukemia virus type-1 (HTLV-1) Tax protein in vitro. In addition, HIC and I-mfa repress Tax-dependent transactivation of an HTLV-1 long terminal repeat (LTR) reporter construct in COS-1, Jurkat and high-Tax-producing HTLV-1-infected T cells. HIC also interacts with Tax through its I-mfa domain in vivo and represses Tax-dependent transactivation of HTLV-1 LTR and NF-κB reporter constructs in an interaction-dependent manner. Furthermore, we show that HIC decreases the nuclear distribution and stimulates the proteasomal degradation of Tax. These data reveal that HIC specifically interacts with HTLV-1 Tax and negatively regulates Tax transactivational activity by altering its subcellular distribution and stability. - Highlights: • I-mfa domain proteins, HIC and I-mfa, specifically interact with HTLV-1 Tax. • HIC and I-mfa repress the Tax-dependent transactivation of HTLV-1 LTR. • HIC represses the Tax-dependent transactivation of NF-κΒ. • HIC decreases the nuclear distribution of Tax. • HIC stimulates the proteasomal degradation of Tax.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

Magnetic Field Apparatus (MFA) Hardware Test

The objectives of this study are threefold: (1) Provide insight into water delivery in microgravity and determine optimal germination paper wetting for subsequent seed germination in microgravity; (2) Observe the behavior of water exposed to a strong localized magnetic field in microgravity; and (3) Simulate the flow of fixative (using water) through the hardware. The Magnetic Field Apparatus (MFA) is a new piece of hardware slated to fly on the Space Shuttle in early 2001. MFA is designed to expose plant tissue to magnets in a microgravity environment, deliver water to the plant tissue, record photographic images of plant tissue, and deliver fixative to the plant tissue.

Anderson, Ken↗
DOE OSTI2020

Effect of 60 Co γ-radiation on the tetrafluoroethylene/perfluoro(methyl vinyl ether) copolymer

The chemical and paramagnetic composition of the surface of the tetrafluoroethylene / perfluoro(methyl vinyl ether) copolymer (MFA) was investigated before and after γ-irradiation. The content of fluorine and carbon decreases at radiolysis of MFA, as well as the proportion of oxygen increases as a result of the oxidation reaction of radicals formed during γ-irradiation. The electron paramagnetic resonance (EPR) spectrum of the copolymer irradiated at 77 K contains the radicals ~CF 2 C∙FCF 2 ~ (1) and >CFOC∙F 2 (2). The absence of ~CF 2 C∙F 2 radicals, which can be formed at radiolytic rupture of the C—C bond of the copolymer main chain is associated with the effect of recombination of the resulting radical pair. The EPR spectrum of irradiated MFA at 300 K only recorded radical (1), which transform into peroxide radicals when air oxygen is introduced into the irradiated sample. Thermomechanical curves of native and radiolyzed MFA show three structural states of the amorphous block (glass, transition region, plateau of highly elastic deformation), as well as melting and molecular flow of the copolymer. The radio thermoluminescence curve shows five maxima associated with the emission of light during the recombination of stabilized ions and radicals during heating of the pre-irradiated MFA at 77 K. The temperature of the luminescence peak maximum at 336 K is close to the temperature of the onset of the devitrification of the chains of the amorphous block of MFA on the thermomechanical curve at 341 K. Irradiation leads to an increase in crystallinity of MFA. The heat of fusion of irradiated MFA on the Differential Scanning Calorimeter (DSC) curve is 50 % higher than the fusion heat of native copolymer. During the heating of γ-irradiated MFA, the relative content of COF molecules in the thermal decomposition products increases by an order of magnitude.

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

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

big scientific dataset↗
DOE OSTI2024

Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST–MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast–growing algae, and produced a synergistic multi–organ flux map that is a first in MFA for any biological system. Finally, these insights could not be elucidated through other approaches and show the potential of INST–MFA to correct an oversimplified understanding of plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Effects of the P2 M-band flux asymmetry of laser-driven gold Hohlraums on the implosion of ICF ignition capsule

Low-mode asymmetries in the laser-indirect-drive inertial confinement fusion implosion experiments conducted on the National Ignition Facility [G. H. Miller et al., Nucl. Fusion 44, S228 (2004)] are deemed the main obstacles hindering further improvement of the nuclear performance of deuterium-tritium-layered capsules. The dominant seeds of these asymmetries include the P2 and P4 asymmetries of x-ray drives and P2 asymmetry introduced by the supporting “tent.” Here, we explore the effects of another possible seed that can lead to low-mode asymmetric implosions, i.e., the M-band flux asymmetry (MFA) in laser-driven cylindrical gold Hohlraums. It is shown that the M-band flux facilitates the ablation and acceleration of the shell, and that positive P2 MFAs can result in negative P2 asymmetries of hot spots and positive P2 asymmetries of shell's ρR. An oblate or toroidal hot spot, depending on the P2 amplitude of MFA, forms at stagnation. The energy loss of such a hot spot via electron thermal conduction is seriously aggravated not only due to the enlarged hot spot surface but also due to the vortices that develop and help transferring thermal energy from the hotter center to the colder margin of such a hot spot. The cliffs of nuclear performance for the two methodologies of applying MFA (i.e., symmetric flux in the presence of MFA and MFA added for symmetric soft x-ray flux) are obtained locating at 9.5% and 5.0% of P2/P0 amplitudes, respectively.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ 2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Identification of bottlenecks in the accumulation of cyclic fatty acids in camelina seed oil

