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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structured interactions drive abrupt transitions in the spatial organization of microbial communities

Bacteria possess diverse mechanisms to regulate their motility in response to environmental and physiological signals, enabling them to navigate complex habitats and adapt their behavior. Some of these mechanisms are species specific and enable cells to modulate their movement based on the ecological identity of neighboring species. Here, we introduce a model in which bacteria interact via local signals that either enhance or suppress the motility of neighboring cells depending on species type. Through large-scale simulations and a coarse-grained stochastic model, we demonstrate the emergence of a sharp transition driven by nucleation processes: increasing the density of motility-suppressing interactions drives the system from a fully mixed, motile phase to a state characterized by large, stationary bacterial clusters. Remarkably, in systems with a large number of interacting species, this transition can be triggered solely by altering the structure of the motility-regulation interaction matrix while maintaining species and interaction densities constant. In particular, we find that heterogeneous and modular interactions promote the transition more readily than homogeneous random ones. These findings add a dimension to the theory of motility-induced phase separation and contribute to the ongoing effort to understand microbial interactions, suggesting that structured, nonrandom ones may be key to reproducing commonly observed spatial patterns in microbial communities.

bacterial communities↗
DOE OSTI2021

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Low Yield Nuclear Monitoring Physics Experiment 1 – Integrated Data Acquisition System Design and Initial Observations

The report documents the design of the Integrated Data AcQuisition (IDAQ) system and observations recorded during the first in a series of underground chemical explosions conducted on the Nevada National Security Site (NNSS) in southern Nevada. Experiments are funded as part of Low Yield Nuclear Monitoring (LYNM) research and development within the United States National Nuclear Security Administration NA-22 nuclear non-proliferation program. The series is part of the broader Physical Experiment 1 (PE1) being conducted in and around the P-tunnel facility on the NNSS. Each explosive experiment utilizes several tons of comp-B to generate signals recorded by a broad suite of instrumentation. The IDAQ serves as the backbone for all subsurface instrumentation providing precise time synchronization, remote control, data exfiltration and backup, along with recording several sensing modalities throughout the underground complex that includes ground motion, environmental conditions, and electromagnetic signals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

To immunity and beyond: the central role of jasmonate signalling in beneficial root–microbe–environment interactions

Jasmonates (JAs) have traditionally been studied for their defensive roles against wounding and detrimental organisms, but they are also crucial hormones for plant–microbe beneficial interactions. Here, we review the most recent advances in this overlooked field. We cover the evolutionary divergences of JA biosynthesis and signalling across various plant lineages and present the molecular mechanisms of action through which beneficial microbes interact with the host JA signalling pathway as well as environmental integration. Special emphasis is given to the cutting‐edge tools to study the spatial compartmentalization and cell and tissue specialization of JA signalling. This review underscores the role of the JA signalling pathway, with the MYELOCYTOMATOSIS2 transcription factor as a potential integrator of biotic and environmental cues, and highlights its significance in mutualistic interactions.

defence↗
DOE OSTI2021

A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ~ 82%) and physiological information (i.e. physiological SIF yield, Φ F , 17% ~ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Φ F indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that Φ F responded to the proxy for physiological stress (partial correlation coefficient, r p = 0.40, p < 0.001) while non-physiological signals of SIF did not respond ( p > 0.1). The field-scale Φ F data showed water deficit stress from the comparison between irrigated and rainfed fields, and Φ F was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r = 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed Φ F was more strongly correlated spatially with air temperature and VPD ( r = 0.23 and 0.39) than SIF ( r = 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that Φ F reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Φ F is spatially scalable. Utilizing Φ F for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cell wall ester modifications and volatile emission signatures of plant response to abiotic stress

