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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multiparametric optical label-free imaging to analyze plant cell wall assembly and metabolism. (Final Report)

Plant tissues are often considered not ideal for fluorescence imaging because of the pervasive intrinsic fluorescence of many plant metabolites and the intricate interactions with light of the many semi-crystalline polymers at the cell wall. Our project aims to take advantage of this observed shortcoming by developing a label-free, optical microscopy platform for characterizing multiple fingerprints of important cell wall components and stress-related, at subcellular scale resolution. The new imaging system can collect fingerprints from both emitted and scattered light that can inform on the chemical nature, subcellular distribution, anisotropy, and molecular environment of multiple cell wall components in intact plant tissues. We are combining these imaging capabilities with computational tools that enable correlated registration, integration, and analysis. This fully integrated, multiparametric optical system will be used to address biological problems connected to cell wall assembly in grasses. This includes a focus on developmental and environmental variation of cell wall impregnation with silica, lignin, suberin, and cutin in different tissues and cell types. Our research plan comprises three main goals: (1) To develop an accessible imaging platform and associated open-source software able to extract and integrate fingerprints from fluorescence-associated (multispectral emission, lifetime, and polarization), wide-field polarimetry, second harmonic generation (SHG), and stimulated Raman scattering signals (SRS); (2) To determine unique combination of fingerprints for various cell wall components and selected metabolites; (3) To analyze the process of cell wall silicification in grasses and determine how silicification affects cell wall properties and lignin, cutin, and suberin deposition in other cell types under differ stress conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

South Africa’s Appliance Energy Efficiency Standards and Labeling Program

Electricity consumption in South Africa comes with a hefty environmental cost to the society. For every kilowatt-hour (kWh) produced, 1 kilogram (kg) of carbon dioxide (CO 2 ) is emitted, 1.4 liters of water are used, and 0.37 grams of particulate emissions are released in the atmosphere. These environmental implications result from the large share of electricity produced from coal (91 percent in 2015). While planned new capacity will ramp up renewable energy, the integrated resource plan for the country still projects the share of col to be 64 percent in 2030. Energy savings provide environmental benefits as well as economic benefits. Energy efficiency standards and labeling (S&L) programs are a policy measure proven to save energy. Such programs have been implemented in many countries to remove inefficient technologies and transform markets to more efficient technologies. In this study, we describe the methodology, assumptions and results of a stock turnover modeling tool that estimates the energy savings achievable by South Africa’s S&L program in the residential sector. We show that if regulations are passed in 2020 and effective in 2021 for 10 major end-use adopting international standards best practices, 6 terawatt-hours (TWh) will be saved in 2030 and 9.5 TWh will be saved in 2040, representing a CO 2 mitigation of 3.7 megatonnes (Mt) in 2030 and 5.8 Mt in 2040, which will contribute to the South African government’s international engagement in fighting against climate change through its National Determined Contribution (NDC). Additional environmental benefits include saving of 6.5 billion liters of water in 2030, representing approximately 100 liters per capita in 2030. Air quality will also improve, as 4 kilotons (kt) of particulate emissions will be avoided, as well as 4.3 Mt of sulfur oxides (SOx) and 23 kt of nitrogen oxides (NOx) in 2030.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Elevated Temperature Effects on Protein Turnover Dynamics in Arabidopsis thaliana Seedlings Revealed by 15 N-Stable Isotope Labeling and ProteinTurnover Algorithm

Global warming poses a threat to plant survival, impacting growth and agricultural yield. Protein turnover, a critical regulatory mechanism balancing protein synthesis and degradation, is crucial for the cellular response to environmental changes. We investigated the effects of elevated temperature on proteome dynamics in Arabidopsis thaliana seedlings using 15 N-stable isotope labeling and ultra-performance liquid chromatography-high resolution mass spectrometry, coupled with the ProteinTurnover algorithm. Analyzing different cellular fractions from plants grown under 22 °C and 30 °C growth conditions, we found significant changes in the turnover rates of 571 proteins, with a median 1.4-fold increase, indicating accelerated protein dynamics under thermal stress. Notably, soluble root fraction proteins exhibited smaller turnover changes, suggesting tissue-specific adaptations. Significant turnover alterations occurred with redox signaling, stress response, protein folding, secondary metabolism, and photorespiration, indicating complex responses enhancing plant thermal resilience. Conversely, proteins involved in carbohydrate metabolism and mitochondrial ATP synthesis showed minimal changes, highlighting their stability. This analysis highlights the intricate balance between proteome stability and adaptability, advancing our understanding of plant responses to heat stress and supporting the development of improved thermotolerant crops.

