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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A synthetic promoter system for well-controlled protein expression with different carbon sources in Saccharomyces cerevisiae

Saccharomyces cerevisiae is an important synthetic biology chassis for microbial production of valuable molecules. Promoter engineering has been frequently applied to generate more synthetic promoters with a variety of defined characteristics in order to achieve a well-regulated genetic network for high production efficiency. Galactose-inducible (GAL) expression systems, composed of GAL promoters and multiple GAL regulators, have been widely used for protein overexpression and pathway construction in S. cerevisiae. However, the function of each element in synthetic promoters and how they interact with GAL regulators are not well known. Here, a library of synthetic GAL promoters demonstrate that upstream activating sequences (UASs) and core promoters have a synergistic relationship that determines the performance of each promoter under different carbon sources. We found that the strengths of synthetic GAL promoters could be fine-tuned by manipulating the sequence, number, and substitution of UASs. Core promoter replacement generated synthetic promoters with a twofold strength improvement compared with the GAL1 promoter under multiple different carbon sources in a strain with GAL1 and GAL80 engineering. These results represent an expansion of the classic GAL expression system with an increased dynamic range and a good tolerance of different carbon sources. In this study, the effect of each element on synthetic GAL promoters has been evaluated and a series of well-controlled synthetic promoters are constructed. By studying the interaction of synthetic promoters and GAL regulators, synthetic promoters with an increased dynamic range under different carbon sources are created.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool-a tetranucleotide-informed metagenome stability diagram-that is publicly available in the integrated microbial genomes and microbiomes (IMG/M) platform for metagenome ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. IMPORTANCE: Microbes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

Kellom, Matthew↗
DOE OSTI2023

Characterizing and Engineering Trehalose Biosynthesis Pathways to Improve Acetate Tolerance in Pseudomonas putida KT2440

Alkaline pretreatment can depolymerize lignin into a diverse mixture of monomers and smaller compounds, enabling bioconversion of these compounds to value-added chemicals in engineered bacterial hosts such as Pseudomonas putida KT2440. Acetate and salts comprise approximately 8% and 32% of the lignin stream, respectively. High concentrations of these components can cause cell death in P. putida, so lignin-derived streams must be fed in lower amounts to decrease stress. Investigation of the mechanisms of bacterial tolerance to chemicals in lignin-rich substrates is therefore required to overcome this obstacle in lignin bioconversion. Previous work has shown that trehalose, an endogenously produced disaccharide, is involved in tolerance to heat, cold, and osmotic stress, as well as implicated in acid stress in other bacteria. As such, trehalose biosynthesis presents a useful target to increase acetate and osmotic tolerance in P. putida. This work investigates the native TreSA, TreSB, and TreY/TreZ trehalose biosynthesis pathways in this bacterial strain as well as the OtsAB pathway of Sphingobium sp. SYK-6. Overexpression of the OtsAB pathway in P. putida increased tolerance to acetate relative to wild-type. Additionally, functional knockouts of treSA, treSB, treY, and treZ in P. putida showed that interruption of any trehalose biosynthesis pathway, and, in the case of TreY/TreZ, interruption of the pathway at any point, significantly hindered growth in acetate as well as on glucose and LB broth. Each trehalose biosynthesis pathway was then engineered for overexpression in P. putida to determine whether overexpression of these genes conferred additional tolerance to acetate. The TreSA, TreSB, OtsA, and OtsB enzymes were also expressed and purified to determine the reaction rates and kinetics of these pathways. The hypothetical glycoside hydrolase family 15 protein, encoded in the gene located in between otsB and otsA in the Sphingobium operon, was purified as well to investigate its potential involvement in the OtsAB pathway. The findings of this work illuminated the mechanisms of trehalose biosynthesis in P. putida and identified genetic targets to improve strain tolerance to common components of alkaline-pretreated lignin streams. These findings could ultimately improve yields of desired products from lignin in engineered strains of P. putida.

alkaline pretreatment↗
DOE OSTI2022

A sensitive and specific genetically-encoded potassium ion biosensor for in vivo applications across the tree of life

