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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Liver proteomics in progressive alcoholic steatosis

Fatty liver is an early stage of alcoholic and nonalcoholic liver disease (ALD and NALD) that progresses to steatohepatitis and other irreversible conditions. In this study, we identified proteins that were differentially expressed in the livers of rats fed 5% ethanol in a Lieber–DeCarli diet daily for 1 and 3 months by discovery proteomics (two-dimensional gel electrophoresis and mass spectrometry) and non-parametric modeling (Multivariate Adaptive Regression Splines). Hepatic fatty infiltration was significantly higher in ethanol-fed animals as compared to controls, and more pronounced at 3 months of ethanol feeding. Discovery proteomics identified changes in the expression of proteins involved in alcohol, lipid, and amino acid metabolism after ethanol feeding. At 1 and 3 months, 12 and 15 different proteins were differentially expressed. Of the identified proteins, down regulation of alcohol dehydrogenase (− 1.6) at 1 month and up regulation of aldehyde dehydrogenase (2.1) at 3 months could be a protective/adaptive mechanism against ethanol toxicity. In addition, betaine-homocysteine S-methyltransferase 2 a protein responsible for methionine metabolism and previously implicated in fatty liver development was significantly up regulated (1.4) at ethanol-induced fatty liver stage (1 month) while peroxiredoxin-1 was down regulated (− 1.5) at late fatty liver stage (3 months). Nonparametric analysis of the protein spots yielded fewer proteins and narrowed the list of possible markers and identified D-dopachrome tautomerase (− 1.7, at 3 months) as a possible marker for ethanol-induced early steatohepatitis. The observed differential regulation of proteins have potential to serve as biomarker signature for the detection of steatosis and its progression to steatohepatitis once validated in plasma/serum. -- Graphical abstract: The figure shows the Hierarchial cluster analysis of differentially expressed protein spots obtained after ethanol feeding for 1 (1–3) and 3 (4–6) months. C and E represent pair-fed control and ethanol-fed rats, respectively. Highlights: ► Proteins related to ethanol-induced steatosis and mild steatohepatitis are identified. ► ADH1C and ALDH2 involved in alcohol metabolism are differentially expressed at 1 and 3 months. ► Discovery proteomics identified a group of proteins to serve as potential biomarkers. ► Using nonparametric analysis DDT is identified as a possible marker for liver damage.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

MARGInS Model-Based Analysis of Realizable Goals in Systems

The high complexity of modern aircraft and spacecraft requires elaborate Verification and Validation (V&V) approaches to make sure that such complex systems work properly and reliably. MARGInS is a framework for the analysis, understanding, and prediction of the behavior of a complex, hybrid system. MARGInS contains a set of machine learning and statistical algorithms for multivariate clustering, treatment learning, critical factor determination, time-series analysis, event prediction, and safety-boundary detection and characterization. The framework supports system testing and can be configured to find novel features in test suites, determine classes of behavior, propose new experiments that can efficiently explore and characterize the boundaries between classes of system behavior, and to create visualizations and reports.

He, Yuning↗
DOE OSTI2018

Factors controlling seasonal groundwater and solute flux from snow‐dominated basins

Abstract Critical zone influences on hydrologic partitioning, subsurface flow paths and reactions along these flow paths dictate the timing and magnitude of groundwater and solute flux to streams. To isolate first‐order controls on seasonal streamflow generation within highly heterogeneous, snow‐dominated basins of the Colorado River, we employ a multivariate statistical approach of end‐member mixing analysis using a suite of daily chemical and isotopic observations. Mixing models are developed across 11 nested basins (0.4 to 85 km 2 ) spanning a gradient of climatological, physical, and geological characteristics. Hydrograph separation using rain, snow, and groundwater as end‐members indicates that seasonal contributions of groundwater to streams is significant. Mean annual groundwater flux ranges from 12% to 33% whereas maximum groundwater contributions of 17% to 50% occur during baseflow. The direct relationship between snow water equivalent and groundwater flux to streams is scale dependent with a trend toward self‐similarity when basins exceed 5.5 km 2 . We find groundwater recharge increases in basins of high relief and within the upper subalpine where maximum snow accumulation is coincident with reduced conifer cover and lower canopy densities. The mixing model developed for the furthest downstream site did not transfer to upstream basins. The resulting error in predicted stream concentrations points toward weathering reactions as a function of source rock and seasonal shifts in flow path. Additionally, the potential for microbial sulfate reduction in floodplain sediments along a low‐gradient, meandering portion of the river is sufficient to modify hillslope contributions and alter mixing ratios in the analysis. Soil flushing in response to snowmelt is not included as an end‐member but is identified as an important mechanism for release of solutes from these mountainous watersheds. End‐member mixing analysis used in combination with high‐frequency observations reveals important aspects of catchment hydrodynamics across scale.

