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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Identification and selection of optimal reference genes for qPCR-based gene expression analysis in Fucus distichus under various abiotic stresses

Quantitative gene expression analysis is an important tool in the scientist’s belt. The identification of evenly expressed reference genes is necessary for accurate quantitative gene expression analysis, whether by traditional RT-PCR (reverse-transcription polymerase chain reaction) or by qRT-PCR (quantitative real-time PCR; qPCR). In the Stramenopiles (the major line of eukaryotes that includes brown algae) there is a noted lack of known reference genes for such studies, largely due to the absence of available molecular tools. Here we present a set of nine reference genes (Elongation Factor 1 alpha (EF1A), Elongation Factor 2 alpha (EF2A), Elongation Factor 1 beta (EF1B), 14-3-3 Protein, Ubiquitin Conjugating Enzyme (UBCE2), Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH), Actin Related Protein Complex (ARP2/3), Ribosomal Protein (40s; S23), and Actin) for the brown alga Fucus distichus . These reference genes were tested on adult sporophytes across six abiotic stress conditions (desiccation, light and temperature modification, hormone addition, pollutant exposure, nutrient addition, and wounding). Suitability of these genes as reference genes was quantitatively evaluated across conditions using standard methods and the majority of the tested genes were evaluated favorably. However, we show that normalization genes should be chosen on a condition-by-condition basis. We provide a recommendation that at least two reference genes be used per experiment, a list of recommended pairs for the conditions tested here, and a procedure for identifying a suitable set for an experimenter’s unique design. With the recent expansion of interest in brown algal biology and accompanied molecular tools development, the variety of experimental conditions tested here makes this study a valuable resource for future work in basic biology and understanding stress responses in the brown algal lineage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

First comprehensive identification of cardiac proteins with putative increased O-GlcNAc levels during pressure overload hypertrophy

Protein posttranslational modifications (PTMs) by O-GlcNAc globally rise during pressure-overload hypertrophy (POH). However, a major knowledge gap exists on the specific proteins undergoing changes in O-GlcNAc levels during POH primarily because this PTM is low abundance and easily lost during standard mass spectrometry (MS) conditions used for protein identification. Methodologies have emerged to enrich samples for O-GlcNAcylated proteins prior to MS analysis. Accordingly, our goal was to identify the specific proteins undergoing changes in O-GlcNAc levels during POH. We used C57/Bl6 mice subjected to Sham or transverse aortic constriction (TAC) to create POH. From the hearts, we labelled the O-GlcNAc moiety with tetramethylrhodamine azide (TAMRA) before sample enrichment by TAMRA immunoprecipitation (IP). We used LC-MS/MS to identify and quantify the captured putative O-GlcNAcylated proteins. We identified a total of 700 putative O-GlcNAcylated proteins in Sham and POH. Two hundred thirty-three of these proteins had significantly increased enrichment in POH over Sham suggesting higher O-GlcNAc levels whereas no proteins were significantly decreased by POH. We examined two MS identified metabolic enzymes, CPT1B and the PDH complex, to validate by immunoprecipitation. We corroborated increased O-GlcNAc levels during POH for CPT1B and the PDH complex. Enzyme activity assays suggests higher O-GlcNAcylation increases CPT1 activity and decreases PDH activity during POH. In summary, we generated the first comprehensive list of proteins with putative changes in O-GlcNAc levels during POH. Our results demonstrate the large number of potential proteins and cellular processes affected by O-GlcNAc and serve as a guide for testing specific O-GlcNAc-regulated mechanisms during POH.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
DOE OSTI2024

Identification and time evolution of thionyl chloride (SOCl 2 ) radiolysis products

In this study, innovative solutions are needed to reduce the amount of high-level waste generated by used nuclear fuel recycling strategies to support the widespread adoption of sustainable nuclear fission energy technologies. To this end, a new sulfur chloride-based process has been developed to recycle zirconium alloy-based materials, which make up a significant fraction of high-level radioactive waste. To support the continued development of this process, we present new data on the potential reaction pathways over time of the products arising from the gamma and electron beam radiolysis of neat thionyl chloride (SOCl 2 ). Interrogation of the gamma irradiated liquid by Raman spectroscopy provided more conclusive identification of the SOCl 2 degradation products, specifically sulfur dichloride (SCl 2 ), molecular chlorine (Cl 2 ), sulfur dioxide (SO 2 ), and sulfuryl chloride (SO 2 Cl 2 ). In comparison, the high dose rate (~10 7 Gy s -1 ) electron beam irradiations formed significantly more degradation products. For both cobalt-60 gamma and electron beam irradiations, the observed degradation products were found to evolve as a function of time post-irradiation via the same reaction pathways, with indication of a solvent regeneration mechanism. These findings are fortuitous for process development, as such a mechanism would be beneficial for process longevity and cost effectiveness.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Wildfires identification: Semantic segmentation using support vector machine classifier

This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with $\mathcal{l}1$-loss and $\mathcal{l}2$-loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.

