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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Genetic diversity within the botulinum neurotoxin-producing bacteria and their neurotoxins

The recent availability of multiple Clostridium botulinum genomic sequences has initiated a new genomics era that strengthens our understanding of the bacterial species that produce botulinum neurotoxins (BoNTs). Analysis of the genomes has reinforced the historical Group I–VI designations and provided evidence that the bont genes can be located within the chromosome, phage or plasmids. The sequences provide the opportunity to examine closely the variation among the toxin genes, the composition and organization of the toxin complex, the regions flanking the toxin complex and the location of the toxin within different bacterial strains. These comparisons provide evidence of horizontal gene transfer and site-specific insertion and recombination events that have contributed to the variation observed among the neurotoxins. In this work, examples that have contributed to the variation observed in serotypes A-H strains are presented to illustrate the mechanisms that have contributed to their variation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2015

Genetic structure of Miscanthus sinensis and Miscanthus sacchariflorus in Japan indicates a gradient of bidirectional but asymmetric introgression

Unilateral introgression from diploids to tetraploids has been hypothesized to be an important evolutionary mechanism in plants. However, few examples have been definitively identified, perhaps because data of sufficient depth and breadth were difficult to obtain before the advent of affordable high-density genotyping. Throughout Japan, tetraploid Miscanthus sacchariflorus and diploid Miscanthus sinensis are common, and occasionally hybridize. In this study, 667 M. sinensis and 78 M. sacchariflorus genotypes from Japan were characterized using 20 704 SNPs and ten plastid microsatellites. Similarity of SNP genotypes between diploid and tetraploid M. sacchariflorus indicated that the tetraploids originated through autopolyploidy. Structure analysis indicated a gradient of introgression from diploid M. sinensis into tetraploid M. sacchariflorus throughout Japan; most tetraploids had some M. sinensis DNA. Among phenotypically M. sacchariflorus tetraploids, M. sinensis ancestry averaged 7% and ranged from 1-39%, with introgression greatest in southern Japan. Unexpectedly, rare (~1%) diploid M. sinensis individuals from northern Japan were found with 6-27% M. sacchariflorus ancestry. Population structure of M. sinensis in Japan included three groups, and was driven primarily by distance, and secondarily by geographic barriers such as mountains and straits. Miscanthus speciation is a complex and dynamic process. In contrast to limited introgression between diploid M. sacchariflorus and M. sinensis in northern China, selection for adaptation to a moderate maritime climate probably favoured cross-ploidy introgressants in southern Japan. Ultimately, these results will help guide the selection of Miscanthus accessions for the breeding of biomass cultivars.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Genetic analysis of Physcomitrella patens identifies ABSCISIC ACID NON-RESPONSIVE , a regulator of ABA responses unique to basal land plants and required for desiccation tolerance

The anatomically simple plants that first colonized land must have acquired molecular and biochemical adaptations to drought stress. Abscisic acid (ABA) coordinates responses leading to desiccation tolerance in all land plants. We identified ABA nonresponsive mutants in the model bryophyte Physcomitrella patens and genotyped a segregating population to map and identify the ABA NON-RESPONSIVE (ANR) gene encoding a modular protein kinase comprising an N-terminal PAS domain, a central EDR domain, and a C-terminal MAPKKK-like domain. anr mutants fail to accumulate dehydration tolerance-associated gene products in response to drought, ABA, or osmotic stress and do not acquire ABA-dependent desiccation tolerance. The crystal structure of the PAS domain, determined to 1.7-Å resolution, shows a conserved PAS-fold that dimerizes through a weak dimerization interface. Targeted mutagenesis of a conserved tryptophan residue within the PAS domain generates plants with ABA nonresponsive growth and strongly attenuated ABA-responsive gene expression, whereas deleting this domain retains a fully ABA-responsive phenotype. ANR orthologs are found in early-diverging land plant lineages and aquatic algae but are absent from more recently diverged vascular plants. Lastly, we propose that ANR genes represent an ancestral adaptation that enabled drought stress survival of the first terrestrial colonizers but were lost during land plant evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An Improved Genetic Algorithm approach to the Unit Commitment/Economic Dispatch problem

