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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2012

Five Year Results of US Intergroup/RTOG 9704 With Postoperative CA 19-9 {<=}90 U/mL and Comparison to the CONKO-001 Trial

Purpose: Radiation Therapy Oncology Group (RTOG) trial 9704 was the largest randomized trial to use adjuvant chemoradiation therapy for patients with pancreatic cancer. This report analyzes 5-year survival by serum level of tumor marker CA 19-9 of {<=}90 vs >90 U/mL and compares results to the those of the CONKO-001 trial. Methods and Materials: CA 19-9 expression was analyzed as a dichotomized variable ({<=}90 vs >90 U/mL). Cox proportional hazard models were used to identify the impact of the CA 19-9 value on overall survival (OS). Actuarial estimates of OS were calculated using the Kaplan-Meier method. Results: Both univariate (hazard ratio [HR] = 3.2; 95% confidence interval [CI], 2.3-4.3, P<.0001) and multivariate (HR = 3.1; 95% CI, 2.2-4.2, P<.0001) analyses demonstrated a statistically significant decrease in OS for CA 19-9 serum level of {>=}90 U/mL. For patients in the gemcitabine (Gem) treatment arm with CA 19-9 <90 U/mL, median survival was 21 months. For patients with CA 19-9 {>=}90 U/mL, this number dropped to 10 months. In patients with pancreatic head tumors in the Gem treatment arm with RT quality assurance per protocol and CA 19-9 of <90 U/mL, median survival and 5-year rate were 24 months and 34%. In comparison, the median survival and 5-year OS rate for patients in the Gem arm of the CONKO trial were 22 months and 21%. Conclusions: This analysis demonstrates that patients with postresection CA 19-9 values {>=}90 U/mL had a significantly worse survival. Patients with pancreatic head tumors treated with Gem with CA 19-9 serum level of <90 U/mL and per protocol RT had favorable survival compared to that seen in the CONKO trial. CA 19-9 is a stratification factor for the current RTOG adjuvant pancreas trial (0848).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factors and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.

42 ENGINEERING↗
DOE OSTI2015

Performance evaluation of a hybrid-passive landfill leachate treatment system using multivariate statistical techniques

Highlights: • Performance of a hybrid passive landfill leachate treatment system was evaluated. • 33 Water chemistry parameters were sampled for 21 months and statistically analyzed. • Parameters were strongly linked and explained most (>40%) of the variation in data. • Alkalinity, ammonia, COD, heavy metals, and iron were criteria for performance. • Eight other parameters were key in modeling system dynamics and criteria. - Abstract: A pilot-scale hybrid-passive treatment system operated at the Merrick Landfill in North Bay, Ontario, Canada, treats municipal landfill leachate and provides for subsequent natural attenuation. Collected leachate is directed to a hybrid-passive treatment system, followed by controlled release to a natural attenuation zone before entering the nearby Little Sturgeon River. The study presents a comprehensive evaluation of the performance of the system using multivariate statistical techniques to determine the interactions between parameters, major pollutants in the leachate, and the biological and chemical processes occurring in the system. Five parameters (ammonia, alkalinity, chemical oxygen demand (COD), “heavy” metals of interest, with atomic weights above calcium, and iron) were set as criteria for the evaluation of system performance based on their toxicity to aquatic ecosystems and importance in treatment with respect to discharge regulations. System data for a full range of water quality parameters over a 21-month period were analyzed using principal components analysis (PCA), as well as principal components (PC) and partial least squares (PLS) regressions. PCA indicated a high degree of association for most parameters with the first PC, which explained a high percentage (>40%) of the variation in the data, suggesting strong statistical relationships among most of the parameters in the system. Regression analyses identified 8 parameters (set as independent variables) that were most frequently retained for modeling the five criteria parameters (set as dependent variables), on a statistically significant level: conductivity, dissolved oxygen (DO), nitrite (NO{sub 2}{sup −}), organic nitrogen (N), oxidation reduction potential (ORP), pH, sulfate and total volatile solids (TVS). The criteria parameters and the significant explanatory parameters were most important in modeling the dynamics of the passive treatment system during the study period. Such techniques and procedures were found to be highly valuable and could be applied to other sites to determine parameters of interest in similar naturalized engineered systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Impact of Insurance Status on Radiation Treatment Modality Selection Among Potential Candidates for Prostate, Breast, or Gynecologic Brachytherapy

