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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Unifying themes and distinct features of carbon and nitrogen assimilation by polysaccharide-degrading bacteria: a summary of four model systems

Our current understanding of enzymatic polysaccharide degradation has come from a huge number of in vitro studies with purified enzymes. While this vast body of work has been invaluable in identifying and characterizing novel mechanisms of action and engineering desirable traits into these enzymes, a comprehensive picture of how these enzymes work as part of a native in vivo system is less clear. Recently, several model bacteria have emerged with genetic systems that allow for a more nuanced study of carbohydrate active enzymes (CAZymes) and how their activity affects bacterial carbon metabolism. With these bacterial model systems it is now possible to not only study a single nutrient system in isolation (i.e. carbohydrate degradation and carbon metabolism), but also how multiple systems are integrated. Given that most environmental polysaccharides are carbon rich but nitrogen poor (e.g. lignocellulose), the interplay between carbon and nitrogen metabolism in polysaccharide-degrading bacteria can now be studied in a physiologically relevant manner. Therefore, in this review we have summarized what has been experimentally determined for CAZyme regulation, production, and export in relation to nitrogen metabolism for two Gram-positive (Caldicellulosiruptor bescii and Clostridium thermocellum) and two Gram-negative (Bacteroides thetaiotaomicron and Cellvibrio japonicus) polysaccharide-degrading bacteria. By comparing and contrasting these four bacteria we have highlighted the shared and unique features of each, with a focus on in vivo studies, in regard to carbon and nitrogen assimilation. Finally, we conclude with what we believe are two important questions that can act as guideposts for future work to better understand the integration of carbon and nitrogen metabolism in polysaccharide-degrading bacteria.

Cellvibrio japonicus↗
DOE OSTI2018

Nominal Power Assimilation in Leningradskaya NPP No. 1 Unit After Service Life Characteristics Restoration of the Graphite Stack

The maximum graphite temperature upon increasing the reactor power in the No. 1 unit of the Leningradskaya NPP to 80% of the nominal value in November–December 2013 after the restoration of the service life characteristics of the graphite stack is presented. A comparative analysis is made of the temperature regime of the graphite stack at power equal to 80 and 100% of nominal value before and after restoration of the service life characteristics at nominal helium content in the gas mixture blown through the reactor space. A comparison is made of the maximum temperature of the graphite stack of the reactors in the No. 1 unit of the Leningradskaya NPP and No. 2 unit of the Kursk NPP upon startup and assimilation of the power after restoration of the service life characteristics. The effect of the restoration of the service life characteristics on the thermohydraulic regime of the fuel channels in the reactor is studied.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Autotrophic, Heterotrophic, and Mixotrophic Nitrogen Assimilation for Single-Cell Protein Production by Two Hydrogen-Oxidizing Bacterial Strains

To recover a nitrogen resource from high-ammonia-nitrogen wastewater, two amphitrophic hydrogen-oxidizing bacteria (HOB), Paracoccus denitrificans Y5 and P. versutus D6, capable of nitrogen assimilation for single-cell protein (SCP) production were isolated. These two HOB strains could grow autotrophically with H{sub 2} as an electron donor, O{sub 2} as an electron acceptor, CO{sub 2} as a carbon source, and ammonia nitrogen (NH{sub 4}{sup +}-N) as a nitrogen source. The cell molecular formulas of strains Y5 and D6 determined by autotrophic cultivation were C{sub 3.33}H{sub 6.83}O{sub 2.58}N{sub 0.77} and C{sub 2.87}H{sub 5.34}O{sub 3.17}N{sub 0.57}, respectively. The isolated strains could synchronously remove NH{sub 4}{sup +}-N and organic carbon and produce SCP via heterotrophic cultivation. The rates of removal of NH{sub 4}{sup +}-N and soluble chemical oxygen demand reached 35.47 and 49.04%, respectively, for Y5 under mixotrophic cultivation conditions with biogas slurry as a substrate. SCP content of strains Y5 and D6 was 67.34–73.73% based on cell dry weight. Compared with soybean meal, the SCP of Y5 contained a variety of amino acids.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Synthetic methylotrophy: Strategies to assimilate methanol for growth and chemicals production

