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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Effects of corpuscular radiation on weather and climate

There is no doubt that the antropogenic effect play an important role in the effects of corpuscular radiation on weather and climate. The task, however, is to distinguish between antropogenic effect in the atmosphere due to human activities and natural climatic fluctuations influencing biological systems. The increase in global temperature during the past 100 years is in relatively good coincidence with the increase in geomagnetic (corpuscular) activity. It is concluded that it could have been the increase in temperature on the Northern Hemisphere, due to the processes occurring in the auroral oval under enhanced corpuscular radiation which led to an increased atmospheric concentration of CO2 in the past. Both processes, i.e., antropogenic and solar activity effects, should be therefore intensively studied due to their important role for elucidating the past and present global change mainly in temperature, climate and biological systems.

Bucha, V.↗
DOE OSTI2021

Crystalline Supramolecular Polymers: Dynamics, Chirality, and Function

Inspired by biological systems, supramolecular polymers have emerged over the past three decades as an important way to form functional structures with potential to tune the internal order and dynamics and to create materials with the potential for responsiveness, reconfigurability, and recyclability. We focus here on several recent examples of ordered supramolecular polymers with a significant extent of internal order that results in crystalline molecular packing, or with dynamics that allow them to undergo structural reconfiguration. These examples highlight the spectrum of molecular mobility that the noncovalently bound constituents can experience. Furthermore, these materials have found interesting functions in their interaction with chiral biological systems and in their ability to form materials with interesting optoelectronic and photocatalytic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Summer Research Paper

Certain populations such as chemotherapy patients and atomic bomb survivors have been exposed to ionizing radiation and experience tissue damage and cancer initiation and progression. One cancer that can be initiated from radiation is esophageal squamous cell carcinoma (ESCC), an epithelial cancer that has a survival rate as low as 20%. Researchers have found that when protein tyrosine kinase receptors (RPTK) activate oncogenes, they can create epithelial tumors and cause deadly cancers like ESCC. The RPTK family has one group, MET, that has only two receptors, MET and RON, present in the human body. MET s ligand is the hepatocyte growth factor (HGF) and RON's ligand is the macrophage-stimulating protein (MSP-1). Both HGF and MSP-1 have been shown to activate their receptors and are implicated in certain processes. Since radiation damages cells throughout the biological system, researchers are investigating whether or not HGF and MSP-1 protects or kills certain normal and cancerous cells by being part of cell recovery processes. One research group recently reviewed that the HGF-MET pathway has an important role in the embryonic development in the liver, migration of myogenic precursor cells, regulation of epithelial morphogenesis and growth, and regeneration and protection in tissues. In addition, since the RON receptor is more commonly expressed in cells of epithelial origin, and when activated is part of epithelial cell matrix invasion, dissociation, and migration processes, scientists conclude that RON might be one of the factors causing epithelial cancer initiation in the biological system. In order to examine HGF and MSP-1 s effect on cancer initiation and progression we used two immortalized esophageal epithelial cell lines. One is a normal human cell line (EPC2-hTERT), while the other had a p53 mutation at the 175th amino acid position (EPC2-hTERT-p53(sup R175H)). For this investigation, we used 0(control), 2, and 4 Gray doses of gamma (Cs137) radiation and selected various concentrations from 0-100 ng/mL of HGF and MSP-1 in our assays. Since the HGF and MSP-1 pathways have proliferative roles in epithelial cells, we conducted the MTT proliferation assay to see if either drug enhances or inhibits cell proliferation over time. Also, a MTT cytotoxicity assay was necessary to observe whether the drugs are protecting the cells from radiation and if a trend is occurring depending upon the amount of dose added. In addition, a wound healing assay was done since both drugs have been to known to promote cell motility. Since cell damage occurs when radiation is added, apoptosis and micronuclei assays are vital to see if HGF and MSP-1 increase or decrease cell death and damage in normal and pre-cancerous cells and by how much based on the radiation dosage. Overall, we used the MTT, wound healing, apoptosis and micronuclei assays to investigate the effects ofHGF and MSP-1 on irradiated esophageal epithelial cells.

