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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

All in the timing: epigenetic control of greening

During germination, the emerging seedling relies on a limited supply of energy and nutrients stored within itself and the seed. This often occurs underground in darkness where seedlings undergo skotomorphogenesis, characterized by hypocotyl elongation, reduced root growth, and small closed cotyledons forming an apical hook to protect the shoot apical meristem as the seedling grows though the soil towards sunlight. Such long and pale dark-grown seedlings are commonly referred to as etiolated (from the French étiolier (to make pale)). However, a seedling must eventually find sunlight and make an irreversible shift to photoautotrophic growth (photomorphogenesis or de-etiolation). This leads to a dramatic change in physiology, including reduced hypocotyl elongation, increased root growth, expansion and unfolding of the cotyledons, and the biogenesis of photosynthetic plastids (i.e. chloroplasts). Furthermore, this shift is accompanied by a massive change in gene expression, with about one-third of genes being reprogrammed. A large amount of the transcriptional response is regulated by the plant photoreceptors (phytochromes and cryptochromes) that activate transcriptional networks. Furthermore, this involves the rapid degradation of PHYTOCHROME INTERACTING FACTORS (PIFs), which are basic helix-loop-helix domain-containing transcription factors that repress photomorphogenesis in the dark.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans

Oogenesis involves transduction of mechanical forces from the cytoskeleton to the nuclear envelope (NE). In Caenorhabditis elegans, oocyte nuclei lacking the single lamin protein LMN-1 are vulnerable to collapse under forces mediated through LINC (linker of nucleoskeleton and cytoskeleton) complexes. Here, we use cytological analysis and in vivo imaging to investigate the balance of forces that drive this collapse and protect oocyte nuclei. We also use a mechano-node-pore sensing device to directly measure the effect of genetic mutations on oocyte nuclear stiffness. We find that nuclear collapse is not a consequence of apoptosis. It is promoted by dynein, which induces polarization of a LINC complex composed of Sad1 and UNC-84 homology 1 (SUN-1) and ZYGote defective 12 (ZYG-12). Lamins contribute to oocyte nuclear stiffness and cooperate with other inner nuclear membrane proteins to distribute LINC complexes and protect nuclei from collapse. We speculate that a similar network may protect oocyte integrity during extended oocyte arrest in mammals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q3

Microbiome data is complex, spanning information from microbial genomes within diverse communities, protein and metabolite readouts, and contextual information (metadata) captured from the environments from which these samples were collected. While the variety and scale of microbiome data generation has dramatically expanded over the past twenty years, infrastructure to support data management, sharing, and access has lagged. New ways to improve interoperability across existing resources and advancing community standards are necessary to support how researchers create, use, and reuse data. The National Microbiome Data Collaborative (NMDC) aims to advance a microbiome data sharing network through infrastructure, data standards, and community building.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Flexibility in the Energy Balancing Network of Photosynthesis Enables Safe Operation under Changing Environmental Conditions

Given their ability to harness chemical energy from the sun and generate the organic compounds necessary for life, photosynthetic organisms have the unique capacity to act simultaneously as their own power and manufacturing plant. This dual capacity presents many unique challenges, chiefly that energy supply must be perfectly balanced with energy demand to prevent photodamage and allow for optimal growth. From this perspective, we discuss the energy balancing network using recent studies and a quantitative framework for calculating metabolic ATP and NAD(P)H demand using measured leaf gas exchange and assumptions of metabolic demand. We focus on exploring how the energy balancing network itself is structured to allow safe and flexible energy supply. We discuss when the energy balancing network appears to operate optimally and when it favors high capacity instead. We also present the hypothesis that the energy balancing network itself can adapt over longer time scales to a given metabolic demand and how metabolism itself may participate in this energy balancing.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Biologically-Inspired Control for a Planetary Exploration Tensegrity Robot

Tensegrity structures are becoming increasingly popular as mechanical structures for robots. Their inherent compliance makes them extremely robust to environmental disturbances, and their design allows them to have a high strength-to-weight ratio whilst being lightweight compared to traditional robots. For these reasons they would be of interest to the aerospace industry, particularly for planetary exploration. However, being such compliant structures thanks to their network of elastic elements also means that their control is not an easy task. Relying solely on traditional control strategies to generate efficient locomotion would surely be near impossible due to the complex oscillatory motions and nonlinear interactions of its members. The goal of this project was to use bio-inspired control techniques to generate locomotion for a tensegrity icosahedron, namely the SUPERball project of the Intelligent Robotics Group of NASA Ames Research Center.

