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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A novel synthetic-genetic-array–based yeast one-hybrid system for high discovery rate and short processing time

Eukaryotic gene expression is often tightly regulated by interactions between transcription factors (TFs) and their DNA cis targets. Yeast one-hybrid (Y1H) is one of the most extensively used methods to discover these interactions. We developed a high-throughput meiosis-directed yeast one-hybrid system using the Magic Markers of the synthetic genetic array analysis. The system has a transcription factor–DNA interaction discovery rate twice as high as the conventional diploid-mating approach and a processing time nearly one-tenth of the haploid-transformation method. The system also offers the highest accuracy in identifying TF–DNA interactions that can be authenticated in vivo by chromatin immunoprecipitation. With these unique features, this meiosis-directed Y1H system is particularly suited for constructing novel and comprehensive genome-scale gene regulatory networks for various organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Direct structural retrieval from gas-phase ultrafast diffraction data using a genetic algorithm

Ultrafast scattering techniques such as ultrafast electron diffraction and ultrafast x-ray diffraction have been utilized to elucidate the structural dynamics, reaction intermediates, and final products in molecular reactions following photoexcitation. The time-dependent structures are typically not directly retrieved from the experimental data, but they rely on comparison with calculations. The genetic algorithm (GA), a global optimization strategy, can be used to retrieve the molecular structures directly from diffraction patterns without any theoretical input. However, the robustness of the GA with respect to real experimental conditions such as a limited momentum transfer range, noise, and artifacts has not been studied in detail. In this work, we characterize the performance of the GA with simulated data that mimic realistic experimental conditions. We have developed and implemented a variant of the GA specific to diffraction measurements which performs better in the presence of imperfect data compared to the standard implementation of the GA. We demonstrate this method with both synthetic data and experimental ultrafast electron diffraction data on the UV-induced photodissociation of trifluoroiodomethane (C⁢F 3⁡ I) molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Experimental demonstration of coupled multi-peak Bragg coherent diffraction imaging with genetic algorithms

Bragg coherent diffraction imaging has the potential to provide significant insight into the structure-properties relationship for crystalline materials by imaging, with nanoscale resolution, three-dimensional strain fields within individual grains and nanoparticles. The capability of present-day synchrotrons to locate and measure a multiplicity of Bragg reflections from a single grain makes it possible to recover the full strain tensor with nanometer resolution. Recent methods for coupling reconstructions from several peaks to determine the strain tensor have been developed and applied to synthetic data, but have not been applied to experimental data. Here, using a coupled genetic reconstruction algorithm, we reconstruct an experimental data set and demonstrate improvements in the ability to resolve vector-valued displacement fields internal to the particle as compared to what is achieved with a noncoupled approach. The coupled approach developed in this work was also validated on simulated data sets. In both simulated and experimental data, reconstructions from our coupled Bragg peak algorithm show improvements over the noncoupled independent reconstruction method of 5% in terms of accuracy and 53% in terms of consistency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Genetic Algorithm-Based Commutation Angle Control for Torque Ripple Mitigation in Switched Reluctance Motor Drives

This work addresses the application of the Genetic Algorithm (GA) technique to optimize the commutation angles of a 2 kW 8/6 switched reluctance machine (SRM). The primary goal is to reduce the well-known drawback of SRMs: the torque ripple. Firstly, the machine was modeled in Matlab /Simulink ® using lookup tables obtained via finite element method (FEM) simulations. Subsequently, the model was used to perform the GA routine aiming to find the optimal phase commutation angles that minimize the torque ripple factor. Notably, the torque performance of the SRM was significantly affected by the commutation angles during the search for the optimal solution. Afterwards, the GA results for four different operation points were verified experimentally through a developed drive platform with digital signal processor-based (DSP) control and an asymmetric bridge converter. As showed by the experiments, the proposed approach was suitable to reduce the torque ripple by more than 50% for one of the evaluated operating points. Furthermore, it was confirmed that the torque ripple mitigation led to acoustic noise improvement.

