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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Augmentation of endogenous GABA pool size induced by Magainin II peptide

Highlights: • Magainin II peptide enhances GABA content by affecting the activity of GABA shunt enzymes both in vitro and in vivo. • The peptide augments the activity of GABA biosynthetic enzyme, while quenching the activities of two catabolizing enzymes. • Magainin II appears to interact directly with the enzymes based on the in vitro model. Gamma aminobutyric acid (GABA), an inhibitory neurotransmitter, is produced via decarboxylation of l-glutamate through the glutamic acid decarboxylase (GAD) enzyme. The synchronic action of GABA-transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSADH) enzymes convert the GABA metabolite into succinate. Given this background, our research was aimed at probing the effect of Magainin II, on the activity of GABA shunt metabolizing enzymes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Heat transfer augmentation of natural convection heat sink through notched fin design

A novel notched fin heat sink with a central opening is proposed in the present study to augment the heat transfer performance of the natural convection heat sink under horizontal configuration. Thermal and airflow behavior in the heat sinks were studied using both experimental and numerical methods. The results showed that the performance of the conventional heat sink can be enhanced by optimizing its fin design. The basic heat sink model provided a 17% reduction in thermal resistance when its fin height and fin spacing were doubled, and it was accompanied by a 21% increase in surface area. The heat sink with a notched fin design with a less surface area offered a 13% lower thermal resistance compared to the basic model by entraining more airflow into the interior fin region. Providing a central opening in the notched fin heat sink can further reduce the thermal resistance by about 13%. Further, this study also analyzed the influence of emissivity of the heat sink on natural convection conditions. The findings demonstrated that the heat sink with an emissivity of 0.9 can reduce the thermal resistance by nearly 23% compared to one with an emissivity of 0.15.

42 ENGINEERING↗
DOE OSTI2017

The Curvature-Augmented Closest Point method with vesicle inextensibility application

Here, the Closest Point method, initially developed by Ruuth and Merriman, allows for the numerical solution of surface partial differential equations without the need for a parameterization of the surface itself. Surface quantities are embedded into the surrounding domain by assigning each value at a given spatial location to the corresponding value at the closest point on the surface. This embedding allows for surface derivatives to be replaced by their Cartesian counterparts (e.g. ∇ s =∇). This equivalence is only valid on the surface, and thus, interpolation is used to enforce what is known as the side condition away from the surface. To improve upon the method, this work derives an operator embedding that incorporates curvature information, making it valid in a neighborhood of the surface. With this, direct enforcement of the side condition is no longer needed. Comparisons in R 2 and R 3 show that the resulting Curvature-Augmented Closest Point method has better accuracy and requires less memory, through increased matrix sparsity, than the Closest Point method, while maintaining similar matrix condition numbers. To demonstrate the utility of the method in a physical application, simulations of inextensible, bi-lipid vesicles evolving toward equilibrium shapes are also included.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

A robust framework for frictional fault contact in geological formations using a stabilized augmented Lagrangian approach

Numerical simulations are essential to evaluate the performance and safety of engineered subsurface systems such as geological carbon storage sites, enhanced geothermal fields, and oil and gas reservoirs. A key challenge lies in accurately modeling the frictional contact behavior along fault surfaces. This problem involves inequality constraints that arise from the physics of frictional slip, requiring specialized numerical methods to handle the resulting highly nonlinear and path-dependent behavior. Here, in this work, we address this challenge using an Augmented Lagrangian Method (ALM) implemented via the Uzawa algorithm. The formulation employs mixed finite element spaces, combining low-order piecewise linear displacements within the 3D domain cells with piecewise constant tractions defined on the fault surfaces. Furthermore, to ensure stability and satisfy the inf-sup condition, the discrete displacement space is enriched with face bubble functions on both sides of the contact interfaces. This approach offers several advantages over other stabilization techniques that rely on additional terms, and it integrates naturally in the Uzawa framework.

58 GEOSCIENCES↗
DOE OSTI2023

Toward prediction of turbulent atmospheric flows over propagating oceanic waves via machine-learning augmented large-eddy simulation

Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Further, our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments.

