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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Research-Driven Radiation Oncology: A Narrative on the Ongoing Legacy of Henry S. Kaplan

In this rapidly evolving time of precision medicine and scientifically based cancer care, how radiation oncology became a strong research-based scientific discipline in the United States after its separation from diagnostic radiology might be lost. The importance of generational mentorship, “family trees,” and interpersonal relationships can be difficult or impossible to trace absent personal narrative recollections of those involved. Henry S. Kaplan is a central figure and the focal point for 3 generations of research-based academic department chairs. This report establishes a first draft of a living record of the radiation oncology history of the Kaplan legacy to serve as an example of how knowledge networks grow and flourish and as an impetus for others to trace the legacy of other radiation oncology academic “trees.”.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Continuous-time probabilistic models for longitudinal electronic health records

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. Here, we present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format

Advances in mass spectrometry (MS) instrumentation, such as higher resolution, faster scan speeds, and improved sensitivity, have significantly increased the volume and complexity of data. The growing adoption of imaging and ion mobility further amplifies these challenges across MS-based omics fields, including proteomics, metabolomics, and lipidomics. While these technologies unlock new possibilities, they also present significant challenges in data management, storage, and accessibility. Existing open formats, such as the XML-based community standards mzML and imzML, struggle to meet the demands of modern MS workflows due to their large file sizes, slow data access, and limited metadata support. Vendor-specific formats, while optimized for proprietary instruments, lack interoperability, comprehensive metadata support and long-term archival reliability. This white paper lays the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multi-dimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will enable enhanced interoperability across platforms, seamless support for complex workflows including ion mobility and MS imaging, and faster data access compared to existing community formats such as mzML. Its design will ensure data is managed in compliance with regulatory standards, essential for applications such as precision medicine and chemical safety, where long-term data integrity and accessibility are critical. For vendors, mzPeak provides a streamlined, open alternative to proprietary formats, reducing the burden of regulatory compliance while aligning with the industry's push for transparency and standardization. By offering a high-performance, interoperable solution, mzPeak positions vendors to meet customer demands for sustainable data management tools which will be able to handle emerging and future data types and workflows. mzPeak aspires to become the cornerstone of MS data management, empowering researchers, vendors, and developers to innovate and collaborate more effectively.

data formats↗
DOE OSTI2023

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations

"Quantile-dependent expressivity" is a dependence of genetic effects on whether the phenotype (e.g., triglycerides) is high or low relative to its distribution in the population. Quantile-specific offspring-parent regression slopes (β OP ) were estimated by quantile regression for 6227 offspring-parent pairs. Quantile-specific heritability (h 2 ), estimated by 2β OP /(1 + $\Gamma$ spouse ), decreased 0.0047 ± 0.0007 (P = 2.9 × 10 -14 ) for each one-percent decrement in fasting triglyceride concentrations, i.e., h 2 ± SE were: 0.428 ± 0.059, 0.230 ± 0.030, 0.111 ± 0.015, 0.050 ± 0.016, and 0.033 ± 0.010 at the 90th, 75th, 50th, 25th, and 10th percentiles of the triglyceride distribution, respectively. Consistent with quantile-dependent expressivity, 11 drug studies report smaller genotype differences at lower (post-treatment) than higher (pre-treatment) triglyceride concentrations. This meant genotype-specific triglyceride changes could not move in parallel when triglycerides were decreased pharmacologically, so that subtracting pre-treatment from post-treatment triglyceride levels necessarily created a greater triglyceride decrease for the genotype with a higher pre-treatment value (purported precision-medicine genetic markers). In addition, sixty-five purported gene-environment interactions were found to be potentially attributable to triglyceride's quantile-dependent expressivity, including gene-adiposity (APOA5, APOB, APOE, GCKR, IRS-1, LPL, MTHFR, PCSK9, PNPLA3, PPARγ2), gene-exercise (APOA1, APOA2, LPL), gene-diet (APOA5, APOE, INSIG2, LPL, MYB, NXPH1, PER2, TNFA), gene-alcohol (ALDH2, APOA5, APOC3, CETP, LPL), gene-smoking (APOC3, CYBA, LPL, USF1), gene-pregnancy (LPL), and gene-insulin resistance interactions (APOE, LPL).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Forces that control self-organization of chemically-propelled Janus tori

Control of the individual and collective behavior of self-propelled synthetic micro-objects has immediate application for nanotechnology, robotics, and precision medicine. Despite significant progress in the synthesis and characterization of self-propelled Janus (two-faced) particles, predictive understanding of their behavior remains challenging, especially if the particles have anisotropic forms. Here, by using molecular simulation, we describe the interactions of chemically-propelled microtori near a wall. The results show that a torus hovers at a certain distance from the wall due to a combination of gravity and hydrodynamic flows generated by the chemical activity. Moreover, electrostatic dipolar interactions between the torus and the wall result in a spontaneous tilt and horizontal translation, in a qualitative agreement with experiment. Simulations of the dynamics of two and four tori near a wall provide evidence for the formation of stable self-propelled bound states. Our results illustrate that self-organization at the microscale occurs due to a combination of multiple factors, including hydrodynamic, chemical, electrostatic and steric interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Top-down proteomics

Proteoforms arising from posttranslational modifications, genetic polymorphisms, and RNA splice variants, play a pivotal role as the key drivers in biology. Thus, a comprehensive understanding of proteoforms is essential for unraveling the intricacies of biological systems and bridging the gap between genotype and phenotype. By analyzing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and presents a next-generation approach for deciphering protein function, uncovering disease mechanisms, and advancing precision medicine. This Primer embarks on a journey into the world of TDP by encapsulating its historical context, underlying principles, recent advances, and an outlook on the future of TDP. The experimental section navigates instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques, and data collection. Results decipher raw data, visualize intact protein spectra, unravel data analysis, and explain proteoform identification, characterization, and quantitation, as well as statistical analysis. Various applications of TDP spanning the human proteoform project, biomedical, biopharmaceutical, and clinical applications are described. These are complemented by discussions on measurement reproducibility, limitations, and a forward-looking perspective outlining uncharted waters where the field can advance, and potential exciting future applications of TDP.

