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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NOAA Arctic Report Card 2024 : Tundra Greenness

The Arctic tundra biome occupies Earth’s northernmost lands, covering a 5.1 million km 2 area that encircles the Arctic Ocean and is bound to the south by the boreal forest biome. Arctic tundra ecosystems are experiencing profound changes as vegetation and underlying permafrost soils are strongly influenced by rising air temperatures and the rapid decline of sea ice (see essays Surface Air Temperature and Sea Ice). By the late 1990s, an increase in the productivity of tundra vegetation became evident in global satellite observations, a phenomenon that continued and soon became known as “the greening of the Arctic.” Arctic greening is dynamically linked with Earth’s changing climate, seasonal snow, permafrost, and sea-ice cover, and remains a focus of multidisciplinary scientific research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO 2 fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystissp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of 13 C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.

Data-Driven Digital Twins↗
DOE OSTI2022

A Laboratory-driven Multiscale Investigation of X-Ray Induced Mass Loss and Photochemical Evolution in Cosmic Carbon and Silicate Dust

We present the results of an integrated laboratory and modeling investigation into the impact of stellar X-rays on cosmic dust. Carbonaceous grains were prepared in a cooled (< 200 K) supersonic expansion from aromatic molecular precursors, and were later irradiated with 970 eV X-rays. Silicate (enstatite) grains were prepared via laser ablation, thermally annealed, and later irradiated with 500 eV X-rays. Infrared spectra of the 3.4 μm band of the carbon sample prepared with benzene revealed 84% ± 5% band area loss for an X-ray dose of 5.2 x 10 23 eV.cm -2 . Infrared spectra of the 8 -12 μm Si-O band of the silicate sample revealed band area loss up to 63% ± 5% for doses of 2.3 x 10 23 eV.cm -2 . A hybrid Monte Carlo particle trajectory approach was used to model the impact of X-rays and ensuing photoelectrons, Auger and collisionally ionized electrons through the bulk. As a result of x-ray ionization and ensuing Coulomb explosions on surface molecules, the calculated mass loss is 60% for the carbonaceous sample and 46% for the silicate sample, within a factor of 2 of the IR band loss, supporting an X-ray induced mass-loss mechanism. We apply the laboratory X-ray destruction rates to estimate the lifetimes of dust grains in protoplanetary disks surrounding 1 M ⊙ and 0.1 M ⊙ G and M stars. In both cases, X-ray destruction timescales are short (a few million years) at the disk surface, but are found to be much longer than typical disk lifetimes (≳10 Myr) over the disk bulk.

79 ASTRONOMY AND ASTROPHYSICS↗