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NASA NTRS2013

Extreme Ionizing-Radiation-Resistant Bacterium

There is a growing concern that desiccation and extreme radiation-resistant, non-spore-forming microorganisms associated with spacecraft surfaces can withstand space environmental conditions and subsequent proliferation on another solar body. Such forward contamination would jeopardize future life detection or sample return technologies. The prime focus of NASA s planetary protection efforts is the development of strategies for inactivating resistance-bearing micro-organisms. Eradi cation techniques can be designed to target resistance-conferring microbial populations by first identifying and understanding their physiologic and biochemical capabilities that confers its elevated tolerance (as is being studied in Deinococcus phoenicis, as a result of this description). Furthermore, hospitals, food, and government agencies frequently use biological indicators to ensure the efficacy of a wide range of radiation-based sterilization processes. Due to their resistance to a variety of perturbations, the nonspore forming D. phoenicis may be a more appropriate biological indicator than those currently in use. The high flux of cosmic rays during space travel and onto the unshielded surface of Mars poses a significant hazard to the survival of microbial life. Thus, radiation-resistant microorganisms are of particular concern that can survive extreme radiation, desiccation, and low temperatures experienced during space travel. Spore-forming bacteria, a common inhabitant of spacecraft assembly facilities, are known to tolerate these extreme conditions. Since the Viking era, spores have been utilized to assess the degree and level of microbiological contamination on spacecraft and their associated spacecraft assembly facilities. Members of the non-sporeforming bacterial community such as Deinococcus radiodurans can survive acute exposures to ionizing radiation (5 kGy), ultraviolet light (1 kJ/m2), and desiccation (years). These resistive phenotypes of Deinococcus enhance the potential for transfer, and subsequent proliferation, on another solar body such as Mars and Europa. These organisms are more likely to escape planetary protection assays, which only take into account presence of spores. Hence, presences of extreme radiation-resistant Deinococcus in the cleanroom facility where spacecraft are assembled pose a serious risk for integrity of life-detection missions. The microorganism described herein was isolated from the surfaces of the cleanroom facility in which the Phoenix Lander was assembled. The isolated bacterial strain was subjected to a comprehensive polyphasic analysis to characterize its taxonomic position. This bacterium exhibits very low 16SrRNA similarity with any other environmental isolate reported to date. Both phenotypic and phylogenetic analyses clearly indicate that this isolate belongs to the genus Deinococcus and represents a novel species. The name Deinococcus phoenicis was proposed after the Phoenix spacecraft, which was undergoing assembly, testing, and launch operations in the spacecraft assembly facility at the time of isolation. D. phoenicis cells exhibited higher resistance to ionizing radiation (cobalt-60; 14 kGy) than the cells of the D. radiodurans (5 kGy). Thus, it is in the best interest of NASA to thoroughly characterize this organism, which will further assess in determining the potential for forward contamination. Upon the completion of genetic and physiological characteristics of D. phoenicis, it will be added to a planetary protection database to be able to further model and predict the probability of forward contamination.

Vaishampayan, Parag A.↗

DOE OSTI2020

Antibiotic tigecycline inhibits cell proliferation, migration and invasion via down‐regulating CCNE2 in pancreatic ductal adenocarcinoma

Abstract Recently, many researches have reported that antibiotic tigecycline has significant effect on cancer treatment. However, biomedical functions and molecular mechanisms of tigecycline in human pancreatic ductal adenocarcinoma (PDAC) remain unclear. In the current study, we tried to assess the effect of tigecycline in PDAC cells. AsPC‐1 and HPAC cells were treated with indicated concentrations of tigecycline for indicated time, and then, MTT, BrdU and soft agar assay were used to test cell proliferation. The effect of tigecycline on cell cycle and cellular apoptosis was tested by cytometry. Migration and invasion were detected by wound healing assay and transwell migration/invasion assay. Expressions of cell cycle‐related and migration/invasion‐related protein were determined by using Western blot. The results revealed that tigecycline observably suppressed cell proliferation by inducing cell cycle arrest at G0/G1 phase and blocked cell migration/invasion via holding back the epithelial‐mesenchymal transition (EMT) process in PDAC. In addition, tigecycline also remarkably blocked tumorigenecity in vivo. Furthermore, the effects of tigecycline alone or combined with gemcitabine in vitro or on PDAC xenografts were also performed. The results showed that tigecycline enhanced the chemosensitivity of PDAC cells to gemcitabine. Interestingly, we found CCNE2 expression was declined distinctly after tigecycline treatment. Then, CCNE2 was overexpressed to rescue tigecycline‐induced effect. The results showed that CCNE2 overexpression significantly rescued tigecycline‐inhibited cell proliferation and migration/invasion. Collectively, we showed that tigecycline inhibits cell proliferation, migration and invasion via down‐regulating CCNE2, and tigecycline might be used as a potential drug for PDAC treatment alone or combined with gemcitabine.