Carroll, Rosemary W. H.↗
DOE OSTI2022

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Label-free molecular imaging of bacterial communities of the opportunistic pathogen Pseudomonas aeruginosa

Biofilms, such as those formed by the opportunistic human pathogen Pseudomonas aeruginosa are complex, matrix enclosed, and surface-associated communities of cells. Bacteria that are part of a biofilm community are much more resistant to antibiotics and the host immune response than their free-floating counterparts. P. aeruginosa biofilms are associated with persistent and chronic infections in diseases such as cystic fibrosis and HIV-AIDS. P. aeruginosa synthesizes and secretes signaling molecules such as the Pseudomonas quinolone signal (PQS) which are implicated in quorum sensing (QS), where bacteria regulate gene expression based on population density. Processes such as biofilms formation and virulence are regulated by QS. This manuscript describes the powerful molecular imaging capabilities of confocal Raman microscopy (CRM) and surface enhanced Raman spectroscopy (SERS) in conjunction with multivariate statistical tools such as principal component analysis (PCA) for studying the spatiotemporal distribution of signaling molecules, secondary metabolites and virulence factors in biofilm communities of P. aeruginosa. Our observations reveal that the laboratory strain PAO1C synthesizes and secretes 2-alkyl-4-hydroxyquinoline N-oxides and 2-alkyl-4-hydroxyquinolones in high abundance, while the isogenic acyl homoserine lactone QS-deficient mutant (ΔlasIΔrhlI) strain produces predominantly 2-alkyl-quinolones during biofilm formation. Furthermore, this study underscores the use of CRM, along with traditional biological tools such as genetics, for studying the behavior of microbial communities at the molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Hierarchical Modeling to Enhance Spectrophotometry Measurements—Overcoming Dynamic Range Limitations for Remote Monitoring of Neptunium

A robust hierarchical model has been demonstrated for monitoring a wide range of neptunium concentrations (0.75–890 mM) and varying temperatures (10–80 °C) using chemometrics and feature selection. The visible–near infrared electronic absorption spectrum (400–1700 nm) of monocharged neptunyl dioxocation (Np(V) = NpO2+) includes many bands, which have molar absorption coefficients that differ by nearly 2 orders of magnitude. The shape, position, and intensity of these bands differ with chemical interactions and changing temperature. These challenges make traditional quantification by univariate methods unfeasible. Measuring Np(V) concentration over several orders of magnitude would typically necessitate cells with varying path length, optical switches, and/or multiple spectrophotometers. Alternatively, the differences in the molar extinction coefficients for multiple absorption bands can be used to quantify Np(V) concentration over 3 orders of magnitude with a single optical path length (1 mm) and a hierarchical multivariate model. In this work, principal component analysis was used to distinguish the concentration regime of the sample, directing it to the relevant partial least squares regression submodels. Each submodel was optimized with unique feature selection filters that were selected by a genetic algorithm to enhance predictions. Through this approach, the percent root mean square error of prediction values were ≤1.05% for Np(V) concentrations and ≤4% for temperatures. This approach may be applied to other nuclear fuel cycle and environmental applications requiring real-time spectroscopic measurements over a wide range of conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

Natural Resources Inventory and Land Evaluation in Switzerland

The author has identified the following significant results. A system was developed to operationally map and measure the areal extent of various land use categories for updating existing and producing new and actual thematic maps showing the latest state of rural and urban landscapes and its changes. The processing system includes: (1) preprocessing steps for radiometric and geometric corrections; (2) classification of the data by a multivariate procedure, using a stepwise linear discriminant analysis based on carefully selected training cells; and (3) output in form of color maps by printing black and white theme overlays of a selected scale with photomation system and its coloring and combination into a color composite.