42 ENGINEERING↗
DOE OSTI2021

Comprehensive Efficiency Analysis of Current Source Inverter Based on CSI-Type Double Pulse Test and Genetic Algorithm

A current-source inverter (CSI) has a natural output voltage boost feature that can be advantageous for traction applications. Switching frequency is one of the easily-controlled variables that can be adjusted to improve the CSI efficiency at different operating points when the boost function is used. This paper investigates a CSI-based double-pulse test (DPT) measurement that mimics normal operation of the CSI. A loss model of the CSI is developed based on the CSI-type DPT experimental results. The impacts of the switching frequency and voltage boost ratio on the CSI efficiency and output voltage ripple are investigated. Based on the loss model, a genetic algorithm has been introduced that makes it possible to optimize the CSI's switching frequency and modulation index to maximize its efficiency under any desired operating condition.

current-source inverter, genetic algorithm, SPM ma↗
DOE OSTI2023

Comprehensive Efficiency Analysis of Seven-Switch Current-Source Inverter Based on Voltage-Boost Function and Genetic Algorithm

This paper investigates the voltage-boost features of the emerging seven-switch current-source inverter (H7-CSI) to improve the efficiency of the CSI-based motor drive system. The switching transient and power loss model of the H7-CSI topology during voltage-boost operation are analyzed in detail. The effects of the boost function on the switching and conduction loss of the H7-CSI topology are investigated. A genetic algorithm has been introduced that makes it possible to optimize the H7-CSI’s modulation index to minimize its power loss. Experimental results for the H7-CSI unit show that this optimization can reduce the power loss by 10.6%.

42 ENGINEERING↗
DOE OSTI2011

A Moving Target Environment for Computer Configurations Using Genetic Algorithms

Moving Target (MT) environments for computer systems provide security through diversity by changing various system properties that are explicitly defined in the computer configuration. Temporal diversity can be achieved by making periodic configuration changes; however in an infrastructure of multiple similarly purposed computers diversity must also be spatial, ensuring multiple computers do not simultaneously share the same configuration and potential vulnerabilities. Given the number of possible changes and their potential interdependencies discovering computer configurations that are secure, functional, and diverse is challenging. This paper describes how a Genetic Algorithm (GA) can be employed to find temporally and spatially diverse secure computer configurations. In the proposed approach a computer configuration is modeled as a chromosome, where an individual configuration setting is a trait or allele. The GA operates by combining multiple chromosomes (configurations) which are tested for feasibility and ranked based on performance which will be measured as resistance to attack. The result of successive iterations of the GA are secure configurations that are diverse due to the crossover and mutation processes. Simulations results will demonstrate this approach can provide at MT environment for a large infrastructure of similarly purposed computers by discovering temporally and spatially diverse secure configurations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified a principal set of 20 time-invariant markers, including 16 single nucleotide polymorphisms and two HLA-DR genotypes, gestational age, and exposure to a prebiotic formula. Integration of the metabolome identified 22 high-priority metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies, dependent upon the time horizon. The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to 3 pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate pathway. TEDDY data suggest that these metabolic processes may play a role in triggering islet autoimmunity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Characterization of zinc oxide thin film using atomic force microscopy and optimized X-ray reflectivity by genetic algorithm

For the nanostructured thin films, the study of surface physical parameters is often of crucial importance. For example, the transport properties of ZnO thin film are strongly affected by the geometry of this film. In this study sol—gel ZnO thin film was spin coated on glass substrate. The inverse problem of thin film characterization is formulated as a parameter identification problem in which a set of parameters corresponding to the thin film property can be found by minimizing fitness functions formulated using theoretical and experimental X-ray reflectivity. Atomic force microscopy (AFM) measurements used as an independent procedure for extracting structural parameters of the film and accurate interpretation of X-ray reflectivity data. After applying Fractal calculations on the AFM measured data the Genetic algorithm (GA) method was applied for extracting other important structural parameters. Optimization of specular X-ray reflectivity by GA method gives information along the depth such as thickness, roughness, and electron density profile of layers in the film.

36 MATERIALS SCIENCE↗
DOE OSTI2015

The use of the taguchi method and a neural-genetic approach to optimize the quality of a pulsed Nd:YAG laser welding process

In the production process of lithium-ion secondary batteries, the lap-joint quality of the safety vent and the cathode lead influences the product quality and production efficiency. A pulsed Nd:YAG laser welding machine was employed herein. The welding parameters that influence the pulsed Nd:YAG laser welding quality was evaluated by measuring the tensile-shear strength. In this study, the Taguchi method was used to perform the initial optimization of the process parameters. A neural network (NN) with the Levenberg–Marquardt back-propagation algorithm was adopted to develop the nonlinear relationship between factors and the response. Then, a genetic algorithm based on a well-trained NN model was applied to determine the optimal factor settings. Experimental results illustrated the proposed approach.