The deployment of new technologies, the importance of accurately modeling the dynamics of the generating units and the introduction of new policies are making the solution of the Unit Commitment/Economic Dispatch problem more and more complicated.In the present scenario, traditionally followed scheduling criteria might not lead to the optimal fleet configuration any more. In addition, most of the widely used techniques have limited capabilities at modeling the nonlinear dynamics of committed power plants. When realistic power systems comprising of several tens of generating units are modeled, the resulting optimization problem turns to be computationally intensive for the current computing capabilities. In this paper, an improved version of a GA-based optimization algorithm is presented. A detailed methodology aimed at obtaining a more efficient version of the GA, and a more detailed and accurate description of the flexible operation flexibility of the power plants is described.

genetic algorithm↗
DOE OSTI2023

Genetic control of photoprotection and photosystem II operating efficiency in plants

Summary Photoprotection against excess light via nonphotochemical quenching (NPQ) is indispensable for plant survival. However, slow NPQ relaxation under low light conditions can decrease yield of field‐grown crops up to 40%. Using semi‐high‐throughput assay, we quantified the kinetics of NPQ and photosystem II operating efficiency (ΦPSII) in a replicated field trial of more than 700 maize ( Zea mays ) genotypes across 2 yr. Parametrized kinetics data were used to conduct genome‐wide association studies. For six candidate genes involved in NPQ and ΦPSII kinetics in maize the loss of function alleles of orthologous genes in Arabidopsis ( Arabidopsis thaliana ) were characterized: two thioredoxin genes, and genes encoding a transporter in the chloroplast envelope, an initiator of chloroplast movement, a putative regulator of cell elongation and stomatal patterning, and a protein involved in plant energy homeostasis. Since maize and Arabidopsis are distantly related, we propose that genes involved in photoprotection and PSII function are conserved across vascular plants. The genes and naturally occurring functional alleles identified here considerably expand the toolbox to achieving a sustainable increase in crop productivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A split ribozyme system for in vivo plant RNA imaging and genetic engineering

RNA plays a central role in plants, governing various cellular and physiological processes. Monitoring its dynamic abundance provides a discerning understanding of molecular mechanisms underlying plant responses to internal (developmental) and external (environmental) stimuli, paving the way for advances in plant biotechnology to engineer crops with improved resilience, quality and productivity. In general, traditional methods for analysis of RNA abundance in plants require destructive, labour-intensive and time-consuming assays. To overcome these limitations, we developed a transformative innovation for in vivo RNA imaging in plants. Specifically, we established a synthetic split ribozyme system that converts various RNA signals to orthogonal protein outputs, enabling in vivo visualisation of various RNA signals in plants. We demonstrated the utility of this system in transient expression experiments (i.e., leaf infiltration in Nicotiana benthamiana ) to detect RNAs derived from transgenes and tobacco rattle virus, respectively. Also, we successfully engineered a split ribozyme-based biosensor in Arabidopsis thaliana for in vivo visualisation of endogenous gene expression at the cellular level, demonstrating the feasibility of multi-scale (e.g., cellular and tissue level) RNA imaging in plants. Furthermore, we developed a platform for easy incorporation of different protein outputs, allowing for flexible choice of reporters to optimise the detection of target RNAs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Automatic Identification of Closed-Loop Wind Turbine Dynamics via Genetic Programming

Wind turbines are nonlinear systems that operate in turbulent environments. As such, their behavior is difficult to characterize accurately across a wide range of operating conditions by physically meaningful models. Customarily, data-based models of wind turbines are defined in 'black box' format, lacking in both conciseness and physical intelligibility. To address this deficiency, we identify models of a modern horizontal-axis wind turbine in symbolic form using a recently developed symbolic regression method. The method used relies on evolutionary multi-objective optimization to produce succinct dynamic models from operational data without 'a priori' knowledge of the system. We compare the produced models with models derived by other methods for their estimation capacity and evaluate the tradeoff between model intelligibility and accuracy. Several succinct models are found that predict wind turbine behavior as well as or better than more complex alternatives derived by other methods.

17 WIND ENERGY↗