Multivariate localization methods for ensemble Kalman filtering

NASA Astrophysics Data System (ADS)

Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.

2015-05-01

In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function.

PubMed

Luan, Xiaoli; Chen, Qiang; Liu, Fei

2014-09-01

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

On multivariate trace inequalities of Sutter, Berta, and Tomamichel

NASA Astrophysics Data System (ADS)

Lemm, Marius

2018-01-01

We consider a family of multivariate trace inequalities recently derived by Sutter, Berta, and Tomamichel. These inequalities generalize the Golden-Thompson inequality and Lieb's triple matrix inequality to an arbitrary number of matrices in a way that features complex matrix powers (i.e., certain unitaries). We show that their inequalities can be rewritten as an n-matrix generalization of Lieb's original triple matrix inequality. The complex matrix powers are replaced by resolvents and appropriate maximally entangled states. We expect that the technically advantageous properties of resolvents, in particular for perturbation theory, can be of use in applications of the n-matrix inequalities, e.g., for analyzing the performance of the rotated Petz recovery map in quantum information theory and for removing the unitaries altogether.

Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM

ERIC Educational Resources Information Center

Mair, Patrick; Satorra, Albert; Bentler, Peter M.

2012-01-01

This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo…

Implementation of hierarchical clustering using k-mer sparse matrix to analyze MERS-CoV genetic relationship

NASA Astrophysics Data System (ADS)

Bustamam, A.; Ulul, E. D.; Hura, H. F. A.; Siswantining, T.

2017-07-01

Hierarchical clustering is one of effective methods in creating a phylogenetic tree based on the distance matrix between DNA (deoxyribonucleic acid) sequences. One of the well-known methods to calculate the distance matrix is k-mer method. Generally, k-mer is more efficient than some distance matrix calculation techniques. The steps of k-mer method are started from creating k-mer sparse matrix, and followed by creating k-mer singular value vectors. The last step is computing the distance amongst vectors. In this paper, we analyze the sequences of MERS-CoV (Middle East Respiratory Syndrome - Coronavirus) DNA by implementing hierarchical clustering using k-mer sparse matrix in order to perform the phylogenetic analysis. Our results show that the ancestor of our MERS-CoV is coming from Egypt. Moreover, we found that the MERS-CoV infection that occurs in one country may not necessarily come from the same country of origin. This suggests that the process of MERS-CoV mutation might not only be influenced by geographical factor.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Arrowheaded enhanced multivariance products representation for matrices (AEMPRM): Specifically focusing on infinite matrices and converting arrowheadedness to tridiagonality

NASA Astrophysics Data System (ADS)

Özdemir, Gizem; Demiralp, Metin

2015-12-01

In this work, Enhanced Multivariance Products Representation (EMPR) approach which is a Demiralp-and-his- group extension to the Sobol's High Dimensional Model Representation (HDMR) has been used as the basic tool. Their discrete form have also been developed and used in practice by Demiralp and his group in addition to some other authors for the decomposition of the arrays like vectors, matrices, or multiway arrays. This work specifically focuses on the decomposition of infinite matrices involving denumerable infinitely many rows and columns. To this end the target matrix is first decomposed to the sum of certain outer products and then each outer product is treated by Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) which has been developed by Demiralp and his group. The result is a three-matrix- factor-product whose kernel (the middle factor) is an arrowheaded matrix while the pre and post factors are invertable matrices decomposed of the support vectors of TMEMPR. This new method is called as Arrowheaded Enhanced Multivariance Products Representation for Matrices. The general purpose is approximation of denumerably infinite matrices with the new method.

Sparsity of the normal matrix in the refinement of macromolecules at atomic and subatomic resolution.

PubMed

Jelsch, C

2001-09-01

The normal matrix in the least-squares refinement of macromolecules is very sparse when the resolution reaches atomic and subatomic levels. The elements of the normal matrix, related to coordinates, thermal motion and charge-density parameters, have a global tendency to decrease rapidly with the interatomic distance between the atoms concerned. For instance, in the case of the protein crambin at 0.54 A resolution, the elements are reduced by two orders of magnitude for distances above 1.5 A. The neglect a priori of most of the normal-matrix elements according to a distance criterion represents an approximation in the refinement of macromolecules, which is particularly valid at very high resolution. The analytical expressions of the normal-matrix elements, which have been derived for the coordinates and the thermal parameters, show that the degree of matrix sparsity increases with the diffraction resolution and the size of the asymmetric unit.

A generalized graph-theoretical matrix of heterosystems and its application to the VMV procedure.

PubMed

Mozrzymas, Anna

2011-12-14

The extensions of generalized (molecular) graph-theoretical matrix and vector-matrix-vector procedure are considered. The elements of the generalized matrix are redefined in order to describe molecules containing heteroatoms and multiple bonds. The adjacency, distance, detour and reciprocal distance matrices of heterosystems, and corresponding vectors are derived from newly defined generalized graph matrix. The topological indices, which are most widely used in predicting physicochemical and biological properties/activities of various compounds, can be calculated from the new generalized vector-matrix-vector invariant. Copyright © 2011 Elsevier Ltd. All rights reserved.

Multivariate localization methods for ensemble Kalman filtering

NASA Astrophysics Data System (ADS)

Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.

2015-12-01

In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

Multidimensional Unfolding by Nonmetric Multidimensional Scaling of Spearman Distances in the Extended Permutation Polytope

ERIC Educational Resources Information Center

Van Deun, Katrijn; Heiser, Willem J.; Delbeke, Luc

2007-01-01

A multidimensional unfolding technique that is not prone to degenerate solutions and is based on multidimensional scaling of a complete data matrix is proposed: distance information about the unfolding data and about the distances both among judges and among objects is included in the complete matrix. The latter information is derived from the…

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

PubMed

Guggenmos, Matthias; Sterzer, Philipp; Cichy, Radoslaw Martin

2018-06-01

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. Copyright © 2018 Elsevier Inc. All rights reserved.

Matrix Completion Optimization for Localization in Wireless Sensor Networks for Intelligent IoT

PubMed Central

Nguyen, Thu L. N.; Shin, Yoan

2016-01-01

Localization in wireless sensor networks (WSNs) is one of the primary functions of the intelligent Internet of Things (IoT) that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes. The task is to find the sensor locations through recovery of missing entries of a squared distance matrix when the dimension of the data is small compared to the number of data points. We solve a relaxation optimization problem using a modification of Newton’s method, where the cost function depends on the squared distance matrix. The solution obtained in our scheme achieves a lower complexity and can perform better if we use it as an initial guess for an interactive local search of other higher precision localization scheme. Simulation results show the effectiveness of our approach. PMID:27213378

Coping with matrix effects in headspace solid phase microextraction gas chromatography using multivariate calibration strategies.

PubMed

Ferreira, Vicente; Herrero, Paula; Zapata, Julián; Escudero, Ana

2015-08-14

SPME is extremely sensitive to experimental parameters affecting liquid-gas and gas-solid distribution coefficients. Our aims were to measure the weights of these factors and to design a multivariate strategy based on the addition of a pool of internal standards, to minimize matrix effects. Synthetic but real-like wines containing selected analytes and variable amounts of ethanol, non-volatile constituents and major volatile compounds were prepared following a factorial design. The ANOVA study revealed that even using a strong matrix dilution, matrix effects are important and additive with non-significant interaction effects and that it is the presence of major volatile constituents the most dominant factor. A single internal standard provided a robust calibration for 15 out of 47 analytes. Then, two different multivariate calibration strategies based on Partial Least Square Regression were run in order to build calibration functions based on 13 different internal standards able to cope with matrix effects. The first one is based in the calculation of Multivariate Internal Standards (MIS), linear combinations of the normalized signals of the 13 internal standards, which provide the expected area of a given unit of analyte present in each sample. The second strategy is a direct calibration relating concentration to the 13 relative areas measured in each sample for each analyte. Overall, 47 different compounds can be reliably quantified in a single fully automated method with overall uncertainties better than 15%. Copyright © 2015 Elsevier B.V. All rights reserved.

Method of multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2004-01-06

A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

LIKELIHOOD RATIO TESTS OF HYPOTHESES ON MULTIVARIATE POPULATIONS, VOLUME II, TEST OF HYPOTHESIS--STATISTICAL MODELS FOR THE EVALUATION AND INTERPRETATION OF EDUCATIONAL CRITERIA. PART 4.

ERIC Educational Resources Information Center

SAW, J.G.

THIS PAPER DEALS WITH SOME TESTS OF HYPOTHESIS FREQUENTLY ENCOUNTERED IN THE ANALYSIS OF MULTIVARIATE DATA. THE TYPE OF HYPOTHESIS CONSIDERED IS THAT WHICH THE STATISTICIAN CAN ANSWER IN THE NEGATIVE OR AFFIRMATIVE. THE DOOLITTLE METHOD MAKES IT POSSIBLE TO EVALUATE THE DETERMINANT OF A MATRIX OF HIGH ORDER, TO SOLVE A MATRIX EQUATION, OR TO…

Protein structure estimation from NMR data by matrix completion.

PubMed

Li, Zhicheng; Li, Yang; Lei, Qiang; Zhao, Qing

2017-09-01

Knowledge of protein structures is very important to understand their corresponding physical and chemical properties. Nuclear Magnetic Resonance (NMR) spectroscopy is one of the main methods to measure protein structure. In this paper, we propose a two-stage approach to calculate the structure of a protein from a highly incomplete distance matrix, where most data are obtained from NMR. We first randomly "guess" a small part of unobservable distances by utilizing the triangle inequality, which is crucial for the second stage. Then we use matrix completion to calculate the protein structure from the obtained incomplete distance matrix. We apply the accelerated proximal gradient algorithm to solve the corresponding optimization problem. Furthermore, the recovery error of our method is analyzed, and its efficiency is demonstrated by several practical examples.

Distance Delivery of Vocational Education Technologies and Planning Matrixes.

ERIC Educational Resources Information Center

Norenberg, Curtis D.; Lundblad, Larry

This document presents a general review of distance education as it currently pertains to secondary, postsecondary, and adult education. Chapter I discusses the general concepts of distance education. It addresses the nature of distance education and distance delivery, the distance learner, the distance instructor, and distance education learning…

Apparatus and system for multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2003-06-24

An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

EPA Positive Matrix Factorization (PMF) 3.0 Fundamentals & User Guide

EPA Science Inventory

Positive matrix factorization (PMF) is a multivariate factor analysis tool that decomposes a matrix of ambient data into two matrices - factor contributions and factor profiles - which then need to be interpreted by an analyst as to what source types are represented using measure...

Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces.

PubMed

Uehara, Takashi; Sartori, Matteo; Tanaka, Toshihisa; Fiori, Simone

2017-06-01

The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery-based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)-based MI-BCI. Tangent space mapping is a powerful method of feature extraction and strongly depends on the selection of a reference covariance matrix. In general, the observed signals may include outliers; therefore, taking the geometric mean of SCMs as the reference matrix may not be the best choice. In order to deal with the effects of outliers, robust estimators have to be used. In particular, we discuss and test the use of geometric medians and trimmed averages (defined on the basis of several metrics) as robust estimators. The main idea behind trimmed averages is to eliminate data that exhibit the largest distance from the average covariance calculated on the basis of all available data. The results of the experiments show that while the geometric medians show little differences from conventional methods in terms of classification accuracy in the classification of electroencephalographic recordings, the trimmed averages show significant improvement for all subjects.

Multivariate model of female black bear habitat use for a Geographic Information System

USGS Publications Warehouse

Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.

1993-01-01

Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

Measuring multiple spike train synchrony.

PubMed

Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

2009-10-15

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

Multivariate statistics applied to the reaction of common bean plants to parasitism by Meloidogyne javanica.

