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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows

Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. Here, in this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n-heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks.

42 ENGINEERING↗
DOE OSTI2023

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. In conclusion, overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

New adaptive method to optimize the secondary reflector of linear Fresnel collectors

Performance of linear Fresnel collectors may largely depend on the secondary-reflector profile design when small-aperture absorbers are used. Optimization of the secondary-reflector profile is an extremely challenging task because there is no established theory to ensure superior performance of derived profiles. In this work, an innovative optimization method is proposed to optimize the secondary-reflector profile of a generic linear Fresnel configuration. The method correctly and accurately captures impacts of both geometric and optical aspects of a linear Fresnel collector to secondary-reflector design. The proposed method is an adaptive approach that does not assume a secondary shape of any particular form, but rather, starts at a single edge point and adaptively constructs the next surface point to maximize the reflected power to be reflected to absorber(s). As a test case, the proposed optimization method is applied to an industrial linear Fresnel configuration, and the results show that the derived optimal secondary reflector is able to redirect more than 90% of the power to the absorber in a wide range of incidence angles. Here, the proposed method can be naturally extended to other types of solar collectors as well, and it will be a valuable tool for solar-collector designs with a secondary reflector.

14 SOLAR ENERGY↗
DOE OSTI2026

A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Covariance operator estimation via adaptive thresholding

This paper studies sparse covariance operator estimation for nonstationary processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance component. Building on recent results from empirical process theory, we derive an operator norm bound on the estimation error in terms of the sparsity level of the covariance and the expected supremum of a normalized process. Furthermore, our theory and numerical simulations demonstrate the advantage of adaptive threshold estimators over universal threshold and sample covariance estimators in nonstationary settings.

Al-Ghattas, Omar [University of Chicago, IL (Unite↗
DOE OSTI2015

Thiol dependent NF-κB suppression and inhibition of T-cell mediated adaptive immune responses by a naturally occurring steroidal lactone Withaferin A

Withaferin A (WA), a steroidal lactone isolated from ayurvedic medicinal plant Withania somnifera, was shown to inhibit tumor growth by inducing oxidative stress and suppressing NF-κB pathway. However, its effect on T-cell mediated adaptive immune responses and the underlying mechanism has not been investigated. Since both T-cell responses and NF-κB pathway are known to be redox sensitive, the present study was undertaken to elucidate the effect of WA on adaptive immune responses in vitro and in vivo. WA inhibited mitogen induced T-cell and B-cell proliferation in vitro without inducing any cell death. It inhibited upregulation of T-cell (CD25, CD69, CD71 and CD54) and B-cell (CD80, CD86 and MHC-II) activation markers and secretion of Th1 and Th2 cytokines. WA induced oxidative stress by increasing the basal ROS levels and the immunosuppressive effects of WA were abrogated only by thiol anti-oxidants. The redox modulatory effects of WA in T-cells were attributed to its ability to directly interact with free thiols. WA inhibited NF-κB nuclear translocation in lymphocytes and prevented the direct binding of nuclear NF-κB to its consensus sequence. MALDI-TOF analysis using a synthetic NF-κB-p50 peptide containing Cys-62 residue suggested that WA can modify the cysteine residue of NF-κB. The pharmacokinetic studies for WA were also carried out and in vivo efficacy of WA was studied using mouse model of Graft-versus-host disease. In conclusion, WA is a potent inhibitor of T-cell responses and acts via a novel thiol dependent mechanism and inhibition of NF-κB pathway. - Highlights:: • Withaferin A (WA) inhibited T-cell and B-cell mediated immune responses. • WA increased basal ROS levels in lymphocytes. • WA directly interacted with GSH as studied using spectrophotometry and HPLC. • WA inhibited NF-κB nuclear translocation and binding of nuclear NF-κB to DNA. • WA inhibited induction of the graft-versus-host disease in mice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Adaptation of HepG2 cells to a steady-state reduction in the content of protein phosphatase 6 (PP6) catalytic subunit

Protein phosphatase 6 (PP6) is a ubiquitous Ser/Thr phosphatase involved in an array of cellular processes. To assess the potential of PP6 as a therapeutic target in liver disorders, we attenuated expression of the PP6 catalytic subunit in HepG2 cells using lentiviral-transduced shRNA. Two PP6 knock-down (PP6KD) cell lines (90% reduction of PP6-C protein content) were studied in depth. Both proliferated at a rate similar to control cells. However, flow cytometry indicated G2/M cell cycle arrest that was accounted for by a shift of the cells from a diploid to tetraploid state. PP6KD cells did not show an increase in apoptosis, nor did they exhibit reduced viability in the presence of bleomycin or taxol. Gene expression analysis by microarray showed attenuated anti-inflammatory signaling. Genes associated with DNA replication were downregulated. Mass spectrometry-based phosphoproteomic analysis yielded 80 phosphopeptides representing 56 proteins that were significantly affected by a stable reduction in PP6-C. Proteins involved in DNA replication, DNA damage repair and pre-mRNA splicing were overrepresented among these. PP6KD cells showed intact mTOR signaling. Our studies demonstrated involvement of PP6 in a diverse set of biological pathways and an adaptive response that may limit the effectiveness of targeting PP6 in liver disorders. - Highlights: • Lentiviral-transduced shRNA was used to generate a stable knockdown of PP6 in HepG2 cells. • Cells adapted to reduced PP6; cell proliferation was unaffected, and cell survival was normal. • However, PP6 knockdown was associated with a transition to a tetraploid state. • Genomic profiling showed downregulated anti-inflammatory signaling and DNA replication. • Phosphoproteomic profiling showed changes in proteins associated with DNA replication and repair.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Nrf2 mediates redox adaptation in NOX4-overexpressed non-small cell lung cancer cells