Abstract Growth suppression and defence signalling are simultaneous strategies that plants invoke to respond to abiotic stress. Here, we show that the drought stress response of poplar trees ( Populus trichocarpa ) is initiated by a suppression in cell wall derived methanol (MeOH) emissions and activation of acetic acid (AA) fermentation defences. Temperature sensitive emissions dominated by MeOH (AA/MeOH <30%) were observed from physiologically active leaves, branches, detached stems, leaf cell wall isolations and whole ecosystems. In contrast, drought treatment resulted in a suppression of MeOH emissions and strong enhancement in AA emissions together with volatiles acetaldehyde, ethanol, and acetone. These drought‐induced changes coincided with a reduction in stomatal conductance, photosynthesis, transpiration, and leaf water potential. The strong enhancement in AA/MeOH emission ratios during drought (400%–3500%) was associated with an increase in acetate content of whole leaf cell walls, which became significantly 13 C 2 ‐labelled following the delivery of 13 C 2 ‐acetate via the transpiration stream. The results are consistent with both enzymatic and nonenzymatic MeOH and AA production at high temperature in hydrated tissues associated with accelerated primary cell wall growth processes, which are downregulated during drought. While the metabolic source(s) require further investigation, the observations are consistent with drought‐induced activation of aerobic fermentation driving high rates of foliar AA emissions and enhancements in leaf cell wall O ‐acetylation. We suggest that atmospheric AA/MeOH emission ratios could be useful as a highly sensitive signal in studies investigating environmental and biological factors influencing growth‐defence trade‐offs in plants and ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

SDSS-IV MaNGA: The Effect of Stellar Mass and Halo Mass on the Assembly Histories of Satellite Galaxies

We combine an unprecedented MaNGA sample of over 3000 passive galaxies in the stellar mass range 10 9 –10 12 M ⊙ with the Sloan Digital Sky Survey group catalog by Tinker to quantify how central and satellite formation, quantified by radial profiles in stellar age, [Fe/H], and [Mg/Fe], depends on the stellar mass of the galaxy (M * ) and the mass of the host halo (M h ). After controlling for M * and M h , the stacked spectra of centrals and satellites beyond the effective radius (r e ) show small, yet significant differences in multiple spectral features at the 1% level. According to spectral fitting with the code alf , a primary driver of these differences appears to be [Mg/Fe] variations, suggesting that stellar populations in the outskirts of satellites formed more rapidly than the outer populations of centrals. To probe the physical mechanisms that may be responsible for this signal, we examined how satellite stellar populations depend on M h . We find that satellites in high-M h halos show older stellar ages, lower [Fe/H], and higher [Mg/Fe] compared to satellites in low-M h halos, especially for M * = 10 9.5 –10 10.5 M ⊙ . These signals lend support to environmentally driven processes that quench satellite galaxies, although variations in the merger histories of central and satellite galaxies also emerge as a viable explanation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Role of Chloroplast Membrane Lipid Metabolism in Plant Environmental Responses

Plants are nonmotile life forms that are constantly exposed to changing environmental conditions during the course of their life cycle. Fluctuations in environmental conditions can be drastic during both day–night and seasonal cycles, as well as in the long term as the climate changes. Plants are naturally adapted to face these environmental challenges, and it has become increasingly apparent that membranes and their lipid composition are an important component of this adaptive response. Plants can remodel their membranes to change the abundance of different lipid classes, and they can release fatty acids that give rise to signaling compounds in response to environmental cues. Chloroplasts harbor the photosynthetic apparatus of plants embedded into one of the most extensive membrane systems found in nature. In part one of this review, we focus on changes in chloroplast membrane lipid class composition in response to environmental changes, and in part two, we will detail chloroplast lipid-derived signals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Cellular metabolic stress responses via organelles

Metabolite fluctuations following nutrient metabolism or environmental stresses impact various intracellular signaling networks and stress responses to maintain cellular and organismal homeostasis. It has been shown that subcellular organelles, such as the endoplasmic reticulum, the Golgi apparatus, lysosomes and mitochondria serve as crucial hubs linking alterations in metabolite levels to cellular responses. This role is coordinated by molecular machineries that are associated with the lipid membranes of organelles, which sense the fluctuations in specific metabolites and activate the appropriate signaling and effector molecules. Moreover, recent studies have demonstrated that membraneless organelles, such as the nucleolus and stress granules, are involved in the metabolic stress response. Metabolite-induced post-translational modifications appear to play an important role in this process. Here, we review the molecular mechanisms of metabolite sensing and metabolite-mediated stress responses through membrane-bound and membraneless organelles in mammalian cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Target of 1 Rapamycin kinase is a positive regulator of plant fatty acid 2 and lipid synthesis