Purpose: The Patient Protection and Affordable Care Act looks to expand both private and Medicaid insurance. To evaluate how these changes may affect the field of radiation oncology, we evaluated the association of insurance status with the use of brachytherapy in cancers for which this treatment technique is used. Methods and Materials: A total of 190,467 patients met the inclusion criteria, of whom 95,292 (50.0%) had breast cancer, 61,096 (32.1%) had prostate cancer, 28,194 (14.8%) had endometrial cancer, and 5885 (3.1%) had cervical cancer. A multivariate logistic regression model was used to determine the association between insurance status and receipt of brachytherapy among patients treated definitively for prostate and cervical cancer or postoperatively for breast and endometrial cancer. Results: The rates of non-Medicaid insurance were 49.9% (cervical), 85.3% (endometrial), 87.4% (breast), and 90.9% (prostate) (P<.001). In a logistic regression, patients who received radiation therapy were less likely to receive brachytherapy if they had Medicaid coverage (odds ratio [OR] 0.57, 95% confidence interval [CI] 0.53-0.61, P<.001) or did not have insurance coverage (OR 0.50, 95% CI 0.45-0.56, P<.001) compared with those with non-Medicaid insurance. On subset analysis, patients with Medicaid coverage or without insurance coverage were significantly less likely to receive brachytherapy than were those with non-Medicaid insurance for all 4 sites, except for patients with endometrial cancer. Conclusions: Despite being a cost-effective treatment modality, brachytherapy is less often used in the definitive or postoperative management of cancer in patients with Medicaid coverage or without insurance. Upcoming health policy changes resulting in the expansion of private insurance and Medicaid will likely increase access to and demand for brachytherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1994

Approximate analysis for repeated eigenvalue problems with applications to controls-structure integrated design

A method for eigenvalue and eigenvector approximate analysis for the case of repeated eigenvalues with distinct first derivatives is presented. The approximate analysis method developed involves a reparameterization of the multivariable structural eigenvalue problem in terms of a single positive-valued parameter. The resulting equations yield first-order approximations to changes in the eigenvalues and the eigenvectors associated with the repeated eigenvalue problem. This work also presents a numerical technique that facilitates the definition of an eigenvector derivative for the case of repeated eigenvalues with repeated eigenvalue derivatives (of all orders). Examples are given which demonstrate the application of such equations for sensitivity and approximate analysis. Emphasis is placed on the application of sensitivity analysis to large-scale structural and controls-structures optimization problems.

Kenny, Sean P.↗
DOE OSTI2017

Spectroscopic analysis and control

Apparatus for spectroscopic analysis which includes a tunable diode laser spectrometer having a digital output signal and a digital computer for receiving the digital output signal from the spectrometer, the digital computer programmed to process the digital output signal using a multivariate regression algorithm. In addition, a spectroscopic method of analysis using such apparatus. Finally, a method for controlling an ethylene cracker hydrogenator.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Parametric Testing of Launch Vehicle FDDR Models

For the safe operation of a complex system like a (manned) launch vehicle, real-time information about the state of the system and potential faults is extremely important. The on-board FDDR (Failure Detection, Diagnostics, and Response) system is a software system to detect and identify failures, provide real-time diagnostics, and to initiate fault recovery and mitigation. The ERIS (Evaluation of Rocket Integrated Subsystems) failure simulation is a unified Matlab/Simulink model of the Ares I Launch Vehicle with modular, hierarchical subsystems and components. With this model, the nominal flight performance characteristics can be studied. Additionally, failures can be injected to see their effects on vehicle state and on vehicle behavior. A comprehensive test and analysis of such a complicated model is virtually impossible. In this paper, we will describe, how parametric testing (PT) can be used to support testing and analysis of the ERIS failure simulation. PT uses a combination of Monte Carlo techniques with n-factor combinatorial exploration to generate a small, yet comprehensive set of parameters for the test runs. For the analysis of the high-dimensional simulation data, we are using multivariate clustering to automatically find structure in this high-dimensional data space. Our tools can generate detailed HTML reports that facilitate the analysis.

Schumann, Johann↗
DOE OSTI2013

Big Data Visual Analytics for Exploratory Earth System Simulation Analysis

Rapid increases in high performance computing are feeding the development of larger and more complex data sets in climate research, which sets the stage for so-called big data analysis challenges. However, conventional climate analysis techniques are inadequate in dealing with the complexities of today s data. In this paper, we describe and demonstrate a visual analytics system, called the Exploratory Data analysis ENvironment (EDEN), with specific application to the analysis of complex earth system simulation data sets. EDEN represents the type of interactive visual analysis tools that are necessary to transform data into insight, thereby improving critical comprehension of earth system processes. In addition to providing an overview of EDEN, we describe real-world studies using both point ensembles and global Community Land Model Version 4 (CLM4) simulations.

big data↗
DOE OSTI2015

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.