Pecha, Marek↗
DOE OSTI2016

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Isotope Identification

The objective of this training modules is to examine the process of using gamma spectroscopy for radionuclide identification; apply pattern recognition to gamma spectra; identify methods of verifying energy calibration; and discuss potential causes of isotope misidentification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Identification and Characterization of Thermal Fluid Phenomena Associated with Selected Operating/Accident Scenarios in Modular High Temperature Gas-cooled Reactors

Dominant phenomena are identified and characterized for the loss-of-forced-convection (LOFC) scenario, the depressurized loss-of-forced-convection (DLOFC) scenario, and the moisture ingress scenario with relief valves available scenario. These scenarios are considered for the modular high temperature gas-cooled reactor (MHTGR) baseline design—with both a prismatic core and a pebble-bed core. The objectives of this effort, described in the above paragraph, were achieved by following a methodology identified by the U.S. Nuclear Regulatory Commission (NRC) and recommended in their Regulatory Guide 1.203. The methodology employed is described. For the above scenarios, baseline phenomena identification and ranking tables (PIRTs) generated by a NRC-sponsored effort in 2008 for the LOFC and DLOFC scenarios and a baseline PIRT generated by a Department of Energy-sponsored effort in 2011 by their Next Generation Nuclear Plant (NGNP) Moisture Ingress Assessment Committee were used to identify the dominant phenomena and existing levels-of-knowledge. Ten dominant phenomena (with only medium or low levels-of-knowledge) were identified for the LOFC and DLOFC scenarios including: (1) Inlet plenum stratification and plumes, (2) core coolant flow distribution, (3) radiant heat transfer from the top of the core to the upper vessel head, (4) reactor cavity air circulation and heat transfer, (5) reactor cavity cooling system behavior, (6) decay heat distributions, (7) core effective thermal conductivity, (8) molecular diffusion in air ingress DLOFC, (9) duct exchange flow, (10) phenomena that affect cavity gas composition for air-ingress. Five dominant phenomena, with high levels of knowledge, were identified for the moisture-ingress scenario with relief valves available including: (1) flow through break, (2) moisture level in the primary, (3) transport to moisture monitor, (4) moisture monitor instrument response, and (5) heat removal by shutdown cooling system. These phenomena are discussed and characterized. A listing of experiments—both completed and ongoing—is given with a view toward including data useful for further more detailed examination of the dominant phenomena listed in the previous paragraph. Note that only data for the LOFC and DLOFC scenarios are listed—since the moisture ingress scenario with relief valves available –have been identified as having adequate levels-of-knowledge by the PIRT committee. Finally, a preliminary computational fluid dynamics (CFD) calculation of inlet plenum stratification and plumes is described. Techniques employed for this calculation are typical for preliminary scoping calculations performed to validate numeric tools.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Optimal Floating Vertical-Axis Wind Turbine Platform Identification Design and Cost Estimation

This report houses the deliverables provided by Stress Engineering Services on the floating platform design identification studies and the detailed final design iterations. The results were obtained under contract to and in partnership with Sandia to iterate between the platform design and the aero-hydro-elastic load simulations of the coupled vertical-axis wind turbine system. Through the analysis summarized in this report, a tension-leg platform with multiple columns was identified as the optimal platform when considering cost and performance. The detailed design and cost estimate of this platform architecture was produced in the final phase of study which is also described within this report.

17 WIND ENERGY↗
DOE OSTI2017

Automated Work Packages: Radio Frequency Identification, Bluetooth Beacons, and Video Applications in the Nuclear Power Industry

The need to move conventional paper-based work packages into an electronic state motivated the development of a large span of electronic work package forms that ranged from simple Portable Document Format (PDF) files to adaptive and intelligent electronic forms. In parallel to this development, the Department of Energy Light Water Reactor Sustainability Program (LWRS) initiated the automated work package as a pilot project to evaluate implementing state of the art automation techniques into the work package process to improve the economic and safety of the work process. Through a multiple year’s effort, the automated work package project have developed a vision of the future work package that relies on automation in all critical and human factor challenging tasks. The vision identified gaps in the work process, the means to automate them, and evaluated the implementation, feasibility and benefits of these means. This was conducted through continues engagement with the nuclear power industry. The aim of this report is to study the potential of three automation technologies, which have been advancing in various industries, in the current work process of nuclear power plants. The study is to determine the feasibility and benefits of incorporating the technologies and determine potential gaps, if any, that need to be addressed to facilitate deployment of the studied technologies. The three automation technologies identified for this effort are ultra-high frequency (UHF) radio frequency identification (RFID), Bluetooth low energy (BLE) beacons, and video monitoring and recording. As part of this effort, the Idaho National Laboratory (INL) collaborated with Xcel Energy Inc. to evaluate these technologies. During the research and development scope of the effort, INL engaged Xcel Energy Inc. periodically and exchanged valuable insight on the means to implement these technologies in a nuclear power plant. Multiple pilot prototypes were developed and tested in a user study conducted at Xcel Energy Inc. in Minneapolis, Minnesota. The outcome of this effort resulted in significant findings that included the detailed means to custom fit the technologies to fit the nuclear power industry need, and identifying and evaluating several applications of high economic or safety potential. The limitations and obstacles that could impact deployment of the technologies were also identified, and the means of how to address these limitations and obstacles were studied.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Real-Time Automated Pathogen Identification by Enhanced Ribotyping (RAPIER) LDRD Final Report