The redox adaptation mechanisms in cancer cells are very complex and remain largely unclear. Our previous studies have confirmed that NADPH oxidase 4 (NOX4) is abundantly expressed in non-small cell lung cancer (NSCLC) and confers apoptosis resistance on NSCLC cells. However, the comprehensive mechanisms for NOX4-mediated oxidative resistance of cancer cells remain still undentified. The present study found that NOX4-derived H{sub 2}O{sub 2} enhanced the nuclear factor erythroid 2-related factor 2 (Nrf2) stability via disruption of redox-dependent proteasomal degradation and stimulated its activity through activation of PI3K signaling. Specifically, the results showed that ectopic NOX4 expression did not induce apoptosis of A549 cells; however, inhibition of Nrf2 resulted in obvious apoptotic death of NOX4-overexpressed A549 cells, accompanied by a significant increase in H{sub 2}O{sub 2} level and decrease in GSH content. Besides, inhibition of Nrf2 could suppress cell growth and efficiently reverse the enhancement effect of NOX4 on cell growth. The in vivo data confirmed that inhibition of Nrf2 could interfere apoptosis resistance in NOX4-overexpressed A549 tumors and led to cell growth inhibition. In conclusion, these results reveal that Nrf2 is critically involved in redox adaptation regulation in NOX4-overexpressed NSCLC cells. Therefore, NOX4 and Nrf2 may be promising combination targets against malignant progression of NSCLC. - Highlights: • NOX4-derived H{sub 2}O{sub 2} upregulates Nrf2 expression and activity in NSCLC. • Nrf2 confers apoptosis resistance in NOX4-overexpressed NSCLC cells. • Inhibition of Nrf2 reverses the enhancement effect of NOX4 on cell growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Tight junction-associated protein GEF-H1 in the neighbours of dividing epithelial cells is essential for adaptation of cell-cell membrane during cytokinesis

Highlights: • GEF-H1 regulates cell-cell contacts deformation during epithelial cell cytokinesis. • GEF-H1 depletion in neighbouring cells induces lateral contacts deformation. • The exocyst complex is involved in the cell-cell contacts geometry regulation. Animal cells divide by a process called cytokinesis which relies on the constriction of a contractile actomyosin ring leading to the production of two daughter cells. Cytokinesis is an intrinsic property of cells which occurs even for artificially isolated cells. During division, isolated cells undergo dramatic changes in shape such as rounding and membrane deformation as the division furrow ingresses. However, cells are often embedded in tissues and thus are surrounded by neighbouring cells. How these neighbours might influence, or might themselves be influenced by, the shape changes of cytokinesis is poorly understood in vertebrates. Here, we show that during cytokinesis of epithelial cells in the Xenopus embryo, lateral cell-cell contacts remain almost perpendicular to the epithelial plane. Depletion of the tight junction-associated protein GEF-H1 leads to a transient and stereotyped deformation of cell-cell contacts. Although, this deformation occurs only during cytokinesis, we show that it originates from immediate neighbours of the dividing cell. Moreover, we show that exocyst and recycling endosome regulation by GEF-H1 are involved in adaptation of cell-cell contacts to deformation. Our results highlight the crucial role of tight junctions and GEF-H1 in cell-cell contact adaptation when cells are exposed to a mechanical stress such as cytokinesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
DOE OSTI2021

Heterogeneous Composition of Oxygen-Evolving Complexes in Crystal Structures of Dark-Adapted Photosystem II

Photosystem II (PSII) is a homodimeric protein complex that catalyzes water oxidation at the oxygen-evolving complex (OEC), a heterocubanoid calcium-tetra-manganese cluster. Here, we analyze the omit electron-density peaks of the OEC’s metal ions in five X-ray free-electron laser PSII structures at a resolution between 2.15 Å and 1.95 Å. The omit peaks can be described by the total number of electrons and approximated by the variance of electron density distribution when the distributions are spherically symmetric. We show that the number of electrons of metal centers are different in the two OECs of PSII dimers, implying that either the oxidation states and/or occupancies of individual metal ions are different in the two monomers. In either case, we find that the two OECs of dark-adapted PSII dimers in crystals are not fully synchronized in the same expected S 1 state. Differences in redox states of the OEC in PSII only partially account for the observation that the electron densities integrate to a smaller number of electrons than expected. Differences between the determined and expected relative electron numbers are much larger than the estimated errors, indicating heterogeneity in the OEC composition. Here, the reported findings suggest that two OECs of dark-adapted PSII crystals are typically not fully synchronized in the S 1 state and likely generate heterogeneity in the crystallographical data of PSII intermediate structures.