PubMed

Santos, L N S; Cabral, P D S; Neves, G A R; Alves, F R; Teixeira, M B; Cunha, F N; Silva, N F

2017-03-16

The availability of common bean cultivars tolerant to Meloidogyne javanica is limited in Brazil. Thus, the present study aimed to evaluate the reactions of 33 common bean genotypes (23 landrace, 8 commercial, 1 susceptible standard and 1 resistant standard) to M. javanica, employing multivariate statistics to discriminate the reaction of the genotypes. The experiment was conducted in a greenhouse using a completely randomized design with seven replicates. The seeds were sown in 1-L pots containing autoclaved soil and sand in a 1:1 ratio (v:v). On day 19, after emergence of the seedlings, the plants were treated with inoculum containing 4000 eggs + second-stage juveniles (J2). At 60 days after inoculation, the seedlings were evaluated based on biometric and parasitism-related traits, such as number of galls, final nematode population per root system, reproduction factor, and percent reduction in the reproduction factor of the nematode (%RRF). The data were subjected to analysis of variance using the F-test. The Mahalanobis generalized distance was used to obtain the dissimilarity matrix, and the average linkage between groups was used for clustering. The use of multivariate statistics allowed groups to be separated according to the resistance levels of genotypes, as observed in the %RRF. The landrace genotypes FORT-09, FORT-17, FORT-31, FORT-32, FORT-34 and FORT-36 presented resistance to M. javanica; thus, these genotypes can be considered potential sources of resistance.

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Distance learning in discriminative vector quantization.

PubMed

Schneider, Petra; Biehl, Michael; Hammer, Barbara

2009-10-01

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods to more general metric structures have been proposed, such as relevance adaptation in generalized LVQ (GLVQ) and matrix learning in GLVQ. In these approaches, metric parameters are learned based on the given classification task such that a data-driven distance measure is found. In this letter, we consider full matrix adaptation in advanced LVQ schemes. In particular, we introduce matrix learning to a recent statistical formalization of LVQ, robust soft LVQ, and we compare the results on several artificial and real-life data sets to matrix learning in GLVQ, a derivation of LVQ-like learning based on a (heuristic) cost function. In all cases, matrix adaptation allows a significant improvement of the classification accuracy. Interestingly, however, the principled behavior of the models with respect to prototype locations and extracted matrix dimensions shows several characteristic differences depending on the data sets.

Occupational exposures are associated with worse morbidity in patients with chronic obstructive pulmonary disease.

PubMed

Paulin, Laura M; Diette, Gregory B; Blanc, Paul D; Putcha, Nirupama; Eisner, Mark D; Kanner, Richard E; Belli, Andrew J; Christenson, Stephanie; Tashkin, Donald P; Han, MeiLan; Barr, R Graham; Hansel, Nadia N

2015-03-01

Links between occupational exposures and morbidity in individuals with established chronic obstructive pulmonary disease (COPD) remain unclear. To determine the impact of occupational exposures on COPD morbidity. A job exposure matrix (JEM) determined occupational exposure likelihood based on longest job in current/former smokers (n = 1,075) recruited as part of the Subpopulations and Intermediate Outcomes in COPD Study, of whom 721 had established COPD. Bivariate and multivariate linear regression models estimated the association of occupational exposure with COPD, and among those with established disease, the occupational exposure associations with 6-minute-walk distance (6MWD), the Modified Medical Research Council Dyspnea Scale (mMRC), the COPD Assessment Test (CAT), St. George's Respiratory Questionnaire (SGRQ), 12-item Short-Form Physical Component (SF-12), and COPD exacerbations requiring health care utilization, adjusting for demographics, current smoking status, and cumulative pack-years. An intermediate/high risk of occupational exposure by JEM was found in 38% of participants. In multivariate analysis, those with job exposures had higher odds of COPD (odds ratio, 1.44; 95% confidence interval, 1.04-1.97). Among those with COPD, job exposures were associated with shorter 6MWDs (-26.0 m; P = 0.006); worse scores for mMRC (0.23; P = 0.004), CAT (1.8; P = 0.003), SGRQ (4.5; P = 0.003), and SF-12 Physical (-3.3; P < 0.0001); and greater odds of exacerbation requiring health care utilization (odds ratio, 1.55; P = 0.03). Accounting for smoking, occupational exposure was associated with COPD risk and, for those with established disease, shorter walk distance, greater breathlessness, worse quality of life, and increased exacerbation risk. Clinicians should obtain occupational histories from patients with COPD because work-related exposures may influence disease burden.

On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms

NASA Astrophysics Data System (ADS)

Schaffrin, Burkhard; Felus, Yaron A.

2008-06-01

The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model ( Y- E Y = ( X- E X ) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E Y and E X . Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem by using the nonlinear Euler-Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition. For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335-342). The differences between the standard least-squares and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion matrix of the estimated parameters.

Graph edit distance from spectral seriation.

PubMed

Robles-Kelly, Antonio; Hancock, Edwin R

2005-03-01

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that they lack some of the formality and rigor of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as a maximum a posteriori probability (MAP) alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which the edit cost is the negative logarithm of the a posteriori sequence alignment probability. We compute the edit distance by finding the sequence of string edit operations which minimizes the cost of the path traversing the edit lattice. The edit costs are determined by the components of the leading eigenvectors of the adjacency matrix and by the edge densities of the graphs being matched. We demonstrate the utility of the edit distance on a number of graph clustering problems.

Distance matrix-based approach to protein structure prediction.

PubMed

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

Breeding Guild Determines Frog Distributions in Response to Edge Effects and Habitat Conversion in the Brazil's Atlantic Forest.

PubMed

Ferreira, Rodrigo B; Beard, Karen H; Crump, Martha L

2016-01-01

Understanding the response of species with differing life-history traits to habitat edges and habitat conversion helps predict their likelihood of persistence across changing landscape. In Brazil's Atlantic Forest, we evaluated frog richness and abundance by breeding guild at four distances from the edge of a reserve: i) 200 m inside the forest, ii) 50 m inside the forest, iii) at the forest edge, and iv) 50 m inside three different converted habitats (coffee plantation, non-native Eucalyptus plantation, and abandoned pastures, hereafter matrix types). By sampling a dry and a wet season, we recorded 622 individual frogs representing 29 species, of which three were undescribed. Breeding guild (i.e. bromeliad, leaf-litter, and water-body breeders) was the most important variable explaining frog distributions in relation to edge effects and matrix types. Leaf-litter and bromeliad breeders decreased in richness and abundance from the forest interior toward the matrix habitats. Water-body breeders increased in richness toward the matrix and remained relatively stable in abundance across distances. Number of large trees (i.e. DBH > 15 cm) and bromeliads best explained frog richness and abundance across distances. Twenty species found in the interior of the forest were not found in any matrix habitat. Richness and abundance across breeding guilds were higher in the rainy season but frog distributions were similar across the four distances in the two seasons. Across matrix types, leaf-litter species primarily used Eucalyptus plantations, whereas water-body species primarily used coffee plantations. Bromeliad breeders were not found inside any matrix habitat. Our study highlights the importance of primary forest for bromeliad and leaf-litter breeders. We propose that water-body breeders use edge and matrix habitats to reach breeding habitats along the valleys. Including life-history characteristics, such as breeding guild, can improve predictions of frog distributions in response to edge effect and matrix types, and can guide more effective management and conservation actions.

Breeding Guild Determines Frog Distributions in Response to Edge Effects and Habitat Conversion in the Brazil’s Atlantic Forest

PubMed Central

Ferreira, Rodrigo B.; Beard, Karen H.; Crump, Martha L.

2016-01-01

Understanding the response of species with differing life-history traits to habitat edges and habitat conversion helps predict their likelihood of persistence across changing landscape. In Brazil’s Atlantic Forest, we evaluated frog richness and abundance by breeding guild at four distances from the edge of a reserve: i) 200 m inside the forest, ii) 50 m inside the forest, iii) at the forest edge, and iv) 50 m inside three different converted habitats (coffee plantation, non-native Eucalyptus plantation, and abandoned pastures, hereafter matrix types). By sampling a dry and a wet season, we recorded 622 individual frogs representing 29 species, of which three were undescribed. Breeding guild (i.e. bromeliad, leaf-litter, and water-body breeders) was the most important variable explaining frog distributions in relation to edge effects and matrix types. Leaf-litter and bromeliad breeders decreased in richness and abundance from the forest interior toward the matrix habitats. Water-body breeders increased in richness toward the matrix and remained relatively stable in abundance across distances. Number of large trees (i.e. DBH > 15 cm) and bromeliads best explained frog richness and abundance across distances. Twenty species found in the interior of the forest were not found in any matrix habitat. Richness and abundance across breeding guilds were higher in the rainy season but frog distributions were similar across the four distances in the two seasons. Across matrix types, leaf-litter species primarily used Eucalyptus plantations, whereas water-body species primarily used coffee plantations. Bromeliad breeders were not found inside any matrix habitat. Our study highlights the importance of primary forest for bromeliad and leaf-litter breeders. We propose that water-body breeders use edge and matrix habitats to reach breeding habitats along the valleys. Including life-history characteristics, such as breeding guild, can improve predictions of frog distributions in response to edge effect and matrix types, and can guide more effective management and conservation actions. PMID:27272328

Degree of coherence for vectorial electromagnetic fields as the distance between correlation matrices.

PubMed

Luis, Alfredo

2007-04-01

We assess the degree of coherence of vectorial electromagnetic fields in the space-frequency domain as the distance between the cross-spectral density matrix and the identity matrix representing completely incoherent light. This definition is compared with previous approaches. It is shown that this distance provides an upper bound for the degree of coherence and visibility for any pair of scalar waves obtained by linear combinations of the original fields. This same approach emerges when applying a previous definition of global coherence to a Young interferometer.

Dependence of Sum Frequency Generation (SFG) Spectral Features on the Mesoscale Arrangement of SFG-Active Crystalline Domains Interspersed in SFG-Inactive Matrix: A Case Study with Cellulose in Uniaxially Aligned Control Samples and Alkali-Treated Secondary Cell Walls of Plants

DOE PAGES

Makarem, Mohamadamin; Sawada, Daisuke; O'Neill, Hugh M.; ...

2017-04-21

Vibrational sum frequency generation (SFG) spectroscopy can selectively detect not only molecules at two-dimensional (2D) interfaces but also noncentrosymmetric domains interspersed in amorphous three-dimensional (3D) matrixes. However, the SFG analysis of 3D systems is more complicated than 2D systems because more variables are involved. One such variable is the distance between SFG-active domains in SFG-inactive matrixes. In this study, we fabricated control samples in which SFG-active cellulose crystals were uniaxially aligned in an amorphous matrix. Assuming uniform separation distances between cellulose crystals, the relative intensities of alkyl (CH) and hydroxyl (OH) SFG peaks of cellulose could be related to themore » intercrystallite distance. The experimentally measured CH/OH intensity ratio as a function of the intercrystallite distance could be explained reasonably well with a model constructed using the theoretically calculated hyperpolarizabilities of cellulose and the symmetry cancellation principle of dipoles antiparallel to each other. In conclusion, this comparison revealed physical insights into the intercrystallite distance dependence of the CH/OH SFG intensity ratio of cellulose, which can be used to interpret the SFG spectral features of plant cell walls in terms of mesoscale packing of cellulose microfibrils.« less

Universality of quantum information in chaotic CFTs

NASA Astrophysics Data System (ADS)

Lashkari, Nima; Dymarsky, Anatoly; Liu, Hong

2018-03-01

We study the Eigenstate Thermalization Hypothesis (ETH) in chaotic conformal field theories (CFTs) of arbitrary dimensions. Assuming local ETH, we compute the reduced density matrix of a ball-shaped subsystem of finite size in the infinite volume limit when the full system is an energy eigenstate. This reduced density matrix is close in trace distance to a density matrix, to which we refer as the ETH density matrix, that is independent of all the details of an eigenstate except its energy and charges under global symmetries. In two dimensions, the ETH density matrix is universal for all theories with the same value of central charge. We argue that the ETH density matrix is close in trace distance to the reduced density matrix of the (micro)canonical ensemble. We support the argument in higher dimensions by comparing the Von Neumann entropy of the ETH density matrix with the entropy of a black hole in holographic systems in the low temperature limit. Finally, we generalize our analysis to the coherent states with energy density that varies slowly in space, and show that locally such states are well described by the ETH density matrix.