In eukaryotes, Target of Rapamycin (TOR), a conserved protein sensor kinase, integrates a diverse set of environmental cues, including growth factor signals, energy availability, and nutritional status, to direct cell growth. In plants, TOR is activated by light and sugars and regulates a wide range of cellular processes, including protein synthesis and metabolism. Fatty acid synthesis is key to membrane biogenesis that in turn, is required for cell growth. To elucidate the primary regulatory role(s) of TOR in lipid metabolism, we followed fatty acid and lipid changes in plants with altered TOR protein levels or activity for short durations, using Nicotiana benthamiana leaves, Arabidopsis seedlings and Brassica napus cell suspension cultures. Transient expression of TOR significantly elevated the levels of total fatty acids in Nicotiana benthamiana leaves, while treatment of Arabidopsis seedlings with Torin 2, a TOR specific inhibitor, for one day, caused significant reductions in fatty acids and membrane lipids. Similarly, incubating oil-producing Brassica napus suspension culture cells with Torin 2 for eight hours led to significant decreases in the levels of TFA and TAG. Taken together the results from three independent systems presented here establishes that TOR positively regulates lipid synthesis in plants, consistent with its role in animals. Furthermore, RNA-seq analysis of Torin 2-treated Arabidopsis seedlings showed that TOR promotes the upregulation of a number of genes involved in de novo fatty acid synthesis while downregulating genes involved in lipid turnover, which we propose as a mechanistic explanation for its promotion of lipid synthesis and accumulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Electrical signaling in fungi: past and present challenges

Abstract Electrical signaling is a fundamental mechanism for integrating environmental stimuli and coordinating responses in living organisms. While extensively studied in animals and plants, the role of electrical signaling in fungi remains a largely underexplored field. Early studies suggested that filamentous fungi generate action potential-like signals and electrical currents at hyphal tips, yet their function in intracellular communication remained unclear. Renewed interest in fungal electrical activity has fueled developments such as the hypothesis that mycorrhizal networks facilitate electrical communication between plants and the emerging field of fungal-based electronic materials. Given their continuous plasma membrane, specialized septal pores, and insulating cell wall structures, filamentous fungi possess architectural features that could support electrical signaling over long distances. However, studying electrical phenomena in fungal networks presents unique challenges due to the microscopic dimensions of hyphae, the structural complexity of highly modular mycelial networks, and the limitations of traditional electrophysiological methods. This review synthesizes current evidence for electrical signaling in filamentous fungi, evaluates methodological approaches, and highlights experimental challenges. By addressing these challenges and identifying best practices, we aim to advance research in this field and provide a foundation for future studies exploring the role of electrical signaling in fungal biology.

Buffi, Matteo↗
DOE OSTI2021

Preserved and variable spatial‐chemical changes of lipids across tomato leaves in response to central vein wounding reveals potential origin of linolenic acid in signal transduction cascade