Funded through the IHNS/E&HS investment area for FY16-18, the RAPIER LDRD sought to evaluate the potential benefits and applicability of the new Oxford MinION nanopore sequencer to pathogen diagnostic applications in biodefense, biosurveillance, and global/public health. The project had four primary objectives: 1) to investigate the performance of the MinION sequencer while building facility with its operation, 2) to develop microfluidic library prep automation facilitating the use of the MinION in field-forward or point-of-care applications, 3) to leverage CRISPR/Cas9 technology to enable targeted identification of bacterial pathogens, and 4) to capitalize on the real- time data output capabilities of the MinION to enable rapid sequence-based diagnostics. While the rapid evolution of the MinION sequencing technology during the course of the project posed a number of challenges and required a reassessment of initial project priorities, it also provided unique opportunities, notably culminating in our development of the RUBRIC real-time selective sequencing software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Identification of Potential Locations for Placement of Strategic Transformers Reserve in Puerto Rico

Extreme weather events such as tropical storms cause extensive damage to energy infrastructures including generator stations, substations, poles, transformers, power transmission lines, etc. due to their presence in hazardous locations. Often these damages lead to long term power outage as was seen in Puerto Rico (PR) following the landfall of Hurricane Maria (2017). A potential solution to long-term power outage is strategic placement of reserve generators and transformers that would ensure continued power supply to enable restoration and recovery activities. The strategic placement of energy storage devices requires identification of potential sites such that (i) the damage from extreme events to energy infrastructures is minimized, (ii) power supply is optimized, and (iii) installation of these infrastructures is cost effective. To meet this goal, in this research, a Multi-Criteria Evaluation (MCE) technique-based site suitability analysis was implemented to identify potential sites for reserve generators and transformers in PR. To determine suitable sites and existing government facilities, the following steps were implemented: (i) identify physical, environmental, technical and socio-economic factors contributing to site suitability; (ii) determine siting constraints based on guidelines and standards used for energy sector (i.e., siting of nuclear plants, solar or wind farms), (iii) implement a raster data model using 30m x 30m spatial resolution to create suitability layers for each factor, (iv) combine suitability and exclusionary layers to create the final suitability layer. Results show that six hospitals, four colleges and universities, and five correctional facilities are in very high suitability areas and could be used for to store reserve generators. These facilities are in the eastern, south-eastern, south-western, and southern part of the island except for one correctional facility that is in the northern part of PR. Not surprisingly, coastal areas were found to be unsuitable for siting any energy facility. More than half (55.9%) of the island was also found to be unsuitable while only 14% of the island was found to be very highly suitable. Further assessment of current power plants and sub-stations indicated that only 16% and 42% of existing power plants and sub-stations respectively are in suitable areas. Given that northern part of the island lacks any facility that could be used for placement of reserve generators, suitable sites identified in this region could be used for placement of storage devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Identification of Hydrogen Material Risk Research Areas

This report presents a research framework for the application of quantitative risk assessment to hydrogen materials, based on the identification of potential areas of research, a literature review, and a plan for future work in the area of hydrogen materials risk. After outlining basics on hydrogen infrastructure, with a focus on pressure vessels, a number of materials risk topics are identified and discussed. Of these, four important areas of risk application to hydrogen materials are highlighted and discussed in further detail. The four topics are initial crack distribution in metals, damage and stress rupture in composites, and polymer behavior in high pressure hydrogen. These topics vary in scope, specificity, level of anticipated resources required, and potential impact to the field. Finally, recommendations are made for future research on the highest priority topics.

08 HYDROGEN↗
DOE OSTI2021

Aftershock Identification Using a Paired Neural Network Applied to Constructed Data

This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Li-ion Identification For the Next Generation of Cathode Recycling

OnTo’s end-to-end solution will demonstrate (in partnership/collaboration with relevant industries) collection of lithium-ion batteries through recycling of cathode material. The DISCC project will prototype 1) deactivation of batteries 2) identification 3) sorting using automated techniques, 4) cutting with waterjet technology and 5) Cathode-Healing™ to produce valuable cathodes from scrap batteries.

25 ENERGY STORAGE↗