Patel, Zarana↗
DOE OSTI2020

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

For centuries, flow visualization has been the art of making fluid motion visible in physical and biological systems. Although such flow patterns can be, in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields directly from the images is challenging. We addressed this problem by developing hidden fluid mechanics (HFM), a physics-informed deep-learning framework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic to the geometry or the initial and boundary conditions. We demonstrate HFM for several physical and biomedical problems by extracting quantitative information for which direct measurements may not be possible.

Raissi, Maziar↗
DOE OSTI2025

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗
DOE OSTI2024

Omics-driven onboarding of the carotenoid producing red yeast Xanthophyllomyces dendrorhous CBS 6938

Transcriptomics is a powerful approach for functional genomics and systems biology, yet it can also be used for genetic part discovery. Here, we derive constitutive and light-regulated promoters directly from transcriptomics data of the basidiomycete red yeast Xanthophyllomyces dendrorhous CBS 6938 (anamorph Phaffia rhodozyma) and use these promoters with other genetic elements to create a modular synthetic biology parts collection for this organism. X. dendrorhous is currently the sole biotechnologically relevant yeast in the Tremellomycete class-it produces large amounts of astaxanthin, especially under oxidative stress and exposure to light. Thus, we performed transcriptomics on X. dendrorhous under different wavelengths of light (red, green, blue, and ultraviolet) and oxidative stress. Differential gene expression analysis (DGE) revealed that terpenoid biosynthesis was primarily upregulated by light through crtI, while oxidative stress upregulated several genes in the pathway. Further gene ontology (GO) analysis revealed a complex survival response to ultraviolet (UV) where X. dendrorhous upregulates aromatic amino acid and tetraterpenoid biosynthesis and downregulates central carbon metabolism and respiration. The DGE data was also used to identify 26 constitutive and regulated genes, and then, putative promoters for each of the 26 genes were derived from the genome. Simultaneously, a modular cloning system for X. dendrorhous was developed, including integration sites, terminators, selection markers, and reporters. Each of the 26 putative promoters were integrated into the genome and characterized by luciferase assay in the dark and under UV light. The putative constitutive promoters were constitutive in the synthetic genetic context, but so were many of the putative regulated promoters. Notably, one putative promoter, derived from a hypothetical gene, showed ninefold activation upon UV exposure. Thus, this study reveals metabolic pathway regulation and develops a genetic parts collection for X. dendrorhous from transcriptomic data. Therefore, this study demonstrates that combining systems biology and synthetic biology into an omics-to-parts workflow can simultaneously provide useful biological insight and genetic tools for nonconventional microbes, particularly those without a related model organism. This approach can enhance current efforts to engineer diverse microbes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Intracellular pathways for lignin catabolism in white-rot fungi

White-rot fungi, the most efficient organisms at breaking down lignin from wood in Nature, utilize lignin degradation products as a carbon source. 15 Lignin is a biopolymer found in plant cell walls that accounts for 30% of the organic carbon in the biosphere. White-rot fungi (WRF) are considered the most efficient organisms at degrading lignin in Nature. While lignin depolymerization by WRF has been exhaustively studied, the possibility that WRF are able to utilize lignin as a carbon source is still a matter of controversy. Here we employ 13C-labeling and systems biology approaches to demonstrate that two WRF, Trametes versicolor and Gelatoporia subvermispora, funnel lignin-derived aromatic compounds into central carbon metabolism via intracellular catabolic pathways. These results provide insights into global carbon cycling in 20 soil ecosystems, and furthermore establishes a foundation for employing WRF in simultaneous lignin depolymerization and bioconversion to bioproducts – a key step towards enabling a sustainable bioeconomy.