Methanol is an attractive and broadly available substrate for large-scale bioproduction of fuels and chemicals. It contains more energy and electrons per carbon than carbohydrates and can be cheaply produced from natural gas. Synthetic methylotrophy refers to the development of non-native methylotrophs such as Escherichia coli and Corynebacterium glutamicum to utilize methanol as a carbon source. Furthermore, we discuss recent advances in engineering these industrial hosts to assimilate methanol for growth and chemicals production through the introduction of the ribulose monophosphate (RuMP) cycle. In addition, we present novel strategies based on flux coupling and adaptive laboratory evolution to engineer new strains that can grow exclusively on methanol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

Time-lapse electrical resistivity tomography (ERT) measurements provide indirect observations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. In this paper, we demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems.

58 GEOSCIENCES↗
DOE OSTI2021

Microbial assimilation of lignin-derived aromatic compounds and conversion to value-added products

Lignin is an abundant and sustainable source of aromatic compounds that can be converted to value-added products. However, lignin is underutilized, since depolymerization produces a complex mixture of aromatic compounds that is difficult to convert to a single product. Microbial conversion of mixed aromatic substrates provides a potential solution to this conversion challenge. Recent advances have expanded the range of lignin-derived aromatic substrates that can be assimilated and demonstrated efficient conversion via central metabolism to new potential products. Here, the development of additional non-model microbial hosts and genetic tools for these hosts have accelerated engineering efforts. However, yields with real depolymerized lignin are still low, and additional work will be required to achieve viable conversion processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2018

Improving formaldehyde consumption drives methanol assimilation in engineered E. coli

Abstract Due to volatile sugar prices, the food vs fuel debate, and recent increases in the supply of natural gas, methanol has emerged as a promising feedstock for the bio-based economy. However, attempts to engineer Escherichia coli to metabolize methanol have achieved limited success. Here, we provide a rigorous systematic analysis of several potential pathway bottlenecks. We show that regeneration of ribulose 5-phosphate in E. coli is insufficient to sustain methanol assimilation, and overcome this by activating the sedoheptulose bisphosphatase variant of the ribulose monophosphate pathway. By leveraging the kinetic isotope effect associated with deuterated methanol as a chemical probe, we further demonstrate that under these conditions overall pathway flux is kinetically limited by methanol dehydrogenase. Finally, we identify NADH as a potent kinetic inhibitor of this enzyme. These results provide direction for future engineering strategies to improve methanol utilization, and underscore the value of chemical biology methodologies in metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Data Assimilation using Non-invasive Monte Carlo Sensitivity Analysis of Reactor Kinetics Parameters

Accurately predicting the criticality of an experiment before interacting with the experimental components is very important for criticality safety. Radiation transport software can be utilized to calculate the effective neutron multiplication factor of a nuclear system. Because of the integral nature of the effective neutron multiplication factor, the value calculated contains various sources of nuclear-data induced uncertainty. The sensitivity analysis and data assimilation technique presented in this paper exhibit one possible method of identifying and reducing the effective neutron multiplication factor nuclear-data induced uncertainty. The results presented in this work show that it is possible to use relative sensitivity coefficients of the prompt neutron decay constant and the effective delayed neutron fraction to 239 Pu nuclear data to reduce nuclear-data induced uncertainties in the effective neutron multiplication factor. This work has been utilized by members of the Los Alamos National Laboratory project EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) for optimally designing a new experiment, which will be used to reduce compensating errors in 239 Pu nuclear data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor-corrector type nudging approaches are still attractive due to their simplicity of implementation when more complex methods need to be avoided. However, optimal estimate of nudging gain matrix might be cumbersome. In this paper, we put forth a fully nonintrusive recurrent neural network approach based on a long short-term memory (LSTM) embedding architecture to estimate the nudging term, which plays a role not only to force the state trajectories to the observations but also acts as a stabilizer. Furthermore, our approach relies on the power of archival data and the trained model can be retrained effectively due to power of transfer learning in any neural network applications. In order to verify the feasibility of the proposed approach, we perform twin experiments using Lorenz 96 system. Our results demonstrate that the proposed LSTM nudging approach yields more accurate estimates than both extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) when only sparse observations are available. With the availability of emerging AI-friendly and modular hardware technologies and heterogeneous computing platforms, we articulate that our simplistic nudging framework turns out to be computationally more efficient than either the EKF or EnKF approaches.

