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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

An Interim Set of TNT Curves for LOX/LNG Explosions

Space launch companies are actively developing, or in some cases have already developed, new vehicles that use large quantities of liquid oxygen (LOX) and liquid natural gas (LNG) propellants. This propellant mixture currently lacks sound/verified LOX/LNG explosive safety standards for use in explosive siting and flight safety analysis. In the U.S., multiple government and commercial organizations have conducted limited testing, or are preparing to conduct tests, intended to provide a sound technical basis for explosive standards applicable to space launch vehicle ground and flight operations. The limited testing and analysis conducted so far indicates the potential to produce extremely energetic explosions due to the miscibility of LOX/LNG (methane), a unique feature relative to previously used propellant mixtures such as hydrogen/oxygen or kerosene/oxygen. MOX is a homogenous mixture of methane and oxygen that is possible because of methane’s 100% miscibility in LOX. Preliminary testing by N2L, Inc. described in this paper shows that it is possible to form MOX with a variety of mixing methods, and that MOX is a sensitive high explosive capable of producing overpressures greater than comparable masses of C-4. The limited amount and quality of large-scale LOX/LNG explosive test data, and the potential formation of high-explosive MOX, create significant unknowns in the determination of hazard areas (cleared of the public) for ground operations (such as a static fire test) and flights of launch vehicles with LOX/LNG propellant. This paper will review the methods used by NASA to develop and validate the LOX/LH2 blast model using large-scale explosive test programs such as Project PYRO, the Hydrogen-Oxygen Vertical Impact (HOVI), and the Large-Scale Hydrogen-Oxygen Explosion (LSHOE) tests. This paper will describe how the same process was used to prepare an interim LOX/LNG blast model that applies to various LV failure scenarios and conservatively accounts for potential MOX formation. This paper also summarizes past and future testing and modeling efforts funded by a consortium of NASA, the US Federal Aviation Administration (FAA), and the US Space Force (USSF). These test programs should be concluded within the next 3 years and are intended to provide empirical data for model verification and validation.

Liquid Oxygen↗
NASA NTRS1983

Compiling the space shuttle wind tunnel data base: An exercise in technical and managerial innovators

Engineers evaluating Space Shuttle flight data and performance results are using a massive data base of wind tunnel test data. A wind tunnel test data base of the magnitude attained is a major accomplishment. The Apollo program spawned an automated wind tunnel data analysis system called SADSAC developed by the Chrysler Space Division. An improved version of this system renamed DATAMAN was used by Chrysler to document analyzed wind tunnel data and data bank the test data in standardized formats. These analysis documents, associated computer graphics and standard formatted data were disseminated nationwide to the Shuttle technical community. These outputs became the basis for substantiating and certifying the flight worthiness of the Space Shuttle and for improving future designs. As an aid to future programs this paper documents the lessons learned in compiling the massive wind tunnel test data base for developing the Space Shuttle. In particular, innovative managerial and technical concepts evolved in the course of conceiving and developing this successful DATAMAN system and the methods and organization for applying the system are presented.

Kemp, N. D.↗
DOE OSTI2019

TableCalculator: a Transparent Public Tool to Replicate US NRC LLW Classification Table Calculations - 19395