Haefner, H.↗
NASA NTRS1976

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

Source record↗
NASA NTRS1979

On certain families of rational functions arising in dynamics

It is noted that linear systems, depending on parameters, can occur in diverse situations including families of rational solutions to the Korteweg-de Vries equation or to the finite Toda lattice. The inverse scattering method used by Moser (1975) to obtain canonical coordinates for the finite homogeneous Toda lattice can be used for the synthesis of RC networks. It is concluded that the multivariable RC setting is ideal for the analysis of the periodic Toda lattice.

Byrnes, C. I.↗
NASA NTRS1982

Lateral control system design for VTOL landing on a DD963 in high sea states

The problem of designing lateral control systems for the safe landing of VTOL aircraft on small ships is addressed. A ship model is derived. The issues of estimation and prediction of ship motions are discussed, using optimal linear linear estimation techniques. The roll motion is the most important of the lateral motions, and it is found that it can be predicted for up to 10 seconds in perfect conditions. The automatic landing of the VTOL aircraft is considered, and a lateral controller, defined as a ship motion tracker, is designed, using optimal control techniqes. The tradeoffs between the tracking errors and the control authority are obtained. The important couplings between the lateral motions and controls are demonstrated, and it is shown that the adverse couplings between the sway and the roll motion at the landing pad are significant constraints in the tracking of the lateral ship motions. The robustness of the control system, including the optimal estimator, is studied, using the singular values analysis. Through a robustification procedure, a robust control system is obtained, and the usefulness of the singular values to define stability margins that take into account general types of unstructured modelling errors is demonstrated. The minimal destabilizing perturbations indicated by the singular values analysis are interpreted and related to the multivariable Nyquist diagrams.

Bodson, M.↗
NASA NTRS1991

Dynamics of aerospace vehicles

Papers on the following subjects are presented: (1) multivariable flight control synthesis and literal robustness analysis for an aeroelastic vehicles; (2) numerical and literal aeroelastic-vehicle-model reduction for feedback control synthesis; and (3) dynamics of aerospace vehicles.

Schmidt, David K.↗
NASA NTRS2008

Population Analysis: Communicating in Context

Providing accommodation to a widely varying user population presents a challenge to engineers and designers. It is often even difficult to quantify who is accommodated and who is not accommodated by designs, especially for equipment with multiple critical anthropometric dimensions. An approach to communicating levels of accommodation referred to as population analysis applies existing human factors techniques in novel ways. This paper discusses the definition of population analysis as well as major applications and case studies. The major applications of population analysis consist of providing accommodation information for multivariate problems and enhancing the value of feedback from human-in-the-loop testing. The results of these analyses range from the provision of specific accommodation percentages of the user population to recommendations of design specifications based on quantitative data. Such feedback is invaluable to designers and results in the design of products that accommodate the intended user population.

Rajulu, Sudhakar↗
DOE OSTI2014

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component in the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.

Ladd-Lively, Jennifer L↗
DOE OSTI2025

Remote quantification of Cm(III) and HNO 3 by fluorescence spectroscopy and chemometrics

A unique approach to remotely quantify Cm(III) (0–100 µg mL −1 ) in HNO 3 (1–12 M) using steady-state laser fluorescence spectroscopy and multivariate regression models was developed. Photoluminescence is amenable to remote measurements using fiber-optic cables and is sensitive to numerous lanthanide and actinide species. In-line measurements can provide feedback to support complex processing in harsh environments (e.g., hot cells) to help guide and optimize radiochemical separations. In this work, Cm(III) spectra were acquired remotely in a glove box as a function of HNO 3 concentration to better understand spectral characteristics and evaluate the utility of multivariate regression models in this system. Furthermore, the Cm(III) fluorescence peak shape, width, position, and intensity changed significantly as a function of HNO 3 concentration, likely because of the displacement of emission quenching inner-sphere water molecules and complexation with nitrate ions. Despite significant covariance and nonlinearity in the data, a D-optimal design strategy successfully minimized training set sample size and was used to build effective partial least squares regression models for Cm(III) and HNO 3 concentrations without a priori knowledge of solution conditions. Chemometrics for modeling complex fluorescence spectra are promising and may find widespread applicability for online analysis in numerous chemical systems found in the nuclear field.