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

PubMed

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM.

PubMed

Mair, Patrick; Satorra, Albert; Bentler, Peter M

2012-07-01

This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo evaluation of structural equation models within the context of nonnormal data. The new procedure for nonnormal data simulation is theoretically described and also implemented in the widely used R environment. The quality of the method is assessed by Monte Carlo simulations. A 1-sample test on the observed covariance matrix based on the copula methodology is proposed. This new test for evaluating the quality of a simulation is defined through a particular structural model specification and is robust against normality violations.

Distribution of the Determinant of the Sample Correlation Matrix: Monte Carlo Type One Error Rates.

ERIC Educational Resources Information Center

Reddon, John R.; And Others

1985-01-01

Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

A mixed model for the relationship between climate and human cranial form.

PubMed

Katz, David C; Grote, Mark N; Weaver, Timothy D

2016-08-01

We expand upon a multivariate mixed model from quantitative genetics in order to estimate the magnitude of climate effects in a global sample of recent human crania. In humans, genetic distances are correlated with distances based on cranial form, suggesting that population structure influences both genetic and quantitative trait variation. Studies controlling for this structure have demonstrated significant underlying associations of cranial distances with ecological distances derived from climate variables. However, to assess the biological importance of an ecological predictor, estimates of effect size and uncertainty in the original units of measurement are clearly preferable to significance claims based on units of distance. Unfortunately, the magnitudes of ecological effects are difficult to obtain with distance-based methods, while models that produce estimates of effect size generally do not scale to high-dimensional data like cranial shape and form. Using recent innovations that extend quantitative genetics mixed models to highly multivariate observations, we estimate morphological effects associated with a climate predictor for a subset of the Howells craniometric dataset. Several measurements, particularly those associated with cranial vault breadth, show a substantial linear association with climate, and the multivariate model incorporating a climate predictor is preferred in model comparison. Previous studies demonstrated the existence of a relationship between climate and cranial form. The mixed model quantifies this relationship concretely. Evolutionary questions that require population structure and phylogeny to be disentangled from potential drivers of selection may be particularly well addressed by mixed models. Am J Phys Anthropol 160:593-603, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Building Academic Quality in Distance Higher Education. A Monograph in Higher Education Evaluation and Policy.

ERIC Educational Resources Information Center

Chacon-Duque, Fabio J.

The factors that determine course completion and achievement in college distance education were investigated using a sample of 25 courses offered through the Independent Learning Program at the Pennsylvania State University. The main objective was to develop a multivariate model to explain and predict outcomes of distance education. Additional…

Automatic face naming by learning discriminative affinity matrices from weakly labeled images.

PubMed

Xiao, Shijie; Xu, Dong; Wu, Jianxin

2015-10-01

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

Nearest neighbors by neighborhood counting.

PubMed

Wang, Hui

2006-06-01

Finding nearest neighbors is a general idea that underlies many artificial intelligence tasks, including machine learning, data mining, natural language understanding, and information retrieval. This idea is explicitly used in the k-nearest neighbors algorithm (kNN), a popular classification method. In this paper, this idea is adopted in the development of a general methodology, neighborhood counting, for devising similarity functions. We turn our focus from neighbors to neighborhoods, a region in the data space covering the data point in question. To measure the similarity between two data points, we consider all neighborhoods that cover both data points. We propose to use the number of such neighborhoods as a measure of similarity. Neighborhood can be defined for different types of data in different ways. Here, we consider one definition of neighborhood for multivariate data and derive a formula for such similarity, called neighborhood counting measure or NCM. NCM was tested experimentally in the framework of kNN. Experiments show that NCM is generally comparable to VDM and its variants, the state-of-the-art distance functions for multivariate data, and, at the same time, is consistently better for relatively large k values. Additionally, NCM consistently outperforms HEOM (a mixture of Euclidean and Hamming distances), the "standard" and most widely used distance function for multivariate data. NCM has a computational complexity in the same order as the standard Euclidean distance function and NCM is task independent and works for numerical and categorical data in a conceptually uniform way. The neighborhood counting methodology is proven sound for multivariate data experimentally. We hope it will work for other types of data.

Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning.

PubMed

Ying, Shihui; Wen, Zhijie; Shi, Jun; Peng, Yaxin; Peng, Jigen; Qiao, Hong

2017-05-18

In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.

Positive edge effects on forest-interior cryptogams in clear-cuts.

PubMed

Caruso, Alexandro; Rudolphi, Jörgen; Rydin, Håkan

2011-01-01

Biological edge effects are often assessed in high quality focal habitats that are negatively influenced by human-modified low quality matrix habitats. A deeper understanding of the possibilities for positive edge effects in matrix habitats bordering focal habitats (e.g. spillover effects) is, however, essential for enhancing landscape-level resilience to human alterations. We surveyed epixylic (dead wood inhabiting) forest-interior cryptogams (lichens, bryophytes, and fungi) associated with mature old-growth forests in 30 young managed Swedish boreal forest stands bordering a mature forest of high conservation value. In each young stand we registered species occurrences on coarse dead wood in transects 0-50 m from the border between stand types. We quantified the effect of distance from the mature forest on the occurrence of forest-interior species in the young stands, while accounting for local environment and propagule sources. For comparison we also surveyed epixylic open-habitat (associated with open forests) and generalist cryptogams. Species composition of epixylic cryptogams in young stands differed with distance from the mature forest: the frequency of occurrence of forest-interior species decreased with increasing distance whereas it increased for open-habitat species. Generalists were unaffected by distance. Epixylic, boreal forest-interior cryptogams do occur in matrix habitats such as clear-cuts. In addition, they are associated with the matrix edge because of a favourable microclimate closer to the mature forest on southern matrix edges. Retention and creation of dead wood in clear-cuts along the edges to focal habitats is a feasible way to enhance the long-term persistence of epixylic habitat specialists in fragmented landscapes. The proposed management measures should be performed in the whole stand as it matures, since microclimatic edge effects diminish as the matrix habitat matures. We argue that management that aims to increase habitat quality in matrix habitats bordering focal habitats should increase the probability of long-term persistence of habitat specialists.

Positive Edge Effects on Forest-Interior Cryptogams in Clear-Cuts

PubMed Central

Caruso, Alexandro; Rudolphi, Jörgen; Rydin, Håkan

2011-01-01

Biological edge effects are often assessed in high quality focal habitats that are negatively influenced by human-modified low quality matrix habitats. A deeper understanding of the possibilities for positive edge effects in matrix habitats bordering focal habitats (e.g. spillover effects) is, however, essential for enhancing landscape-level resilience to human alterations. We surveyed epixylic (dead wood inhabiting) forest-interior cryptogams (lichens, bryophytes, and fungi) associated with mature old-growth forests in 30 young managed Swedish boreal forest stands bordering a mature forest of high conservation value. In each young stand we registered species occurrences on coarse dead wood in transects 0–50 m from the border between stand types. We quantified the effect of distance from the mature forest on the occurrence of forest-interior species in the young stands, while accounting for local environment and propagule sources. For comparison we also surveyed epixylic open-habitat (associated with open forests) and generalist cryptogams. Species composition of epixylic cryptogams in young stands differed with distance from the mature forest: the frequency of occurrence of forest-interior species decreased with increasing distance whereas it increased for open-habitat species. Generalists were unaffected by distance. Epixylic, boreal forest-interior cryptogams do occur in matrix habitats such as clear-cuts. In addition, they are associated with the matrix edge because of a favourable microclimate closer to the mature forest on southern matrix edges. Retention and creation of dead wood in clear-cuts along the edges to focal habitats is a feasible way to enhance the long-term persistence of epixylic habitat specialists in fragmented landscapes. The proposed management measures should be performed in the whole stand as it matures, since microclimatic edge effects diminish as the matrix habitat matures. We argue that management that aims to increase habitat quality in matrix habitats bordering focal habitats should increase the probability of long-term persistence of habitat specialists. PMID:22114728

Collision for Li++He System. I. Potential Curves and Non-Adiabatic Coupling Matrix Elements

NASA Astrophysics Data System (ADS)

Yoshida, Junichi; O-Ohata, Kiyosi

1984-02-01

The potential curves and the non-adiabatic coupling matrix elements for the Li++He collision system were computed. The SCF molecular orbitals were constructed with the CGTO atomic bases centered on each nucleus and the center of mass of two nuclei. The SCF and CI calculations were done at various internuclear distances in the range of 0.1˜25.0 a.u. The potential energies and the wavefunctions were calculated with good approximation over whole internuclear distance. The non-adiabatic coupling matrix elements were calculated with the tentative method in which the ETF are approximately taken into account.

An alternative derivation of the stationary distribution of the multivariate neutral Wright-Fisher model for low mutation rates with a view to mutation rate estimation from site frequency data.

PubMed

Schrempf, Dominik; Hobolth, Asger

2017-04-01

Recently, Burden and Tang (2016) provided an analytical expression for the stationary distribution of the multivariate neutral Wright-Fisher model with low mutation rates. In this paper we present a simple, alternative derivation that illustrates the approximation. Our proof is based on the discrete multivariate boundary mutation model which has three key ingredients. First, the decoupled Moran model is used to describe genetic drift. Second, low mutation rates are assumed by limiting mutations to monomorphic states. Third, the mutation rate matrix is separated into a time-reversible part and a flux part, as suggested by Burden and Tang (2016). An application of our result to data from several great apes reveals that the assumption of stationarity may be inadequate or that other evolutionary forces like selection or biased gene conversion are acting. Furthermore we find that the model with a reversible mutation rate matrix provides a reasonably good fit to the data compared to the one with a non-reversible mutation rate matrix. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide

EPA Science Inventory

PMF is a multivariate factor analysis tool that decomposes a matrix of speciated sample data into two matrices: factor contributions (G) and factor profiles (F). These factor profiles need to be interpreted by the user to identify the source types that may be contributing to the ...

Heterogeneity Measurement Based on Distance Measure for Polarimetric SAR Data

NASA Astrophysics Data System (ADS)

Xing, Xiaoli; Chen, Qihao; Liu, Xiuguo

2018-04-01

To effectively test the scene heterogeneity for polarimetric synthetic aperture radar (PolSAR) data, in this paper, the distance measure is introduced by utilizing the similarity between the sample and pixels. Moreover, given the influence of the distribution and modeling texture, the K distance measure is deduced according to the Wishart distance measure. Specifically, the average of the pixels in the local window replaces the class center coherency or covariance matrix. The Wishart and K distance measure are calculated between the average matrix and the pixels. Then, the ratio of the standard deviation to the mean is established for the Wishart and K distance measure, and the two features are defined and applied to reflect the complexity of the scene. The proposed heterogeneity measure is proceeded by integrating the two features using the Pauli basis. The experiments conducted on the single-look and multilook PolSAR data demonstrate the effectiveness of the proposed method for the detection of the scene heterogeneity.

A spectral method to detect community structure based on distance modularity matrix

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

Model transformations for state-space self-tuning control of multivariable stochastic systems

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Bao, Yuan L.; Coleman, Norman P.

1988-01-01

The design of self-tuning controllers for multivariable stochastic systems is considered analytically. A long-division technique for finding the similarity transformation matrix and transforming the estimated left MFD to the right MFD is developed; the derivation is given in detail, and the procedures involved are briefly characterized.

Clinical predictors of the optimal spectacle correction for comfort performing desktop tasks.