42 ENGINEERING↗
DOE OSTI2019

Reduction-dependent siderophore assimilation in a model pennate diatom

Significance Diatoms can access inorganic iron with remarkable efficiency, but this process is contingent on carbonate ion concentration. As ocean acidification reduces carbonate concentration, inorganic iron uptake may be discouraged in favor of carbonate-independent uptake. We report details of an iron assimilation process that needs no carbonate but requires exogenous compounds produced by cooccurring organisms. We show this process to be critical for diatom growth at high siderophore concentrations, but ineffective at acquiring iron from low-affinity organic chelators or lithogenic particulates. Understanding the caveats associated with iron source preference in diatoms will help predict the impacts of climate change on microbial community structure in high-nitrate low-chlorophyll ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Structural and biochemical analyses indicate that a bacterial persulfide dioxygenase–rhodanese fusion protein functions in sulfur assimilation

Hydrogen sulfide (H 2 S) is a signaling molecule that is toxic at elevated concentrations. In eukaryotes, it is cleared via a mitochondrial sulfide oxidation pathway, which comprises sulfide quinone oxidoreductase, persulfide dioxygenase (PDO), rhodanese, and sulfite oxidase and converts H 2 S to thiosulfate and sulfate. Natural fusions between the non-heme iron containing PDO and rhodanese, a thiol sulfurtransferase, exist in some bacteria. However, little is known about the role of the PDO–rhodanese fusion (PRF) proteins in sulfur metabolism. Herein, we report the kinetic properties and the crystal structure of a PRF from the Gram-negative endophytic bacterium Burkholderia phytofirmans. The crystal structures of wild-type PRF and a sulfurtransferase-inactivated C314S mutant with and without glutathione were determined at 1.8, 2.4, and 2.7 Å resolution, respectively. We found that the two active sites are distant and do not show evidence of direct communication. The B. phytofirmans PRF exhibited robust PDO activity and preferentially catalyzed sulfur transfer in the direction of thiosulfate to sulfite and glutathione persulfide; sulfur transfer in the reverse direction was detectable only under limited turnover conditions. Together with the kinetic data, our bioinformatics analysis reveals that B. phytofirmans PRF is poised to metabolize thiosulfate to sulfite in a sulfur assimilation pathway rather than in sulfide stress response as seen, for example, with the Staphylococcus aureus PRF or sulfide oxidation and disposal as observed with the homologous mammalian proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Assimilating electronic tagging, oceanographic modelling, and fisheries data to estimate movements and connectivity of swordfish in the North Atlantic

Abstract Broadbill swordfish (Xiphias gladius) is a key species in commercial fisheries, but management efforts continue to be hindered by data gaps in their basic biology, including ongoing debate over stock boundaries and movement between management zones. We deployed 20 pop-up satellite archival transmitting (PSAT) tags on juvenile swordfish in the Azores and adults in the northwest Atlantic (NWA). We applied a recently developed geolocation approach that synthesizes tag data with a global observation-assimilating ocean model to improve geolocation accuracy. Resulting tracks from 16 individuals averaged 3751 km (range 345–7247 km) over 93 days (range 17–181 days). Juveniles tagged in the Azores made regional movements while adults tagged in the NWA moved between summer foraging grounds near the Grand Banks to winter habitats near the Antillean Arc. All individuals spent considerable time in the mesopelagic zone during the day, and this behaviour increased with fish size. We integrate results from our PSAT-based movements with conventional tag and catch-per-unit effort data, which indicates complex stock structure within the North Atlantic that largely supports current stock boundaries. Our work demonstrates the utility in synthesizing fishery-independent electronic tag data and fisheries datasets to improve our understanding of large pelagic fish ecology.

