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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Novel compound heterozygous CPLANE1 variants identified in a Chinese family with Joubert syndrome

Abstract Joubert syndrome (JS) and JS‐related disorders (JSRD) are a group of neurodevelopmental diseases that share the “molar tooth sign” on axial brain magnetic resonance imaging (MRI), accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. To identify variants responsible for the clinical symptoms of a Chinese family with JS and to explore the genotype–phenotype associations, we conducted a series of clinical examinations, including blood tests, brain MRI scans, ultrasound imaging, and ophthalmologic examination. Genomic DNA was extracted from the peripheral blood of the six‐person family, and the pathogenic variants were detected by whole‐exome sequencing (WES) and verified by Sanger sequencing. WES revealed two novel compound heterozygous variants in CPLANE1 : c.1270C>T (p.Arg424*) in exon 10 and c.8901C>A (p.Tyr2967*) in exon 48 of one child, inherited from each parent. Both variants were absent in ethnically matched Chinese control individuals and were either absent or present at very low frequencies in public databases, suggesting that these variants could be the pathogenic triggers of the JS phenotype. Notably, these CPLANE1 sequence variants were related to the pathogenesis of autosomal recessive JS in this study. The newly discovered variants expand the mutation spectrum of CPLANE1 , which assists in understanding the molecular mechanism underlying JS and improving the recognition of genetic counseling, particularly for families with a history of autosomal recessive JS.

Zhang, Cheng↗
DOE OSTI2015

Monoamine oxidase: Radiotracer chemistry and human studies

Monoamine oxidase (MAO) oxidizes amines from both endogenous and exogenous sources thereby regulating the concentration of neurotransmitter amines such as serot onin, norepinephrine and dopamine as well as many xenobiotics. MAO inhibitor drugs are used in the treatment of Parkinson’s disease and in depression stimulating the development of radiotracer tools to probe the role of MAO in normal human biology and in disease. Over the past 30 since the first radiotracers were developed and the first PET images of MAO in humans were carried out, PET studies of brain MAO in healthy volunteers and in patients have identified different variables which have contributed to different MAO levels in brain and in peripheral organs. MAO radiotracers and PET have also been used to study the current and developing MAO inhibitor drugs including the selection of doses for clinical trials. In this article, we describe (1) the development of MAO radiotracers; (2) human studies including the relationship of brain MAO levels to genotype, personality, neurological and psychiatric disorders; (3) examples of the use of MAO radiotracers in drug research and development. We will conclude with outstanding needs to improve the radiotracers which are currently used and possible new applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem

Abstract Solving multi‐objective combinatorial optimization problems can quickly become computationally challenging when applied to large networks generated from big data. Present‐day first‐rate Mixed Integer Programming (MIP) solvers have parallelism built‐in to take advantage of multicore architectures; but specialized network optimization algorithms that can often solve graph problems more efficiently than a general MIP solver are typically programmed serially. Thus, these network algorithm implementations do not take full advantage of modern multicore computer capabilities. Many parallel computing languages include a fork/join framework into their concurrency packages that allow for simple conversion of recursive serial algorithms to operate in parallel without having to use complicated low‐level message‐passing interfaces. Fork/join is particularly well suited to divide and conquer algorithms such as Non‐Inferior Set Estimation (NISE) method for computing the supported Pareto front of a multi‐objective optimization problem. This work develops a simple general parallel NISE (pNISE) implementation using the Java 7 concurrency tools, and then offers results from a specific implementation of solving a bi‐objective shortest path problem. The results indicate that this method is capable of sizeable speed‐ups on both small‐scale personal computers as well as large‐scale shared memory supercomputers. Finally, other network problems suitable to this method are discussed, including multi‐objective variants of the minimum spanning tree problem, transportation problem, assignment problem, maximum flow problem, and the minimum‐cost flow problem. Copyright © 2015 John Wiley & Sons, Ltd.

Medrano, Fernando Antonio↗
DOE OSTI2024

The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection

A National Institutes of Health (NIH) and U.S. Department of Energy (DOE) Office of Science virtual workshop on shared general topics was held in July of 2021 and reported on in this publication in January of 2023. Following the inaugural 2021 joint meeting representatives from the DOE Office of Science and NIH met to discuss organizing a second joint workshop that would concentrate on radiation detection to bring together teams from both agencies and their grantee populations to stimulate collaboration and efficiency. To meet this scientific mission within the NIH and DOE radiation detection space, the organizers assembled workshop sessions covering the state–of–the–art in cameras, detectors, and sensors for radiation external and internal (diagnostic and therapeutic) to human, data acquisition and electronics, image reconstruction and processing, and the application of artificial intelligence. NIH and DOE are committed to continuing the process of convening a joint workshop every 12–24 months. This Special Report recaps the findings of this second workshop. Beyond showing only the innovations and areas of success, important gaps in our knowledge were defined and presented. Finally, we summarize by defining four areas of greatest opportunity and need that emerged from the unique, dynamic dialogue the in–person workshop provided the attendees.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