Potassium ion (K + ) plays a critical role as an essential electrolyte in all biological systems. Genetically-encoded fluorescent K + biosensors are promising tools to further improve our understanding of K + -dependent processes under normal and pathological conditions. Here, we report the crystal structure of a previously reported genetically-encoded fluorescent K + biosensor, GINKO1, in the K + -bound state. Using structure-guided optimization and directed evolution, we have engineered an improved K + biosensor, designated GINKO2, with higher sensitivity and specificity. We have demonstrated the utility of GINKO2 for in vivo detection and imaging of K + dynamics in multiple model organisms, including bacteria, plants, and mice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Harnessing evolution: leveraging bacterial isoprenoid pathway diversity toward improved bioengineering strategies

Isoprenoids play vital roles in all domains of life, from beta-carotene in bacteria to heme in humans. Two distinct metabolic pathways have evolved to synthesize the critical precursor of all mature isoprenoids: the mevalonate (MEV) and the methylerythritol phosphate (MEP) pathways. Here, we quantify the extensive inter- and intra-genus heterogeneity in the usage of these two pathways with particular emphasis on rare bacteria that encode both, or neither, pathways. Furthermore, MEP intermediates themselves have non-isoprenogenic roles that may underlie evolutionary pressures driving pathway diversification. Understanding isoprenoid biosynthesis in bacteria offers new avenues toward more sustainable engineering of economically relevant molecules in microbes.

Biotechnology and Synthetic Biology↗
NASA NTRSDate not supplied

Engineered Yeast to Test Risks for Human Exploration of the Lunar Surface

Jessica W. Chau, Natalie N. Ball, Aditya Hindupur, Sandra T. Vu, Jennifer Gil Acevedo, Lauren C. Liddell, Chinmayee Govinda Raj, Gentry, Sergio R. Santa Maria, A. Mark Settles Crewed exploration of the Moon carries risks of long duration exposure to reduced gravity and to deep space radiation. The Lunar Explorer Instrument for space biology Applications (LEIA) investigates the effects of increased radiation and reduced gravity on yeast viability and growth in a Commercial Lunar Payload Services (CLPS) surface mission to the south polar region. LEIA conducts yeast genetics experiments to quantify growth, metabolism, and synthetic biology-enabled production of human nutrients, while taking real time measurements of biologically relevant radiation exposure on the lunar surface. We have engineered beta-carotene producing yeast strains to test the importance of selected DNA damage repair and reactive oxygen species (ROS) defense pathways in mitigating cellular damage from lunar surface radiation. Carotenoids are important dietary antioxidants, and beta-carotene is pro-vitamin A, which is needed for vision and immune function. Carotenoids are sensitive to ROS produced by ionizing radiation and NASA is testing on-demand production of carotenoids from yeast in the BioNutrients space flight experiments. In LEIA, we test the effects of deep space on carotenoid yield in engineered yeast strains. The LEIA team uses CRISPR-Cas9 to engineer yeast to express carotenoids as well as to generate loss-of-function mutations. We are generating mutations in the RAD51 DNA damage repair locus and three genes that function to reduce oxidative damage to the cell: SOD1, SOD2, and TSA1. These strains are tested for carotenoid production using microfluidics and LED spectroscopy to allow remote sensing of cellular growth and carotenoid levels. Keywords: synthetic biology, oxidative stress tolerance, biosensors, space radiation, beyond low Earth orbit, lunar surface, CRISPR/Cas9, gene editing, desiccation, carotenoids.

synthetic biology↗
DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗
NASA NTRS1998

SoftLab: A Soft-Computing Software for Experimental Research with Commercialization Aspects

SoftLab is a software environment for research and development in intelligent modeling/control using soft-computing paradigms such as fuzzy logic, neural networks, genetic algorithms, and genetic programs. SoftLab addresses the inadequacies of the existing soft-computing software by supporting comprehensive multidisciplinary functionalities from management tools to engineering systems. Furthermore, the built-in features help the user process/analyze information more efficiently by a friendly yet powerful interface, and will allow the user to specify user-specific processing modules, hence adding to the standard configuration of the software environment.

