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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Generalizability of heat-related health risk associations observed in a large healthcare claims database of patients with commercial health insurance

Extreme ambient heat is unambiguously associated with higher risk of illness and death. The Optum Labs Data Warehouse (OLDW), a database of medical claims from US-based patients with commercial or Medicare Advantage health insurance, has been used to quantify heat-related health impacts. Whether results for the insured sub-population are generalizable to the broader population has to our knowledge not been documented. We sought to address this question, for the US population in California from 2012 to 2019. We examined changes in daily rates of emergency department (ED) encounters and in-patient hospitalization encounters for all-causes, heat-related outcomes, renal disease, mental/behavioral disorders, cardiovascular disease, and respiratory disease. OLDW was the source for health data for insured individuals in California, and health data for the broader population were gathered from the California Department of Health Care Access and Information (HCAI). We defined extreme heat exposure as any day in a group of 2 or more days with maximum temperatures exceeding the county-specific 97.5 th percentile and used a space-time-stratified case–crossover design to assess and compare the impacts of heat on health. Average incidence rates of medical encounters differed by dataset. However, rate ratios for ED encounters were similar across datasets for all causes (ratio of incidence rate ratios (rIRR) = 0.989; 95% confidence interval (CI) = 0.973, 1.011), heat-related causes (rIRR = 1.080; 95% CI = 0.999, 1.168), renal disease (rIRR = 0.963; 95% CI = 0.718, 1.292), and mental health disorders (rIRR = 1.098; 95% CI = 1.004, 1.201). Rate ratios for inpatient encounters were also similar. This work presents evidence that OLDW can continue to be a resource for estimating the health impacts of extreme heat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Synthetic diversity in the preparation of metallic uranium

Uranium metal is associated with several aspects of nuclear technology; it is used as fuel for research and power reactors, targets for medical isotope productions, explosive for nuclear weapons and precursors in synthetic chemistry. The study of uranium metal at the laboratory scale presents the opportunity to evaluate metallic nuclear fuels, develop new methods for metallic spent fuel reprocessing and advance the science relevant to nuclear forensics and medical isotope production. Since its first isolation in 1841, from the reaction of uranium chloride and potassium metal, uranium metal has been prepared by solid-state reactions and in solution by electrochemical, chemical and radiochemical methods. The present review summarizes the methods outlined above and describes the chemistry associated with each preparation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Measurements of the 96 Zr(α,n) cross section for astrophysics and applications

The reaction 96 Zr(α,n) plays an important role in ν-driven wind nucleosynthesis in core-collapse supernovae and is a possible avenue for medical isotope production. Here, cross section measurements were performed using the activation technique at the Edwards Accelerator Laboratory. Results were analyzed along with world data on the 96 Zr(α, n) cross section and 96 Zr(α, α) differential cross section using large-scale Hauser-Feshbach calculations. We find general agreement between our data, previous measurements, and a statistical description of the reaction. However, we find a larger cross section at low energies compared to prior experimental results, allowing for a larger astrophysical reaction rate. This may impact results of core-collapse supernova ν-driven wind nucleosynthesis calculations, but does not significantly alter prior conclusions about 99 Mo production for medical physics applications. The results from our large-scale Hauser-Feshbach calculations demonstrate that phenomenological optical potentials may yet be adequate to describe (α, n) reactions of interest for ν-driven wind nucleosynthesis, albeit with regionally-adjusted model parameters.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Characterizing Sub-Cohorts via Data Normalization and Representation Learning

The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.