Del Cerro, Carlos↗
DOE OSTI2021

Composition and Structure of Fluorescent Graphene Quantum Dots Generated by Enzymatic Degradation of Graphene Oxide

The broad applications of carbon nanomaterials (CNMs) in both materials and life sciences necessitate investigation of their metabolites due to the inevitable contact of CNMs and biological systems. Graphene oxide (GO), along with other types of CNMs, can be enzymatically degraded by myeloperoxidase (MPO), an enzyme released during the innate immune response. However, the enzymatic degradation products are neither well-defined nor well-understood. Some products generated during MPO-catalyzed degradation of GO could emit blue photoluminescence (PL) and were simply dubbed graphene quantum dots (GQDs) without further elucidating their structures. In this work, we use liquid chromatography-mass spectrometry (LC-MS) to isolate and elucidate chemical structures of the MPO-catalyzed degradation products. A general chemical formula screening workflow was developed for the GQDs, which are in the form of polyaromatic hydrocarbons (PAHs), obtained in the degradation products. Structures of the PAHs responsible for the blue PL were further proposed using density functional theory (DFT) calculations. Our results indicated that structures with several conjugated benzene rings are likely to generate the observed PL. This work provides insights into the mechanism of enzymatic degradation and open opportunities for fluorescence imaging of GO in biological systems.

composition and structure screening↗
DOE OSTI2026

Data for Development of Vegetative Oil Sorghum: From Lab-to-Field

Biomass crops engineered to accumulate energy-dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar-accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high-yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push-pull-protect’ strategy. The top TAG-yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium-chain acyl-acyl carrier protein thioesterases. Though intended to generate oils with medium-chain fatty acids, engineered lines accumulated oleic acid-rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36-fold and 49-fold increases relative to wild-type (WT) plants, respectively. Under field conditions, the top-performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78-fold and 58-fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab-to-field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

Biofuels↗
DOE OSTI2022

Bioinspired mechanically interlocking holey graphene@SiO 2 anode

Mechanically interlocking structures that can enhance adhesion at the interface and regulate the stress distribution have been widely observed in biological systems. Inspired by the biological structures in the wings of beetles, we synthesized a holey graphene@SiO 2 anode with strong mechanical interlocking, characterized it electrochemically, and explained its performance by finite element analysis and density functional calculations. The mechanically interlocking structure enhances lithium-ion (Li + ) storage by transmitting the strain from SiO 2 to the holey graphene and by a mechano-electrochemical coupling effect. The interlocking fit hinders the abscission of SiO 2 and the distinctive structure reduces the stress and strain of SiO 2 during (de)lithiation. The positive mechano-electrochemical coupling effect preserves the amount of electrochemically active phase (Li x Si) during cycles and facilitates Li + diffusion. Therefore, the capacity shows only a slight attenuation after 8000 cycles (cycling stability), and the specific capacity is ~1200 mA h g –1 at 5 A/g (rate-performance). This study furnishes a novel way to design high-performance Li + /Na + /K + /Al 3+ anodes with large volume expansion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

New Opportunities for Neutrons in Environmental and Biological Sciences

The use of neutron methods in environmental and biological sciences is rapidly emerging and accelerating with the development of new instruments at neutron user facilities. This article, based on a workshop held at Oak Ridge National Laboratory (ORNL), offers insights into the application of neutron techniques in environmental and biological sciences. Here we highlight recent advances and identify key challenges and potential future research areas. These include soil and rhizosphere processes, root water dynamics, plant-microbe interactions, structure and dynamics of biological systems, applications in synthetic biology and enzyme engineering, next-generation bioproducts, biomaterials and bioenergy, nanoscale structure, and fluid dynamics of porous materials in geochemistry. We provide an outlook on emerging opportunities with an emphasis on new capabilities that will be enabled at the Spallation Neutron Source Second Target Station currently under design at ORNL. The mission of scientific neutron user facilities worldwide is to enable science using state-of-the-art neutron capabilities. We aim to encourage researchers in the environmental and biological research community to explore the unique capability afforded by neutrons at these facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics

