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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The co-crystal structure of Cbl-b and a small-molecule inhibitor reveals the mechanism of Cbl-b inhibition

Cbl-b is a RING-type E3 ubiquitin ligase that is expressed in several immune cell lineages, where it negatively regulates the activity of immune cells. Cbl-b has specifically been identified as an attractive target for cancer immunotherapy due to its role in promoting an immunosuppressive tumor environment. A Cbl-b inhibitor, Nx-1607, is currently in phase I clinical trials for advanced solid tumor malignancies. Using a suite of biophysical and cellular assays, we confirm potent binding of C7683 (an analogue of Nx-1607) to the full-length Cbl-b and its N-terminal fragment containing the TKBD-LHR-RING domains. To further elucidate its mechanism of inhibition, we determined the co-crystal structure of Cbl-b with C7683, revealing the compound’s interaction with both the TKBD and LHR, but not the RING domain. Here, we provide structural insights into a novel mechanism of Cbl-b inhibition by a small-molecule inhibitor that locks the protein in an inactive conformation by acting as an intramolecular glue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Compact, portable, automatic sample changer stick for cryostats and closed-cycle refrigerators

Beamlines are facilities that produce and deliver highly focused and intense beams of radiation, typically x rays, synchrotron radiation, or neutrons, for scientific research purposes. Millions of dollars are spent annually to maintain and operate these scientific beamlines, oftentimes running continuously between cycles. To reduce human intervention and improve productivity, mechanical sample changers are often commissioned for use. Designing sample changers is difficult because mechanical parts can be bulky, expensive, and challenging to design for instruments with low volume access, high radiation, and cryogenic environments. We present a portable and inexpensive sample changer stick that can hold and manipulate up to four samples, specifically designed for use with cryogenic closed-cycle refrigerators. The sample changer stick enables rapid and efficient exchange of samples without manual intervention, and is compatible with standard sample mounts such as vanadium cans. The sample changer stick includes a motorized rotation and lancing mechanism, which enables the precise positioning of each sample in the neutron beam, while ensuring compatibility with the operating temperatures and vacuum conditions required for closed-cycle refrigerators. The design has been successfully tested at the VISION beamline at the Spallation Neutron Source. The mechanical action and software controls are detailed. Furthermore, the sample changer stick is a valuable tool for scientists working with cryogenic closed-cycle refrigerators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Magnetic properties and signatures of moment ordering in the triangular lattice antiferromagnet KCeO 2

The magnetic ground state and the crystalline electric field level scheme of the triangular lattice antiferromagnet KCeO 2 are investigated here. Below $T_N$ = 300 mK, KCeO 2 develops signatures of magnetic order in specific heat measurements and low energy inelastic neutron scattering data. Trivalent Ce 3+ ions in the $D_{3d}$ local environment of this compound exhibit large splittings among the lowest three 4$f^1$ Kramers doublets defining for the free ion the $\textit{J}$ = 5/2 sextet and a ground state doublet with dipole character, consistent with recent theoretical predictions by Eldeeb et al. [Phys. Rev. Materials 4, 124001 (2020)]. An unexplained, additional local mode appears, and potential origins of this anomalous mode are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Invited: Software defined accelerators from learning tools environment

Next generation systems, such as edge devices, will need to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. In this paper we present the Software Defined Accelerators From Learning Tools Environment (SODALITE), an automated open source high-level ML framework-to-verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets. The SODALITE approach will implement optimal designs by seamlessly combining custom components generated through high-level synthesis (HLS) with templated and fully tunable Intellectural Properties (IPs) and macros, integrated in an extendable resource library. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimensions.

High-Level Synthesis, Accelerators, Hardware-Softw↗
DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗
DOE OSTI2024

Leveraging Additive Manufacturing to Fabricate High Temperature Alloys With Co-Designed Mechanical Properties and Environmental Resistance

A paradigm shift in the traditional sequential design approaches is critically essential to create application-specific hierarchical and multifunctional materials with superior long-term performance for next-generation energy technologies involving extreme environments. In the current work, we aim to leverage the flexibility and geometric/compositional complexity offered by additive manufacturing to demonstrate this new approach by codesigning a compositionally graded Ni-based alloy for molten salts\sCO 2 heat exchangers to enable mitigation of environmental degradation of surfaces exposed to molten halide salts, while simultaneously suppressing the consequent deterioration in mechanical stability. Further, thermokinetic modeling describing the underlying physics of thermally- and environmentally induced spatiotemporal compositional and microstructural evolution will be employed to predict the parameter space of material deposition processes and precisely identify the required composition gradient. Preliminary corrosion and mechanical testing of the dual material demonstrated the potential of the material to replace existing solid solution strengthened materials for this application.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Cellular signaling within aged skeletal muscle reveals a dysregulated stress-induced remodeling response following volumetric muscle loss in female mice