Rush III, Everett↗
DOE OSTI2025

Evaluating the Impact of Power Outages on Occupancy Patterns During the 2021 Texas Power Crisis

Large-scale power outages, such as those caused by extreme weather events, have a big impact on human behavior. A short power outage is merely a nuisance for most, and may not change people's locations. An outage that lasts for a few hours can result in spoiled food and medical supplies, and people will have to restock spoiled items. Long outages result in temperatures outside tolerable levels in homes, and may prompt people to acquire supplies, such as generators and gas, or change location. The long outages during Winter Storm Uri in Texas resulted in millions of dollars in property damage due to freezing pipes. This level of damage is expected to result in a sharp increase in supply runs and contractor activity. In this paper, we present a tool to explore differences in visiting patterns before, during, and after power outages. It allows to compare different points of interest like medical facilities, grocery stores, hardware stores, and other types of businesses.

big data↗
DOE OSTI2021

Applying Utility's Advanced Grid Technologies to Improve Resiliency of a Critical Load

The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

BESS↗
DOE OSTI2021

Perspectives on Machine Learning-assisted Plasma Medicine: Towards Automated Plasma Treatment

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications. Predictive modeling of the mutual interactions between the plasma and biological surfaces and, thus, systematic approaches to quantify and predict plasma treatment outcomes remain largely elusive due to the lack of mechanistic understanding of plasma-surface interactions that can span across vastly different length-and time-scales. In addition, real-time sensing capabilities in biomedical CAP devices are often limited, which can be detrimental to plasma treatment due to the intrinsic plasma and surface variability during the treatment, as well as sensitivity to external perturbations. All of these challenges can make reproducible and effective plasma treatment of biological surfaces difficult to realize, which is further compounded by errors due to human operation of hand-held CAP devices. Machine learning and data-driven approaches can be particularly useful in addressing these challenges in three major ways: (i) data-driven modeling of hard-to-model plasma-surface interactions and plasma treatment outcomes; (ii) learning data analytics for plasma and surface diagnostics in real-time; and (iii) developing predictive controllers that enable reliable and effective CAP treatments. Furthermore, this paper discusses the promise of machine learning to accelerate plasma medicine research in these areas, toward machine learning-assisted and automated CAP treatment of complex biological surfaces.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Initial Process Planning of a Hybrid Multi-Tasking Platform

Abstract Applications of hybrid technology are expanding from refurbishment and repair to low quantity, specialty part production, which are staple characteristics in medical implant, energy, and aerospace industry sectors, among others. This expansion has led to the development of the Mazak VC-500A/5X AM HWD, a wire fed laser cladding unit equipped with a standard 5 axis CNC. This unit is capable of building near net geometry of complex medium to large parts within a profitable timeframe, due to its comparatively high rate of deposition to that of a powder fed hybrid system. In this study, deposition and machining capabilities of the VC-500A/5X AM HWD are assessed through the production of three different test geometries by different process plans. Production of these test geometries is supported by an open loop sensor package primarily for monitoring machine health, data collection, and machine operator aid. The viability of extended deposition followed by machining is evaluated against a more cyclical strategy of reoccurring deposition and machining operations. Lastly, common defects in as-built geometries are evaluated and addressed through revisions to original process plans and toolpaths, indicating the need for continued innovation in hybrid manufacturing specific CAM/CAD software, as well as closed loop machine monitoring and quality control.

DeWitte, Lisa N.↗
DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗
DOE OSTI2025

Optimizing transmit field inhomogeneity of parallel RF transmit design in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B+1) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B+1 field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient’s presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B+1 fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B+1 fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method’s advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.

Lu, Zhengyi [Vanderbilt University]↗
DOE OSTI2021

Capturing an elusive but critical element: Natural protein enables actinium chemistry