Akbarzadeh-T, M.-R.↗
NASA NTRS2016

Innovation at the Edge of Space- An Engine for Global Opportunities

Microgravity based commercial opportunities are broad, with applications ranging from fiber optics, device-grade semiconductor crystals, space beads, new materials, cells microencapsulation, 3D tissues and cell cultures, genetic and molecular changes of immune suppression, protein and virus crystal growth, perfume and hair care. To date, primarily the knowledge gained from observing and understanding new end states of systems unraveled in microgravity has been translated into unique technologies and business opportunities on Earth. In some instances existing flight qualified hardware is immediately available for commercial RD for small-scale in-space manufacturing. Overall, products manufactured in microgravity have key properties usually surpassing the best terrestrial counterparts. Commercially, these products have an attractive touch of space which aids in marketing.The talk will addresses the potential benefits of microgravity research for a variety of terrestrial markets by (1) surveying the level of awareness and interest of parties from different commercial sectors (2) assessing the potential of microgravity to stimulate innovation in future product development and (3) formulating a synergistic strategy to use microgravity for economic growth and public benefit.Our findings originating primarily from discussions with 100+ non-aerospace private companies among the high-tech Silicon Valley ecosystem, show that the opportunities and benefits of using the ISS are largely not considered by experts, primarily due to a lack of awareness of the breadth of terrestrial applications that have been enabled or enhanced by microgravity RD. Based on this dialogue, the concept of microgravity verticals is developed to translate the benefits of the microgravity environment into blue ocean business opportunities for various key US commercial sectors.

orbital economy↗
DOE OSTI2026

Multimodal Approaches for Leveraging Domain Knowledge with State-of-the-Art Machine Learning to Engineer Biocatalysts

This grant aimed to accelerate the development of specialized enzymes—biological catalysts essential for sustainable manufacturing and medicine—by integrating traditional laboratory evolution with cutting-edge artificial intelligence. To achieve this, we developed a suite of high-throughput sequencing tools and a centralized database to bridge the gap between a protein’s genetic "code" and its physical function. By training machine learning models on large datasets, we also demonstrated the ability to move beyond slow, trial-and-error testing to a "generative" approach, where AI can independently design new, versatile enzymes like tryptophan synthases. Ultimately, these findings demonstrate that combining laboratory data with computer-guided design enables the engineering of highly efficient biological tools with unprecedented speed and precision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Bacteria-mediated dsRNA delivery for mosquito-borne virus control

Mosquito-borne viruses represent an increasing global public health threat, exacerbated by urbanisation and climate change, thus making effective mosquito control essential. RNA interference (RNAi), a sequence-specific gene regulation mechanism, can be a flexible vector control tool. RNAi effectors, such as double-stranded RNA (dsRNA), can target mosquito genes or the viruses they carry, disrupting development or suppressing infection. However, current RNAi delivery methods are ineffective. Engineered bacterial symbionts offer a promising alternative for delivery, as they can produce dsRNA directly within mosquitoes. However, bacterial RNAi delivery in mosquitoes remains underexplored. We review emerging genetic tools, insights from RNAi and bacteria–mosquito interactions to outline priorities for realising bacterial RNAi as an efficient and sustainable vector control strategy.

Biological and medical sciences↗
DOE OSTI2020

Phase-Only Beam Broadening of Contiguous Uniform Subarrayed Arrays

In modern antenna systems, beam broadening of subarrayed arrays provides continuous coverage of a wide angular extent in a cost-effective manner. While many methods have been published that address beam broadening of traditional (non-subarrayed) arrays, there is a knowledge gap in the published literature with respect to efficient beam broadening of contiguous uniform subarrayed arrays. This article presents efficient methods for beam broadening of contiguous uniform subarrayed arrays where the excitation of each element is not individually controlled, but the elements of the array are grouped together as subarrays to have the same element excitations. Particularly, this article focuses on phase-only optimization to preserve maximum power output. Additionally, three modilied iterative Fourier transform (IFT) methods and one genetic algorithm (GA) are presented to efliciently search the vast solution space of possible phase settings for a solution that satislies the desired broadened pattern. These methods are evaluated on idealized 1 x 40 and 1 x 80 linear arrays with live element subarrays and 40 x 40 and 80 x 80 element rectangular arrays with 5 x 5 element subarrays. The proposed modilied IFT methods are found to be faster than the GA approach, while the GA approach only offers a few percentage points of better effectiveness.