PubMed

Leffler, Christopher T; Davenport, Byrd; Rentz, Jodi; Miller, Amy; Benson, William

2008-11-01

The best strategy for spectacle correction of presbyopia for near tasks has not been determined. Thirty volunteers over the age of 40 years were tested for subjective accommodative amplitude, pupillary size, fusional vergence, interpupillary distance, arm length, preferred working distance, near and far visual acuity and preferred reading correction in the phoropter and trial frames. Subjects performed near tasks (reading, writing and counting change) using various spectacle correction strengths. Predictors of the correction maximising near task comfort were determined by multivariable linear regression. The mean age was 54.9 years (range 43 to 71) and 40 per cent had diabetes. Significant predictors of the most comfortable addition in univariate analyses were age (p<0.001), interpupillary distance (p=0.02), fusional vergence amplitude (p=0.02), distance visual acuity in the worse eye (p=0.01), vision at 40 cm in the worse eye with distance correction (p=0.01), duration of diabetes (p=0.01), and the preferred correction to read at 40 cm with the phoropter (p=0.002) or trial frames (p<0.001). Target distance selected wearing trial frames (in dioptres), arm length, and accommodative amplitude were not significant predictors (p>0.15). The preferred addition wearing trial frames holding a reading target at a distance selected by the patient was the only independent predictor. Excluding this variable, distance visual acuity was predictive independent of age or near vision wearing distance correction. The distance selected for task performance was predicted by vision wearing distance correction at near and at distance. Multivariable linear regression can be used to generate tables based on distance visual acuity and age or near vision wearing distance correction to determine tentative near spectacle addition. Final spectacle correction for desktop tasks can be estimated by subjective refraction with trial frames.

Three-dimensional structure of the human immunodeficiency virus type 1 matrix protein.

PubMed

Massiah, M A; Starich, M R; Paschall, C; Summers, M F; Christensen, A M; Sundquist, W I

1994-11-25

The HIV-1 matrix protein forms an icosahedral shell associated with the inner membrane of the mature virus. Genetic analyses have indicated that the protein performs important functions throughout the viral life-cycle, including anchoring the transmembrane envelope protein on the surface of the virus, assisting in viral penetration, transporting the proviral integration complex across the nuclear envelope, and localizing the assembling virion to the cell membrane. We now report the three-dimensional structure of recombinant HIV-1 matrix protein, determined at high resolution by nuclear magnetic resonance (NMR) methods. The HIV-1 matrix protein is the first retroviral matrix protein to be characterized structurally and only the fourth HIV-1 protein of known structure. NMR signal assignments required recently developed triple-resonance (1H, 13C, 15N) NMR methodologies because signals for 91% of 132 assigned H alpha protons and 74% of the 129 assignable backbone amide protons resonate within chemical shift ranges of 0.8 p.p.m. and 1 p.p.m., respectively. A total of 636 nuclear Overhauser effect-derived distance restraints were employed for distance geometry-based structure calculations, affording an average of 13.0 NMR-derived distance restraints per residue for the experimentally constrained amino acids. An ensemble of 25 refined distance geometry structures with penalties (sum of the squares of the distance violations) of 0.32 A2 or less and individual distance violations under 0.06 A was generated; best-fit superposition of ordered backbone heavy atoms relative to mean atom positions afforded root-mean-square deviations of 0.50 (+/- 0.08) A. The folded HIV-1 matrix protein structure is composed of five alpha-helices, a short 3(10) helical stretch, and a three-strand mixed beta-sheet. Helices I to III and the 3(10) helix pack about a central helix (IV) to form a compact globular domain that is capped by the beta-sheet. The C-terminal helix (helix V) projects away from the beta-sheet to expose carboxyl-terminal residues essential for early steps in the HIV-1 infectious cycle. Basic residues implicated in membrane binding and nuclear localization functions cluster about an extruded cationic loop that connects beta-strands 1 and 2. The structure suggests that both membrane binding and nuclear localization may be mediated by complex tertiary structures rather than simple linear determinants.

DENBRAN: A basic program for a significance test for multivariate normality of clusters from branching patterns in dendrograms

NASA Astrophysics Data System (ADS)

Sneath, P. H. A.

A BASIC program is presented for significance tests to determine whether a dendrogram is derived from clustering of points that belong to a single multivariate normal distribution. The significance tests are based on statistics of the Kolmogorov—Smirnov type, obtained by comparing the observed cumulative graph of branch levels with a graph for the hypothesis of multivariate normality. The program also permits testing whether the dendrogram could be from a cluster of lower dimensionality due to character correlations. The program makes provision for three similarity coefficients, (1) Euclidean distances, (2) squared Euclidean distances, and (3) Simple Matching Coefficients, and for five cluster methods (1) WPGMA, (2) UPGMA, (3) Single Linkage (or Minimum Spanning Trees), (4) Complete Linkage, and (5) Ward's Increase in Sums of Squares. The program is entitled DENBRAN.

MULTIVARIATERESIDUES : A Mathematica package for computing multivariate residues

NASA Astrophysics Data System (ADS)

Larsen, Kasper J.; Rietkerk, Robbert

2018-01-01

Multivariate residues appear in many different contexts in theoretical physics and algebraic geometry. In theoretical physics, they for example give the proper definition of generalized-unitarity cuts, and they play a central role in the Grassmannian formulation of the S-matrix by Arkani-Hamed et al. In realistic cases their evaluation can be non-trivial. In this paper we provide a Mathematica package for efficient evaluation of multivariate residues based on methods from computational algebraic geometry.

Rapid construction of pinhole SPECT system matrices by distance-weighted Gaussian interpolation method combined with geometric parameter estimations

NASA Astrophysics Data System (ADS)

Lee, Ming-Wei; Chen, Yi-Chun

2014-02-01

In pinhole SPECT applied to small-animal studies, it is essential to have an accurate imaging system matrix, called H matrix, for high-spatial-resolution image reconstructions. Generally, an H matrix can be obtained by various methods, such as measurements, simulations or some combinations of both methods. In this study, a distance-weighted Gaussian interpolation method combined with geometric parameter estimations (DW-GIMGPE) is proposed. It utilizes a simplified grid-scan experiment on selected voxels and parameterizes the measured point response functions (PRFs) into 2D Gaussians. The PRFs of missing voxels are interpolated by the relations between the Gaussian coefficients and the geometric parameters of the imaging system with distance-weighting factors. The weighting factors are related to the projected centroids of voxels on the detector plane. A full H matrix is constructed by combining the measured and interpolated PRFs of all voxels. The PRFs estimated by DW-GIMGPE showed similar profiles as the measured PRFs. OSEM reconstructed images of a hot-rod phantom and normal rat myocardium demonstrated the effectiveness of the proposed method. The detectability of a SKE/BKE task on a synthetic spherical test object verified that the constructed H matrix provided comparable detectability to that of the H matrix acquired by a full 3D grid-scan experiment. The reduction in the acquisition time of a full 1.0-mm grid H matrix was about 15.2 and 62.2 times with the simplified grid pattern on 2.0-mm and 4.0-mm grid, respectively. A finer-grid H matrix down to 0.5-mm spacing interpolated by the proposed method would shorten the acquisition time by 8 times, additionally.

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

Key-Generation Algorithms for Linear Piece In Hand Matrix Method

NASA Astrophysics Data System (ADS)

Tadaki, Kohtaro; Tsujii, Shigeo

The linear Piece In Hand (PH, for short) matrix method with random variables was proposed in our former work. It is a general prescription which can be applicable to any type of multivariate public-key cryptosystems for the purpose of enhancing their security. Actually, we showed, in an experimental manner, that the linear PH matrix method with random variables can certainly enhance the security of HFE against the Gröbner basis attack, where HFE is one of the major variants of multivariate public-key cryptosystems. In 1998 Patarin, Goubin, and Courtois introduced the plus method as a general prescription which aims to enhance the security of any given MPKC, just like the linear PH matrix method with random variables. In this paper we prove the equivalence between the plus method and the primitive linear PH matrix method, which is introduced by our previous work to explain the notion of the PH matrix method in general in an illustrative manner and not for a practical use to enhance the security of any given MPKC. Based on this equivalence, we show that the linear PH matrix method with random variables has the substantial advantage over the plus method with respect to the security enhancement. In the linear PH matrix method with random variables, the three matrices, including the PH matrix, play a central role in the secret-key and public-key. In this paper, we clarify how to generate these matrices and thus present two probabilistic polynomial-time algorithms to generate these matrices. In particular, the second one has a concise form, and is obtained as a byproduct of the proof of the equivalence between the plus method and the primitive linear PH matrix method.

Factorial Design Based Multivariate Modeling and Optimization of Tunable Bioresponsive Arginine Grafted Poly(cystaminebis(acrylamide)-diaminohexane) Polymeric Matrix Based Nanocarriers.

PubMed

Yang, Rongbing; Nam, Kihoon; Kim, Sung Wan; Turkson, James; Zou, Ye; Zuo, Yi Y; Haware, Rahul V; Chougule, Mahavir B

2017-01-03

Desired characteristics of nanocarriers are crucial to explore its therapeutic potential. This investigation aimed to develop tunable bioresponsive newly synthesized unique arginine grafted poly(cystaminebis(acrylamide)-diaminohexane) [ABP] polymeric matrix based nanocarriers by using L9 Taguchi factorial design, desirability function, and multivariate method. The selected formulation and process parameters were ABP concentration, acetone concentration, the volume ratio of acetone to ABP solution, and drug concentration. The measured nanocarrier characteristics were particle size, polydispersity index, zeta potential, and percentage drug loading. Experimental validation of nanocarrier characteristics computed from initially developed predictive model showed nonsignificant differences (p > 0.05). The multivariate modeling based optimized cationic nanocarrier formulation of <100 nm loaded with hydrophilic acetaminophen was readapted for a hydrophobic etoposide loading without significant changes (p > 0.05) except for improved loading percentage. This is the first study focusing on ABP polymeric matrix based nanocarrier development. Nanocarrier particle size was stable in PBS 7.4 for 48 h. The increase of zeta potential at lower pH 6.4, compared to the physiological pH, showed possible endosomal escape capability. The glutathione triggered release at the physiological conditions indicated the competence of cytosolic targeting delivery of the loaded drug from bioresponsive nanocarriers. In conclusion, this unique systematic approach provides rational evaluation and prediction of a tunable bioresponsive ABP based matrix nanocarrier, which was built on selected limited number of smart experimentation.

Approximating Multivariate Normal Orthant Probabilities. ONR Technical Report. [Biometric Lab Report No. 90-1.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

The probability integral of the multivariate normal distribution (ND) has received considerable attention since W. F. Sheppard's (1900) and K. Pearson's (1901) seminal work on the bivariate ND. This paper evaluates the formula that represents the "n x n" correlation matrix of the "chi(sub i)" and the standardized multivariate…

A General Exponential Framework for Dimensionality Reduction.

PubMed

Wang, Su-Jing; Yan, Shuicheng; Yang, Jian; Zhou, Chun-Guang; Fu, Xiaolan

2014-02-01

As a general framework, Laplacian embedding, based on a pairwise similarity matrix, infers low dimensional representations from high dimensional data. However, it generally suffers from three issues: 1) algorithmic performance is sensitive to the size of neighbors; 2) the algorithm encounters the well known small sample size (SSS) problem; and 3) the algorithm de-emphasizes small distance pairs. To address these issues, here we propose exponential embedding using matrix exponential and provide a general framework for dimensionality reduction. In the framework, the matrix exponential can be roughly interpreted by the random walk over the feature similarity matrix, and thus is more robust. The positive definite property of matrix exponential deals with the SSS problem. The behavior of the decay function of exponential embedding is more significant in emphasizing small distance pairs. Under this framework, we apply matrix exponential to extend many popular Laplacian embedding algorithms, e.g., locality preserving projections, unsupervised discriminant projections, and marginal fisher analysis. Experiments conducted on the synthesized data, UCI, and the Georgia Tech face database show that the proposed new framework can well address the issues mentioned above.

Matrix metalloproteinase-2 gene variants and abdominal aortic aneurysm.

PubMed

Smallwood, L; Warrington, N; Allcock, R; van Bockxmeer, F; Palmer, L J; Iacopetta, B; Golledge, J; Norman, P E

2009-08-01

To investigate associations between two polymorphisms of the matrix metalloproteinase-2 gene (MMP2) and the incidence and progression of abdominal aortic aneurysm (AAA). Cases and controls were recruited from a trial of screening for AAAs. The association between two variants of MMP2 (-1360C>T, and +649C>T) in men with AAA (n=678) and in controls (n=659) was examined using multivariate analyses. The association with AAA expansion (n=638) was also assessed. In multivariate analyses with adjustments for multiple testing, no association between either SNP and AAA presence or expansion was detected. MMP2 -1360C>T and +649C>T variants are not risk factors for AAA.