Yang, Jie↗

DOE OSTI2020

Acoustic Explosion Data Archive for Machine Learning

The prompt detection of explosions is a key element of the nuclear non-proliferation mission. With traditional sensors being limited in number and scale, smartphones as compact and economical multi-modal sensors are gaining traction and are being deployed. To address the flood of heterogeneous smartphone data, our team proposes a feature extraction using standardized constant-Q frequency bands across acoustic, barometric, and accelerometer data. The work in this presentation contains data collected at Idaho National Laboratory during planned detonations.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2020

Data Fusion and Feature Extraction of Explosions Recorded on Smartphones

The prompt detection of explosions is a key element of the nuclear non-proliferation mission. With traditional sensors being limited in number and scale, smartphones as compact and economical multi-modal sensors are gaining traction and are being deployed. To address the flood of heterogeneous smartphone data, our team proposes a feature extraction using standardized constant-Q frequency bands across acoustic, barometric, and accelerometer data. The work presented in this poster utilizes three explosions collected on smartphones with Idaho National Lab. More explosions are planned in the near future at INL and NNSS. These extracted features will be used in machine learning methods in future iterations.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2025

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗

DOE OSTI2025

A distributed voltage inference framework for cyber-physical attacks detection and localization in active distribution grids

The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.

active distribution grids↗

DOE OSTI2022

Light output quenching in response to deuterium-ions and alpha particles and pulse shape discrimination in deuterated trans-stilbene

We characterized the light output response of a new 140 cm 3 stilbene-d 12 crystal up to 14.1 MeV neutron energies using a coincidence neutron scattering system. We also characterized its light output response to alpha particles in the 5 to 6 MeV energy range. The excellent PSD capability of the stilbene-d 12 detector allowed us to select light pulses produced by particles of increasing ionization density, namely electrons, protons, deuterium-ions, and alpha particles. The measured fast decay component of the light pulses is increasingly quenched as the ionization density of the particle in the crystal increases. Consistently with this finding, the Birks’ quenching parameter of alpha particles is approximately 8.5 times larger compared to the quenching of deuterium ions, produced by neutron scattering interactions. The reported experimental characterization will allow high-fidelity modeling of the detector enabling its application for fast-neutron detection and spectroscopy in nuclear physics, radiation protection, nuclear security, and non-proliferation.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Quantum Devices for Neutrino and Rare Particle Detection (Final Report)

This report summarizes the technical work completed under the DOE-QuanISED program. This grant allowed the MIT group --and associated groups on the project, including UMass Amherst and Argonne National Laboratory -- to explore the use of superconducting bolometers for the purpose of neutrino and low mass dark matter detection. These low threshold bolometric detection techniques have wide applicability in nuclear and particle physics, including searching for low mass weakly-interacting dark matter; detecting neutrinos via coherent neutrino scattering; searching for axions and nuclear non-proliferation. In addition, we explored how using techniques readily used by the quantum computing community–such as multiplexed microwave resonators and quantum amplifiers—could be used as a low noise readout scheme for an array of superconducting detectors. We briefly summarized the accomplishments under the project, and what future research directions have spawned from this research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

Background: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Methods: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. Results: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. Conclusions: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2011

Assessment of Gamma Radiation Resistance of Spores Isolated from the Spacecraft Assembly Facility During MSL Assembly

Spore forming bacteria, a common inhabitant of spacecraft assembly facilities, are known to tolerate extreme environmental conditions such as radiation, desiccation, and high temperatures. Since the Viking era (early 1970's), spores have been utilized to assess the degree and level of microbiological contamination on spacecraft and their associated spacecraft assembly facilities. There is a growing concern that desiccation and extreme radiation resistant spore forming microorganisms associated with spacecraft surfaces can withstand space environmental conditions and subsequently proliferate on another solar body. Such forward contamination would certainly jeopardize future life detection or sample return technologies. It is important to recognize that different classes of organisms are critical while calculating the probability of contamination, and methods must be devised to estimate their abundances. Microorganisms can be categorized based on radiation sensitivity as Type A, B, C, and D. Type C represents spores resistant to radiation (10% or greater survival above 0.8 mRad gamma radiation). To address these questions we have purified 96 spore formers, isolated during planetary protection efforts of Mars Science Laboratory assembly for gamma radiation resistance. The spores purified and stored will be used to generate data that can be used further to model and predict the probability of forward contamination.

