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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System

Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology. Since 2011, the authors, along with a community of open-source contributors, have worked on developing an open-source radiology information system (RIS) across two communities—OpenMRS and LibreHealth. The main purpose of the RIS is to implement core radiology workflows, on which others can build and test new radiology standards. This work has resulted in three major releases of the system, with current architectural changes driven by changing technology, development of new standards in health and imaging informatics, and changing user needs. At their core, both these communities are focused on building general-purpose EHR systems, but based on user contributions from the fringes, we have been able to create an innovative system that has been used by hospitals and clinics in four different countries. We provide an overview of the history of the LibreHealth RIS, the architecture of the system, overview of standards integration, describe challenges of developing an open-source product, and future directions. Our goal is to attract more participation and involvement to further develop the LibreHealth RIS into an Enterprise Imaging System that can be used in other clinical imaging including pathology and dermatology.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Characterization of gamma-ray background outside of the High Flux Isotope Reactor

Static gamma-ray measurements collected between September 2017 and March 2018 in the vicinity of the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at Oak Ridge National Laboratory are presented. Temporal events are identified and tracked, including radioactive material transfers, rain events, and 41 Ar emissions. The remaining data is used to determine the average background and its variation. Finally, these results will be used to benchmark Monte Carlo transport models of the HFIR/REDC vicinity and provide training data for material transfer tracking algorithms and data fusion algorithms to determine HFIR operational status under the Multi-Informatics for Nuclear Operations Scenarios program.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric – number of objects and total surface – information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Data-driven discovery of a formation prediction rule on high-entropy ceramics

The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ B-TM ) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Diagonal entropy and topological phase transitions in extended Kitaev chains

We investigate the diagonal entropy for ground states of the extended Kitaev chains with extensive pairing and hopping terms. The systems contain rich topological phases equivalently represented by topological invariant winding numbers and Majorana zero modes. Both the finite size scaling law and block scaling law of the diagonal entropy are studied, which indicates that the diagonal entropy demonstrates volume effect. The parameter of volume term is regarded as the diagonal entropy density, which can identify the critical points of symmetry-protected topological phase transitions efficiently in the studied models, even for those with higher winding numbers. The formulation of block scaling law and the capability of diagonal entropy density in detecting topological phase transitions are independent of the chosen bases. In order to manifest the advantage of diagonal entropy, we also calculate the global entanglement, which cannot show clear signatures of the topological phase transitions. This work provides a new quantum-informatic approach to characterize the feature of the topologically ordered states and may motivate a deep understanding of the quantum coherence and diagonal entropy in various condensed matter systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

MiR-145 regulates PAK4 via the MAPK pathway and exhibits an antitumor effect in human colon cells

Highlights: Black-Right-Pointing-Pointer MiR-145 targets a putative binding site in the 3 Prime UTR of PAK4. Black-Right-Pointing-Pointer MiR-145 played an important role in inhibiting cell growth by directly targeting PAK4. Black-Right-Pointing-Pointer MiR-145 may function as tumor suppressors. -- Abstract: MicroRNAs (miRNAs) are regulators of numerous cellular events; accumulating evidence indicates that miRNAs play a key role in a wide range of biological functions, such as cellular proliferation, differentiation, and apoptosis in cancer. Down-regulated expression of miR-145 has been reported in colon cancer tissues and cell lines. The molecular mechanisms underlying miR-145 and the regulation of colon carcinogenesis remain unclear. In this study, we investigated the levels of miR-145 in human colon cancer cells using qRT-PCR and found markedly decreased levels compared to normal epithelial cells. We identified PAK4 as a novel target of miR-145 using informatics screening. Additionally, we demonstrated that miR-145 targets a putative binding site in the 3 Prime UTR of PAK4 and that its abundance is inversely associated with miR-145 expression in colon cancer cells; we confirmed this relationship using the luciferase reporter assay. Furthermore, restoration of miR-145 by mimics in SW620 cells significantly attenuated cell growth in vitro, in accordance with the inhibitory effects induced by siRNA mediated knockdown of PAK4. Taken together, these findings demonstrate that miR-145 downregulates P-ERK expression by targeting PAK4 and leads to inhibition of tumor growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

MiR-181b targets Six2 and inhibits the proliferation of metanephric mesenchymal cells in vitro

Highlights: •We do bio-informatics websites to analysis of Six2 3′UTR. •MiR181b is a putative miRNA which can targets Six2 3′UTR. •MiR-181b binding site in the 3′UTR of Six2 is functional. •MiR-181b suppresses MK3 cells cell proliferation by targeting Six2. -- Abstract: MicroRNAs (miRNAs) are small non-coding RNAs that down-regulate gene expression by binding to target mRNA for cleavage or translational repression, and play important regulatory roles in renal development. Despite increasing genes have been predicted to be miRNA targets by bioinformatic analysis during kidney development, few of them have been verified by experiment. The objective of our study is to identify the miRNAs targeting Six2, a critical transcription factor that maintains the mesenchymal progenitor pool via self-renewal (proliferation) during renal development. We initially analyzed the 3′UTR of Six2 and found 37 binding sites targeted by 50 putative miRNAs in the 3′UTR of Six2. Among the 50 miRNAs, miR-181b is the miRNAs predicted by the three used websites. In our study, the results of luciferase reporter assay, realtime-PCR and Western blot demonstrated that miR-181b directly targeted on the 3′UTR of Six2 and down-regulate the expression of Six2 at mRNA and protein levels. Furthermore, EdU proliferation assay along with the Six2 rescue strategy showed that miR-181b suppresses the proliferation of metanephric mesenchymal by targeting Six2 in part. In our research, we concluded that by targeting the transcription factor gene Six2, miR-181b inhibits the proliferation of metanephric mesenchymal cells in vitro and might play an important role in the formation of nephrons.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Fibulin-4 is a novel Wnt/β-Catenin pathway activator in human osteosarcoma