Occupational Exposures Are Associated with Worse Morbidity in Patients with Chronic Obstructive Pulmonary Disease

PubMed Central

Paulin, Laura M.; Diette, Gregory B.; Blanc, Paul D.; Putcha, Nirupama; Eisner, Mark D.; Kanner, Richard E.; Belli, Andrew J.; Christenson, Stephanie; Tashkin, Donald P.; Han, MeiLan; Barr, R. Graham

2015-01-01

Rationale: Links between occupational exposures and morbidity in individuals with established chronic obstructive pulmonary disease (COPD) remain unclear. Objectives: To determine the impact of occupational exposures on COPD morbidity. Methods: A job exposure matrix (JEM) determined occupational exposure likelihood based on longest job in current/former smokers (n = 1,075) recruited as part of the Subpopulations and Intermediate Outcomes in COPD Study, of whom 721 had established COPD. Bivariate and multivariate linear regression models estimated the association of occupational exposure with COPD, and among those with established disease, the occupational exposure associations with 6-minute-walk distance (6MWD), the Modified Medical Research Council Dyspnea Scale (mMRC), the COPD Assessment Test (CAT), St. George’s Respiratory Questionnaire (SGRQ), 12-item Short-Form Physical Component (SF-12), and COPD exacerbations requiring health care utilization, adjusting for demographics, current smoking status, and cumulative pack-years. Measurements and Main Results: An intermediate/high risk of occupational exposure by JEM was found in 38% of participants. In multivariate analysis, those with job exposures had higher odds of COPD (odds ratio, 1.44; 95% confidence interval, 1.04–1.97). Among those with COPD, job exposures were associated with shorter 6MWDs (−26.0 m; P = 0.006); worse scores for mMRC (0.23; P = 0.004), CAT (1.8; P = 0.003), SGRQ (4.5; P = 0.003), and SF-12 Physical (−3.3; P < 0.0001); and greater odds of exacerbation requiring health care utilization (odds ratio, 1.55; P = 0.03). Conclusions: Accounting for smoking, occupational exposure was associated with COPD risk and, for those with established disease, shorter walk distance, greater breathlessness, worse quality of life, and increased exacerbation risk. Clinicians should obtain occupational histories from patients with COPD because work-related exposures may influence disease burden. PMID:25562375

The influence of hydrology and waterway distance on population structure of Chinook salmon Oncorhynchus tshawytscha in a large river.

PubMed

Olsen, J B; Beacham, T D; Wetklo, M; Seeb, L W; Smith, C T; Flannery, B G; Wenburg, J K

2010-04-01

Adult Chinook salmon Oncorhynchus tshawytscha navigate in river systems using olfactory cues that may be influenced by hydrologic factors such as flow and the number, size and spatial distribution of tributaries. Thus, river hydrology may influence both homing success and the level of straying (gene flow), which in turn influences population structure. In this study, two methods of multivariate analysis were used to examine the extent to which four indicators of hydrology and waterway distance explained population structure of O. tshawytscha in the Yukon River. A partial Mantel test showed that the indicators of hydrology were positively associated with broad-scale (Yukon basin) population structure, when controlling for the influence of waterway distance. Multivariate multiple regression showed that waterway distance, supplemented with the number and flow of major drainage basins, explained more variation in broad-scale population structure than any single indicator. At an intermediate spatial scale, indicators of hydrology did not appear to influence population structure after accounting for waterway distance. These results suggest that habitat changes in the Yukon River, which alter hydrology, may influence the basin-wide pattern of population structure in O. tshawytscha. Further research is warranted on the role of hydrology in concert with waterway distance in influencing population structure in Pacific salmon.

Person Re-Identification via Distance Metric Learning With Latent Variables.

PubMed

Sun, Chong; Wang, Dong; Lu, Huchuan

2017-01-01

In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.

Relation of Cloud Occurrence Frequency, Overlap, and Effective Thickness Derived from CALIPSO and CloudSat Merged Cloud Vertical Profiles

NASA Technical Reports Server (NTRS)

Kato, Seiji; Sun-Mack, Sunny; Miller, Walter F.; Rose, Fred G.; Chen, Yan; Minnis, Patrick; Wielicki, Bruce A.

2009-01-01

A cloud frequency of occurrence matrix is generated using merged cloud vertical profile derived from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR). The matrix contains vertical profiles of cloud occurrence frequency as a function of the uppermost cloud top. It is shown that the cloud fraction and uppermost cloud top vertical pro les can be related by a set of equations when the correlation distance of cloud occurrence, which is interpreted as an effective cloud thickness, is introduced. The underlying assumption in establishing the above relation is that cloud overlap approaches the random overlap with increasing distance separating cloud layers and that the probability of deviating from the random overlap decreases exponentially with distance. One month of CALIPSO and CloudSat data support these assumptions. However, the correlation distance sometimes becomes large, which might be an indication of precipitation. The cloud correlation distance is equivalent to the de-correlation distance introduced by Hogan and Illingworth [2000] when cloud fractions of both layers in a two-cloud layer system are the same.

Manifold learning-based subspace distance for machinery damage assessment

NASA Astrophysics Data System (ADS)

Sun, Chuang; Zhang, Zhousuo; He, Zhengjia; Shen, Zhongjie; Chen, Binqiang

2016-03-01

Damage assessment is very meaningful to keep safety and reliability of machinery components, and vibration analysis is an effective way to carry out the damage assessment. In this paper, a damage index is designed by performing manifold distance analysis on vibration signal. To calculate the index, vibration signal is collected firstly, and feature extraction is carried out to obtain statistical features that can capture signal characteristics comprehensively. Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace. The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment. Finally, Grassmann distance between manifold subspaces is defined as a damage index. The Grassmann distance reflecting manifold structure is a suitable metric to measure distance between subspaces in the manifold. The defined damage index is applied to damage assessment of a rotor and the bearing, and the result validates its effectiveness for damage assessment of machinery component.

Distance-Dependent Multimodal Image Registration for Agriculture Tasks

PubMed Central

Berenstein, Ron; Hočevar, Marko; Godeša, Tone; Edan, Yael; Ben-Shahar, Ohad

2015-01-01

Image registration is the process of aligning two or more images of the same scene taken at different times; from different viewpoints; and/or by different sensors. This research focuses on developing a practical method for automatic image registration for agricultural systems that use multimodal sensory systems and operate in natural environments. While not limited to any particular modalities; here we focus on systems with visual and thermal sensory inputs. Our approach is based on pre-calibrating a distance-dependent transformation matrix (DDTM) between the sensors; and representing it in a compact way by regressing the distance-dependent coefficients as distance-dependent functions. The DDTM is measured by calculating a projective transformation matrix for varying distances between the sensors and possible targets. To do so we designed a unique experimental setup including unique Artificial Control Points (ACPs) and their detection algorithms for the two sensors. We demonstrate the utility of our approach using different experiments and evaluation criteria. PMID:26308000

Impact of distance to a urologist on early diagnosis of prostate cancer among black and white patients.

PubMed

Holmes, Jordan A; Carpenter, William R; Wu, Yang; Hendrix, Laura H; Peacock, Sharon; Massing, Mark; Schenck, Anna P; Meyer, Anne-Marie; Diao, Kevin; Wheeler, Stephanie B; Godley, Paul A; Stitzenberg, Karyn B; Chen, Ronald C

2012-03-01

We examined whether an increased distance to a urologist is associated with a delayed diagnosis of prostate cancer among black and white patients, as manifested by higher risk disease at diagnosis. North Carolina Central Cancer Registry data were linked to Medicare claims for patients with incident prostate cancer diagnosed in 2004 to 2005. Straight-line distances were calculated from the patient home to the nearest urologist. Race stratified multivariate ordinal logistic regression was used to examine the association between distance to a urologist and prostate cancer risk group (low, intermediate, high or very high/metastasis) at diagnosis for black and white patients while accounting for age, comorbidity, marital status and diagnosis year. An overall model was then used to examine the distance × race interaction effect. Included in analysis were 1,720 white and 531 black men. In the overall cohort the high risk cancer rate increased monotonically with distance to a urologist, including 40% for 0 to 10, 45% for 11 to 20 and 57% for greater than 20 miles. Correspondingly the low risk cancer rate decreased with longer distance. On race stratified multivariate analysis longer distance was associated with higher risk prostate cancer for white and black patients (p = 0.04 and <0.01, respectively) but the effect was larger in the latter group. The distance × race interaction term was significant in the overall model (p = 0.03). Longer distance to a urologist may disproportionally impact black patients. Decreasing modifiable barriers to health care access, such as distance to care, may decrease racial disparities in prostate cancer. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Why Do Long-Distance Travelers Have Improved Pancreatectomy Outcomes?

PubMed

Jindal, Manila; Zheng, Chaoyi; Quadri, Humair S; Ihemelandu, Chukwuemeka U; Hong, Young K; Smith, Andrew K; Dudeja, Vikas; Shara, Nawar M; Johnson, Lynt B; Al-Refaie, Waddah B

2017-08-01

Centralization of complex surgical care has led patients to travel longer distances. Emerging evidence suggested a negative association between increased travel distance and mortality after pancreatectomy. However, the reason for this association remains largely unknown. We sought to unravel the relationships among travel distance, receiving pancreatectomy at high-volume hospitals, delayed surgery, and operative outcomes. We identified 44,476 patients who underwent pancreatectomy for neoplasms between 2004 and 2013 at the reporting facility from the National Cancer Database. Multivariable analyses were performed to examine the independent relationships between increments in travel distance mortality (30-day and long-term survival) after adjusting for patient demographics, comorbidity, cancer stage, and time trend. We then examined how additional adjustment of procedure volume affected this relationship overall and among rural patients. Median travel distance to undergo pancreatectomy increased from 16.5 to 18.7 miles (p for trend < 0.001). Although longer travel distance was associated with delayed pancreatectomy, it was also related to higher odds of receiving pancreatectomy at a high-volume hospital and lower postoperative mortality. In multivariable analysis, difference in mortality among patients with varying travel distance was attenuated by adjustment for procedure volume. However, longest travel distance was still associated with a 77% lower 30-day mortality rate than shortest travel among rural patients, even when accounting for procedure volume. Our large national study found that the beneficial effect of longer travel distance on mortality after pancreatectomy is mainly attributable to increase in procedure volume. However, it can have additional benefits on rural patients that are not explained by volume. Distance can represent a surrogate for rural populations. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Cultural interaction and biological distance in postclassic period Mexico.

PubMed

Ragsdale, Corey S; Edgar, Heather J H

2015-05-01

Economic, political, and cultural relationships connected virtually every population throughout Mexico during Postclassic period (AD 900-1520). Much of what is known about population interaction in prehistoric Mexico is based on archaeological or ethnohistoric data. What is unclear, especially for the Postclassic period, is how these data correlate with biological population structure. We address this by assessing biological (phenotypic) distances among 28 samples based upon a comparison of dental morphology trait frequencies, which serve as a proxy for genetic variation, from 810 individuals. These distances were compared with models representing geographic and cultural relationships among the same groups. Results of Mantel and partial Mantel matrix correlation tests show that shared migration and trade are correlated with biological distances, but geographic distance is not. Trade and political interaction are also correlated with biological distance when combined in a single matrix. These results indicate that trade and political relationships affected population structure among Postclassic Mexican populations. We suggest that trade likely played a major role in shaping patterns of interaction between populations. This study also shows that the biological distance data support the migration histories described in ethnohistoric sources. © 2015 Wiley Periodicals, Inc.