Leroy, Marc↗
NASA NTRS1999

Research and Educational Opportunities with NASA GSFC

The research carried out in the Earth sciences at NASA's Goddard Space Flight Center will be the focus of the presentation. These research interests span the full breath of disciplines in Earth System Science. Branches and research groups focus on areas as diverse as planetary geomagnetics and atmospheric chemistry. There are 4 major laboratories in the directorate and 2 organizations that focus on scientific information technology. These laboratories focus on atmospheric sciences (atmospheric chemistry, climate and radiation, regional processes, atmospheric modeling), hydrological sciences (snow, ice, oceans, and seasonal-to-interannual prediction), terrestrial physics (geology, terrestrial biology, land-atmosphere interactions, geophysics), climate modeling (global warming, greenhouse gases, climate change). Two of the labs also have branches that focus on sensor development especially using lidar and microwave technologies. In the information technologies, areas of scientific and technical research, and expertise include massively parallel computers, technologies for high speed electronic networks, scientific visualization, information systems, archive and distribution systems, and information processing. Examples will be presented from each of these research disciplines.

Meeson, Blanche W.↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dynamic conformational switching underlies TFIIH function in transcription and DNA repair and impacts genetic diseases

Transcription factor IIH (TFIIH) is a protein assembly essential for transcription initiation and nucleotide excision repair (NER). Yet, understanding of the conformational switching underpinning these diverse TFIIH functions remains fragmentary. TFIIH mechanisms critically depend on two translocase subunits, XPB and XPD. To unravel their functions and regulation, we build cryo-EM based TFIIH models in transcription- and NER-competent states. Using simulations and graph-theoretical analysis methods, we reveal TFIIH’s global motions, define TFIIH partitioning into dynamic communities and show how TFIIH reshapes itself and self-regulates depending on functional context. Our study uncovers an internal regulatory mechanism that switches XPB and XPD activities making them mutually exclusive between NER and transcription initiation. By sequentially coordinating the XPB and XPD DNA-unwinding activities, the switch ensures precise DNA incision in NER. Mapping TFIIH disease mutations onto network models reveals clustering into distinct mechanistic classes, affecting translocase functions, protein interactions and interface dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Datasets used in manuscript, 'High-Throughput Chromosomal Confirmation Capture (Hi-C) Metagenome Sequencing Reveals Moisture Impact on Soil Phage-Host Interactions'

Soil moisture shifts have largely unknown impacts on soil virus-host interactions. Here, we applied high-throughput chromosomal confirmation capture (Hi-C) metagenomics to link phage with their hosts in soils under wet and dry conditions. Bulk metagenomes and metatranscriptomes were analyzed from the same soil incubations. Host-associated phage diversity and the number of viruses per host increased following soil desiccation. Under wet conditions, the viral enrichment and host abundances were significantly negatively correlated, but the transcriptional activities of the phage were higher. Together, these results suggest that there was a general transition of phage from lytic to lysogenic during drying and that viral infection was higher under dry conditions. Soil desiccation also caused shifts in phage hosts and some of these were central in microbial co-occurrence networks, highlighting the impact of soil phages on microbiome structure. This study provides the first empirical evidence of phage-mediated bacterial dynamics during soil desiccation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hyperaccumulation of Gadolinium by Methylorubrum extorquens AM1 Reveals Impacts of Lanthanides on Cellular Processes Beyond Methylotrophy

Lanthanides (Ln) are a new group of life metals, and many questions remain regarding how they are acquired and used in biology. Methylotrophic bacteria can acquire, transport, biomineralize, and use Ln as part of a cofactor complex with pyrroloquinoline quinone (PQQ) in alcohol dehydrogenases. For most methylotrophic bacteria use is restricted to the light Ln, which range from lanthanum to samarium (atomic numbers 57–62). Understanding how the cell differentiates between light and heavy Ln, and the impacts of these metals on the metabolic network, will advance the field of Ln biochemistry and give insights into enzyme catalysis, stress homeostasis, and metal biomineralization and compartmentalization. We report robust methanol growth with the heavy Ln gadolinium by a genetic variant of the model methylotrophic bacterium Methylorubrum extorquens AM1, named evo -HLn, for “ evo lved for H eavy L antha n ides.” A non-synonymous single nucleotide polymorphism in a cytosolic hybrid histidine kinase/response regulator allowed for sweeping transcriptional alterations to heavy metal stress response, methanol oxidation, and central metabolism. Increased expression of genes for Ln acquisition and uptake, production of the Ln-chelating lanthanophore, PQQ biosynthesis, and phosphate transport and metabolism resulted in gadolinium hyperaccumulation of 36-fold with a trade-off for light Ln accumulation. Gadolinium was hyperaccumulated in an enlarged acidocalcisome-like compartment. This is the first evidence of a bacterial intracellular Ln-containing compartment that we name the “lanthasome.” Carotenoid and toblerol biosynthesis were also upregulated. Due to its unique capabilities, evo -HLn can be used to further magnetic resonance imaging (MRI) and bioremediation technologies. In this regard, we show that gadolinium hyperaccumulation was sufficient to produce MRI contrast in whole cells, and that evo -HLn was able to readily acquire the metal from the MRI contrast agent gadopentetic acid. Finally, hyperaccumulation of gadolinium, differential uptake of light and heavy Ln, increased PQQ levels, and phosphate transport provide new insights into strategies for Ln recovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Pan-cancer evaluation of clinical value of mitotic network activity index (MNAI) and its predictive value for immunotherapy