UnigeneFinder: An Automated Pipeline for Gene Calling From Transcriptome Assemblies Without a Reference Genome

ABSTRACT For most species, transcriptome data are much more readily available than genome data. Without a reference genome, gene calling is cumbersome and inaccurate because of the high degree of redundancy in de novo transcriptome assemblies. To simplify and increase the accuracy of de novo transcriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared with existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high‐quality reference genomes. These features, coupled with the pipeline’s cross‐platform implementation, focus on automation, and an accessible, user‐friendly interface, make UnigeneFinder a useful tool for many downstream sequence‐based analyses in nonmodel organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high‐performance computing (HPC) systems and personal computers, further reducing barriers to use.

Xue, Bo [Plant Resilience Institute Michigan State↗
DOE OSTI2017

Proteomic analysis of hair shafts from monozygotic twins: Expression profiles and genetically variant peptides

Forensic association of hair shaft evidence with individuals is currently assessed by comparing mitochondrial DNA haplotypes of reference and casework samples, primarily for exclusionary purposes. Present work tests and validates more recent proteomic approaches to extract quantitative transcriptional and genetic information from hair samples of monozygotic twin pairs, which would be predicted to partition away from unrelated individuals if the datasets contain identifying information. Protein expression profiles and polymorphic, genetically variant hair peptides were generated from ten pairs of monozygotic twins. Profiling using the protein tryptic digests revealed that samples from identical twins had typically an order of magnitude fewer protein expression differences than unrelated individuals. The data did not indicate that the degree of difference within twin pairs increased with age. In parallel, data from the digests were used to detect genetically variant peptides that result from common nonsynonymous single nucleotide polymorphisms in genes expressed in the hair follicle. Compilation of the variants permitted sorting of the samples by hierarchical clustering, permitting accurate matching of twin pairs. The results demonstrate that genetic differences are detectable by proteomic methods and provide a framework for developing quantitative statistical estimates of personal identification that increase the value of hair shaft evidence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Strangely attractive: Collaboration and feedback in the field of molecular magnetism

The fluid synergy between experimentalists and theoreticians has, for decades, led to a deeper understanding of the processes that govern single-molecule magnets. This approach has allowed for the establishment of proven design criteria for the control of magnetic properties through molecular architecture and the development of new magnetic measurement techniques and innovative computational methodologies. Here, we give an account of the experimental and theoretical joint endeavor carried out as part of the synthesis group led by David Mills and the computational/theoretical team led by Nicholas Chilton, together with colleagues in synthetic f-element chemistry and magnetism at the University of Manchester. In addition, we provide a personal perspective on collaborative work in molecular magnetism and how such collaborations are essential for advancing the field further.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

XPS Determination of Uranium Oxidations States

This contribution is both a review of different aspects of the XPS spectra that can help one determine U oxidation states and a personal perspective on how to effectively model the XPS of complicated mixed valence U phases. After a discussion of the valence band, the focus lingers on the U4f region, where the use of binding energies, satellite structures, and peak shapes is discussed in some detail. Binding energies were shown to be very dependent on composition/structure and consequently unreliable guides to oxidation state, particularly where assignment of composition is difficult. Likewise, the spin orbit split 4f7/2 and 4f5/2 peak shapes do not carry significant information on oxidation states. In contrast, both satellite-primary peak binding energy separations, as well as intensities too lesser extent, are relatively insensitive to composition/structure within the oxide-hydroxide-hydrate system and can be used to both identify and help quantify U oxidation states in mixed valence phases. An example of the usefulness of the satellite structure in constraining the interpretation of a complex multivalence U compound is given.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Kılınç, Deniz↗
DOE OSTI2021

Fluorescence Microscopic Investigations of Molecular Dynamics in Self–Assembled Nanostructures

Many analytical methods employ self-assembled nanostructured materials as chemical recognition media. Molecular permeation through these materials exhibits unique selectivity owing to nanoconfinement-induced enhancement of permeant–nanostructure interactions. This Personal Account introduces our efforts to investigate the detailed dynamics of single or a small number of molecules in nanostructured materials. Here, we developed new experimental and analysis approaches built upon laser-based fluorescence microscopy to measure the detailed translational and orientational dynamics of molecules diffusing in horizontally-oriented, cylindrical nanostructures, including surfactant micelles, silica mesopores, block copolymer microdomains, and bolaamphiphile-based organic nanotubes. Our studies clarified nanoscale details on the structural/chemical heterogeneity of the nanostructures, and their impacts on molecular mass transport dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Commercialization of Ionic Liquids in Pursuit of Green Chemistry: Must we Each Become an Entrepreneur?