Roberts, David S.↗
DOE OSTI2022

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks

Abstract As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Zheng, Kai↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗
DOE OSTI2022

A reference tissue atlas for the human kidney

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2022

Towards a robust out-of-the-box neural network model for genomic data

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity. Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study. While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Phenopacket-tools: Building and validating GA4GH Phenopackets

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Protein Factory on a Chip for Rapid Therapeutics

The potential for new, re-emerging, or engineered pandemic threats could challenge our nation's biosecurity, requiring rapid and flexible countermeasure development platforms. In healthcare, one of the main hindrances to the timely development of protein-based therapeutics for cancer, infectious disease, and other diseases has been a slow and expensive quality-controlled production stage. We present a protein-based technology that has the potential to address these crucial problems in biosecurity and healthcare by maximizing protein production and system portability, while minimizing production latency and cost. Specifically, we have developed a reusable, portable first-generation platform that leverages the advantages of cell-free protein synthesis (CFPS) and flow cells to create practical amounts of clinically relevant therapeutics. We have demonstrated that this technology is reusable and programmable, enabling a single platform that can synthesize multiple dosages of various therapeutics and vaccine in the field. Implementing this technology could mitigate pandemic threats by providing a rapidly-deployable (just-in-time), versatile (precision medicine) production of biologic therapeutics to the infected patient’s locale (point-of-care). In drug development, once promising drug variations are identified, the platform can be scaled to quickly produce practical amounts of protein for patient testing in the matter of days (vs. months) at a fraction of the cost of existing technology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Support for Students, Postdoctoral Fellows and Trainees to Attend Radiochemistry-Related Symposia at Pacifichem 2020

This project was undertaken to help meet the need for training personnel who will be involved in professional careers requiring knowledge of radiochemistry, such as those working in radionuclide production, and in biological, industrial, medical and environmental fields that use radionuclides in their work. The goal of the project was to provide financial assistance to students and trainees from US academic and government institutions to attend selected radiochemistry related symposia at the Pacifichem 2020 meeting, which was to be held in Honolulu, Hawaii in December 2020. The funding, meant to provide a portion of an awardee’s travel cost, was specifically directed at awardees attendance to the following symposia: #190, Advancements in Isotope production-Providing Important Materials for Research and Applications; #180, Advancements in the Chemistry of Targeted Alpha Therapy, and #178, The Chemistry of 18 F, 11 C and Radiometal-based Probes for Molecular Imaging & Precision Medicine. It was/is believed that attendance of students/postdocs at the meeting would point out the critical role radiochemistry and radionuclides can have in the development of future diagnostic and therapeutic agents which would stimulate student interest in radiochemistry, and thus, provide support for making, or an impetus to make, this technical area a career choice.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

The First Virtual Human Global Summit: Prepublication Meeting Report

This is the prepublication report for the First Virtual Human Global Summit held in October 2023. Organized collaboratively by Frederick National Laboratory for Cancer Research, Brookhaven National Laboratory, University College London, and Eviden, the 2023 Virtual Human Global Summit convened global thought leaders to bring together multiple perspectives across domains and organizations. The intent of the Summit was to foster collaboration among key leaders internationally whose combined efforts are required to advance patient-focused precision medicine through medical digital twins. Participants represented cancer and biomedical research, industry, infrastructure, clinical research, community health, non-profit organizations, government, and general public interests. The Summit included over 80 attendees across three continents including native American tribes. The Summit was held to share insights about the state of the art for medical digital twins, provide motivating opportunities and identify key challenges along the path to improved health and wellness through virtual human models and personalized digital twins. The Summit included sessions emphasizing the primary areas of research, infrastructure, clinical application, government support, and adoption/sustainability. Several examples of digital twins were referenced or mentioned through the course of the Summit including digital twin approaches in cancer, radiation oncology, molecular scale digital twins, diabetes, and sepsis. The summit report includes perspectives on the current state, challenges and guidance across research, infrastructure, clinical translation and community adoption, as well as multiple key insights from industry perspectives.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

“Production of High Specific Activity 155 Tb, 161 Tb and 203 Pb for Research and Clinical Applications: Effective Target Design, Target Material Recycling and Radioisotope Separation”

Radioisotopes are essential for the development and application of radiopharmaceuticals that target specific diseases, such as cancer, offering unique potential for precision medicine. The growing demand for theranostic radioisotopes underscores their critical role in personalized medicine, where they enhance diagnostic imaging, minimize patient radiation exposure, and improve targeted tissue uptake, particularly in receptor- and antigen-directed therapies. The theranostic pair terbium-155 (diagnostic) and terbium-161 (therapeutic) holds significant promise for advancing individualized, targeted, and dosimetry-driven radiotherapies. However, the United States currently lacks routine and reliable production of these isotopes. This project made significant progress toward addressing this supply issue by developing production and separation methods for terbium-155 and terbium-161 while also training the next generation of the nuclear and radiochemistry workforce. This grant also strengthened collaboration between scientists at the University of Washington, the University of Missouri and Brookhaven National Laboratory. The research effort focused on evaluating target preparation methods, optimizing irradiation parameters, and refining isolation processes. In addition, the project provided extensive hands-on training to graduate students and postdoctoral fellows, equipping them with expertise in radioisotope production technologies and fostering the growth of the nuclear science workforce.

07 ISOTOPE AND RADIATION SOURCES↗