42 ENGINEERING↗
DOE OSTI2023

Climate lags and genetics determine phenology in quaking aspen ( Populus tremuloides )

Spatiotemporal patterns of phenology may be affected by mosaics of environmental and genetic variation. Environmental drivers may have temporally lagged impacts, but patterns and mechanisms remain poorly known. Here, we combine multiple genomic, remotely sensed, and physically modeled datasets to determine the spatiotemporal patterns and drivers of canopy phenology in quaking aspen, a widespread clonal dioecious tree species with diploid and triploid cytotypes. We show that over 391 km 2 of southwestern Colorado: greenup date, greendown date, and growing season length vary by weeks and differ across sexes, cytotypes, and genotypes; phenology has high phenotypic plasticity and heritabilities of 31–61% (interquartile range); and snowmelt date, soil moisture, and air temperature predict phenology, at temporal lags of up to 3 yr. Our study shows that lagged environmental effects are needed to explain phenological variation and that the effect of cytotype on phenology is obscured by its correlation with topography. Phenological patterns are consistent with responses to multiyear accumulation of carbon deficit or hydraulic damage.

carbon allocation↗
DOE OSTI2016

In silico discovery of metal-organic frameworks for precombustion CO 2 capture using a genetic algorithm

Discovery of new adsorbent materials with a high CO 2 working capacity could help reduce CO 2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. Here in this paper we report the in silico discovery of high-performing adsorbents for precombustion CO 2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO 2 working capacity and a high CO 2 /H 2 selectivity. One of the synthesized MOFs shows a higher CO 2 working capacity than any MOF reported in the literature under the operating conditions investigated here.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic overlapping genes stabilize genetic systems

Overlapping genes—wherein two different proteins are translated from alternative reading frames of the same DNA sequence—provide a means to stabilize an engineered gene by directly linking its evolutionary fate with that of an overlapping gene. However, creating overlapping gene pairs is challenging, as it requires redesigning both protein products to accommodate overlap constraints. Here, we present a new “overlapping, alternate-frame insertion” (OAFI) method for creating synthetic overlapping genes by inserting an “inner” gene, encoded in an alternate frame, into a flexible region of an “outer” gene. Using OAFI, we create new overlapping gene pairs of genetic reporters and bacterial toxins within an antibiotic resistance gene. We show that both the inner and outer genes retain function despite redesign, with translation of the inner gene influenced by its overlap position in the outer gene. Importantly, we show that, despite these inner gene sequences not contributing to outer gene function, selection for the outer gene alters the permitted inactivating mutations in the inner gene, and that overlapping toxins can restrict horizontal gene transfer of the antibiotic resistance gene. Overall, OAFI offers a versatile tool for synthetic biology, expanding the applications of overlapping genes in gene stabilization and biocontainment.

Biological and medical sciences↗
DOE OSTI2023

Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm

To sustain the high-rate current required for fast charging electric vehicle batteries, electrodes must exhibit sufficiently high effective ionic diffusion. Additionally, to reduce battery manufacturing costs, wetting time must decrease. Both of these issues can be addressed by structuring the electrodes with mesoscale pore channels. However, their optimal spatial distribution, or patterns, is unknown. Herein, a genetic algorithm has been developed to identify these optimal patterns using a CPU-cheap proxy distance-based model to evaluate the impact of the added pore networks. Both coin-cell and pouch cell form factors have been considered for the wetting analysis, with their respective electrolyte infiltration mode. Regular hexagonal and mud-crack-like patterns, respectively, for fast charging and fast wetting were found to be optimal and have been compared with pre-determined, easier to manufacture, patterns. The model predicts that using cylindrical channels arranged in a regular hexagonal pattern is ∼6.25 times more efficient for fast charging as compared to grooved lines with both structuring strategies being restricted to a 5% electrode total volume loss. The model also shows that only a very limited electrode volume loss (1%–2%) is required to dramatically improve the wetting (5–20 times) compared to an unstructured electrode.

25 ENERGY STORAGE↗
DOE OSTI2012

Genetic diversity within the genus Francisella as revealed by comparative analyses of the genomes of two North American isolates from environmental sources

