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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 OSTI2022

SIRT1 targeted by miR ‐211‐5p regulated proliferation and apoptosis of Dex‐treated growth plate chondrocytes via mediating SOX2

Abstract Dexamethasone (Dex) is reported to cause bone growth retardation in children, which is associated with the increased apoptosis and decreased proliferation of growth plate chondrocytes. Sirtuin 1 (SIRT1) plays an important role in chondrocyte function and homeostasis. Thus, we further explored the regulatory mechanism of SIRT1 in Dex‐induced growth plate chondrocyte dysfunction. SIRT1 expression was detected in Dex‐treated growth plate chondrocytes using RT‐qPCR and western blot assay. The modulation of SIRT1 on SOX2 expression was evaluated. Besides, we identified that SIRT1 was targeted by miR‐211‐5p using TargetScan and RNA pull‐down assay. A loss‐of‐function assay was performed to evaluate the effects of miR‐211‐5p on Dex‐induced growth plate chondrocyte dysfunction in vitro and in vivo. We found that SIRT1 was downregulated in Dex‐treated growth plate chondrocytes. The expression of SOX2 was upregulated by overexpression SIRT1. Meanwhile, downregulation of SOX2 weakened the positive function of SIRT1 overexpression on Dex‐induced growth plate chondrocytes dysfunction. Subsequently, we confirmed that SIRT1 was targeted by miR‐211‐5p. MiR‐211‐5p inhibitor increased the expression levels of SIRT1 and SOX2, and restored the Dex‐treated growth plate chondrocyte function. Animal assays further demonstrated that the effects of miR‐211‐5p on the growth plate chondrogenesis. In conclusion, our data suggest that SIRT1 exerts a protective effect on growth plate chondrocyte under Dex stimulation. MiR‐211‐5p/SIRT1/SOX2 axis regulates the process of Dex‐inhibited growth plate chondrogenesis.

Cheng, Deliang↗
DOE OSTI2020

MSR Proliferation Resistance and Physical Protection White Paper

Molten Salt Reactors (MSRs) have seen a resurgence of interest in the past decade around the world. Support for these activities is provided from both national and private sources. The largest difference from the 2011 GIF MSR PR&PP evaluation consequently is the transition from evaluating academic systems focused on exploring the technical potential of MSRs to those of companies and countries focusing on near-term deployment. A wide variety of designs currently exist ranging from solid to liquid-fueled designs, with salt processing on-site or off-site, and a variety of fuel choices. As such, the proliferation resistance and physical protection aspects will have significantly more variation depending on reactor design than the other advanced reactors. The rapid introduction and evolution of innovative MSR designs inevitably means that technology specific details of overview reports, such as this one, become rapidly outdated. Consequently, this report focuses on essential features required for any MSR rather than specific design aspects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Numerical Simulations of Non-Proliferation Experiments

We have implemented a set of numerical simulations to numerically investigate the outcomes of non-proliferation experiments. The high explosive (HE) in the simulations is in the form of a cylinder. The two-dimensional simulations are carried out in the r-z cylindrical coordinate, and the three-dimensional simulation is in the Cartesian coordinate. From the simulation, the cavity formed after detonation stops its expansion after about 0.15 sec. At the time the cavity stops expanding, the cavity shape seems quite spherical although the initial HE is in the shape of cylinder, but the radius in the vertical direction, which is about 19.7m, is a little less than the one in the horizonal direction, which is about 21.5m. Although there is no layer of air between the HE and tuff in real experiments, in addition to the simulations without air, we add a layer of air in one simulation to assess any possible influence of the air on the shape of the cavity.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Considerations in Proliferation Resistance Optimization of Medical Isotope Facilities

The U.S. Proliferation Resistance and Optimization (PRO-X) program provides a framework for design optimization of a system, component, or feature of a nuclear facility to minimize potential Special Nuclear Material production and diversion pathways while optimizing system performance for peaceful, asintended missions.

Groden, Nicholas D.↗
DOE OSTI2025

Study of Applications of Radiological Data Fusion for Nuclear Security and Non-proliferation Applications

This report describes the radiological Scene Data Fusion (SDF) technology and the potential to apply the SDF technology to a variety of problems related to nuclear non-proliferation, radiological security, and other missions related to the national nuclear security agency and the US government, more broadly. The application space was conceived by both the authors of this report and the many scientists and stakeholders they interviewed. In order to successfully apply the SDF technology to the various applications, further research and development is often necessary and those R&D steps are also outlined herein.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Importance of 3-D S{sub N} depletion in non-proliferation using BSOLVE

