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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-scale signaling and tumor evolution in high-grade gliomas

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Understanding Competing Risks

Highlights: • Clinicians and researchers need to be cognizant of competing risks (ie, the occurrence of an event that precludes future development of the outcome of interest). • Censoring is appropriate when a patient being censored at a particular time (eg, owing to loss to follow-up) is deemed to have approximately the same future risk of developing the outcome of interest as patients still being followed, and when the reason for censoring is not related to events of interest. • Censoring is not appropriate in many scenarios; for example, a dead patient can never develop a future cancer recurrence or late effect. • Kaplan-Meier (KM) analysis is commonly used for a time-to-event analysis when the primary outcome (eg, overall survival) has no meaningful competing risk and censoring is appropriate. Cox proportional hazards modeling can be used for multivariable analysis to examine the association between covariates and the primary outcome. • In the presence of competing events, incorrect use of the KM analysis will likely lead to an overestimation of the risk of the outcome of interest and biased estimates from both univariable and multivariable analyses. Therefore, KM usually should not be used to determine local recurrence risk because humanity is always at risk for dying, which is a competing risk. • A competing risk analysis is therefore most appropriate when there are competing events to the primary outcome of interest. The most commonly used multivariable model in these situations is the Fine-Gray model. • Many fundamental questions in radiation oncology—local and regional recurrence, treatment-related toxicity—are best addressed using competing risk methodologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Long noncoding RNA AK023096 interacts with hnRNP-K and contributes to the maintenance of self-renewal in bladder cancer stem-like cells

Highlights: • BCSLCs play a critical role for the high risk of recurrence and we identified that lncRNA-AK023096 was required for controls stemness of BCSLCs. • LncRNA-AK023096 recruits hnRNP-K and epigenetically upregulated SOX2 expression by modulating H3K4 trimethylation of SOX2 promoter. • Overexpression of lncRNA-AK023096 expression in the tumor tissue was significantly associated with high risk of recurrence following TUR-Bt. LncRNA contribution to self-renewal of bladder cancer stem-like cells (CSLCs) remains largely unknown. We investigated the expression profile and biological function of lncRNAs in urothelial CSLCs by microarray analysis. Among these, lncRNA-AK023096 was identified as potentially playing a role in maintaining self-renewal of CSLCs. Knockdown of this transcript inhibited spheroid formation and tumor formation. We found that AK023096 mediates recruitment of hnRNP-K to SOX2 promoter and increases H3K4 trimethylation status on SOX2 promoter, leading to a robust change in SOX2 mRNA and protein levels. Moreover, AK023096 expression in primary tumors was found to be a powerful predictor of recurrence following transurethral resection in patients with nonmuscle-invasive bladder cancer, highlighting the critical role of lncRNA in the bladder cancer regulatory network.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Online evolutionary neural architecture search for multivariate non-stationary time series forecasting

Time series forecasting (TSF) is one of the most important tasks in data science. TSF models are usually pre-trained with historical data and then applied on future unseen datapoints. However, real-world time series data is usually non-stationary and models trained offline usually face problems from data drift. Models trained and designed in an offline fashion can not quickly adapt to changes quickly or be deployed in real-time. To address these issues, this work presents the Online NeuroEvolution-based Neural Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Without any pre-training, ONE-NAS utilizes populations of RNNs that are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world, large-scale multivariate wind turbine data as well as the univariate Dow Jones Industrial Average (DJIA) dataset. These results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies. Additionally, results show that utilizing multiple populations of RNNs which are periodically repopulated provide significant performance improvements, allowing this online neural network architecture design and training to be successful.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. Here, this study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

False positive PSMA PET for tumor remnants in the irradiated prostate and other interpretation pitfalls in a prospective multi-center trial

Readers need to be informed about potential pitfalls of [ 68 Ga]Ga-PSMA-11 PET interpretation. Here we report [ 68 Ga]Ga-PSMA-11 PET findings discordant with the histopathology/composite reference standard in a recently published prospective trial on 635 patients with biochemically recurrent prostate cancer. Consensus reads were false positive in 20 regions of 17/217 (8%) patients with lesion validation. Majority of the false positive interpretations (13 of 20, 65%) occurred in the context of suspected prostate (bed) relapse (T) after radiotherapy ( n = 11); other false positive findings were noted for prostate bed post prostatectomy (T, n = 2), pelvic nodes (N, n = 2), or extra pelvic lesions (M, n = 5). Major sources of false positive findings were PSMA-expressing residual adenocarcinoma with marked post-radiotherapy treatment effect. False negative interpretation occurred in 8 regions of 6/79 (8%) patients with histopathology validation, including prostate (bed) ( n = 5), pelvic nodes ( n = 1), and extra pelvic lesions ( n = 2). Lesions were missed mostly due to small metastases or adjacent bladder/urine uptake.[ 68 Ga]Ga-PSMA-11 PET at biochemical recurrence resulted in less than 10% false positive interpretations. Post-radiotherapy prostate uptake was a major source of [ 68 Ga]Ga-PSMA-11 PET false positivity. In few cases, PET correctly detects residual PSMA expression post-radiotherapy, originating however from treated, benign tissue or potentially indolent tumor remnants.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2020

A Bayesian modeling framework for crash severity effects of active traffic management systems

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. In this paper, we used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A most misunderstood conditionally-solvable quantum-mechanical model