Severe muscle trauma disrupts endogenous repair mechanisms, producing chronic functional deficits that are incompletely characterized in aged populations. This study investigated inflammatory, molecular, and physiological responses to volumetric muscle loss in young adult and aged female mice. Serum cytokine profiling revealed elevated baseline inflammation in aged animals and a blunted response to injury, with cytokines such as IL-6 increasing 4.4-fold in young versus 1.8-fold in aged mice at day 3 compared to baseline. By day 28 post-injury, histological analyses showed comparable reductions in muscle size and increases in fibrosis across ages. Despite these similar tissue-level outcomes, age-dependent differences emerged in downstream functional and molecular responses. Muscle functional testing demonstrated persistent force deficits independent of age but altered muscle relaxation kinetics in aged mouse muscles (p < 0.001), suggesting dysregulated excitation-contraction coupling. Additionally, mice displayed age-associated differences in post-injury limb loading. Global proteomic analyses further confirmed age-associated enrichment of complement and antigen-processing pathways alongside metabolic dysfunction (p < 0.05). Phosphoproteomic profiling revealed reduced basal kinase activity in the muscles of aged mice yet exaggerated injury-induced phosphorylation of Mapk1-associated phosphosites, indicating a dysregulated stress response. Collectively, these findings suggest that aged murine muscles function within a heightened inflammatory and perturbed kinase-signaling environment that may hinder the coordination of regenerative programs, underscoring the need for regenerative strategies that address age-specific molecular contexts to improve functional recovery across the lifespan.

Habing, Krista M.↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning

Deep neural networks have emerged as a leading set of algorithms to infer information from a variety of data sources such as images and time series data. In their most basic form, neural networks lack the ability to adapt to new classes of information. Continual learning is a field of study attempting to give previously trained deep learning models the ability to adapt to a changing environment. Previous work developed a CL method called Neurogenesis for Deep Learning (NDL). Here, we combine NDL with a specific neural network architecture (the Ladder Network) to produce a system capable of automatically adapting a classification neural network to new classes of data. The NDL Ladder Network was evaluated against other leading CL methods. While the NDL and Ladder Network system did not match the cutting edge performance achieved by other CL methods, in most cases it performed comparably and is the only system evaluated that can learn new classes of information with no human intervention.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Who Is Metabolizing What? Discovering Novel Biomolecules in the Microbiome and the Organisms Who Make Them

Even as the field of microbiome research has made huge strides in mapping microbial community composition in a variety of environments and organisms, explaining the phenotypic influences on the host by microbial taxa – both known and unknown – and their specific functions still remain major challenges. A pressing need is the ability to assign specific functions in terms of enzymes and small molecules to specific taxa or groups of taxa in the community. This knowledge will be crucial for advancing personalized therapies based on the targeted modulation of microbes or metabolites that have predictable outcomes to benefit the human host. This perspective article advocates for the combined use of standards-free metabolomics and activity-based protein profiling strategies to address this gap in functional knowledge in microbiome research via the identification of novel biomolecules and the attribution of their production to specific microbial taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Antibiotic resistance and host immune system-induced metal bactericidal control are key factors for microbial persistence in the developing human preterm infant gut microbiome

The human gut microbiome, which develops and stabilizes during the early stages of infant life, plays an essential role in host health through the production of metabolic resources and the stimulation and training of the immune system. To study colonization and community functional dynamics of the microbiota based on responses to host immune processes during the normal and dysbiotic establishment of the gut, metaproteomics was conducted on 91 fecal samples collected over the first 90 days of life from 17 hospitalized premature infants. Microbial responses to antibiotic administration and host-imposed metal bactericidal control correlated with community assembly and resiliency of microbes in the developing preterm gut. Specifically, proteins related to antibiotic resistance and metal homeostasis mechanisms were predominant in persisting members in the infant gut environment over the first several weeks of life. Overall, this metaproteomics study provides a unique approach to examine the temporal expansion and resilience of microbial colonization, as it allows simultaneous examination of both host and microbial metabolic activities. Understanding the interplay between host and microbes may elucidate the microbiome’s potential immunomodulatory roles relevant to necrotizing enterocolitis and other dysbiotic conditions in preterm infants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The Amylolytic Regulator AmyR of Aspergillus niger Is Involved in Sucrose and Inulin Utilization in a Culture-Condition-Dependent Manner