extreme environments↗

NASA NTRS2011

Assessment of Gamma Radiation Resistance of Spores Isolated from the Spacecraft Assembly Facility During MSL Assembly

Spore forming bacteria, a common inhabitant of spacecraft assembly facilities, are known to tolerate extreme environmental conditions such as radiation, desiccation, and high temperatures. Since the Viking era (early 1970's), spores have been utilized to assess the degree and level of microbiological contamination on spacecraft and their associated spacecraft assembly facilities. There is a growing concern that desiccation and extreme radiation resistant spore forming microorganisms associated with spacecraft surfaces can withstand space environmental conditions and subsequently proliferate on another solar body. Such forward contamination would certainly jeopardize future life detection or sample return technologies. It is important to recognize that different classes of organisms are critical while calculating the probability of contamination, and methods must be devised to estimate their abundances. Microorganisms can be categorized based on radiation sensitivity as Type A, B, C, and D. Type C represents spores resistant to radiation (10% or greater survival above 0.8 Mrad gamma radiation). To address these questions we have purified 96 spore formers, isolated during planetary protection efforts of Mars Science Laboratory assembly for gamma radiation resistance. The spores purified and stored will be used to generate data that can be used further to model and predict the probability of forward contamination.

spacecraft assembly↗

DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

100% microgrid↗

DOE OSTI2020

Innovation in Radiological Security, Part 1 of 2 – Using a Cloud Solution to Compress the Timely Detection Model

The design and implementation of new security technologies must account for numerous and complex operational challenges. For example, traditionally isolated systems must now survive amid the proliferation of network connectivity and endure the dynamic environments created by organizations tolerating bring-your-own-device policies. When evaluating the cyber, physical, or cyber-physical security of an asset, a common practice in the security industry is to apply the Timely Detection Model (TDM)—a versatile concept that relates the security functions of detection, delay, and response to the progression of an oppositional force’s attack timeline. If implemented correctly, security enhancements can compress the TDM to offer efficiencies to the stakeholders responsible for adjudicating threat scenarios. The efficiencies gained by the response force allow for more effective protection strategies to be realized. One such enhancement, deployed within the radiological security domain—the Sentry-Remote Monitoring System (Sentry-RMS)—is a stand-alone security system that detects, assesses, and communicates priority alarms as a means of thwarting internal and external threats. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform—an optional feature of the Sentry-RMS—is being developed to facilitate more efficient alarm adjudication by site stakeholders and, if necessary, a faster response by law enforcement. This Cloud-hosted platform receives protected information from deployed Sentry-RMS units and relays it to stakeholders that have vested interests in maintaining an elevated level of situational awareness. Operationally, this enables real-time delivery of high-priority alarm and video imagery directly to an identified response stakeholder, such as local law enforcement or site management, via natively developed mobile applications or full platform integration. The Sentry-SECURE platform, as implemented by the U.S. Department of Energy’s Office of Radiological Security, is an example of an innovative security technology that compresses the TDM by (1) enabling a more efficient time to target and (2) better informing a response force’s predetermined tactics, techniques, and procedures. This paper explores the platform’s operational roles, highlights its principal functions, and presents a use case that demonstrates how the platform provides enhanced situational awareness when adjudicating priority alarms.

Sentry-RMS, Timely Detection Model, Sentry-SECURE↗

NASA NTRS2016

Caribbean Oceans: Utilizing NASA Earth Observations to Detect, Monitor, and Respond to Unprecedented Levels of Sargassum in the Caribbean Sea

In 2011 and 2015, the nations of the Caribbean Sea were overwhelmed by the unprecedented quantity of Sargassum that washed ashore. This issue prompted international discussion to better understand the origins, distribution, and movement of Sargassum, a free-floating brown macro alga with ecological, environmental, and commercial importance. In the open ocean, Sargassum mats serve a vital ecological function. However, when large quantities appear onshore without warning, Sargassum threatens local tourist industries and nearshore ecosystems within the Caribbean. As part of the international response, this project investigated the proliferation of this macro alga within the Caribbean Sea from 2003-2015, and used NASA Earth observations to detect and model Sargassum growth across the region. The Caribbean Oceans team calculated the Floating Algal Index (FAI) using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data, and compared the FAI to various oceanic variables to determine the ideal pelagic environment for Sargassum growth. The project also examined the annual spread of Sargassum throughout the region by using Earth Trends Modeler (ETM) in Clark Labs' TerrSet software. As part of the international effort to better understand the life cycle of Sargassum in the Caribbean, the results of this project will help local economies promote sustainable management practices in the region.