58 GEOSCIENCES↗
NASA NTRS1991

Using Gaussian windows to explore a multivariate data set

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

Jaeckel, Louis A.↗
DOE OSTI2023

The State of the Art in Visualizing Dynamic Multivariate Networks

Abstract Most real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

Kale, Bharat↗
DOE OSTI2019

Development and Comparison of Purgeable Mercury Values in SRR Samples Measured by SRNL and Eurofins FGS

Savannah River Remediation (SRR) requested the development of mercury speciation capabilities at the Savannah River National Laboratory (SRNL) to support the Liquid Waste Operations at SRS. As part of that method development, SRR requested that SRNL Analytical Development (AD) compare their results with those obtained from their outside contract laboratory, Eurofins Frontier Global Sciences (FGS). This document reports on this method development work performed at SRNL as well as the comparative analyses conducted between the two laboratories. Development, optimization, and validation were undertaken at SRNL to produce a method for the species-specific analysis of dissolved gaseous mercury (DGM) in samples from SRR. It was determined that DGM present in SRR samples represented a combination of various soluble and volatile mercury species- and is thus referred to in this document as purgeable mercury. A method has been developed and analytically validated for the analysis of purgeable mercury in caustic nuclear waste. Using multivariate optimization, an efficient and sensitive method was produced. Total recovery of spiked purgeable mercury from deionized water was 99.4 ±10.6% and 91.6 ±14.9% from spiked high-activity tank waste samples. Calibration, by external calibration and standard addition calibration, maintained a mean accuracy of 101 ±5% and 99.9 ±1.7%, respectively. An interlaboratory comparison was performed between SRNL and Eurofins FGS in the co-analysis of three quarterly SRR Tank 50 samples. The statistical evaluations presented in this document support the following conclusions: for internal validation data, the two laboratories demonstrate biases of -4.3% for SRNL and 4.4% for Eurofins FGS, though neither are statistically significant at the 5% level. While the percent relative standard deviation for each of the laboratories is rather large (21.5% for SRNL and 18.4% for Eurofins FGS), there is no indication of a statistically significant difference between the two laboratories in the precision of these results. For the three sets of SRR quarterly Tank 50 samples, there is no indication of a statistically significant bias between the two laboratories at the 5% significance level. These results indicate that SRNL may provide data to the customer of statistically similar quality to Eurofins FGS.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Robustness analysis of an air heating plant and control law by using polynomial chaos

This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputs (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Searching for Low-Mass Dark Matter with SuperCDMS Soudan Detectors

SuperCDMS is a direct-detection dark matter (DM) experiment that uses cryogenically cooled germanium and silicon detectors to search for interac- tions between DM particles and detector nuclei, and in this thesis I describe my contributions to the experiment. I start with a brief review of DM and motivate the possibility of its de- tection in underground laboratories with sensitive detectors, and I review the SuperCDMS detector fundamentals. Then I focus on detector develop- ment for the future generation of the experiment, which will deploy an array of detectors at SNOLAB in Sudbury, Canada. Specifically I describe char- acterization of prototype detectors from surface facility testing, and discuss measurements of critical values that determine the detectors’ sensitivity to DM particles, such as the baseline resolution and the phonon collection ef- ficiency. I also describe analysis techniques developed to measure intrinsic detector noise in a high radiation environment such as a surface test facility. In the final chapters I describe a DM search analysis using four months of data from operation of SuperCDMS detectors in the Soudan Mine in northern Minnesota. I discuss how a particular detector operating mode, called CDMSlite, lowers the energy threshold of the detectors in order to improve the sensitivity to low-mass DM particles. I also present new analysis techniques that optimize the sensitivity to low-mass DM particles, including noise discrimination with multivariate classifiers, instrumental background modeling, and a profile likelihood signal and background fitting approach. In this analysis we set an upper limit on the DM-nucleon scattering cross section in germanium that is a factor of 2.5 improvement over the previous CDMSlite result for a DM mass that is five times the proton rest mass.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Modern CACSD using the Robust-Control Toolbox

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

Chiang, Richard Y.↗