42 ENGINEERING↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
DOE OSTI2023

The engineered single guide RNA structure as a biomarker for gene-editing reagent exposure

Abstract CRISPR arrays and CRISPR-associated (Cas) proteins comprise a prevalent adaptive immune system in bacteria and archaea. These systems defend against exogenous parasitic mobile genetic elements. The adaption of single effector CRISPR-Cas systems has massively facilitated gene-editing due to the reprogrammable guide RNA. The guide RNA affords little priming space for conventional PCR-based nucleic acid tests without foreknowledge of the spacer sequence. Further impeding detection of gene-editor exposure, these systems are derived from human microflora and pathogens ( Staphylococcus pyogenes , Streptococcus aureus , etc.) that contaminate human patient samples. The single guide RNA—formed from the CRISPR RNA (crRNA) and transactivating RNA (tracrRNA)—harbors a variable tetraloop sequence between the two RNA segments, complicating PCR assays. Identical single effector Cas proteins are used for gene-editing and naturally by bacteria. Antibodies raised against these Cas proteins are unable to distinguish CRISPR-Cas gene-editors from bacterial contaminant. To overcome the high potential for false positives, we have developed a DNA displacement assay to specifically detect gene-editors. We leveraged the single guide RNA structure as an engineered moiety for gene-editor exposure that does not cross-react with bacterial CRISPRs. Our assay has been validated for five common CRISPR systems and functions in complex sample matrices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A landing pad system for multicopy gene integration in Issatchenkia orientalis

The robust nature of the non-conventional yeast Issatchenkia orientalis allows it to grow under highly acidic conditions and therefore, has gained increasing interest in producing organic acids using a variety of carbon sources. Recently, the development of a genetic toolbox for I. orientalis, including an episomal plasmid, characterization of multiple promoters and terminators, and CRISPR-Cas9 tools, has eased the metabolic engineering efforts in I. orientalis. However, multiplex engineering is still hampered by the lack of efficient multicopy integration tools. To facilitate the construction of large, complex metabolic pathways by multiplex CRISPR-Cas9-mediated genome editing, we developed a bioinformatics pipeline to identify and prioritize genome-wide intergenic loci and characterized 47 gRNAs located in 21 intergenic regions. These loci are screened for guide RNA cutting efficiency, integration efficiency of a gene cassette, the resulting cellular fitness, and GFP expression level. We further developed a landing pad system using components from these well-characterized loci, which can aid in the integration of multiple genes using single guide RNA and multiple repair templates of the user’s choice. We have demonstrated the use of the landing pad for simultaneous integrations of 2, 3, 4, or 5 genes to the target loci with efficiencies greater than 80%. As a proof of concept, we showed how the production of 5-aminolevulinic acid can be improved by integrating five copies of genes at multiple sites in one step. We have further demonstrated the efficiency of this tool by constructing a metabolic pathway for succinic acid production by integrating five gene expression cassettes using a single guide RNA along with five different repair templates, leading to the production of 9 g/L of succinic acid in batch fermentations. Furthermore, this study demonstrates the effectiveness of a single gRNA-mediated CRISPR platform to build complex metabolic pathways in a non-conventional yeast. This landing pad system will be a valuable tool for the metabolic engineering of I. orientalis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mediated Electrochemical Probing: A Systems-Level Tool for Redox Biology

Biology uses well-known redox mechanisms for energy harvesting (e.g., respiration), biosynthesis, and immune defense (e.g., oxidative burst), and now we know biology uses redox for systems-level communication. Currently, we have limited abilities to “eavesdrop” on this redox modality, which can be contrasted with our abilities to observe and actuate biology through its more familiar ionic electrical modality. In this Perspective, we argue that the coupling of electrochemistry with diffusible mediators (electron shuttles) provides a unique opportunity to access the redox communication modality through its electrical features. Here, we highlight previous studies showing that mediated electrochemical probing (MEP) can “communicate” with biology to acquire information and even to actuate specific biological responses (i.e., targeted gene expression). We suggest that MEP may reveal an extent of redox-based communication that has remained underappreciated in nature and that MEP could provide new technological approaches for redox biology, bioelectronics, clinical care, and environmental sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