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

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

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

Adaptive Variational Quantum Computing Approaches for Green’s Functions and Nonlinear Susceptibilities

Here, we present and benchmark quantum computing approaches for calculating real-time single-particle Green’s functions and nonlinear susceptibilities of Hamiltonian systems. The approaches leverage adaptive variational quantum algorithms for state preparation and propagation. Using automatically generated compact circuits, the dynamical evolution is performed over sufficiently long times to achieve adequate frequency resolution of the response functions. We showcase accurate Green’s function calculations using a statevector simulator on classical hardware for Fermi-Hubbard chains of 4 and 6 sites, with maximal ansatz circuit depths of 65 and 424 layers, respectively, and for the molecule LiH with a maximal ansatz circuit depth of 81 layers. Additionally, we consider an antiferromagnetic quantum spin-1 model that incorporates the Dzyaloshinskii-Moriya interaction to illustrate calculations of the third-order nonlinear susceptibilities, which can be measured in two-dimensional coherent spectroscopy experiments. These results demonstrate that real-time approaches using adaptive parametrized circuits to evaluate linear and nonlinear response functions can be feasible with near-term quantum processors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Efficient Use of an Adapting Database of Ab Initio Calculations To Generate Accurate Newtonian Dynamics

We develop and demonstrate a method to efficiently use density functional calculations to drive classical dynamics of complex atomic and molecular systems. The method has the potential to scale to systems and time scales unreachable with current ab initio molecular dynamics schemes. It relies on an adapting dataset of independently computed Hellmann–Feynman forces for atomic configurations endowed with a distance metric. The metric on configurations enables fast database lookup and robust interpolation of the stored forces. Here, we discuss mechanisms for the database to adapt to the needs of the evolving dynamics, while maintaining accuracy, and other extensions of the basic algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Designing Stress-Adaptive Dense Suspensions Using Dynamic Covalent Chemistry

The non-Newtonian behaviors of dense suspensions are central to their use in technological and industrial applications and arise from a network of particle–particle contacts that dynamically adapt to imposed shear. Reported herein are studies aimed at exploring how dynamic covalent chemistry between particles and the polymeric solvent can be used to tailor such stress-adaptive contact networks, leading to their unusual rheological behaviors. Specifically, a room temperature dynamic thia-Michael bond is employed to rationally tune the equilibrium constant (K eq ) of the polymeric solvent to the particle interface. It is demonstrated that low Keq leads to shear thinning, while high K eq produces antithixotropy, a rare phenomenon where the viscosity increases with shearing time. It is proposed that an increase in K eq increases the polymer graft density at the particle surface and that antithixotropy primarily arises from partial debonding of the polymeric graft/solvent from the particle surface and the formation of polymer bridges between particles. Thus, the implementation of dynamic covalent chemistry provides a new molecular handle with which to tailor the macroscopic rheology of suspensions by introducing programmable time dependence. These studies open the door to energy-absorbing materials that not only sense mechanical inputs and adjust their dissipation as a function of time or shear rate but also can switch between these two modalities on demand.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Global isotope metabolomics reveals adaptive strategies for nitrogen assimilation

Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using 15 NO 3 as a nitrogen source. In contrast to a control ( Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee ( Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. In conclusion, the use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea

Seagrasses colonized the sea on at least three independent occasions to form the basis of one of the most productive and widespread coastal ecosystems on the planet. Here we report the genome of Zostera marina (L.), the first, to our knowledge, marine angiosperm to be fully sequenced. This reveals unique insights into the genomic losses and gains involved in achieving the structural and physiological adaptations required for its marine lifestyle, arguably the most severe habitat shift ever accomplished by flowering plants. Key angiosperm innovations that were lost include the entire repertoire of stomatal genes, genes involved in the synthesis of terpenoids and ethylene signalling, and genes for ultraviolet protection and phytochromes for far-red sensing. Seagrasses have also regained functions enabling them to adjust to full salinity. Their cell walls contain all of the polysaccharides typical of land plants, but also contain polyanionic, low-methylated pectins and sulfated galactans, a feature shared with the cell walls of all macroalgae and that is important for ion homoeostasis, nutrient uptake and O 2 /CO 2 exchange through leaf epidermal cells. The Z. marina genome resource will markedly advance a wide range of functional ecological studies from adaptation of marine ecosystems under climate warming, to unravelling the mechanisms of osmoregulation under high salinities that may further inform our understanding of the evolution of salt tolerance in crop plants.

59 BASIC BIOLOGICAL SCIENCES↗