Decoding and optimized implementation of SECDED codes over GF(q)

DOEpatents

Ward, H. Lee; Ganti, Anand; Resnick, David R

2013-10-22

A plurality of columns for a check matrix that implements a distance d linear error correcting code are populated by providing a set of vectors from which to populate the columns, and applying to the set of vectors a filter operation that reduces the set by eliminating therefrom all vectors that would, if used to populate the columns, prevent the check matrix from satisfying a column-wise linear independence requirement associated with check matrices of distance d linear codes. One of the vectors from the reduced set may then be selected to populate one of the columns. The filtering and selecting repeats iteratively until either all of the columns are populated or the number of currently unpopulated columns exceeds the number of vectors in the reduced set. Columns for the check matrix may be processed to reduce the amount of logic needed to implement the check matrix in circuit logic.

Design, decoding and optimized implementation of SECDED codes over GF(q)

DOEpatents

Ward, H Lee; Ganti, Anand; Resnick, David R

2014-06-17

A plurality of columns for a check matrix that implements a distance d linear error correcting code are populated by providing a set of vectors from which to populate the columns, and applying to the set of vectors a filter operation that reduces the set by eliminating therefrom all vectors that would, if used to populate the columns, prevent the check matrix from satisfying a column-wise linear independence requirement associated with check matrices of distance d linear codes. One of the vectors from the reduced set may then be selected to populate one of the columns. The filtering and selecting repeats iteratively until either all of the columns are populated or the number of currently unpopulated columns exceeds the number of vectors in the reduced set. Columns for the check matrix may be processed to reduce the amount of logic needed to implement the check matrix in circuit logic.

Decoding and optimized implementation of SECDED codes over GF(q)

DOEpatents

Ward, H Lee; Ganti, Anand; Resnick, David R

2014-11-18

A plurality of columns for a check matrix that implements a distance d linear error correcting code are populated by providing a set of vectors from which to populate the columns, and applying to the set of vectors a filter operation that reduces the set by eliminating therefrom all vectors that would, if used to populate the columns, prevent the check matrix from satisfying a column-wise linear independence requirement associated with check matrices of distance d linear codes. One of the vectors from the reduced set may then be selected to populate one of the columns. The filtering and selecting repeats iteratively until either all of the columns are populated or the number of currently unpopulated columns exceeds the number of vectors in the reduced set. Columns for the check matrix may be processed to reduce the amount of logic needed to implement the check matrix in circuit logic.

Generating an Empirical Probability Distribution for the Andrews-Pregibon Statistic.

ERIC Educational Resources Information Center

Jarrell, Michele G.

A probability distribution was developed for the Andrews-Pregibon (AP) statistic. The statistic, developed by D. F. Andrews and D. Pregibon (1978), identifies multivariate outliers. It is a ratio of the determinant of the data matrix with an observation deleted to the determinant of the entire data matrix. Although the AP statistic has been used…

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Multivariate Cryptography Based on Clipped Hopfield Neural Network.

PubMed

Wang, Jia; Cheng, Lee-Ming; Su, Tong

2018-02-01

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

Synthesis and crystalline properties of CdS incorporated polyvinylidene fluoride (PVDF) composite film

NASA Astrophysics Data System (ADS)

Patel, Arunendra Kumar; Sunder, Aishwarya; Mishra, Shweta; Bajpai, Rakesh

2018-05-01

This paper gives an insight on the synthesis and crystalline properties of Polyvinylidene Fluoride (PVDF) (host matrix) composites impregnated with Cadmium Sulphide (CdS) using Dimethyl formamide (DMF) as the base, prepared by the well known solvent casting technique. The effect of doping concentration of CdS in to the PVDF matrix was studied using X-ray diffraction technique. The structural properties like crystallinity Cr, interplanar distance d, average size of the crystalline region (D), and average inter crystalline separation (R) have been estimated for the developed composite. The crystallinity index, crystallite size and inter crystalline separation is increasing with increase in the concentration of CdS in to the PVDF matrix while the interplanar distance d is decreasing.

The difference between two random mixed quantum states: exact and asymptotic spectral analysis

NASA Astrophysics Data System (ADS)

Mejía, José; Zapata, Camilo; Botero, Alonso

2017-01-01

We investigate the spectral statistics of the difference of two density matrices, each of which is independently obtained by partially tracing a random bipartite pure quantum state. We first show how a closed-form expression for the exact joint eigenvalue probability density function for arbitrary dimensions can be obtained from the joint probability density function of the diagonal elements of the difference matrix, which is straightforward to compute. Subsequently, we use standard results from free probability theory to derive a relatively simple analytic expression for the asymptotic eigenvalue density (AED) of the difference matrix ensemble, and using Carlson’s theorem, we obtain an expression for its absolute moments. These results allow us to quantify the typical asymptotic distance between the two random mixed states using various distance measures; in particular, we obtain the almost sure asymptotic behavior of the operator norm distance and the trace distance.

Adomian decomposition

NASA Astrophysics Data System (ADS)

Daftardar-Gejji, Varsha; Jafari, Hossein

2005-01-01

Adomian decomposition method has been employed to obtain solutions of a system of fractional differential equations. Convergence of the method has been discussed with some illustrative examples. In particular, for the initial value problem: where A=[aij] is a real square matrix, the solution turns out to be , where E([alpha]1,...,[alpha]n),1 denotes multivariate Mittag-Leffler function defined for matrix arguments and Ai is the matrix having ith row as [ai1...ain], and all other entries are zero. Fractional oscillation and Bagley-Torvik equations are solved as illustrative examples.

Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

ERIC Educational Resources Information Center

Gisburne, Jaclyn M.; Fairchild, Patricia J.

2004-01-01

This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

Outlier Detection in Hyperspectral Imagery Using Closest Distance to Center with Ellipsoidal Multivariate Trimming

DTIC Science & Technology

2011-01-01

where r << P. The use of PCA for finding outliers in multivariate data is surveyed by Gnanadesikan and Kettenring16 and Rao.17 As alluded to earlier...1984. 16. Gnanadesikan R and Kettenring JR. Robust estimates, residu­ als, and outlier detection with multiresponse data. Biometrics 1972; 28: 81–124

Probing the smearing effect by a pointlike graviton in the plane-wave matrix model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Bum-Hoon; Nam, Siyoung; Shin, Hyeonjoon

2010-08-15

We investigate the interaction between a flat membrane and pointlike graviton in the plane-wave matrix model. The one-loop effective potential in the large-distance limit is computed and is shown to be of r{sup -3} type where r is the distance between two objects. This type of interaction has been interpreted as the one incorporating the smearing effect due to the configuration of a flat membrane in a plane-wave background. Our results support this interpretation and provide more evidence about it.

The Matrix Element Method: Past, Present, and Future

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gainer, James S.; Lykken, Joseph; Matchev, Konstantin T.

2013-07-12

The increasing use of multivariate methods, and in particular the Matrix Element Method (MEM), represents a revolution in experimental particle physics. With continued exponential growth in computing capabilities, the use of sophisticated multivariate methods-- already common-- will soon become ubiquitous and ultimately almost compulsory. While the existence of sophisticated algorithms for disentangling signal and background might naively suggest a diminished role for theorists, the use of the MEM, with its inherent connection to the calculation of differential cross sections will benefit from collaboration between theorists and experimentalists. In this white paper, we will briefly describe the MEM and some ofmore » its recent uses, note some current issues and potential resolutions, and speculate about exciting future opportunities.« less

Real-Time Parameter Estimation Method Applied to a MIMO Process and its Comparison with an Offline Identification Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaplanoglu, Erkan; Safak, Koray K.; Varol, H. Selcuk

2009-01-12

An experiment based method is proposed for parameter estimation of a class of linear multivariable systems. The method was applied to a pressure-level control process. Experimental time domain input/output data was utilized in a gray-box modeling approach. Prior knowledge of the form of the system transfer function matrix elements is assumed to be known. Continuous-time system transfer function matrix parameters were estimated in real-time by the least-squares method. Simulation results of experimentally determined system transfer function matrix compare very well with the experimental results. For comparison and as an alternative to the proposed real-time estimation method, we also implemented anmore » offline identification method using artificial neural networks and obtained fairly good results. The proposed methods can be implemented conveniently on a desktop PC equipped with a data acquisition board for parameter estimation of moderately complex linear multivariable systems.« less

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from whichmore » unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.« less

Multivariate approach to matrimonial mobility in Catalonia.

PubMed

Calafell, F; Hernández, M

1993-10-01

Matrimonial mobility in Catalonia was studied using 1986 census data. Comarca (a geographic division) of birth was used as the population unit, and a measure of affinity (a statistical distance) between comarques in spouse geographic origin was defined. This distance was analyzed with multivariate methods drawn from numerical taxonomy to detect any discontinuities in matrimonial mobility and gene flow between comarques. Results show a three-level pattern of gene flow in Catalonia: (1) a strong endogamy within comarques; (2) a 100-km matrimonial circle around every comarca; and (3) the capital, Barcelona, which attracts migrants from all over Catalonia. The regionalization in matrimonial mobility follows the geographically clear-cut groups of comarques almost exactly.

Opportunity potential matrix for Atlantic Canadians

Treesearch

Greg Danchuk; Ed Thomson

1992-01-01

Opportunity for provision of Parks Service benefit to Atlantic Canadians was investigated by mapping travel behaviour into a matrix in terms of origin, season, purpose, distance, time, and destination. Findings identified potential for benefit in several activity areas, particularly within residents' own province.

Double-β decay matrix elements from lattice quantum chromodynamics

NASA Astrophysics Data System (ADS)

Tiburzi, Brian C.; Wagman, Michael L.; Winter, Frank; Chang, Emmanuel; Davoudi, Zohreh; Detmold, William; Orginos, Kostas; Savage, Martin J.; Shanahan, Phiala E.; Nplqcd Collaboration

2017-09-01

A lattice quantum chromodynamics (LQCD) calculation of the nuclear matrix element relevant to the n n →p p e e ν¯eν¯e transition is described in detail, expanding on the results presented in Ref. [P. E. Shanahan et al., Phys. Rev. Lett. 119, 062003 (2017), 10.1103/PhysRevLett.119.062003]. This matrix element, which involves two insertions of the weak axial current, is an important input for phenomenological determinations of double-β decay rates of nuclei. From this exploratory study, performed using unphysical values of the quark masses, the long-distance deuteron-pole contribution to the matrix element is separated from shorter-distance hadronic contributions. This polarizability, which is only accessible in double-weak processes, cannot be constrained from single-β decay of nuclei, and is found to be smaller than the long-distance contributions in this calculation, but non-negligible. In this work, technical aspects of the LQCD calculations, and of the relevant formalism in the pionless effective field theory, are described. Further calculations of the isotensor axial polarizability, in particular near and at the physical values of the light-quark masses, are required for precise determinations of both two-neutrino and neutrinoless double-β decay rates in heavy nuclei.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding

NASA Astrophysics Data System (ADS)

Kang, Shouqiang; Ma, Danyang; Wang, Yujing; Lan, Chaofeng; Chen, Qingguo; Mikulovich, V. I.

2017-03-01

To effectively assess different fault locations and different degrees of performance degradation of a rolling bearing with a unified assessment index, a novel state assessment method based on the relative compensation distance of multiple-domain features and locally linear embedding is proposed. First, for a single-sample signal, time-domain and frequency-domain indexes can be calculated for the original vibration signal and each sensitive intrinsic mode function obtained by improved ensemble empirical mode decomposition, and the singular values of the sensitive intrinsic mode function matrix can be extracted by singular value decomposition to construct a high-dimensional hybrid-domain feature vector. Second, a feature matrix can be constructed by arranging each feature vector of multiple samples, the dimensions of each row vector of the feature matrix can be reduced by the locally linear embedding algorithm, and the compensation distance of each fault state of the rolling bearing can be calculated using the support vector machine. Finally, the relative distance between different fault locations and different degrees of performance degradation and the normal-state optimal classification surface can be compensated, and on the basis of the proposed relative compensation distance, the assessment model can be constructed and an assessment curve drawn. Experimental results show that the proposed method can effectively assess different fault locations and different degrees of performance degradation of the rolling bearing under certain conditions.