Modified fatty acids (mFA) have diverse uses, e.g., cyclopropane fatty acids (CPA) are feedstocks for producing coatings, lubricants, plastics, and cosmetics. The expression of mFA-producing enzymes in crop and model plants generally results in lower levels of mFA accumulation than in their natural-occurring source plants. In order to further our understanding of metabolic bottlenecks that limit mFA accumulation, we generated transgenic Camelina sativa lines co-expressing Escherichia coli cyclopropane synthase (EcCPS) and Sterculia foetida lysophosphatidic acid acyltransferase (SfLPAT). In contrast to transgenic CPA-accumulating Arabidopsis, CPA accumulation in camelina caused only minor changes in seed weight, germination rate, oil accumulation, and seedling development. CPA accumulated to much higher levels in membrane than storage lipids, comprising more than 60% of total fatty acid in both phosphatidylcholine (PC) and phosphatidylethanolamine (PE) versus 26% in diacylglycerol (DAG) and 12% in triacylglycerol (TAG) indicating bottlenecks in the transfer of CPA from PC to DAG and from DAG to TAG. Upon coexpression of SfLPAT with EcCPS, di-CPA-PC increased by ~50% relative to lines expressing EcCPS alone with the di-CPA-PC primarily observed in the embryonic axis and mono-CPA-PC primarily in cotyledon tissue. EcCPS-SfLPAT lines revealed a redistribution of CPA from the sn-1 to sn-2 positions within PC and PE that was associated with a doubling of CPA accumulation in both DAG and TAG. Finally, the identification of metabolic bottlenecks in acyl transfer between site of synthesis (phospholipids) and deposition in storage oils (TAGs) lays the foundation for the optimizing CPA accumulation through directed engineering of oil synthesis in target crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An automated workflow that generates atom mappings for large‐scale metabolic models and its application to Arabidopsis thaliana

SUMMARY Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large‐scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large‐scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana . We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)‐containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N‐MFA studies of A. thaliana . The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph‐theoretic structural analyses with large‐scale metabolic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Systematic comparison of local approaches for isotopically nonstationary metabolic flux analysis

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determine cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA) that relies on the patterns of isotope labeling of metabolites in the network. For metabolic systems, typical for plants, where all potentially labeled atoms effectively have only one source atom pool, only isotopically nonstationary MFA can provide information about intracellular fluxes. There are several global approaches that implement MFA for an entire metabolic network and estimate, at once, a steady-state flux distribution for all reactions with identifiable fluxes in the network. In contrast, local approaches deal with estimation of fluxes for a subset of reactions, with smaller data demand for flux estimation. Here we present a systematic comparative review and benchmarking of the existing local approaches for isotopically nonstationary MFA. The comparison is conducted with respect to the required data and underlying computational problems solved on a synthetic network example. Furthermore, we benchmark the performance of these approaches in estimating fluxes for a subset of reactions using data obtained from the simulation of nitrogen fluxes in the Arabidopsis thaliana core metabolism. The findings pinpoint practical aspects that need to be considered when applying local approaches for flux estimation in large-scale plant metabolic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13 C Metabolic Flux Analysis

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

13C metabolic flux analysis↗
DOE OSTI2015

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2018

The removal of Zn2+, Pb2+, and As(V) ions by lime activated fly ash and valorization of the exhausted adsorbent

This study focuses on the use of raw fly ash (FA) and modified fly ash – activated by lime (MFA), as effective and low-cost adsorbents for the removal of heavy metals (Zn2+, Pb2+and As(V)), followed by the revalorization of the exhausted adsorbent. The granulometric, elemental analysis, point of zero charge (pHPZC), radiochemical and structural characterization were conducted using X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), scanning electron microscopy (SEM), Fourier transform infrared (FTIR) and gamma spectrometry techniques. The optimal conditions and key factors influencing the adsorption process were assessed using the response surface method (RSM). The adsorption capacity of the MFA adsorbent for Zn2+, Pb2+and As(V) removal, calculated by the Langmuir model, was found to be 33.13, 26.06, and 29.71 mg g−1, respectively. The kinetic and thermodynamic parameters indicated that the adsorption process is spontaneous and endothermic. Due to their low desorption potential of the exhausted adsorbents, their effective reuse was established to be feasible. For this reason, the valorization of this material as an additive in construction materials was thereafter studied, where testing its toxicity leaching (TCLP) as well as the mechanical properties of construction material containing exhausted MFA confirmed its safe use. Hence, this study points to a possible “two-in-one” reuse of coal ash, initially as an adsorbent and later as an additive in a construction material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