Actinium-based therapies could revolutionize cancer medicine but remain tantalizing due to the difficulties in studying and limited knowledge of Ac chemistry. Current efforts focus on small synthetic chelators, limiting radioisotope complexation and purification efficiencies. Here, we demonstrate a straightforward strategy to purify medically relevant radiometals, actinium(III) and yttrium(III), and probe their chemistry, using the recently discovered protein, lanmodulin. The stoichiometry, solution behavior, and formation constant of the 228 Ac 3+ -lanmodulin complex and its 90 Y 3+ / nat Y 3+ / nat La 3+ analogs were experimentally determined, representing the first actinium-protein and strongest actinide(III)-protein complex (sub-picomolar Kd) to be characterized. Lanmodulin’s unparalleled properties enable the facile purification recovery of radiometals, even in the presence of >10 +10 equivalents of competing ions and at ultratrace levels: down to 2 femtograms 90 Y 3+ and 40 attograms 228 Ac 3+ . The lanmodulin-based approach charts a new course to study elusive isotopes and develop versatile chelating platforms for medical radiometals, both for high-value separations and potential in vivo applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Nurses’ Satisfaction with Patient Room Lighting Conditions: A Study of Nurses in Four Hospitals with Differences in the Environment of Care

Lighting systems in patient rooms must balance energy efficiency concerns with the holistic needs of patients, families, and medical caregivers. This paper reports survey responses from 138 individuals working in medical-surgical units in four hospitals, with three of the hospitals having traditional lighting systems and the other hospital having a more contemporary lighting system. The results add to a growing evidence base on the effects of lighting on nurses’ job performance, job satisfaction, and overall perceptions of the environment, and provide evidence that innovative lighting approaches and technologies are worth considering as an investment by hospital administrators looking to improve employees’ and patients’ perceptions of the patient room environment. This knowledge is intended to help facility designers and managers incorporate nurses’ perspectives in their decisions, as well as to inform technologists and engineers developing future generations of lighting systems for patient rooms. These future systems will utilize solid-state lighting technologies, such as LEDs, and will provide new opportunities for adapting the intensity, distribution, and spectrum of light to better satisfy the holistic needs of patients and caregivers at different times of day or night.

Davis, Robert G.↗
DOE OSTI2022

The 6-minute walk test predicts mortality in a pulmonary nontuberculous mycobacteria-predominant bronchiectasis cohort

Background: Bronchiectasis is a chronic lung condition frequently associated with nontuberculous mycobacteria pulmonary (NTM) disease. Persons with these conditions are at increased risk of mortality. Patient reported outcome (PRO) instruments and the 6-minute walk test (6MWT) have been shown to predict mortality for several lung conditions, but these measures have not been fully evaluated for bronchiectasis and NTM. Methods: We conducted a retrospective cohort study among adult patients enrolled in a natural history study of bronchiectasis at the National Heart, Lung, and Blood Institute. Electronic medical records were queried for demographic, clinical, microbiologic, radiographic, and PRO instrument data: St. George’s Respiratory Questionnaire (SGRQ), Medical Research Council Dyspnea Scale, and the Pulmonary Symptom Severity Score (PSSS). The study baseline date was defined as the patient’s first visit after January 1st, 2015 with a SGRQ or 6MWT completed. Follow-up was defined as the interval between the study baseline visit and date of death or December 31st, 2019. Sex-stratified Cox proportional-hazards regression was conducted to identify predictors of mortality. Separate models were run for each PRO and 6MWT measure, controlling for age, body mass index (BMI), fibrocavitary disease status, and M. abscessus infection. Results: In multivariable Cox proportional-hazards regression models, the PSSS-severity (aHR 1.29, 95% CI 1.04–1.59), the 6MWT total distance walked (aHR 0.938, 95% CI 0.896–0.981) and distance saturation product (aHR 0.930, 95% CI 0.887–0.974) independently predicted mortality. In addition, BMI was significantly predictive of mortality in all models. Conclusions: The 6MWT and a PRO instrument capturing symptom severity are independently predictive of mortality in our cohort of bronchiectasis patients.