Short-distance matrix elements for $D$-meson mixing for 2+1 lattice QCD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Chia Cheng

2015-01-01

We study the short-distance hadronic matrix elements for D-meson mixing with partially quenched N f = 2+1 lattice QCD. We use a large set of the MIMD Lattice Computation Collaboration's gauge configurations with a 2 tadpole-improved staggered sea quarks and tadpole-improved Lüscher-Weisz gluons. We use the a 2 tadpole-improved action for valence light quarks and the Sheikoleslami-Wohlert action with the Fermilab interpretation for the valence charm quark. Our calculation covers the complete set of five operators needed to constrain new physics models for D-meson mixing. We match our matrix elements to the MS-NDR scheme evaluated at 3 GeV. We reportmore » values for the Beneke-Buchalla-Greub-Lenz-Nierste choice of evanescent operators.« less

IVisTMSA: Interactive Visual Tools for Multiple Sequence Alignments.

PubMed

Pervez, Muhammad Tariq; Babar, Masroor Ellahi; Nadeem, Asif; Aslam, Naeem; Naveed, Nasir; Ahmad, Sarfraz; Muhammad, Shah; Qadri, Salman; Shahid, Muhammad; Hussain, Tanveer; Javed, Maryam

2015-01-01

IVisTMSA is a software package of seven graphical tools for multiple sequence alignments. MSApad is an editing and analysis tool. It can load 409% more data than Jalview, STRAP, CINEMA, and Base-by-Base. MSA comparator allows the user to visualize consistent and inconsistent regions of reference and test alignments of more than 21-MB size in less than 12 seconds. MSA comparator is 5,200% efficient and more than 40% efficient as compared to BALiBASE c program and FastSP, respectively. MSA reconstruction tool provides graphical user interfaces for four popular aligners and allows the user to load several sequence files at a time. FASTA generator converts seven formats of alignments of unlimited size into FASTA format in a few seconds. MSA ID calculator calculates identity matrix of more than 11,000 sequences with a sequence length of 2,696 base pairs in less than 100 seconds. Tree and Distance Matrix calculation tools generate phylogenetic tree and distance matrix, respectively, using neighbor joining% identity and BLOSUM 62 matrix.

A novel edge-preserving nonnegative matrix factorization method for spectral unmixing

NASA Astrophysics Data System (ADS)

Bao, Wenxing; Ma, Ruishi

2015-12-01

Spectral unmixing technique is one of the key techniques to identify and classify the material in the hyperspectral image processing. A novel robust spectral unmixing method based on nonnegative matrix factorization(NMF) is presented in this paper. This paper used an edge-preserving function as hypersurface cost function to minimize the nonnegative matrix factorization. To minimize the hypersurface cost function, we constructed the updating functions for signature matrix of end-members and abundance fraction respectively. The two functions are updated alternatively. For evaluation purpose, synthetic data and real data have been used in this paper. Synthetic data is used based on end-members from USGS digital spectral library. AVIRIS Cuprite dataset have been used as real data. The spectral angle distance (SAD) and abundance angle distance(AAD) have been used in this research for assessment the performance of proposed method. The experimental results show that this method can obtain more ideal results and good accuracy for spectral unmixing than present methods.

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

PubMed

Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

New robust bilinear least squares method for the analysis of spectral-pH matrix data.

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C

2005-07-01

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.

Multivariate geometry as an approach to algal community analysis

USGS Publications Warehouse

Allen, T.F.H.; Skagen, S.

1973-01-01

Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

thoroughly in the ecology literature. Permutation Multivariate Analysis of Variance ( PerMANOVA ). We used PerMANOVA to test the null-hypothesis of no...difference between the bacterial communities found within a single wound compared to those from different patients (α = 0.05). PerMANOVA is a...permutation-based version of the multivariate analysis of variance (MANOVA). PerMANOVA uses the distances between samples to partition variance and

Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research.

PubMed

Graffelman, Jan; van Eeuwijk, Fred

2005-12-01

The scatter plot is a well known and easily applicable graphical tool to explore relationships between two quantitative variables. For the exploration of relations between multiple variables, generalisations of the scatter plot are useful. We present an overview of multivariate scatter plots focussing on the following situations. Firstly, we look at a scatter plot for portraying relations between quantitative variables within one data matrix. Secondly, we discuss a similar plot for the case of qualitative variables. Thirdly, we describe scatter plots for the relationships between two sets of variables where we focus on correlations. Finally, we treat plots of the relationships between multiple response and predictor variables, focussing on the matrix of regression coefficients. We will present both known and new results, where an important original contribution concerns a procedure for the inclusion of scales for the variables in multivariate scatter plots. We provide software for drawing such scales. We illustrate the construction and interpretation of the plots by means of examples on data collected in a genomic research program on taste in tomato.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Tensor manifold-based extreme learning machine for 2.5-D face recognition

NASA Astrophysics Data System (ADS)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Effects of landscape matrix on population connectivity of an arboreal mammal, Petaurus breviceps.

PubMed

Malekian, Mansoureh; Cooper, Steven J B; Saint, Kathleen M; Lancaster, Melanie L; Taylor, Andrea C; Carthew, Susan M

2015-09-01

Ongoing habitat loss and fragmentation is considered a threat to biodiversity as it can create small, isolated populations that are at increased risk of extinction. Tree-dependent species are predicted to be highly sensitive to forest and woodland loss and fragmentation, but few studies have tested the influence of different types of landscape matrix on gene flow and population structure of arboreal species. Here, we examine the effects of landscape matrix on population structure of the sugar glider (Petaurus breviceps) in a fragmented landscape in southeastern South Australia. We collected 250 individuals across 12 native Eucalyptus forest remnants surrounded by cleared agricultural land or exotic Pinus radiata plantations and a large continuous eucalypt forest. Fifteen microsatellite loci were genotyped and analyzed to infer levels of population differentiation and dispersal. Genetic differentiation among most forest patches was evident. We found evidence for female philopatry and restricted dispersal distances for females relative to males, suggesting there is male-biased dispersal. Among the environmental variables, spatial variables including geographic location, minimum distance to neighboring patch, and degree of isolation were the most important in explaining genetic variation. The permeability of a cleared agricultural matrix to dispersing gliders was significantly higher than that of a pine matrix, with the gliders dispersing shorter distances across the latter. Our results added to previous findings for other species of restricted dispersal and connectivity due to habitat fragmentation in the same region, providing valuable information for the development of strategies to improve the connectivity of populations in the future.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

A comparison of likelihood ratio tests and Rao's score test for three separable covariance matrix structures.

PubMed

Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha

2017-01-01

The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Monte Carlo simulations are then used to study the comparative behavior of the null distribution of the RST statistic, as well as that of the LRT statistic, in terms of sample size considerations, and for the estimation of the empirical percentiles. Our findings are compared with existing results where the first component of the separable structure is a compound symmetry (CS) correlation matrix. It is also shown by simulations that the empirical null distribution of the RST statistic converges faster than the empirical null distribution of the LRT statistic to the limiting χ 2 distribution. The tests are implemented on a real dataset from medical studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

NASA Astrophysics Data System (ADS)

Qin, Xulei; Cong, Zhibin; Fei, Baowei

2013-11-01

An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

Predicting the required number of training samples. [for remotely sensed image data based on covariance matrix estimate quality criterion of normal distribution

NASA Technical Reports Server (NTRS)

Kalayeh, H. M.; Landgrebe, D. A.

1983-01-01

A criterion which measures the quality of the estimate of the covariance matrix of a multivariate normal distribution is developed. Based on this criterion, the necessary number of training samples is predicted. Experimental results which are used as a guide for determining the number of training samples are included. Previously announced in STAR as N82-28109

Sequential design of discrete linear quadratic regulators via optimal root-locus techniques

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Yates, Robert E.; Ganesan, Sekar

1989-01-01

A sequential method employing classical root-locus techniques has been developed in order to determine the quadratic weighting matrices and discrete linear quadratic regulators of multivariable control systems. At each recursive step, an intermediate unity rank state-weighting matrix that contains some invariant eigenvectors of that open-loop matrix is assigned, and an intermediate characteristic equation of the closed-loop system containing the invariant eigenvalues is created.

Structural analysis and design of multivariable control systems: An algebraic approach

NASA Technical Reports Server (NTRS)

Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

1988-01-01

The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

A Note on Asymptotic Joint Distribution of the Eigenvalues of a Noncentral Multivariate F Matrix.

DTIC Science & Technology

1984-11-01

Krishnaiah (1982). Now, let us consider the samples drawn from the k multivariate normal popuiejons. Let (Xlt....Xpt) denote the mean vector of the t...to maltivariate problems. Sankh-ya, 4, 381-39(s. (71 KRISHNAIAH , P. R. (1982). Selection of variables in discrimlnant analysis. In Handbook of...Statistics, Volume 2 (P. R. Krishnaiah , editor), 805-820. North-Holland Publishing Company. 6. Unclassifie INSTRUCTIONS REPORT DOCUMENTATION PAGE

A way around the Nyquist lag

NASA Astrophysics Data System (ADS)

Penland, C.

2017-12-01

One way to test for the linearity of a multivariate system is to perform Linear Inverse Modeling (LIM) to a multivariate time series. LIM yields an estimated operator by combining a lagged covariance matrix with the contemporaneous covariance matrix. If the underlying dynamics is linear, the resulting dynamical description should not depend on the particular lag at which the lagged covariance matrix is estimated. This test is known as the "tau test." The tau test will be severely compromised if the lag at which the analysis is performed is approximately half the period of an internal oscillation frequency. In this case, the tau test will fail even though the dynamics are actually linear. Thus, until now, the tau test has only been possible for lags smaller than this "Nyquist lag." In this poster, we investigate the use of Hilbert transforms as a way to avoid the problems associated with Nyquist lags. By augmenting the data with dimensions orthogonal to those spanning the original system, information that would be inaccessible to LIM in its original form may be sampled.

Direct calculation of modal parameters from matrix orthogonal polynomials

NASA Astrophysics Data System (ADS)

El-Kafafy, Mahmoud; Guillaume, Patrick

2011-10-01

The object of this paper is to introduce a new technique to derive the global modal parameter (i.e. system poles) directly from estimated matrix orthogonal polynomials. This contribution generalized the results given in Rolain et al. (1994) [5] and Rolain et al. (1995) [6] for scalar orthogonal polynomials to multivariable (matrix) orthogonal polynomials for multiple input multiple output (MIMO) system. Using orthogonal polynomials improves the numerical properties of the estimation process. However, the derivation of the modal parameters from the orthogonal polynomials is in general ill-conditioned if not handled properly. The transformation of the coefficients from orthogonal polynomials basis to power polynomials basis is known to be an ill-conditioned transformation. In this paper a new approach is proposed to compute the system poles directly from the multivariable orthogonal polynomials. High order models can be used without any numerical problems. The proposed method will be compared with existing methods (Van Der Auweraer and Leuridan (1987) [4] Chen and Xu (2003) [7]). For this comparative study, simulated as well as experimental data will be used.

A multivariate assessment of changes in wetland habitat for waterbirds at Moosehorn National Wildlife Refuge, Maine, USA

USGS Publications Warehouse

Hierl, L.A.; Loftin, C.S.; Longcore, J.R.; McAuley, D.G.; Urban, D.L.

2007-01-01

We assessed changes in vegetative structure of 49 impoundments at Moosehorn National Wildlife Refuge (MNWR), Maine, USA, between the periods 1984-1985 to 2002 with a multivariate, adaptive approach that may be useful in a variety of wetland and other habitat management situations. We used Mahalanobis Distance (MD) analysis to classify the refuge?s wetlands as poor or good waterbird habitat based on five variables: percent emergent vegetation, percent shrub, percent open water, relative richness of vegetative types, and an interspersion juxtaposition index that measures adjacency of vegetation patches. Mahalanobis Distance is a multivariate statistic that examines whether a particular data point is an outlier or a member of a data cluster while accounting for correlations among inputs. For each wetland, we used MD analysis to quantify a distance from a reference condition defined a priori by habitat conditions measured in MNWR wetlands used by waterbirds. Twenty-five wetlands declined in quality between the two periods, whereas 23 wetlands improved. We identified specific wetland characteristics that may be modified to improve habitat conditions for waterbirds. The MD analysis seems ideal for instituting an adaptive wetland management approach because metrics can be easily added or removed, ranges of target habitat conditions can be defined by field-collected data, and the analysis can identify priorities for single or multiple management objectives.