Abstract While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii , we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N -glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A standardized workflow for kinetic metabolic model curation and dissemination

Kinetic metabolic models provide invaluable insights into cellular metabolism, supporting applications in synthetic biology, metabolic engineering, and systems biology. However, reproducibility and utility of these models hinge on clear and rigorous documentation, standardized annotation, and accessible visualization. This paper presents a workflow for building, annotating, visualizing, and sharing kinetic metabolic models. Our method integrates community standards and open-source tools to ensure reproducibility, interoperability, and user accessibility. This procedure enables researchers to produce reusable and well-documented kinetic models, advancing their role as powerful tools in metabolic research.

Cook, Margaret [Univ. of Washington, Seattle, WA (↗
DOE OSTI2020

Nanoengineering of Complex Materials

We are primarily interested in two important biomimetic principles, namely, self-assembly based on supramolecular chemistry and the use of these structures to template in organic phases as observed in biomineralization. The projects proposed are inspired by some of our achievements in the previous funding period described above, and also by preliminary data gathered recently which we describe below. In addition to our broad objective of acquiring knowledge in supramolecular self-assembly and templating of organic–inorganic hybrid materials, we also propose here work on biomolecular structures based on DNA, peptide nucleic acids, and peptides which can serve as models for artificial materials based on biomimetic principles. The proposed work is divided into three sections, supramolecular systems, hybrid systems, and biomolecular and biological systems. In terms of functions, we are using systems that have relevance to solar cells, catalysis, dynamic/active materials, and ferroelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Systems Biology-Based Optimization of Extremely Thermophilic Lignocellulose Conversion to Bioproducts

This was a collaborative project involving researchers at the University of Georgia, North Carolina State University, Sanford‐Burnham‐Prebys Med. Discovery Institute and the University of Rhode Island. The over-arching goal was to demonstrate that non-model microorganisms, specifically extreme thermophiles, can be a strategic metabolic engineering platform for industrial biotechnology. We engineered the most thermophilic lignocellulose-degrading organism known, Caldicellulosiruptor bescii (Cbes) , which grows optimally near 80°C, and the most thermophilic fermentative organism known, Pyrococcus furiosus (Pfu) , which grows optimally at 100°C, to produce several key industrial chemicals. This work leveraged recent breakthrough advances in the development of molecular genetic tools for these organisms, complemented by a deep understanding of its metabolism and physiology gained over the past decade of study in the PIs’ laboratories. We applied the latest metabolic reconstruction and modeling approaches to optimize biomass to product conversion. Bio-processing above 70°C can have important advantages over near-ambient operations. Highly genetically-modified microorganisms usually have a fitness disadvantage and can be easily overtaken in culture when contaminating microbes are present. The high growth temperature of extreme thermophiles precludes growth or survival of virtually any contaminating organism or phage. This reduces operating costs associated with reactor sterilization and maintaining a sterile facility. In addition, at industrial scales, heat production from microbial metabolic activity vastly outweighs heat loss through bioreactor walls such that cooling is required. Extreme thermophiles have the advantage that non-refrigerated cooling water can be used if needed, and heating requirements can be met with low-grade steam typically in excess capacity on plant sites. We assembled a highly interdisciplinary team that brought together all of the expertise for the project to have successful outcomes. This project also built upon and utilized extensive information already available in the PIs’ labs for both Cbes and Pfu to develop models that provide a comprehensive description of these organisms’ physiology and metabolism that were utilized to inform metabolic engineering strategies. The models were validated with experimental data and the results demonstrated that unpretreated lignocellulose has the potential to be converted into value-added industrial chemicals at high loading at bioreactor scale. The data generated from this research were published in twenty-one peer-reviewed papers in international journals with online access.

60 APPLIED LIFE SCIENCES↗