Background: Francisella tularensis is an intracellular pathogen that causes tularemia in humans and the public health importance of this bacterium has been well documented in recent history. Francisella philomiragia, a distant relative of F. tularensis, is thought to constitute an environmental lineage along with Francisella novicida. Nevertheless, both F. philomiragia and F. novicida have been associated with human disease, primarily in immune-compromised individuals. To understand the genetic relationships and evolutionary contexts among different lineages within the genus Francisella, the genome of Francisella spp. strain TX07-7308 was sequenced and compared to the genomes of F. philomiragia strains ATCC 25017 and 25015, F. novicida strain U112, and F. tularensis strain Schu S4. Results: The size of strain ATCC 25017 chromosome was 2,045,775 bp and contained 1,983 protein-coding genes. The size of strain TX07-7308 chromosome was 2,035,931 bp and contained 1,980 protein-coding genes. Pairwise BLAST comparisons indicated that strains TX07-7308 and ATCC 25017 contained 1,700 protein coding genes in common. NUCmer analyses revealed that the chromosomes of strains TX07-7308 and ATCC 25017 were mostly collinear except for a few gaps, translocations, and/or inversions. Using the genome sequence data and comparative analyses with other members of the genus Francisella (e.g., F. novicida strain U112 and F. tularensis strain Schu S4), several strain-specific genes were identified. Strains TX07-7308 and ATCC 25017 contained an operon with six open reading frames encoding proteins related to enzymes involved in thiamine biosynthesis that was absent in F. novicida strain U112 and F. tularensis strain Schu S4. Strain ATCC 25017 contained an operon putatively involved in lactose metabolism that was absent in strain TX07-7308, F. novicida strain U112, and F. tularensis strain Schu S4. In contrast, strain TX07-7308 contained an operon putatively involved in glucuronate metabolism that was absent in the genomes of strain ATCC 25017, F. novicida strain U112, and F. tularensis strain Schu S4. The polymorphic nature of polysaccharide biosynthesis/modification gene clusters among different Francisella strains was also evident from genome analyses. Conclusions: From genome comparisons, it appeared that genes encoding novel functions have contributed to the metabolic enrichment of the environmental lineages within the genus Francisella. The inability to acquire new genes coupled with the loss of ancestral traits and the consequent reductive evolution may be a cause for, as well as an effect of, niche selection of F. tularensis. Sequencing and comparison of the genomes of more isolates are required to obtain further insights into the ecology and evolution of different species within the genus Francisella.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

High-resolution genetic mapping of allelic variants associated with cell wall chemistry in Populus

We report the identification of six genetic loci and the allelic-variants associated with Populus cell wall phenotypes determined independently using pyrolysis Molecular Beam Mass Spectrometry (pyMBMS), saccharification assay and wet chemistry in two partially overlapping populations of P. trichocarpa genotypes sampled from multiple environments in the Pacific Northwest of North America. All 6 variants co-located with a quantitative trait locus (QTL) hotspot on chromosome XIV for lignin content, syringyl to guaiacyl (S/G) ratio, 5- and 6- carbon sugars identified in an interspecific P. trichocarpa x P. deltoides pseudo-backcross mapping pedigree. Genomic intervals containing an amino acid transporter, a MYB transcription factor, an angustifolia CtBP transcription factor, a copper transport protein ATOX1-related, a Ca 2+ transporting ATPase and a protein kinase were identified within 5 QTL regions. Each interval contained single nucleotide polymorphisms (SNPs) that were significantly associated to cell-wall phenotypes, with associations exceeding the chromosome-wise Bonferroni-adjusted p-values in at least one environment. cDNA sequencing for allelic variants of 3 of the 6 genes identified polymorphisms leading to premature stop codons in the MYB transcription factor and protein kinase. On the other hand, variants of the Angustifolia CtBP transcription factor exhibited a polyglutamine (PolyQ) length polymorphism. Results from transient protoplast assays suggested that each of the polymorphisms conferred allelic differences in activation of cellulose, hemicelluloses and lignin pathway marker genes, with truncated and short PolyQ alleles exhibiting significantly reduced marker gene activation. Genes identified in this study represent novel targets for reducing cell wall recalcitrance for lignocellulosic biofuels production using plant biomass.

09 BIOMASS FUELS↗
DOE OSTI2022

Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach

Codon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from various selective pressures, including selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression. Here we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure in S. cerevisiae and E. coli. Unlike other metrics of codon usage, our approach explicitly separates the effects of natural selection, scaled by gene expression, and mutation bias while naturally accounting for a region’s amino acid usage. Bayesian model comparisons suggest selection on codon usage varies only slightly between helix, sheet, and coil secondary structures and, similarly, between structured and intrinsically-disordered regions. Similarly, in contrast to previous findings, we find selection on codon usage only varies slightly at the termini of helices in E. coli. Using simulated data, we show this previous work indicating “non-optimal” codons are enriched at the beginning of helices in S. cerevisiae was due to failure to control for various confounding factors (e.g. amino acid biases, gene expression, etc.), and rather than selection to modulate cotranslational folding. Our results reveal a weak relationship between codon usage and protein structure, indicating that differences in selection on codon usage between structures are slight. In addition to the magnitude of differences in selection between protein structures being slight, the observed shifts appear to be idiosyncratic and largely codon-specific rather than systematic reversals in the nature of selection. Overall, our work demonstrates the statistical power and benefits of studying selective shifts on codon usage or other genomic features from an explicitly evolutionary approach. Limitations of this approach and future potential research avenues are discussed.

59 BASIC BIOLOGICAL SCIENCES↗