Highlights: • The Schrödinger equation for some quantum-mechanical models is separable in cylindrical coordinates. • The radial equation exhibits harmonic, linear and Coulomb-like interactions. • The Frobenius method leads to three-term recurrence relations. • Some particular energies are obtained from truncation of the recurrence relation. • Many authors misunderstood these results. In this paper we show that several authors have derived wrong physical conclusions from a gross misunderstanding of the exact eigenvalues and eigenfunctions of a conditionally-solvable quantum-mechanical model. It consists of an eigenvalue equation with seemingly Coulomb, linear and harmonic terms. Here we compare the results derived by those authors with the actual eigenvalues of the models calculated by means of the Ritz variational method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Validating Modern NRG Oncology Pelvic Nodal and Groupe Francophone de Radiothérapie Urologique Prostate Bed Contouring Guidelines for Post-Prostatectomy Salvage Radiation: A Secondary Analysis of the LOCATE Trial

We used the patterns of recurrence on {sup 18}F-fluciclovine positron emission tomography (PET)–computed tomography (CT) in patients enrolled in the LOCATE trial after prostatectomy to evaluate how well the most recent NRG Oncology and Groupe Francophone de Radiothérapie Urologique (GFRU) contouring recommendations encompassed all sites of recurrence in the prostate fossa and pelvic nodes in comparison to former Radiation Therapy Oncology Group (RTOG) recommendations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Prognostic Implications of MRI-Detected EMVI and Tumor Deposits and Their Response to Neoadjuvant Therapy in cT3 and cT4 Rectal Cancer

Magnetic resonance imaging–detected extramural venous invasion (mrEMVI) and tumor deposits (TDs) are risk factors for the development of local recurrence and distant metastases (DMs) in rectal cancer. However, little is known about their response to neoadjuvant treatment and its relation to oncologic outcomes. This study evaluated the incidence and features of mrEMVI and TDs before and after neoadjuvant treatment in relation to the development of local recurrence and DMs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Predicting material microstructure evolution via data-driven machine learning

The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.

machine learning (ML), materials characterization↗
DOE OSTI2021

Mitochondrial integration and ovarian cancer chemotherapy resistance

Ovarian cancer has been nicknamed the “silent killer”. Most patients with ovarian cancer are diagnosed at an advanced stage of the disease for the first time because of its insignificant early clinical symptoms. In addition to the difficulty of early screening and delay in diagnosis, the high recurrence rate and relapsed refractory status of patients with ovarian cancer are also important factors for their high mortality. Patients with recurrent ovarian cancer often use neoadjuvant chemotherapy followed by surgery as the first choice. However, this is often accompanied by chemotherapy resistance, leading to treatment failure and a mortality rate of more than 90%. In the past, it was believed that the anti-tumor effect of chemotherapeutics represented by cisplatin was entirely attributable to its irreversible damage to DNA, but current research has found that it can inhibit cell growth and cytotoxicity via nuclear and cytoplasmic coordinated integration. As an important hub and integration platform for intracellular signal communication, mitochondria are responsible for multiple key factors during tumor occurrence and development, such as metabolic reprogramming, acquisition of metastatic ability, and chemotherapy drug response. The role of mitochondria in ovarian cancer chemotherapy resistance is becoming increasingly recognized. In this review, we discuss the cellular interactive regulatory network surrounding mitochondria, elucidate the mechanisms of tumor cell survival under chemotherapy, and discuss potential means of interfering with mitochondrial function as a novel anti-cancer therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

The Leading Modes of Asian Summer Monsoon Variability as Pulses of Atmospheric Energy Flow

Monsoon rainfall anomalies are often organized into dynamical modes that are more predictable than rainfall at specific locations. Here a linear response function (LRF) is constructed to extract the neutral modes (NM) of the dynamical atmospheric system and their optimal forcing that govern precipitation variability, providing a dynamical underpinning for the empirically derived, recurrent rainfall patterns. The leading mode (NM1) features a shift in the Atlantic ITCZ, a drying over the Indian Ocean and Indian subcontinent, and a “South-Flood-North Drought (SFND)” pattern over eastern China, a pattern inferable from changes in the atmospheric energy flow forced by the corresponding optimal forcing and further enhanced by the water vapor and cloud feedbacks. Moreover, the slow evolving component of the NM1 in response to CO2 forcing can be skillfully predicted by running the ocean heat uptake through the LRF, implicating a deterministic ocean dynamic source for the NM1-like trend under climate warming.

monsoon mode of variability, EBM, climate change, ↗
DOE OSTI2023

Dispersal, habitat filtering, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography

Abstract Wetlands store 20–30% of the world’s soil carbon, and identifying the microbial controls on these carbon reserves is essential to predicting feedbacks to climate change. Although viral infections likely play important roles in wetland ecosystem dynamics, we lack a basic understanding of wetland viral ecology. Here 63 viral size-fraction metagenomes (viromes) and paired total metagenomes were generated from three time points in 2021 at seven fresh- and saltwater wetlands in the California Bodega Marine Reserve. We recovered 12,826 viral population genomic sequences (vOTUs), only 4.4% of which were detected at the same field site two years prior, indicating a small degree of population stability or recurrence. Viral communities differed most significantly among the seven wetland sites and were also structured by habitat (plant community composition and salinity). Read mapping to a new version of our reference database, PIGEONv2.0 (515,763 vOTUs), revealed 196 vOTUs present over large geographic distances, often reflecting shared habitat characteristics. Wetland vOTU microdiversity was significantly lower locally than globally and lower within than between time points, indicating greater divergence with increasing spatiotemporal distance. Viruses tended to have broad predicted host ranges via CRISPR spacer linkages to metagenome-assembled genomes, and increased SNP frequencies in CRISPR-targeted major tail protein genes suggest potential viral eco-evolutionary dynamics in response to both immune targeting and changes in host cell receptors involved in viral attachment. Together, these results highlight the importance of dispersal, environmental selection, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography.

Environmental Sciences & Ecology↗