Braun, Camrin D.↗
DOE OSTI2021

Inference of neutrino flavor evolution through data assimilation and neural differential equations

The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for the estimation of model parameters and neutrino flavor evolution histories. Here, we combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for nonlinear dynamical systems. Using this architecture, and a simple two-neutrino-beam, two-flavor model, we compare the performances of nine different optimization algorithms and expand upon previous assessments of the efficacy of inference for tackling problems in flavor evolution. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the small-scale model and allows us to quickly explore both model parameters and initial flavor content. In future work we plan to extend these inference techniques to large numbers of neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Terrestrial biosphere models underestimate photosynthetic capacity and CO 2 assimilation in the Arctic

Terrestrial biosphere models (TBMs) are highly sensitive to model representation of photosynthesis, in particular the parameters maximum carboxylation rate and maximum electron transport rate at 25°C (V c,max.25 and J max.25 , respectively). Many TBMs do not include representation of Arctic plants, and those that do rely on understanding and parameterization from temperate species. We then measured photosynthetic CO 2 response curves and leaf nitrogen (N) content in species representing the dominant vascular plant functional types found on the coastal tundra near Barrow, Alaska. The activation energies associated with the temperature response functions of Vc,max and Jmax were 17% lower than commonly used values. When scaled to 25°C, Vc,max.25 and J max.25 were two- to five-fold higher than the values used to parameterize current TBMs. This high photosynthetic capacity was attributable to a high leaf N content and the high fraction of N invested in Rubisco. Leaf-level modeling demonstrated that current parameterization of TBMs resulted in a two-fold underestimation of the capacity for leaf-level CO 2 assimilation in Arctic vegetation. Our study highlights the poor representation of Arctic photosynthesis in TBMs, and provides the critical data necessary to improve our ability to project the response of the Arctic to global environmental change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Constraining estimates of terrestrial carbon uptake: new opportunities using long‐term satellite observations and data assimilation

Summary The response of terrestrial carbon uptake to increasing atmospheric [ CO 2 ], that is the CO 2 fertilization effect ( CFE ), remains a key area of uncertainty in carbon cycle science. Here we provide a perspective on how satellite observations could be better used to understand and constrain CFE . We then highlight data assimilation ( DA ) as an effective way to reconcile different satellite datasets and systematically constrain carbon uptake trends in Earth System Models. As a proof‐of‐concept, we show that joint DA of multiple independent satellite datasets reduced model ensemble error by better constraining unobservable processes and variables, including those directly impacted by CFE . DA of multiple satellite datasets offers a powerful technique that could improve understanding of CFE and enable more accurate forecasts of terrestrial carbon uptake.

Smith, William K.↗
DOE OSTI2019

Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets v1.1.0

BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets) uses a data brokering approach to synthesize diverse earth science data from a variety of remote sources in real-time without the need for additional storage. BASIN-3D is an extendable Django web framework application using a generalized data synthesis model that makes the synthesized data available via REpresentational State Transfer (REST) Application Programming Interface (API). We have currently implemented our data synthesis model to represent sensor-based time series earth science observations across a hierarchy of spatial locations. Supporting our data synthesis model is a plugin framework that allows developers to map data sources of interest to the BASIN-3D synthesis model

Varadharajan, Charuleka↗
DOE OSTI2023

PYOED: AN ETENSIBLE SUITE FOR DATA ASSIMILATION AND MODEL-CONSTRAINED OPTIMAL DESIGN OF EXPERIMENTS

SF-23-005 PyOED is a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to ``enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.'' PyOED will be continuously expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators.

ATTIA, AHMEDMOHAMED↗