Classification of Low-Level Waste (LLW) as Class A, B, C, or Greater than Class C (GTCC) is based on tables provided in Section 61.55 of Title 10 of the Code of Federal Regulations (CFR). Those tables were developed from model projections of dose to an inadvertent intruder in agricultural and construction scenarios at different times after closure of a LLW land disposal site. The calculations were performed with two FORTRAN codes, which are documented in US NRC guidance documents published in the early 1980's. Although the equations and parameter values used in the codes are publicly available, no modern, user-friendly implementation of the codes is known to US NRC staff. Because Very Low-Level Waste (VLLW) and GTCC waste disposal are related to the low end and high end of the existing LLW waste classifications, respectively, recent interest in options for disposal of VLLW and GTCC waste have generated public interest in the original assumptions made during the development of the LLW classification tables. Certain stakeholder analyses have recommended alternatives to the existing LLW concentration limits by calculating a projected intruder dose under different conditions than were evaluated in the original development of 10 CFR Part 61 (e.g., using more modern dosimetry or assuming deeper disposal) and comparing the radionuclide concentrations that result in a projected annual dose of 5 milliSieverts (mSv) (500 millirem (mrem)) total effective dose equivalent (TEDE) to the concentrations in the waste classification tables. However, beginning with the 10 CFR Part 61 LLW classification limits and modifying only one or two assumptions to generate a proposed concentration limit could lead to misleading results without a thorough analysis of whether other assumptions made in the original development of the waste classification tables remain valid. In particular, the US NRC made adjustments to the 10 CFR 61.55 table values during the rulemaking process based on qualitative considerations. Those adjustments mean that many of the values in the final tables cannot simply be assumed to quantitatively represent a 5 mSv (500 mrem) projected inadvertent intruder dose without an assessment of whether the technical bases used to develop the adjustments are applicable to the new analysis. Furthermore, the waste classification limits were based on the limiting critical organ dose rather than a TEDE. Therefore, any calculation based on the assumption that the waste classification limits correspond to 5 mSv (500 mrem) TEDE could benefit from consideration of how differences in exposure pathways could affect the limiting critical organ dose differently from the TEDE. To facilitate a more comprehensive understanding of the calculations used to develop the waste classification tables, the US NRC staff has developed a user-friendly tool to replicate the original calculations. Results were verified against output from the FORTRAN codes and the classification table values. The tool, TableCalculator, is expected to be made publicly available and will allow the user to trace the original calculations and to observe the effects of changes in disposal assumptions and other parameter values. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Light Water Sustainability Program: Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis

This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening control structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2021

NASA Engineering and Safety Center Technical Bulletin No. 21-03-1: Best Practices for the Elemental Profiling of High-Purity Hydrazine

Trace contaminants in high-purity hydrazine (HPH) propellant impact a wide variety of commercial, Department of Defense (DoD), and NASA missions. Depending on thruster design, elemental contaminants must be kept at extremely low levels and are verified as such by routine analysis. A number of these contaminants have recently undergone an assessment to shed light on their quantities present following changes in the HPH supply chain. A round robin analysis utilizing four separate laboratories resulted in unacceptably high variability in the quantification of these contaminants. The principal objective of this technical bulletin is to signal the availability of a new analysis methodology which yields accurate and repeatable quantification by providing best practices for both quantitation methodology and strategies for avoiding sample contamination during analysis.

High-Purity Hydrazine↗
DOE OSTI2026

Boride-based Ceramic Super-high Temperature Thermocouples in Harsh Environments (Final Scientific/Technical Report)

An electromotive force (emf) can be generated along a temperature gradient between the cold end and hot end of a thermoelectric material, termed the Seebeck effect. Based on the Seebeck effect, metallic alloys have been extensively employed to detect temperatures for centuries, named thermocouples. However, commercially available thermocouple alloys suffer from limitations, such as oxidation, chemical degradation, and poor long-term stability under high-temperature harsh environments. This DOE-funded project aimed to develop high-temperature, chemically tolerant thermocouples suitable for operation in extreme environments relevant to semiconducting thermoelectric materials. The research focused on boride-based semiconducting thermoelectric compounds as candidates for next-generation thermocouples with enhanced oxidation resistance, chemical stability, and thermal robustness under conditions representative of charcoal-fired electricity facilities. During the funded years, boride materials were synthesized using an arc-plasma technique under ambient air and argon atmospheres, enabling scalable and cost-effective production compared with conventional boride fabrication methods. The synthesized borides were processed into nanostructured powders, followed by consolidation into dense bulk materials using a spark plasma sintering (SPS) bottom-up approach. Comprehensive characterization was performed, including microstructural analysis, electrical transport measurements, and optical and thermal property evaluation. Both p-type and n-type boride electric legs were fabricated and integrated into boride-based thermocouples. The thermal and irradiation stabilities of the boride nanomaterials and bulk thermoelectric materials were systematically evaluated to assess suitability for long-term operation in harsh environments. Additionally, 12 students were broadly hands-on trained spanning the full research workflow, including word processing and technical editing (e.g., LATEX for manuscript and poster preparation), data collection and analysis (using Python and related libraries and hardware interfaces), sample preparation (including arc-plasma synthesis and spark plasma sintering), and advanced characterization techniques (such as X-ray diffraction, UV–vis spectroscopy, electron microscopy, differential thermal analysis (DTA), and Seebeck coefficient measurements, etc). Overall, this project demonstrated the feasibility of boride-based thermoelectric materials as durable high-temperature thermocouples, providing a promising pathway toward robust temperature sensing technologies aligned with DOE energy infrastructure and extreme-environment monitoring needs.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2019

Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science

Numerical physics-based models are useful in understanding battery performance and devel- oping optimal battery design architectures. Data science developments have enabled software algorithms to perform data analysis and decision making that traditionally could only be per- formed by technical experts. Traditional workflows of model development - manual parameter estimation through visual comparison of simulations to experimental observations, and model discrimination through expert intuition - are time-consuming, and difficult to justify. This paper compares the conclusions derived from computationally scalable algorithms to conclu- sions developed by expert researchers. This paper illustrates how data science techniques, such as cross-validation and lasso regression, can be used to augment physics-based simulations to perform data analysis such as parameter estimation, model selection, variable selection, and model-guided design of experiment. The validation of these algorithms is that they produce results similar to those of the expert modeler. The algorithms outlined are well-established and the approaches are general, so can be applied to a variety of battery chemistries and architectures. The conclusions reached using these approaches are in agreement with expert analysis (literature results), were reached with minimal human intervention, and provide quantitative justification. By minimizing the amount of expert time, and providing rigorous quantitative justifications, these methods may accelerate battery development.

25 ENERGY STORAGE↗
DOE OSTI2018

Seven good practices for the environmental licensing of coastal interventions: Lessons from the Italian, Cuban, Spanish and Colombian regulatory frameworks and insights on coastal processes

Highlights: • Environmental licensing procedures (ELP) line up by projects instead of environments. • The ELP of four countries was compared according to the generic EIA stages. • The study relies upon the revision of EIA legislation and technical guidelines. • Seven EIA good practices were extracted from the ELP analysis of the four countries. • A changing approach is proposed for the EPL performance on coastal interventions. - Abstract: Environmental licensing is the regulatory procedure that enforces the environmental impact assessment (EIA) of human activities inside a given country. Despite worldwide acceptance of EIA as a valid tool, its application in coastal environments is still too diverse and limited regarding the specificity of the natural processes influencing the shore. This paper compares the Environmental Licensing Procedure (ELP) of four countries, focusing on the activities that could affect the coastal geomorphology. The acquisition and validation of information were done through interviews with EIA representatives in each country, who signalized the official documents of environmental licensing and coastal management to be considered in the documentary review. The results present those differences and similarities among ELP stages in each country, based on the principles of the International Association of Impact Assessment and the national documents analyzed. In sum, 59 interventions associated with human uses and activities in the coastal zone were compared according to the prescriptive character of the environmental licensing in Italy, Spain, Cuba and Colombia. The natural processes influencing coastal geomorphology were also analyzed within the technical criteria included in the official guidelines for the EIA, finding a generalized weakness in processes associated with geochemical courses on coastal environments. By way of discussion, seven good practices are illustrated, according to their pertinence to the impact assessment of the coastal zone: 1) The integration of screening and scoping; 2) Evaluation focusing on the environment rather than the intervention; 3) Binding the coastal zone delimitation; 4) Institutional articulation; 5) Accreditation of environmental consultancies; 6) Official guidelines by types of environment; 7) The integration of environmental geographic information. Finally, general conclusions to assist EIA practitioners operating in the four countries and recommendations to lead further research are provided, introducing a novel process-oriented approach for ELP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Biositing Webtool, v1

The webtool displays the following types of data: Wastewater treatment plants (WWTP) with anaerobic digestion Facilities with anaerobic digestion onsite other than WWTP Combustion plants. Manure locations. Manure (non points) Municipal solid waste locations Crop locations Processor residue (non points) District energy systems Mixed use developments (non points) Process heating and cooling locations Basic Usage Users can interact with the map in the following ways: Select the year of the analysis from a drop-down menu. Set the background layer as biomass availability or thermal consumption density from a drop-down menu. Select whether the facility of interest is an Anaerobic Digestion or a Therochemical facility from a drop-down menu. This filters the biomass data by moisture and only shows the relevant portions based on the facility selection. Select from a drop-down menu whether the biomass amounts will be shown as gross amounts or technical amounts. Set the radius for the search buffer in which the analysis will be performed. Click on any point data on the map to get more information. Click on the map, to given the signal of the query point location, from where the algorithmic process is going to initiate. This will show the available biomass locations and amounts that fall within the predefined buffer zone. Download the data in a csv format for the entire county or the selected biomass that is inside the predefined buffer. Report whether there are errors in the data to us and we will work to the best of our abilities to confirm and fix any issues. The outputs of the algorithmic process are going to appear on the screen after the computation is done. The outputs consist of the locations of all available biomass that are within the buffer zone from the clicked location. By clicking on any point the user can get more information on the nature and amounts of the biomass.