Fibulin-4, an extracellular glycoprotein implicated in connective tissue development and elastic fiber formation, draws increasing focuses in cancer research. However, little is known about the underlying oncogenic roles of Fibulin-4 in human osteosarcoma (OS). In this study, by immunohistochemical analysis, upregulated expression of Fibulin-4 was found in the OS clinical specimens and cell lines compared to their normal counterparts. Fibulin-4 was positively correlated with the T stage of OS patients, and the proliferation index Ki67. Based on informatics analysis and functional verification, microRNA-137 was identified as a potential upstream regulator of Fibulin-4. Knockdown of Fibulin-4 or introduction of microRNA-137 inhibited cell proliferation and promoted cell apoptosis, and adverse effects were observed by overexpression of Fibulin-4. Furthermore, the tumor-suppressive functions of microRNA-137 were markedly abolished by restoration of Fibulin-4 expression in OS cells. Mechanistically, Fibulin-4 activated Wnt/β-Catenin pathway and promoted the expression of its downstream targets, including CCND2, c-Myc and VEGF. Taken together, Fibulin-4 plays critical neoplastic roles in tumor growth of human OS by activating Wnt/β-Catenin signaling and may represent a potential therapeutic target. -- Highlights: •Upregulated Fibulin-4 correlates tumor growth in human OS. •MicroRNA-137 is a critical regulator of Fibulin-4 expression. •Deregulated miR-137/Fibulin-4 axis promotes tumor growth of human OS. •Wnt/β-Catenin pathway is activated by Fibulin-4 stimulation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. Finally, the needed transformation of the LTG data culture requires emphasis in student education on science and management of data.

58 GEOSCIENCES↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗
DOE OSTI2013

Increasing Confidence of LC-MS Identifications by Utilizing Ion Mobility Spectrometry

Ion mobility spectrometry in conjunction with liquid chromatography separations and mass spectrometry offers a range of new possibilities for analyzing complex biological samples. To fully utilize the information obtained from these three measurement dimensions, informatics tools based on the accurate mass and time tag methodology were modified to incorporate ion mobility spectrometry drift times for peptides observed in human serum. A reference human serum database was created using 12,139 peptides, tracking the monoisotopic mass, liquid chromatography normalized elution time, and ion mobility spectrometry drift time(s) for each peptide. We demonstrate that the use of three dimensions for peak matching during the peptide identification process resulted in increased numbers of identifications and lower false discovery rates relative to the use of only the mass and normalized elution time dimensions.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2022

Novel cis-regulatory elements as synthetic promoters to drive recombinant protein expression from the Chlamydomonas reinhardtii nuclear genome

Eukaryotic green microalgae represent a sustainable, photosynthetic biotechnology platform for generating high-value products. The model green alga Chlamydomonas reinhardtii has already been used to generate high value bioproducts such as recombinant proteins and terpenoids. However, low, unstable, and variable nuclear transgene expression has limited the ease and speed of metabolic engineering and recombinant protein expression in this system. Here, novel genetic devices for transgene expression in C. reinhardtii have been developed by identifying cis-regulatory DNA elements capable of driving high transgene expression in C. reinhardtii promoters using de novo motif discovery informatics approaches. Thirteen putative motifs were synthesized as concatemers, linked to a common minimal basal promoter, and assayed for their activity to drive expression of a yellow fluorescent protein reporter gene. Following transformation of the vectors into C. reinhardtii by electroporation, in vivo measurements of yellow fluorescent protein expression by flow cytometry revealed that five of the DNA motifs analyzed displayed significantly higher reporter expression compared to the basal promoter control. Two of the concatemerized motifs, despite being much smaller minimal cis-regulatory elements, drove reporter expression at levels approaching that of the conventionally-used AR1 promoter. This analysis provides insight into C. reinhardtii promoter structure and gene regulation, and provides a new toolbox of cis-regulatory elements that can be used to drive transgene expression at a variety of expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Transcriptional Regulation of Enhancers Active in Protodomains of the Developing Cerebral Cortex

Elucidating the genetic control of cerebral cortical (pallial) development is essential for understanding function, evolution, and disorders of the brain. Transcription factors (TFs) that embryonically regulate pallial regionalization are expressed in gradients, raising the question of how discrete domains are generated. Here, we provide evidence that small enhancer elements active in protodomains integrate broad transcriptional information. CreER T2 and GFP expression from 14 different enhancer elements in stable transgenic mice allowed us to define a comprehensive regional fate map of the pallium. We explored transcriptional mechanisms that control the activity of the enhancers using informatics, in vivo occupancy by TFs that regulate cortical patterning ( CoupTFI, Pax6 , and Pbx1 ), and analysis of enhancer activity in Pax6 mutants. Overall, the results provide insights into how broadly expressed patterning TFs regulate the activity of small enhancer elements that drive gene expression in pallial protodomains that fate map to distinct cortical regions.

59 BASIC BIOLOGICAL SCIENCES↗