42 ENGINEERING↗
DOE OSTI2025

ESAC (EQ-SANS Assisting Chatbot): Application of large language models and retrieval-augmented generation for enhanced user experience at EQ-SANS

Neutron scattering experiments have played vital roles in exploring materials properties in the past decades. While user interfaces have been improved over time, neutron scattering experiments still require specific knowledge or training by an expert due to the complexity of such advanced instrumentation and the limited number of experiments each person may perform each year. This paper introduces an innovative chatbot application that leverages Large Language Models(LLM) and Retrieval-Augmented Generation (RAG) technologies to significantly enhance the user experience at the EQ-SANS, a small-angle neutron scattering instrument at the Spallation Neutron Source of Oak Ridge National Laboratory. Through a user-centric design approach, the EQ-SANS Assisting Chatbot (ESAC) serves as an interactive reference for users, thereby facilitating the use of the instrument by visiting scientists. By bridging the gap between the users of EQ-SANS and the control systems required to perform their experiments, the ESAC sets a new standard for interactive learning and support for the scientific community using large-scale scientific facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Fetal and neonatal exposure to nicotine leads to augmented hepatic and circulating triglycerides in adult male offspring due to increased expression of fatty acid synthase

While nicotine replacement therapy is assumed to be a safer alternative to smoking during pregnancy, the long-term consequences for the offspring remain elusive. Animal studies now suggest that maternal nicotine exposure during perinatal life leads to a wide range of adverse outcomes for the offspring including increased adiposity. The focus of this study was to investigate if nicotine exposure during pregnancy and lactation leads to alterations in hepatic triglyceride synthesis. Female Wistar rats were randomly assigned to receive daily subcutaneous injections of saline (vehicle) or nicotine bitartrate (1 mg/kg/day) for two weeks prior to mating until weaning. At postnatal day 180 (PND 180), nicotine exposed offspring exhibited significantly elevated levels of circulating and hepatic triglycerides in the male offspring. This was concomitant with increased expression of fatty acid synthase (FAS), the critical hepatic enzyme in de novo triglyceride synthesis. Given that FAS is regulated by the nuclear receptor Liver X receptor (LXRα), we measured LXRα expression in both control and nicotine-exposed offspring. Nicotine exposure during pregnancy and lactation led to an increase in hepatic LXRα protein expression and enriched binding to the putative LXRE element on the FAS promoter in PND 180 male offspring. This was also associated with significantly enhanced acetylation of histone H3 [K9,14] surrounding the FAS promoter, a hallmark of chromatin activation. Collectively, these findings suggest that nicotine exposure during pregnancy and lactation leads to an increase in circulating and hepatic triglycerides long-term via changes in the transcriptional and epigenetic regulation of the hepatic lipogenic pathway. - Highlights: • Our data reveals the links nicotine exposure in utero and long-term hypertriglyceridemia. • It is due to nicotine-induced augmented expression of hepatic FAS and LXRα activity. • Moreover, this involves nicotine-induced enhanced acetylation of histone H3 [K9,14]. • This provides a mechanism for developmental origins of health and disease (DOHaD)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Estrogen and pure antiestrogen fulvestrant (ICI 182 780) augment cell–matrigel adhesion of MCF-7 breast cancer cells through a novel G protein coupled estrogen receptor (GPR30)-to-calpain signaling axis

Fulvestrant (ICI 182 780, ICI) has been used in treating patients with hormone-sensitive breast cancer, yet initial or acquired resistance to endocrine therapies frequently arises and, in particular, cancer recurs as metastasis. We demonstrate here that both 17-beta-estradiol (E2) and ICI enhance cell adhesion to matrigel in MCF-7 breast cancer cells, with increased autolysis of calpain 1 (large subunit) and proteolysis of focal adhesion kinase (FAK), indicating calpain activation. Additionally, either E2 or ICI induced down-regulation of estrogen receptor α without affecting G protein coupled estrogen receptor 30 (GPR30) expression. Interestingly, GPR30 agonist G1 triggered calpain 1 autolysis but not calpain 2, whereas ER agonist diethylstilbestrol caused no apparent calpain autolysis. Furthermore, the actions of E2 and ICI on calpain and cell adhesion were tremendously suppressed by G15, or knockdown of GPR30. E2 and ICI also induced phosphorylation of extracellular regulated protein kinases 1 and 2 (ERK1/2), and suppression of ERK1/2 phosphorylation by U0126 profoundly impeded calpain activation triggered by estrogenic and antiestrogenic stimulations indicating implication of ERK1/2 in the GPR30-mediated action. Lastly, the E2- or ICI-induced cell adhesion was dramatically impaired by calpain-specific inhibitors, ALLN or calpeptin, suggesting requirement of calpain in the GPR30-associated action. These data show that enhanced cell adhesion by E2 and ICI occurs via a novel GPR30-ERK1/2-calpain pathway. Our results indicate that targeting the GPR30 signaling may be a potential strategy to reduce metastasis and improve the efficacy of antiestrogens in treatment of advanced breast cancer. - Highlights: • Estrogen and ICI augment adhesion to matrigel with calpain activation in MCF-7 cells. • GPR30 mediates cell–matrigel adhesion and calpain activation via ERK1/2. • Calpain is required in the cell–matrigel adhesion induced by E2 and ICI.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Dietary n-3 PUFAs augment caspase 8 activation in Staphylococcal aureus enterotoxin B stimulated T-cells