Ped, Jordan↗

DOE OSTI2024

Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications

We report digital twin technology has the potential to improve the effectiveness of international safeguards inspectors by providing a tool which can: first, perform an accurate diversion path analysis, identify their indicators, and required sensors to detect them; and second, monitor facilities in real-time using critical data streams that benefit from this safeguards-by-design approach. Safeguards inspectors are required to visit facilities and verify the nuclear material to ensure no diversion has taken place and detect misuse of the facility; however, this analysis and verification effort is time consuming, and with limited funding it is imperative that time spent at a nuclear facility is focused on key areas. A virtual digital twin of three prototypic sodium fast reactors was developed, where diversion and misuse scenarios were explored to determine how a digital twin could provide inspectors with an understanding of how proliferation may occur and where the most likely areas for proliferation would be. For each of the three reactors, an optimization algorithm was able to find core designs which would be difficult to detect via sensors alone; however, the use of a machine learning adapter provided by the digital twin was able to show general trends in where proliferation as likely to take place.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Radiation Detection Data Competition Report

In FY2018 through FY2020, NA-22, the Defense Nuclear Nonproliferation Research and Development Program, funded a Data Science project to develop and implement statistical methodology to effectively host data competitions with the goal of leveraging the opportunity provided by crowdsourcing. By accessing and engaging expertise from a broader research community, there is an opportunity to attract innovative solutions from a variety of different research disciplines to advance the ability to solve important non-proliferation problems. This report summarizes the key results of this project after hosting two data competitions focused on urban radiation detection. The first competition was focused on attracting participants from the U.S. national laboratories, while the second, hosted by TopCoder, was open to the broader international community and awarded prize money to the top 10 competitors. At the start of the project, there was strong interest from NA-22 to explore and develop the capability to host data competitions as a means of leveraging the broader community to solve important nuclear nonproliferation problems. Having a standard data set on which to compare different approaches based on clearly defined criteria was desirable to be able to evaluate the state of solutions for important problems. Initially, it was not clear that it would even be possible logistically and bureaucratically to host a competition with an international field of competitors and to award the prize money needed to attract solutions from top competitors. Happily, a path to host the competitions was ultimately found that allowed this powerful accelerator of improvements to be leveraged.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens

Abstract Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3–29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%–97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%–94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or “molecular tipping points” that are predictive of later-life health outcomes.

Hill, III,, Thomas↗

DOE OSTI2020

Integrating Data Sources for Improved Safeguards and Accountancy of Electrochemical Fuel Reprocessing Systems. Final Report

Electrochemical reprocessing (also commonly known as “pyroprocessing”) of used nuclear fuel is an alternative to aqueous reprocessing that confers a number of advantages, including the ability to process more recently-discharged fuel, smaller resultant waste volumes, and the lack of isolation of plutonium in the product stream. While electrochemical reprocessing systems have seen a significant research and development effort, nuclear safeguards and security of these systems remains under-developed, particularly given the significant differences in operating environment and process flowsheet compared with established aqueous methods. Objective 4 of DOE’s nuclear energy research and development activities in Nuclear Energy Research and Development Roadmap is: “Understand and minimize the risks of nuclear proliferation and terrorism.” Material Control & Accountancy (MC&A) programs at nuclear processing plants deter and detect theft and diversion of nuclear material by both outside and inside adversaries. Empirical modeling and data analytics of online measurements and state indicators can improve the timeliness of detection and reduce the uncertainty in accountancy measures. To support the development and demonstration of such methods, data are needed that capture expected measurement values collected throughout electrochemical reprocessing facilities. A review of the available literature of current research in measurements for electrochemical reprocessing systems identified twenty-two candidate measurement methodologies, which characterize fuel/salt composition, concentrations of various isotopes and elements, and operational parameters throughout the reprocessing facility. Four primary measurement methodologies have been simulated based on either physical simulations or empirical correlations: gamma emissions, neutron emissions, hybrid k-edge densitometry (HKED), and cyclic voltammetry. Initial investigation into the use of these measures for process monitoring and detection suggests the approach is viable and worthy of further investigation. This report summarizes efforts to generate signatures of key measurements during operation of electrochemical reprocessing facilities and initial investigation of data analytics to support process monitoring, diversion detection, and accountancy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