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA): Component 5: Imaging Protein Conformations, Shapes & Assemblies in Solution & Administration project (Final Scientific/Technical Report, Subcontract No. 6974584)

We set ambitious goals to examine microorganism communities and measure both their chemical input and output as a read out of specific biochemical activity. These scientific goals are driving the development of sophisticated algorithms to analyze large amounts of experimental measurements made using high throughput technologies to explain and predict how the environment influences biological function at multiple scales and how the microbial systems in tum modify the environment. By examining how bacteria communities rely on symbiotic metabolic relationships for survival and reproductive success, and how these relationships consequently affect their biochemical capabilities. This was accomplished using state-of-the-art mass spectrometry-based methods, and metabolic fingerprinting approaches with a high degree of chemical specificity and sensitivity. During this period the original effort transitioned and was consolidated into what is now ENIGMA, the efforts on technology development expanded to include more untargeted metabolomics and its application to organisms on a systems level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Mutualism increases diversity, stability, and function of multiplex networks that integrate pollinators into food webs

Ecosystems are composed of complex networks of many species interacting in different ways. While ecologists have long studied food webs of feeding interactions, recent studies increasingly focus on mutualistic networks including plants that exchange food for reproductive services provided by animals such as pollinators. Here, we synthesize both types of consumer-resource interactions to better understand the controversial effects of mutualism on ecosystems at the species, guild, and whole-community levels. We find that consumer-resource mechanisms underlying plant-pollinator mutualisms can increase persistence, productivity, abundance, and temporal stability of both mutualists and non-mutualists in food webs. These effects strongly increase with floral reward productivity and are qualitatively robust to variation in the prevalence of mutualism and pollinators feeding upon resources in addition to rewards. This work advances the ability of mechanistic network theory to synthesize different types of interactions and illustrates how mutualism can enhance the diversity, stability, and function of complex ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Abstract Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Evolutionary Biology↗
DOE OSTI2020

Elucidating Anthracnose Resistance Mechanisms in Sorghum—A Review

Sorghum (Sorghum bicolor) is the fifth most cultivated cereal crop in the world, traditionally providing food, feed, and fodder, but more recently also fermentable sugars for the production of renewable fuels and chemicals. The hemibiotrophic fungal pathogen Colletotrichum sublineola, the causal agent of anthracnose disease in sorghum, is prevalent in the warm and humid climates where much of the sorghum is cultivated and poses a serious threat to sorghum production. The use of anthracnose-resistant sorghum germplasm is the most environmentally and economically sustainable way to protect sorghum against this pathogen. Even though multiple anthracnose resistance loci have been mapped in diverse sorghum germplasm in recent years, the diversity in C. sublineola pathotypes at the local and regional levels means that these resistance genes are not equally effective in different areas of cultivation. This review summarizes the genetic and cytological data underlying sorghum’s defense response and describes recent developments that will enable a better understanding of the interactions between sorghum and C. sublineola at the molecular level. This includes releases of the sorghum genome and the draft genome of C. sublineola, the use of next-generation sequencing technologies to identify gene expression networks activated in response to infection, and improvements in methodologies to validate resistance genes, notably virus-induced and transgenic gene silencing approaches.

Plant Sciences↗
DOE OSTI2020

Generating a Fractal Microstructure of Laminin-111 to Signal to Cells

Laminin-111 (Ln1) is an essential part of the extracellular matrix in epithelia, muscle and neural systems. We have previously demonstrated that the microstructure of Ln1 alters the way that it signals to cells, possibly because Ln1 assembly into networks exposes different adhesive domains. In this paper, we describe three methods to generate polymerized Ln1.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