Filamentous fungi degrade complex plant material to its monomeric building blocks, which have many biotechnological applications. Transcription factors play a key role in plant biomass degradation, but little is known about their interactions in the regulation of polysaccharide degradation. Here, we deepened the knowledge about the storage polysaccharide regulators AmyR and InuR in Aspergillus niger. AmyR controls starch degradation, while InuR is involved in sucrose and inulin utilization. In our study, the phenotypes of A. niger parental, ΔamyR, ΔinuR and ΔamyRΔinuR strains were assessed in both solid and liquid media containing sucrose or inulin as carbon source to evaluate the roles of AmyR and InuR and the effect of culture conditions on their functions. In correlation with previous studies, our data showed that AmyR has a minor contribution to sucrose and inulin utilization when InuR is active. In contrast, growth profiles and transcriptomic data showed that the deletion of amyR in the ΔinuR background strain resulted in more pronounced growth reduction on both substrates, mainly evidenced by data originating from solid cultures. Overall, our results show that submerged cultures do not always reflect the role of transcription factors in the natural growth condition, which is better represented on solid substrates. Importance: The type of growth has critical implications in enzyme production by filamentous fungi, a process that is controlled by transcription factors. Submerged cultures are the preferred setups in laboratory and industry and are often used for studying the physiology of fungi. In this study, we showed that the genetic response of A. niger to starch and inulin was highly affected by the culture condition, since the transcriptomic response obtained in a liquid environment did not fully match the behavior of the fungus in a solid environment. These results have direct implications in enzyme production and would help industry choose the best approaches to produce specific CAZymes for industrial purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Study of the Cosmic Rays and Interstellar Medium in Local H I Clouds Using Fermi -LAT Gamma-Ray Observations

An accurate estimate of the interstellar gas density distribution is crucial to understanding the interstellar medium (ISM) and Galactic cosmic rays (CRs). To comprehend the ISM and CRs in a local environment, a study of the diffuse $γ$-ray emission in a midlatitude region of the third quadrant was performed. Specifically, the $γ$-ray data in the 0.1–25.6 GeV energy range of the Fermi Large Area Telescope (LAT) and other interstellar gas tracers such as the HI4PI survey data and the Planck dust thermal emission model were used, and the northern and southern regions were analyzed separately. The variation of the dust emission D em with the total neutral gas column density N H was studied in high dust temperature areas, and the N H /D em ratio was calibrated using $γ$-ray data under the assumption of a uniform CR intensity in the studied regions. The measured integrated $γ$-ray emissivities above 100 MeV are (1.58 ± 0.04) x 10 -26 photons s -1 sr -1 H-atom-1 and (1.59 ± 0.02) x 10-26 photons s-1 sr-1 H-atom -1 in the northern and southern regions, respectively, supporting the existence of a uniform CR intensity in the vicinity of the solar system. While most of the gas can be interpreted to be H I with a spin temperature of T S = 125 K or higher, an area dominated by optically thick H I with T S ~ 40 K was identified.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Plausibility of the Formose Reaction in Alkaline Hydrothermal Vent Environments

Prebiotic processes required a reliable source of free energy and complex chemical mixtures that may have included sugars. The formose reaction is a potential source of those sugars. At moderate to elevated temperature and pH ranges, these sugars rapidly decay. Here it is shown that CaCO 3 -based chemical gardens catalyze the formose reaction to produce glucose, ribose, and other monosaccharides. These thin inorganic membranes are explored as analogs of hydrothermal vent materials—a possible place for the origin of life—and similarly exposed to very steep pH gradients. Supported by simulations of a simple reaction-diffusion model, this study shows that such gradients allow for the dynamic accumulation of sugars in specific layers of the thin membrane, effectively protecting formose sugar yields. Furthermore, the formose reaction may be a plausible prebiotic reaction in alkaline hydrothermal vent environments, possibly setting the stage for an RNA world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Ultrasonic Measurements of Temperature Distribution in Extreme Environments: Results of Power Plant Testing

The most hardened insertion sensors do not perform reliably in extreme environmental conditions. We address this problem by developing an ultrasonic thermometry system for the measurements of segmental temperature distributions (MSTD) in solid materials and, specifically, across containments of energy conversion processes. The MSTD approach uses an echogenically segmented ultrasound propagation path, structured to contain engineered or naturally occurring echogenic features. In pulse-echo mode, these features produce a train of echoes in response to an external pulse of ultrasonic excitation created and acquired by a transducer located outside the extreme environment. The delays between the echoes encode the information on the temperature distribution in the corresponding segments of the ultrasonic propagation path, which we use to reconstruct an unknown temperature profile. This note describes the preliminary results and initial experience in applying the developed MSTD ultrasonic thermometry system to the measurements of temperatures in a utility-scale 500MW coal-fired boiler.

John, Mason↗
DOE OSTI2023

Soil microbiome engineering for sustainability in a changing environment

Recent advances in microbial ecology and synthetic biology have the potential to mitigate damage caused by anthropogenic activities that are deleteriously impacting Earth’s soil ecosystems. Here, we discuss challenges and opportunities for harnessing natural and synthetic soil microbial communities, focusing on plant growth promotion under different scenarios. We explore current needs for microbial solutions in soil ecosystems, how these solutions are being developed and applied, and the potential for new biotechnology breakthroughs to tailor and target microbial products for specific applications. We highlight several scientific and technological advances in soil microbiome engineering, including characterization of microbes that impact soil ecosystems, directing how microbes assemble to interact in soil environments, and the developing suite of gene-engineering approaches. This Review underscores the need for an interdisciplinary approach to understand the composition, dynamics and deployment of beneficial soil microbiomes to drive efforts to mitigate or reverse environmental damage by restoring and protecting healthy soil ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2022

Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

NMDC, Microbiome↗