We present a single Pressurized Water Reactor (PWR) 3-D fuel rod design for depletion analysis using BSOLVE, our newly developed Runge-Kutta-Fehlberg based depletion code. BSOLVE is coupled with the deterministic 3-D S{sub N} particle transport code, PENTRAN, applied here with a 4-neutron energy group comparison to Continuous Energy (C/E) SERPENT2 Monte Carlo results. Differences are expected, as PENTRAN+BSOLVE retains full (multi-group) energy information for reactions, nuclide specific fission contributions, and energy dependent fission yields, using the latest available ENDF-BVIII data, important to retain accurate burned fuel inventories; SERPENT2 collapses burnup reactions to a single energy value. For depletion times up to ∼ 700 days and typical PWR power densities, relative differences between multigroup 3-D S{sub N} with full energy data and Monte Carlo one group burnup for trans-uranium nuclide concentrations and fission products are up to ∼20%. System eigenvalues are consistent, but with differences early and late in the cycle attributed to multigroup vs. C/E Monte Carlo cross sections. This work highlights the importance of low variance transport driven burnup for non-proliferation concerns, since plutonium quality varies significantly along axial lengths, and is more challenging to converge using Monte Carlo; details of depletion steps with spatial/zone dependent plutonium quality are provided. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Sorafenib derivatives-functionalized gold nanoparticles confer protection against tumor angiogenesis and proliferation via suppression of EGFR and VEGFR-2

Sorafenib is a multi-kinase inhibitor that has been highlighted as a tumor suppressor due to its anti-proliferative and anti-angiogenic properties, whereas the clinical application of Sorafenib is restricted by the side effects it may cause. The past decade has witnessed the development of a series of sorafenib derivatives to improve the clinical performance of sorafenib. Gold nanoparticles (AuNPs) have been widely utilized in drug delivery systems due to their unique properties, including biocompatible nature, simple preparation, and easy surface modification. Herein, this study is aimed to investigate the anti-tumor effects of new sorafenib derivatives-capped gold nanoparticles (AuNPs-New Sor) in tumor formation and metastasis as well as the underlying mechanisms. Initially, new sorafenib derivatives were constructed and combined with AuNPs to form AuNPs-New Sor, and the properties of synthesized AuNPs-New Sor were identified in a mouse model of tumorigenesis. The effect of AuNPs-New Sor on tumor vascular normalization was investigated by assessing vascular permeability and perfusion rate. Next, we evaluated the effect of AuNPs-New Sor on migration and viability of tumor cells and human umbilical vein endothelial cells (HUVECs) as well as on HUVEC angiogenesis in vitro. A melanoma mouse model was further established for in vivo substantiation of the anti-tumor effect of AuNPs-New Sor. According to the results, AuNPs could deliver new sorafenib derivatives into tumor tissues and downregulate the expression of epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor-2 (VEGFR-2), thereby suppressing tumor migration, EMT, and angiogenesis in vitro. In addition, AuNPs-New Sor displayed competitive anti-tumor activities in vivo. Taken together, AuNPs-New Sor may attenuate tumor development and angiogenesis through downregulation of EGFR and VEGFR-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Path Toward a Breeding, Proliferation-Resistant, Thermal-Spectrum Molten Salt Reactor

Liquid-fueled, thermal-spectrum molten salt breeder reactors (TS-MSBRs) offer the potential for affordable, safe, inexhaustible energy with minimal potential for nuclear material misuse and without significant actinide waste generation. Realizing the full set of TS-MSBR capabilities is only now becoming possible with the advent of advanced fuel-salt processing techniques, improved materials, and a more detailed understanding of fuel-salt properties. Additionally, modern higher-fidelity modeling and simulation methods enable a more detailed evaluation of TS-MSBR design options. TS-MSBRs, however, remain immature and will require substantial, sustained development resources.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Event Definition for the Automated Detection of Nuclear Proliferation Activities

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Discovery Analytics Center (DAC) at Virginia Polytechnic Institute and State University (VT) began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on largescale open data sources to identify new, developing, or undeclared nuclear programs. One of the most challenging aspects of applying machine learning techniques to such a problem is the high likelihood of extremely sparse data from disparate sources. To overcome this challenge, the current work will use a strategic combination of supervised, semi-supervised, and unsupervised learning techniques to ingest and fuse data streams to make a forecast of nuclear activities in a targeted geospatial location. Identifying potential data sources and training supervised learning algorithms is dependent upon the development of a robust foundation of targeted event domains that fundamentally define the nuclear activities of interest. This report documents the definition of a hierarchical structure for both nuclear activity and event domains that will be used to guide the research team in development or use of existing semantic dictionaries that are instrumental to searching, parsing, and categorizing events for the forecasting system’s use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Machine Learning Using Open Data Sources for Detection of Nuclear Proliferation Activities (U)

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics (SCAIDA) at Virginia Polytechnic Institute and State University (VT) and funded by the Department of Energy’s (DOE) Defense Nuclear Nonproliferation Research and Development, began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on large-scale open data sources to identify new, developing, and/or undeclared nuclear programs. Using the announcement in May 2018 of the proposed Savannah River Plutonium Processing Facility (SRPPF) as a test subject, the goal of this 2-year project is to forecast the SRPPF using only data prior to May 2018. The project work is split into a preliminary prototype development for the first year with an initial evaluation of viability followed by the second year of development to create an integrated prototype system and more extensive performance evaluation. This report documents the results of the preliminary-phase tasks.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