Epidemiological studies have linked consumption of n-3 PUFAs with a variety of beneficial health benefits, particularly with respect to putative anti-inflammatory effects. Unfortunately, many of these results remain somewhat controversial because in most instances there has not been a linkage to specific molecular mechanisms. For instance, dietary exposure to low levels of mercury has been shown to be damaging to neural development, but concomitant ingestion of n-3 PUFAs as occurs during consumption of fish, has been shown to counteract the detrimental effects. As the mechanisms mediating the neurotoxicity of environmental mercury are not fully delineated, it is difficult to conceptualize a testable molecular mechanism explaining how n-3 PUFAs negate its neurotoxic effects. However, environmental exposure to mercury also has been linked to increased autoimmunity. By way of a molecular understanding of this immuno-toxic association, disruption of CD95 signaling is well established as a triggering factor for autoimmunity, and we have previously shown that environmentally relevant in vitro and dietary exposures to mercury interfere with CD95 signaling. In particular we have shown that activation of caspase 8, as well as downstream activation of caspase 3, in response to CD95 agonist stimulation is depressed by mercury. More recently we have shown in vitro that the n-3 PUFA docosahexaenoic acid counteracts the negative effect of mercury on CD95 signaling by restoring caspase activity. We hypothesized that concomitant ingestion of n-3 PUFAs with mercury might be protective from the immuno-toxic effects of mercury, as it is with mercury's neuro-toxic effects, and in the case of immuno-toxicity this would be related to restoration of CD95 signal strength. We now show that dietary ingestion of n-3 PUFAs generally promotes CD95 signaling by upregulating caspase 8 activation. Apart from accounting for the ability of n-3 PUFAs to specifically counteract autoimmune sequelae of mercury exposure, this novel finding for the first time suggests a testable molecular mechanism explaining the overall anti-inflammatory properties of n-3 PUFAs. - Highlights: • Dietary n-3 PUFAs counter Hg{sup 2+} immunotoxicity • Hg{sup 2+} interference with SEB-mediated signal transduction is ameliorated by n-3 PUFA rich diets. • Dietary n-3 PUFAs augment SEB-mediated activation of caspase 8 in vivo.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Augmenter of liver regeneration inhibits TGF-β1-induced renal tubular epithelial-to-mesenchymal transition via suppressing TβR II expression in vitro

Tubular epithelial-to-mesenchymal transition (EMT) plays a crucial role in the progression of renal tubular interstitial fibrosis (TIF), which subsequently leads to chronic kidney disease (CKD) and eventually, end-stage renal disease (ESRD). We propose that augmenter of liver regeneration (ALR), a member of the newly discovered ALR/Erv1 protein family shown to ameliorate hepatic fibrosis, plays a similar protective role in renal tubular cells and has potential as a new treatment option for CKD. Here, we showed that recombinant human ALR (rhALR) inhibits EMT in renal tubular cells by antagonizing activation of the transforming growth factor-β1 (TGF-β1) signaling pathway. Further investigation revealed that rhALR suppresses the expression of TGF-β receptor type II (TβR II) and significantly alleviates TGF-β1-induced phosphorylation of Smad2 and nuclear factor-κB (NF-κB). No apparent adverse effects were observed upon the addition of rhALR alone to cells. These findings collectively suggest that ALR plays a role in inhibiting progression of renal tubular EMT, supporting its potential utility as an effective antifibrotic strategy to reverse TIF in CKD. - Highlights: • ALR is involved in the pathological progression of renal EMT in NRK-52E cells. • ALR suppresses the expression of TβRII and the phosphorylation of Smad2 and NF-κB. • ALR plays a role in inhibiting progression of renal tubular EMT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Alleviation of CCCP-induced mitochondrial injury by augmenter of liver regeneration via the PINK1/Parkin pathway-dependent mitophagy