Abstract There will be common challenges to scaling‐up any ionic liquids separations technologies which require very large volumes of ionic liquid. Some of these challenges are illustrated in this personal account which chronicles the extraction of chitin from shrimp shell from discovery to current commercialization efforts. The road being taken from discovery in an academic laboratory, through attempts to navigate the scaling‐up to commercial scale using the vehicle of a faculty startup company is rewarding, but fraught with roadblocks, detours, and unexpected challenges. The differences in ‘technically feasible’ and ‘commercially viable’ are not always evident from the beginning of the journey, however, one wonders what achievements we miss as a Society because it was assumed to not be commercially viable.

Shamshina, Julia L.↗
DOE OSTI2022

“I Would Purposely Try to Keep them Separated”: Language Ideologies, Language Policy, and Beliefs about Emergent Bilinguals in Career and Technical Education

Abstract In the policy context of college and career readiness, career and technical education (CTE) is growing in popularity, especially for students who are underserved in academic high school programs. While CTE has shown promise for some students, very little research exists on its potential for emergent bilingual students (EBs). Thus, building on research of deficit perceptions of EBs and language ideologies (LIs), this study examines how LIs and deficit perspectives are reproduced in CTE contexts across Pennsylvania. Using a sequential mixed‐methods design, we found that, while CTE educators held generally positive attitudes toward EBs, they were influenced by monoglossic language ideology and standard language ideology. These LIs, along with other institutional factors, led educators to implement restrictive language policies within their classrooms. These implications serve to inform how, with a lack of clear language policy within CTE programs, teachers relied on their personal beliefs and reproduced deficit ideologies in the creation of classroom‐level policies.

Emerick, Mark R.↗
DOE OSTI2017

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 This article is categorized under: Analytical and Computational Methods > Dynamical Methods Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Metabolism

Voit, Eberhard O.↗
DOE OSTI2018

Activity-based protein profiling – enabling multimodal functional studies of microbial communities

Microorganisms living in community are critical to life on Earth, playing numerous and profound roles in the environment and human and animal health. Though their essentiality to life is clear, the mechanistic underpinnings of community structure, in-teractions, and functions is largely unexplored and in need of function-dependent technologies to unravel the mysteries. Activity-based protein profiling holds much promise for mechanistic microbial community investigations and will also yield an ave-nue to determine how external exposures result in functional alterations to microbi-omes. Herein, we illuminate the current state and prospective contributions of ABPP as it relates to microbial communities. We provide details on the design, develop-ment, and validation of probes, challenges associated with probing in complex micro-bial communities, provide some specific examples of the biological applications of ABPP in microbes and microbial communities, and highlight potential areas for devel-opment. The future of ABPP holds real promise for understanding and considerable impact in microbiome studies associated with personalized medicine, precision agricul-ture, veterinary health, environmental studies, and beyond.

Whidbey, Christopher-Mychael↗
DOE OSTI2019

Investigating the Role of VR in a Simulation-Based Medical Planning System for Coronary Interventions

Virtual reality (VR) based computational fluid dynamics (CFD) simulations are emerging as a viable solution to guide complex surgical or invasive procedures, such as percutaneous coronary intervention (PCI), as they enable a realistic first-person experience of underlying patient anatomy and physiology. Realistic VR experience can be assessed by immersion, an objective VR property. However, it remains unclear how immersion influences virtual PCI procedures and what level of immersion is required. This study answers both these questions by evaluating the application of a CFD-based VR system and comparing semi-immersive and fully immersive VR displays. Nine patient-specific arterial models were simulated using CFD and used to conduct a quantitative user evaluation (n = 31) with both types of VR displays. The findings of this study reveal that VR immersion significantly improves the accuracy of simulated stent placement in complex arterial geometries, relative to traditional desktops with no immersion (p < 0.05). Higher accuracy is noted by the use of semi-immersive VR display, which offers higher display fidelity as compared to the fully immersive VR display (p < 0.05). Interestingly, CFD data mapped on to arterial geometries strongly influences the location of stent placement. This finding is demonstrated by the lack of significant accuracy deviation between the two immersive displays when CFD data is shown. This study provides compelling evidence that a CFD-based VR system rendered on semi-immersive displays can enable more accurate and efficient stent placement.

Vardhan, Madhurima↗
DOE OSTI2020

Dynamic instructions for Lock-out Tag-out

Lock-out tag-out (LOTO) instructions protect personnel from un-expected re-energization or start-up of equipment that is under maintenance. Even though there is rigorous training associated with LOTO activities, inci-dents still occur. Idaho National Laboratory is researching and developing dynamic LOTO instructions, which supports automatic place keeping, per-sonnel working on multiple instructions, and multiple work groups complet-ing the same instructions. Researchers identified user needs through subject matter expert interviews, observations, and classroom training. Failing to identify potential hazards during LOTO can cause injury or death to person-nel. Dynamic instructions can provide planning, incorporate supplemental documentation into the instruction, and improve instruction compliance. Combining these functions may lead to clearer, more applicable instructions which addresses the reason for compliance issues with current paper-based procedures.

99 GENERAL AND MISCELLANEOUS↗