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

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mercury Methylation and Soil Organic Matter Degradation in Incubations with Added Acetate, Isotope-Labeled Mercury, and Selective Microbial Inhibitors, Teller and Council Sites, Seward Peninsula, 2018-2019

This dataset provides the results of laboratory soil microcosm incubation experiments conducted under anaerobic conditions with added acetate, isotope-labeled mercury, and selective microbial inhibitors (2-bromoethanesulfonate and sodium molybdate). The microcosms contained soil collected in August 2018 from the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) near Intensive Site 9 (TL_IS_9) and from the Council Road Site (CN_MM71). Incubations were conducted at 8°C for 180 days (Teller incubations) or 60 days (Council incubations). The microcosm headspace was sampled at designated intervals for greenhouse gas concentrations, then destructively harvested and extracted using ultrapure water and 0.1 M KCl for analysis of the porewater geochemistry. This dataset contains one pdf, two csv data files, and one zipped file with raw data.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

177 Lu-Labeled Phosphoramidate-Based PSMA Inhibitors: The Effect of an Albumin Binder on Biodistribution and Therapeutic Efficacy in Prostate Tumor-Bearing Mice

Prostate-specific membrane antigen (PSMA) continues to be an active biomarker for small-molecule PSMA-targeted imaging and therapeutic agents for prostate cancer and various non-prostatic tumors that are characterized by PSMA expression on their neovasculature. One of the challenges for small-molecule PSMA inhibitors with respect to delivering therapeutic payloads is their rapid renal clearance. In order to overcome this pharmacokinetic challenge, we outfitted a 177 Lu-labeled phosphoramidate-based PSMA inhibitor (CTT1298) with an albumin-binding motif (CTT1403) and compared its in vivo performance with that of an analogous compound lacking the albumin-binding motif (CTT1401). The radiolabeling of CTT1401 and CTT1403 was achieved using click chemistry to connect 177 Lu-DOTA-N3 to the dibenzocyclooctyne (DBCO)-bearing CTT1298 inhibitor cores. A direct comparison in vitro and in vivo performance was made for CTT1401 and CTT1403; the specificity and efficacy by means of cellular uptake and internalization, biodistribution, and therapeutic efficacy were determined for both compounds. And while both compounds displayed excellent uptake and rapid internalization in PSMA+ PC3-PIP cells, the albumin binding moiety in CTT1403 conferred clear advantages to the PSMA-inhibitor scaffold including increased circulating half-life and prostate tumor uptake that continued to increase up to 168 h post-injection. This then increased tumor uptake translated into superior therapeutic efficacy of CTT1403 in PSMA+ PC3-PIP human xenograft tumors.

177Lu↗
DOE OSTI2021

Partial connectomes of labeled dopaminergic circuits reveal non-synaptic communication and axonal remodeling after exposure to cocaine

Dopaminergic (DA) neurons exert profound influences on behavior including addiction. However, how DA axons communicate with target neurons and how those communications change with drug exposure remains poorly understood. We leverage cell type-specific labeling with large volume serial electron microscopy to detail DA connections in the nucleus accumbens (NAc) of the mouse (Mus musculus) before and after exposure to cocaine. We find that individual DA axons contain different varicosity types based on their vesicle contents. Spatially ordering along individual axons further suggests that varicosity types are non-randomly organized. DA axon varicosities rarely make specific synapses (<2%, 6/410), but instead are more likely to form spinule-like structures (15%, 61/410) with neighboring neurons. Days after a brief exposure to cocaine, DA axons were extensively branched relative to controls, formed blind-ended ‘bulbs’ filled with mitochondria, and were surrounded by elaborated glia. Finally, mitochondrial lengths increased by ~2.2 times relative to control only in DA axons and NAc spiny dendrites after cocaine exposure. We conclude that DA axonal transmission is unlikely to be mediated via classical synapses in the NAc and that the major locus of anatomical plasticity of DA circuits after exposure to cocaine are large-scale axonal re-arrangements with correlated changes in mitochondria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
NASA NTRS1967

Tritiated alumina serves as reagent for self-labeling analysis

Tritiated alumina, prepared by exchange of the surface hydroxyl groups with tritiated water, is a suitable reagent for exchange-labeling of specific compounds in low concentrations prior to chromatographic analysis. In a chromatographic column, it detects and measures submicrogram quantities of material.

Erenrich, E. H.↗