The occurrence of liver diseases is attributed to mitochondrial damage. Mitophagy selectively removes dysfunctional mitochondria, thereby preserving mitochondrial function. Augmenter of liver regeneration (ALR) protects the mitochondria from injury. However, whether ALR protection is associated with mitophagy remains unclear. In this study, mitochondrial damage was induced by carbonyl cyanide 3-chlorophenylhydrazone (CCCP), and long-form ALR (lfRNA)-mediated protection against this damage was investigated. Treatment of HepG2 cells with CCCP elevated the level of intracellular ROS, inhibited ATP production, and increased the mitochondrial membrane potential and cell apoptotic rate. However, in lfALR-transfected cells, CCCP-induced cell injury was clearly alleviated, the apoptosis and ROS levels clearly declined, and the ATP production was significantly enhanced as compared with that in vector-Tx cells. Furthermore, lfALR overexpression promoted autophagy and mitophagy via a PINK1/Parkin-dependent pathway, whereas knockdown of ALR suppressed mitophagy. In lfALR-transfected cells, the phosphorylation of AKT was decreased, thus, downregulating the phosphorylation of the transcription factor FOXO3a at Ser315. In contrast, the phosphorylation of AMPK was enhanced, thereby upregulating the phosphorylation of FOXO3a at Ser413. Consequently, FOXO3a′s nuclear translocation and binding to the promoter region of PINK1 was enhanced, and the accumulation of PINK1/Parkin in mitochondria increased. Meanwhile, short-form ALR (sfALR) also increased PINK1 expression through FOXO3a with the similar pathway to lfALR. In conclusion, our data suggest a novel mechanism through which both lfALR and sfALR protect mitochondria by promoting PINK1/Parkin-dependent mitophagy through FOXO3a activation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Data augmentation for disruption prediction via robust surrogate models

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student t process regression. We apply Student t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Tandem mass spectrometry (MS/MS) is a primary tool for identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra datasets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (~10% more spectra correctly identified) on large datasets (2,000 - 10,000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50 - 90 eV), and with smaller datasets available through MassBank.

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 OSTI2019

Formulation of Multicomponent Lattice Gas Model Cluster Expansions Parameterized on Ab Initio Data: An Introduction to the Ab Initio Mean-Field Augmented Lattice Gas Modeling Code

A formalism is presented for the construction of multicomponent lattice gas models parameterized with ab initio (typically density functional theory) data. The leave-multiple-out and the leave-one-out cross validation score are showcased, and practical algorithms are developed and implemented in a new code called the Ab initio Mean-field Augmented Lattice Gas Modeling (AMALGM) code. Here, the functionality of these algorithms is demonstrated with a fully worked out example using the O/Fe(100) system. AMALGM and the formalism on which it is based are envisioned as a surface-oriented lattice gas alternative to other cluster expansion codes that are typically geared toward bulk systems whose lateral interactions between components are parameterized using an Ising model. While the formalism is created within the context of surfaces, it is equally applicable to bulk materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Augmenting drug–carrier compatibility improves tumour nanotherapy efficacy

A major goal of cancer nanotherapy is to use nanoparticles as carriers for targeted delivery of anti-tumour agents. The drug–carrier association after intravenous administration is essential for efficient drug delivery to the tumour. However, a large number of currently available nanocarriers are self-assembled nanoparticles whose drug-loading stability is critically affected by the in vivo environment. Here we used in vivo FRET imaging to systematically investigate how drug–carrier compatibility affects drug release in a tumour mouse model. We found the drug’s hydrophobicity and miscibility with the nanoparticles are two independent key parameters that determine its accumulation in the tumour. Next, we applied these findings to improve chemotherapeutic delivery by augmenting the parent drug’s compatibility; as a result, we achieved better antitumour efficacy. Lastly, our results help elucidate nanomedicines’ in vivo fate and provide guidelines for efficient drug delivery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Augmenting the Calvin–Benson–Bassham cycle by a synthetic malyl-CoA-glycerate carbon fixation pathway

The Calvin–Benson–Bassham (CBB) cycle is presumably evolved for optimal synthesis of C3 sugars, but not for the production of C2 metabolite acetyl-CoA. The carbon loss in producing acetyl-CoA from decarboxylation of C3 sugar limits the maximum carbon yield of photosynthesis. Here we design a synthetic malyl-CoA-glycerate (MCG) pathway to augment the CBB cycle for efficient acetyl-CoA synthesis. This pathway converts a C3 metabolite to two acetyl-CoA by fixation of one additional CO2 equivalent, or assimilates glyoxylate, a photorespiration intermediate, to produce acetyl-CoA without net carbon loss. We first functionally demonstrate the design of the MCG pathway in vitro and in Escherichia coli. We then implement the pathway in a photosynthetic organism Synechococcus elongates PCC7942, and show that it increases the intracellular acetyl-CoA pool and enhances bicarbonate assimilation by roughly 2-fold. This work provides a strategy to improve carbon fixation efficiency in photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