6-minute walk test↗
DOE OSTI2025

Simultaneous on-chip generation of violet, blue, cyan, green, yellow, orange, and red light from an octave-spanning infrared frequency comb

An integrated, multi-spectral visible-light source could significantly benefit technologies such as displays, medical imaging, spectroscopy, visible-light communications, and astrophysics. However, despite recent advances in chip-scale visible lasers, simultaneously generating light of all colors in a single chip has been challenging. Existing solutions are either not suitable for full chip-scale integration, or are fundamentally difficult to scale. Here we demonstrate the simultaneous on-chip generation of infrared, red, orange, yellow, green, cyan, blue, and violet light. Leveraging the low loss, low dispersion, and high density of modes of an adiabatic multimode silicon nitride (SiN) microresonator, we use a single infrared pump of moderate power (~130 mW) to produce an octave-spanning infrared frequency comb that is then converted to different portions of the visible spectrum. We measure non-mode-locked combs and soliton steps corresponding to mode-locked states, making our comb generator suitable for applications that demand either low or high coherence. Since the required pump power is compatible with high-power lasers demonstrated in the same SiN platform, our multi-octave light generator can be fully integrated in a chip-scale form factor. We envision that such a light source will be a catalyst for the development and deployment of miniaturized multi-spectral technologies for quantum systems, medical imaging, displays, and spectroscopy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗
DOE OSTI2020

Multi-Scale Lithofacies and Chemostratigraphic Analysis of Two Middle Devonian Marcellus Shale Wells in Northern West Virginia, USA

The Marcellus Shale Energy and Environmental Laboratory (MSEEL) consists of two project areas within the dry gas producing region of the Marcellus shale play in Monongalia County, West Virginia. MSEEL is a collaborative field project led by West Virginia University, with Northeast Natural Energy LLC, several industrial partners, and sponsored by the US Department of Energy National Energy Technology Laboratory. The study areas are drilled approximately 8.5 miles apart to better understand the vertical and lateral changes in stratigraphy over a short distance. Two vertical pilot wells, MIP-3H and Boggess 17H were drilled in the fall of 2015 and spring of 2019, respectively. Core was recovered from the MIP-3H (API: 47-061-01707-00-00) 112 feet (34m) between depths of 7445 to 7557 feet, and from the Boggess 17H (API: 47-061-01812-00-00) 139 feet (42m) between depths of 7908 and 8012 ft. A full suite of triple combo (gamma ray, neutron, density logs), image logs, and advanced logging tools were run in both wells and calibrated to core analysis. Core analysis includes medical computed tomography (CT) scans, mineralogy and chemostratigraphy determined from handheld X-Ray fluorescence (hhXRF) and X-Ray powder diffraction (XRD) measurements, and determination of total organic content (TOC). Lithofacies were determined at core-scale using traditional core description techniques and medical CT-scan images. Log-scale facies are based on mineralogy and TOC data and developed using petrophysical logging data calibrated to core data (XRD and pyrolysis data). Chemostratigraphic analysis utilized hhXRF data to determine the major and trace element trends in the cores. In the two wells six shale lithofacies were recognized at the core and log scale. Both wells show organic-rich facies (TOC > 6.5%) primarily in the middle and lower Marcellus, with a slight decrease in thickness of this interval in the Boggess 17H. This interval is interpreted as an increase in paleo-productivity (increased Ni, Zn, and V), decreased sedimentation (decreased detrital proxies), and anoxic to euxinic conditions (increased Mo and chalcophile elements). Paleo-redox conditions in both wells are dynamic throughout deposition transitioning between euxinic/anoxic to dysoxic/oxic. This trend is seen through elemental proxies and calcite/pyrite concretion distributions.

03 NATURAL GAS↗
DOE OSTI2020

Additive manufacturing for COVID-19: Devices, materials, prospects, and challenges

The current COVID-19 pandemic has caused the shortage of personal protective equipment (PPE) where improvised manufacturing in particular 3D printing has addressed many needs. This prospective discusses the current global crisis, then follows the wide interest in addressing the shortage of medical devices and PPEs used for treatment and protection against pathogens. An overview of the 3D printing process with polymer materials is given followed by the different 3D printing projects of PPEs and medical devices that emerged for the pandemic (including validation/testing). The potential for rapid prototyping with different polymer materials and eventual high-throughput production is emphasized.

36 MATERIALS SCIENCE↗