Actinide↗
DOE OSTI2017

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictates the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Circulating levels of micronutrients and risk of osteomyelitis: a Mendelian randomization study

Background Few observational studies have investigated the effect of micronutrients on osteomyelitis, and these findings are limited by confounding and conflicting results. Therefore, we conducted Mendelian randomization (MR) analyses to evaluate the association between blood levels of eight micronutrients (copper, selenium, zinc, vitamin B12, vitamin C, and vitamin D, vitamin B6, vitamin E) and the risk of osteomyelitis. Methods We performed the two-sample and multivariable Mendelian randomization (MVMR) to investigate causation, where instrument variables for the predictor (micronutrients) were derived from the summary data of micronutrients from independent cohorts of European ancestry. The outcome instrumental variables were used from the summary data of European-ancestry individuals ( n = 486,484). The threshold of statistical significance was set at p < 0.00625. Results We found a significant causal association that elevated zinc heightens the risk of developing osteomyelitis in European ancestry individuals OR = 1.23 [95% confidence interval (CI) [1.07, 1.43]; p = 4.26E-03]. Similarly, vitamin B6 showed a similar significant causal effect on osteomyelitis as a risk factor OR = 2.78 (95% CI [1.34, 5.76]; p = 6.04E-03; in the secondary analysis). Post-hoc analysis suggested this result (vitamin B6). However, the multivariable Mendelian randomization (MVMR) provides evidence against the causal association between zinc and osteomyelitis OR = 0.98(95% CI [−0.11, 0.07]; p = 7.20E-1). After searching in PhenoScanner, no SNP with confounding factors was found in the analysis of vitamin B6. There was no evidence of a reverse causal impact of osteomyelitis on zinc and vitamin B6. Conclusion This study supported a strong causal association between vitamin B6 and osteomyelitis while reporting a dubious causal association between zinc and osteomyelitis.

Zhang, Xu↗
DOE OSTI2012

Risk Factors and Complications Associated with Difficult Retrieval of Embolic Protection Devices in Carotid Artery Stenting

Purpose: This study was designed to investigate the risk factors and complications of difficult retrieval (DR) of embolic protection devices (EPDs) in carotid artery stenting (CAS). Methods: A total of 195 consecutive patients who underwent CAS between December 2007 and March 2010 in a general hospital were enrolled and divided into two groups: with DR and without DR. The risk factors of DR were analyzed with univariate and multivariate analyses. The complications of DR were recorded and analyzed. Results: A total of 30 (15.4%) patients with DR of EPDs were observed, of whom EPDs were successfully retrieved in 29, and in 1 was it taken out by surgical treatment. The retrieval time was significantly longer in patients with DR (490.5 {+-} 143.9 s) than in patients without DR (157.2 {+-} 15.7 s, p = 0.000). Vasospasm was observed more frequently in patients with DR than in patients without DR (55.2% vs. 14.5%, p = 0.000). Intracranial compilations were more frequent in patients with DR than in those without DR (17.2% vs. 0.6%, p = 0.000). Calcified plaques, degree of residual stenosis, types of the stents, and tortuosity index (TI > 80 Degree-Sign) were all associated with DR. A logistic regression analysis indicated that calcified plaques (odds ratio (OR) = 6.5; p = 0.000) and TI > 80 Degree-Sign (OR = 18.8; p = 0.000) were independent predictors of DR. Conclusions: Calcified plaques and TI > 80 Degree-Sign may be related to DR in patients with CAS. DR may lengthen the retrieving time and increase the complications of the procedure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