Scown, Corinne↗
NASA NTRS2014

A Framework for Assessment of Aviation Safety Technology Portfolios

The programs within NASA's Aeronautics Research Mission Directorate (ARMD) conduct research and development to improve the national air transportation system so that Americans can travel as safely as possible. NASA aviation safety systems analysis personnel support various levels of ARMD management in their fulfillment of system analysis and technology prioritization as defined in the agency's program and project requirements. This paper provides a framework for the assessment of aviation safety research and technology portfolios that includes metrics such as projected impact on current and future safety, technical development risk and implementation risk. The paper also contains methods for presenting portfolio analysis and aviation safety Bayesian Belief Network (BBN) output results to management using bubble charts and quantitative decision analysis techniques.

Jones, Sharon M.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2018

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), is a data acquisition technique used in targeted analysis of molecules, including targeted proteomic studies. It exploits the unique capability of triple-quadrupole (QQQ) mass spectrometers to monitor the predefined precursor and fragment ion pairs of specific molecules of interest throughout a liquid chromatography (LC) elution profile. Compared to shotgun proteomics, targeted proteomics using SRM has high selectivity, high sensitivity, and a wide linear dynamic range,(1-3) which makes it especially useful in the accurate and reproducible quantification of low-abundance proteins in highly complex biological samples. SRM has been widely used in the fields of biomarker discovery,(4-7) analysis of protein post-translational modifications(8) and characterization of biological protein networks.(4,9) In the recent years, multiple technical advances have greatly improved the throughput of SRM analyses, allowing for the quantification of hundreds of peptides in a single analysis.(6,7,10) For example, a single 800-plex SRM assay (e.g., 400 unlabeled and heavy-labeled peptide pairs and 2400 transitions with retention time scheduling) using ultrahigh-performance liquid chromatography (UHPLC) has been developed to quantify proteins in plasma.(11) Advanced labeling techniques utilizing in vitro proteome-assisted MRM for protein absolute quantification (iMPAQT) demonstrated the capability of SRM in genome-wide protein quantification of over 18?000 human proteins.(12) Moreover, the scan speed of QQQ instrumentation has been greatly improved in recent releases of commercial instrumentation. The newly developed TSQ Altis (released in 2017) can scan more than 600 transitions per second, which is 6 times more than a traditional QQQ scan speed of 100 transitions per second. The breadth of measurement enabled by these technological improvements to QQQ mass spectrometry has increased the feasibility and popularity of large scale targeted proteomics studies,(13) a major application of which will be in clinical studies in which up to hundreds of protein candidates need to be quantified in hundreds of clinical specimens.(14) Quantitative accuracy is a primary motivating factor for utilizing a targeted proteomics protocol. The precise and reproducible absolute quantification produced by SRM assays is essential to many clinical and laboratory experiments.(15,16) Because the abundance of an endogenous peptide is calculated from the measurement of the spiked-in reference standard, it is essential to assess the data quality of these references.(17) In early applications of targeted proteomics, when instrument speed greatly limited the number of transitions that could be monitored, much of this quality assessment was done manually. However, recent improvements in instrument performance and experimental design have enabled a dramatic increase in the number of target peptides and associated SRM transitions, which makes manual quality assessment an untenable and laborious task. A variety of computational tools assist in SRM experiment design and data analysis. The first task in creating an SRM experiment is the choice of proteins and representative peptides to monitor. Achieving a reliable protein abundance requires appropriately choosing peptides that have a strong signal and are free from interferences in the biological matrix. Numerous computational tools exist to facilitate assay design by identifying peptides and refining SRM transitions.(18-21) To help share these assays and eliminate time spent designing the same transitions at multiple institutions, community portals have begun to host well-designed and vetted assays.(22-24) Analyzing the experimental data requires significant computational effort to align files across replicates and experimental conditions, pick peaks and produce quantitative values, normalize data and perform statistical tests, etc.(25-27)

Gibbons, Bryson C.↗
NASA NTRS1973

Adaptive on-line classification of multi-spectral scanner data

A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

Fromm, F. R.↗