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DOE OSTI2026

Multi-omics data resource: Data package 23 (Pck023)

The data package consists of isolated pancreatic islets from adult male C57BL6/J mice treated with IL-1β + IFNγ, IL-1β + IFNγ + NMMA, or NMMA alone for 18 h and submitted for scRNA-seq. This study focused on the cell-type-specific effects of nitric oxide signaling in islets and characterized the heterogeneity of responses. Data contributors: Jennifer S Stancill & John A Corbett: Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA Data repository: GSE183010 Publication: 10.1093/function/zqab063

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

NASA NTRS1997

The Aggregate Description of Semi-Arid Vegetation with Precipitation-Generated Soil Moisture Heterogeneity

Meteorological measurements in the Walnut Gulch catchment in Arizona were used to synthesize a distributed, hourly-average time series of data across a 26.9 by 12.5 km area with a grid resolution of 480 m for a continuous 18-month period which included two seasons of monsoonal rainfall. Coupled surface-atmosphere model runs established the acceptability (for modelling purposes) of assuming uniformity in all meteorological variables other than rainfall. Rainfall was interpolated onto the grid from an array of 82 recording rain gauges. These meteorological data were used as forcing variables for an equivalent array of stand-alone Biosphere-Atmosphere Transfer Scheme (BATS) models to describe the evolution of soil moisture and surface energy fluxes in response to the prevalent, heterogeneous pattern of convective precipitation. The calculated area-average behaviour was compared with that given by a single aggregate BATS simulation forced with area-average meteorological data. Heterogeneous rainfall gives rise to significant but partly compensating differences in the transpiration and the intercepted rainfall components of total evaporation during rain storms. However, the calculated area-average surface energy fluxes given by the two simulations in rain-free conditions with strong heterogeneity in soil moisture were always close to identical, a result which is independent of whether default or site-specific vegetation and soil parameters were used. Because the spatial variability in soil moisture throughout the catchment has the same order of magnitude as the amount of rain failing in a typical convective storm (commonly 10% of the vegetation's root zone saturation) in a semi-arid environment, non-linearitv in the relationship between transpiration and the soil moisture available to the vegetation has limited influence on area-average surface fluxes.

White, Cary B.↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRS1993

Security aspects of space operations data

This paper deals with data security. It identifies security threats to European Space Agency's (ESA) In Orbit Infrastructure Ground Segment (IOI GS) and proposes a method of dealing with its complex data structures from the security point of view. It is part of the 'Analysis of Failure Modes, Effects Hazards and Risks of the IOI GS for Operations, including Backup Facilities and Functions' carried out on behalf of the European Space Operations Center (ESOC). The security part of this analysis has been prepared with the following aspects in mind: ESA's large decentralized ground facilities for operations, the multiple organizations/users involved in the operations and the developments of ground data systems, and the large heterogeneous network structure enabling access to (sensitive) data which does involve crossing organizational boundaries. An IOI GS data objects classification is introduced to determine the extent of the necessary protection mechanisms. The proposal of security countermeasures is oriented towards the European 'Information Technology Security Evaluation Criteria (ITSEC)' whose hierarchically organized requirements can be directly mapped to the security sensitivity classification.