Abstract Membrane lipids serve as substrates for the generation of numerous signaling lipids when plants are exposed to environmental stresses, and jasmonic acid, an oxidized product of 18‐carbon unsaturated fatty acids (e.g., linolenic acid), has been recognized as the essential signal in wound‐induced gene expression. Yet, the contribution of individual membrane lipids in linolenic acid generation is ill‐defined. In this work, we performed spatial lipidomic experiments to track lipid changes that occur locally at the sight of leaf injury to better understand the potential origin of linolenic and linoleic acids from individual membrane lipids. The central veins of tomato leaflets were crushed using surgical forceps, leaves were cryosectioned and analyzed by two orthogonal matrix‐assisted laser desorption/ionization mass spectrometry imaging platforms for insight into lipid spatial distribution. Significant changes in lipid composition are only observed 30 min after wounding, while after 60 min lipidome homeostasis has been re‐established. Phosphatidylcholines exhibit a variable pattern of spatial behavior in individual plants. Among lysolipids, lysophosphatidylcholines strongly co‐localize with the injured zone of wounded leaflets, while, for example, lysophosphatidylglycerol (LPG) (16:1) accumulated preferentially toward the apex in the injured zone of wounded leaflets. In contrast, two other LPGs (LPG [18:3] and LPG [18:2]) are depleted in the injured zone. Our high‐resolution co‐localization imaging analyses suggest that linolenic acids are predominantly released from PCs with 16_18 fatty acid composition along the entire leaf, while it seems that in the apex zone PG (16:1_18:3) significantly contributes to the linolenic acid pool. These results also indicate distinct localization and/or substrate preferences of phospholipase isoforms in leaf tissue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2025

Radiation portal monitor data file format for comprehensive background radiation monitoring

Radiation portal monitors (RPMs) are widely used at border security checkpoints to detect the presence of radioactive materials in people, vehicles, and cargo. Typically, RPM detection systems consist of two pillars equipped with gamma and neutron detectors. To improve detection efficiency, RPMs employ techniques such as a limited energy window, dynamic alarm thresholds, and lead shielding. However, without continuous monitoring of background radiation, signal interpretation can be compromised, because environmental factors and mechanical failures can cause fluctuations. Here, we introduce a daily file format that logs gamma background and neutron background radiation levels continuously over a 24 h period; this format is different from traditional formats that record data only when the RPM is active or occupied. The approach enables RPM operators and analysts to (1) identify and diagnose malfunctioning components, (2) adjust system settings to account for dynamic environmental factors, and (3) use the recorded data to characterize outer space phenomena. Continuous background reporting is essential for identifying issues such as faulty connections, voltage divider failures, and errors in background updates. Continuous background reporting also enables the detection of external influences, including nearby X-ray scanners, temperature fluctuations, rainfall, cosmic radiation, and lunar phase changes. These data files are designed to be easily evaluated and parsed using common tools, and a quick review by an expert is often sufficient for problem diagnosis. We anticipate that continuous background radiation monitoring and these new strategies will significantly improve the accuracy and reliability of RPM systems, reducing the rate of false alarms and enhancing overall system performance.

Background radiation monitoring↗
DOE OSTI2022

Sugar loading is not required for phloem sap flow in maize plants

We report phloem transport of photoassimilates from leaves to non-photosynthetic organs, such as the root and shoot apices and reproductive organs, is crucial to plant growth and yield. For nearly 90 years, evidence has been generally consistent with the theory of a pressure-flow mechanism of phloem transport. Central to this hypothesis is the loading of osmolytes, principally sugars, into the phloem to generate the osmotic pressure that propels bulk flow. Here we used genetic and light manipulations to test whether sugar import into the phloem is required as the driving force for phloem sap flow. Using carbon-11 radiotracer, we show that a maize sucrose transporter1 (sut1) loss-of-function mutant has severely reduced export of carbon from photosynthetic leaves (only ~4% of the wild type level). Yet, the mutant remarkably maintains phloem pressure at ~100% and sap flow speeds at ~50–75% of those of wild type. Potassium (K+) abundance in the phloem was elevated in sut1 mutant leaves. Fluid dynamic modelling supports the conclusion that increased K+ loading compensated for decreased sucrose loading to maintain phloem pressure, and thereby maintained phloem transport via the pressure-flow mechanism. Furthermore, these results suggest that sap flow and transport of other phloem-mobile nutrients and signalling molecules could be regulated independently of sugar loading into the phloem, potentially influencing carbon–nutrient homoeostasis and the distribution of signalling molecules in plants encountering different environmental conditions.

36 MATERIALS SCIENCE↗