Schmitz, Stefan↗

NASA NTRS1993

The ESIS query environment pilot project

The European Space Information System (ESIS) was originally conceived to provide the European space science community with simple and efficient access to space data archives, facilities with which to examine and analyze the retrieved data, and general information services. To achieve that ESIS will provide the scientists with a discipline specific environment for querying in a uniform and transparent manner data stored in geographically dispersed archives. Furthermore it will provide discipline specific tools for displaying and analyzing the retrieved data. The central concept of ESIS is to achieve a more efficient and wider usage of space scientific data, while maintaining the physical archives at the institutions which created them, and has the best background for ensuring and maintaining the scientific validity and interest of the data. In addition to coping with the physical distribution of data, ESIS is to manage also the heterogenity of the individual archives' data models, formats and data base management systems. Thus the ESIS system shall appear to the user as a single database, while it does in fact consist of a collection of dispersed and locally managed databases and data archives. The work reported in this paper is one of the results of the ESIS Pilot Project which is to be completed in 1993. More specifically it presents the pilot ESIS Query Environment (ESIS QE) system which forms the data retrieval and data dissemination axis of the ESIS system. The others are formed by the ESIS Correlation Environment (ESIS CE) and the ESIS Information Services. The ESIS QE Pilot Project is carried out for the European Space Agency's Research and Information center, ESRIN, by a Consortium consisting of Computer Resources International, Denmark, CISET S.p.a, Italy, the University of Strasbourg, France and the Rutherford Appleton Laboratories in the U.K. Furthermore numerous scientists both within ESA and space science community in Europe have been involved in defining the core concepts of the ESIS system.

Fuchs, Jens J.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-16833-DND

Semantic Infrastructure is central to realizing the first goal of the ASDC's Strategic Plan: expanding the ASDC's customer base by improving access to ASDC data. ASDC data comprises a widely heterogeneous set of complex products which presents two significant challenges in data access: Helping customers discover, among many available options, the most suitable data products for their purpose; and Guiding customers to easily and appropriately use products. Data products differ significantly in terms of how the data was collected and processed, even with similar subject matter. Understanding differences is critical to using data effectively. To reach a broader customer range, the ASDC must provide prospective users with enough information to quickly and meaningfully compare and evaluate data products. Data formats and structures also differ among products. Applications displaying and analyzing data need access to federated and semantically disambiguated data. Semantic technologies offer functionality for addressing this issue. Ontologies can provide robust, stable domain models serving as common schema for discovering, evaluating, comparing, and integrating data from disparate products. Reasoning engines and triple stores can leverage ontologies to support intelligent search applications allowing users to discover, query, retrieve, and easily reformat data from a broad spectrum of sources.

Beth Huffer↗

DOE OSTI2024

Quantitative kinetic rules for plastic strain-induced α - ω phase transformation in Zr under high pressure

Plastic strain-induced phase transformations (PTs) and chemical reactions under high pressure are broadly spread in modern technologies, friction and wear, geophysics, and astrogeology. However, because of very heterogeneous fields of plastic strain $E$ p and stress σ tensors and volume fraction c of phases in a sample compressed in a diamond anvil cell (DAC) and impossibility of measurements of σ and $E$ p , there are no strict kinetic equations for them. Here, we develop a kinetic model, finite element method (FEM) approach, and combined FEM-experimental approaches to determine all fields in strongly plastically predeformed Zr compressed in DAC, and specific kinetic equation for α-ω PT consistent with experimental data for the entire sample. Since all fields in the sample are very heterogeneous, data are obtained for numerous complex 7D paths in the space of 3 components of the plastic strain tensor and 4 components of the stress tensor. Kinetic equation depends on accumulated plastic strain (instead of time) and pressure and is independent of plastic strain and deviatoric stress tensors, i.e., it can be applied for various above processes. Our results initiate kinetic studies of strain-induced PTs and provide efforts toward more comprehensive understanding of material behavior in extreme conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

In this paper, we propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce an order orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm guaranteed to converge to a stationary point. The unique and common representations learned by perTucker reveal intrinsic statistical patterns in data and provide valuable information for a wide range of downstream analytics, including anomaly detection, source classification, and clustering. We demonstrate perTucker’s effectiveness through a simulation study and two case studies on solar flare detection and tonnage signal classification.

14 SOLAR ENERGY↗

DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗

DOE OSTI2024

Characterization of pitch carbon coating properties affecting the electrochemical behavior of silicon nanoparticle lithium-ion battery anodes

Silicon is an exciting material for next-generation lithium-ion battery anodes, due to its high theoretical capacity and availability, but its widespread implementation has been limited by extensive volume changes during cycling that causes mechanical damage and limits cycle life. One approach to mitigating these deleterious effects while still maintaining silicon's benefits is the addition of a pitch carbon coating to nanometer-sized silicon particles. Here, in this study, we present characterization of pitch-coated silicon electrodes without annealing and annealed at 700 °C and 1000 °C to determine the optimum temperature treatment, as well as pitch-only and silicon-only electrodes with and without annealing to elucidate the impacts of each component on early electrochemical behavior. Using Fourier transform infrared and Raman spectroscopies, atomic force and scanning spreading resistance microscopy, and electrochemical analysis, we find 700 °C to be the optimal annealing temperature, as amorphous carbon is created, which improves conductivity prior to cycling and facilitates ion storage during cycling that increases capacity. We also present in situ Raman spectroscopy data demonstrating the heterogeneous aging that occurs across the electrode surface during cycling.

25 ENERGY STORAGE↗

DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Structural characterization of ligand binding and pH-specific enzymatic activity of mouse Acidic Mammalian Chitinase

Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high-resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1973

A critical review of the experimental data for developed free turbulent shear layers

Experimental shear layer data are reviewed and the results are compared to numerical predictions for three test cases. It was concluded from the study that many, if not most, of the apparent inconsistencies which exist in the interpretation of the experimental data for free shear layers result from confusing data taken in developed turbulent flows with those taken in transitional or developing flows. Other conclusions drawn from the study include the following: (1) The effects of Mach number are more uncertain primarily because of limited data and the absence of any turbulence measurements for supersonic shear layers. (2) The data available for heterogeneous shear layers are not sufficient to clearly establish the effect of density ratio on mixing rate.

Birch, S. F.↗

NASA NTRS1980

Evaluating LANDSAT wildland classification accuracies

Procedures to evaluate the accuracy of LANDSAT derived wildland cover classifications are described. The evaluation procedures include: (1) implementing a stratified random sample for obtaining unbiased verification data; (2) performing area by area comparisons between verification and LANDSAT data for both heterogeneous and homogeneous fields; (3) providing overall and individual classification accuracies with confidence limits; (4) displaying results within contingency tables for analysis of confusion between classes; and (5) quantifying the amount of information (bits/square kilometer) conveyed in the LANDSAT classification.

Toll, D. L.↗

NASA NTRS1985

Uranium distribution and 'excessive' U-He ages in iron meteoritic troilite

Fission tracking techniques were used to measure the uranium distribution in meteoritic troilite and graphite. The obtained fission tracking data showed a heterogeneous distribution of tracks with a significant portion of track density present in the form of uranium clusters at least 10 microns in size. The matrix containing the clusters was also heterogeneous in composition with U concentrations of about 0.2-4.7 ppb. U/He ages could not be estimated on the basis of the heterogeneous U distributions, so previously reported estimates of U/He ages in the presolar range are probably invalid.

Fisher, D. E.↗

NASA NTRS1988

Shock-induced melting and shear banding in single-crystal NaCl

Radiative color temperatures were measured in single-crystal sodium chloride under shock compression parallel to 100-line orientation over a pressure range from 20 to 35 GPa. Color temperatures from 2500 to 4500 K and emittances from 0.003 to 0.3 were determined by fitting observed spectra (450-850 nm) to the Planck greybody radiation law. These data support a heterogeneous shock deformation model of shocked halite in this pressure range. A 2500-K temperature rise, of unknown origin, is observed over the B1-B2 mixed phase region from 25 to 30 GPa. Assuming that shock deformation occurs via yielding in localized planar zones which become melt and the melting temperature at high pressure controls the temperature, it is inferred that the temperature of the B2 fusion curve from 30 to 35 GPa rises from 3200 to 3300 K. The B1-B2-liquid triple point is predicted to be at a temperature of 2250 K and 23.5 GPa.

Schmitt, Douglas R.↗