Correction of spin diffusion during iterative automated NOE assignment

NASA Astrophysics Data System (ADS)

Linge, Jens P.; Habeck, Michael; Rieping, Wolfgang; Nilges, Michael

2004-04-01

Indirect magnetization transfer increases the observed nuclear Overhauser enhancement (NOE) between two protons in many cases, leading to an underestimation of target distances. Wider distance bounds are necessary to account for this error. However, this leads to a loss of information and may reduce the quality of the structures generated from the inter-proton distances. Although several methods for spin diffusion correction have been published, they are often not employed to derive distance restraints. This prompted us to write a user-friendly and CPU-efficient method to correct for spin diffusion that is fully integrated in our program ambiguous restraints for iterative assignment (ARIA). ARIA thus allows automated iterative NOE assignment and structure calculation with spin diffusion corrected distances. The method relies on numerical integration of the coupled differential equations which govern relaxation by matrix squaring and sparse matrix techniques. We derive a correction factor for the distance restraints from calculated NOE volumes and inter-proton distances. To evaluate the impact of our spin diffusion correction, we tested the new calibration process extensively with data from the Pleckstrin homology (PH) domain of Mus musculus β-spectrin. By comparing structures refined with and without spin diffusion correction, we show that spin diffusion corrected distance restraints give rise to structures of higher quality (notably fewer NOE violations and a more regular Ramachandran map). Furthermore, spin diffusion correction permits the use of tighter error bounds which improves the distinction between signal and noise in an automated NOE assignment scheme.

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

Multivariate analysis of nystatin and metronidazole in a semi-solid matrix by means of diffuse reflectance NIR spectroscopy and PLS regression.

PubMed

Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A

2006-01-23

A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.

Non-fragile multivariable PID controller design via system augmentation

NASA Astrophysics Data System (ADS)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Research on propagation properties of controllable hollow flat-topped beams in turbulent atmosphere based on ABCD matrix

NASA Astrophysics Data System (ADS)

Liu, Huilong; Lü, Yanfei; Zhang, Jing; Xia, Jing; Pu, Xiaoyun; Dong, Yuan; Li, Shutao; Fu, Xihong; Zhang, Angfeng; Wang, Changjia; Tan, Yong; Zhang, Xihe

2015-01-01

This paper studies the propagation properties of controllable hollow flat-topped beams (CHFBs) in turbulent atmosphere based on ABCD matrix, sets up a propagation model and obtains an analytical expression for the propagation. With the help of numerical simulation, the propagation properties of CHFBs in different parameters are studied. Results indicate that in turbulent atmosphere, with the increase of propagation distance, the darkness of CHFBs gradually annihilate, and eventually evolve into Gaussian beams. Compared with the propagation properties in free space, the turbulent atmosphere enhances the diffraction effect of CHFBs and reduces the propagation distance for CHFBs to evolve into Gaussian beams. In strong turbulence atmospheric propagation, Airy disk phenomenon will disappear. The study on the propagation properties of CHFBs in turbulence atmosphere by using ABCD matrix is simple and convenient. This method can also be applied to study the propagation properties of other hollow laser beams in turbulent atmosphere.

Determination of matrix composition based on solute-solute nearest-neighbor distances in atom probe tomography.

PubMed

De Geuser, F; Lefebvre, W

2011-03-01

In this study, we propose a fast automatic method providing the matrix concentration in an atom probe tomography (APT) data set containing two phases or more. The principle of this method relies on the calculation of the relative amount of isolated solute atoms (i.e., not surrounded by a similar solute atom) as a function of a distance d in the APT reconstruction. Simulated data sets have been generated to test the robustness of this new tool and demonstrate that rapid and reproducible results can be obtained without the need of any user input parameter. The method has then been successfully applied to a ternary Al-Zn-Mg alloy containing a fine dispersion of hardening precipitates. The relevance of this method for direct estimation of matrix concentration is discussed and compared with the existing methodologies. Copyright © 2010 Wiley-Liss, Inc.

Morphological parameters associated with ruptured posterior communicating aneurysms.

PubMed

Ho, Allen; Lin, Ning; Charoenvimolphan, Nareerat; Stanley, Mary; Frerichs, Kai U; Day, Arthur L; Du, Rose

2014-01-01

The rupture risk of unruptured intracranial aneurysms is known to be dependent on the size of the aneurysm. However, the association of morphological characteristics with ruptured aneurysms has not been established in a systematic and location specific manner for the most common aneurysm locations. We evaluated posterior communicating artery (PCoA) aneurysms for morphological parameters associated with aneurysm rupture in that location. CT angiograms were evaluated to generate 3-D models of the aneurysms and surrounding vasculature. Univariate and multivariate analyses were performed to evaluate morphological parameters including aneurysm volume, aspect ratio, size ratio, distance to ICA bifurcation, aneurysm angle, vessel angles, flow angles, and vessel-to-vessel angles. From 2005-2012, 148 PCoA aneurysms were treated in a single institution. Preoperative CTAs from 63 patients (40 ruptured, 23 unruptured) were available and analyzed. Multivariate logistic regression revealed that smaller volume (p = 0.011), larger aneurysm neck diameter (0.048), and shorter ICA bifurcation to aneurysm distance (p = 0.005) were the most strongly associated with aneurysm rupture after adjusting for all other clinical and morphological variables. Multivariate subgroup analysis for patients with visualized PCoA demonstrated that larger neck diameter (p = 0.018) and shorter ICA bifurcation to aneurysm distance (p = 0.011) were significantly associated with rupture. Intracerebral hemorrhage was associated with smaller volume, larger maximum height, and smaller aneurysm angle, in addition to lateral projection, male sex, and lack of hypertension. We found that shorter ICA bifurcation to aneurysm distance is significantly associated with PCoA aneurysm rupture. This is a new physically intuitive parameter that can be measured easily and therefore be readily applied in clinical practice to aid in the evaluation of patients with PCoA aneurysms.

Morphological Parameters Associated with Ruptured Posterior Communicating Aneurysms

PubMed Central

Ho, Allen; Lin, Ning; Charoenvimolphan, Nareerat; Stanley, Mary; Frerichs, Kai U.; Day, Arthur L.; Du, Rose

2014-01-01

The rupture risk of unruptured intracranial aneurysms is known to be dependent on the size of the aneurysm. However, the association of morphological characteristics with ruptured aneurysms has not been established in a systematic and location specific manner for the most common aneurysm locations. We evaluated posterior communicating artery (PCoA) aneurysms for morphological parameters associated with aneurysm rupture in that location. CT angiograms were evaluated to generate 3-D models of the aneurysms and surrounding vasculature. Univariate and multivariate analyses were performed to evaluate morphological parameters including aneurysm volume, aspect ratio, size ratio, distance to ICA bifurcation, aneurysm angle, vessel angles, flow angles, and vessel-to-vessel angles. From 2005–2012, 148 PCoA aneurysms were treated in a single institution. Preoperative CTAs from 63 patients (40 ruptured, 23 unruptured) were available and analyzed. Multivariate logistic regression revealed that smaller volume (p = 0.011), larger aneurysm neck diameter (0.048), and shorter ICA bifurcation to aneurysm distance (p = 0.005) were the most strongly associated with aneurysm rupture after adjusting for all other clinical and morphological variables. Multivariate subgroup analysis for patients with visualized PCoA demonstrated that larger neck diameter (p = 0.018) and shorter ICA bifurcation to aneurysm distance (p = 0.011) were significantly associated with rupture. Intracerebral hemorrhage was associated with smaller volume, larger maximum height, and smaller aneurysm angle, in addition to lateral projection, male sex, and lack of hypertension. We found that shorter ICA bifurcation to aneurysm distance is significantly associated with PCoA aneurysm rupture. This is a new physically intuitive parameter that can be measured easily and therefore be readily applied in clinical practice to aid in the evaluation of patients with PCoA aneurysms. PMID:24733151

VizieR Online Data Catalog: Outliers and similarity in APOGEE (Reis+, 2018)

NASA Astrophysics Data System (ADS)

Reis, I.; Poznanski, D.; Baron, D.; Zasowski, G.; Shahaf, S.

2017-11-01

t-SNE is a dimensionality reduction algorithm that is particularly well suited for the visualization of high-dimensional datasets. We use t-SNE to visualize our distance matrix. A-priori, these distances could define a space with almost as many dimensions as objects, i.e., tens of thousand of dimensions. Obviously, since many stars are quite similar, and their spectra are defined by a few physical parameters, the minimal spanning space might be smaller. By using t-SNE we can examine the structure of our sample projected into 2D. We use our distance matrix as input to the t-SNE algorithm and in return get a 2D map of the objects in our dataset. For each star in a sample of 183232 APOGEE stars, the APOGEE IDs of the 99 stars with most similar spectra (according to the method described in paper), ordered by similarity. (3 data files).

A novel three-stage distance-based consensus ranking method

NASA Astrophysics Data System (ADS)

Aghayi, Nazila; Tavana, Madjid

2018-05-01

In this study, we propose a three-stage weighted sum method for identifying the group ranks of alternatives. In the first stage, a rank matrix, similar to the cross-efficiency matrix, is obtained by computing the individual rank position of each alternative based on importance weights. In the second stage, a secondary goal is defined to limit the vector of weights since the vector of weights obtained in the first stage is not unique. Finally, in the third stage, the group rank position of alternatives is obtained based on a distance of individual rank positions. The third stage determines a consensus solution for the group so that the ranks obtained have a minimum distance from the ranks acquired by each alternative in the previous stage. A numerical example is presented to demonstrate the applicability and exhibit the efficacy of the proposed method and algorithms.

Wave bandgap formation and its evolution in two-dimensional phononic crystals composed of rubber matrix with periodic steel quarter-cylinders

NASA Astrophysics Data System (ADS)

Li, Peng; Wang, Guan; Luo, Dong; Cao, Xiaoshan

2018-02-01

The band structure of a two-dimensional phononic crystal, which is composed of four homogenous steel quarter-cylinders immersed in rubber matrix, is investigated and compared with the traditional steel/rubber crystal by the finite element method (FEM). It is revealed that the frequency can then be tuned by changing the distance between adjacent quarter-cylinders. When the distance is relatively small, the integrality of scatterers makes the inner region inside them almost motionless, so that they can be viewed as a whole at high-frequencies. In the case of relatively larger distance, the interaction between each quarter-cylinder and rubber will introduce some new bandgaps at relatively low-frequencies. Lastly, the point defect states induced by the four quarter-cylinders are revealed. These results will be helpful in fabricating devices, such as vibration insulators and acoustic/elastic filters, whose band frequencies can be manipulated artificially.

Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric.

PubMed

Lee, Hyekyoung; Chung, Moo K; Kang, Hyejin; Kim, Boong-Nyun; Lee, Dong Soo

2011-01-01

The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.

Whitby Mudstone, flow from matrix to fractures

NASA Astrophysics Data System (ADS)

Houben, Maartje; Hardebol, Nico; Barnhoorn, Auke; Boersma, Quinten; Peach, Colin; Bertotti, Giovanni; Drury, Martyn

2016-04-01

Fluid flow from matrix to well in shales would be faster if we account for the duality of the permeable medium considering a high permeable fracture network together with a tight matrix. To investigate how long and how far a gas molecule would have to travel through the matrix until it reaches an open connected fracture we investigated the permeability of the Whitby Mudstone (UK) matrix in combination with mapping the fracture network present in the current outcrops of the Whitby Mudstone at the Yorkshire coast. Matrix permeability was measured perpendicular to the bedding using a pressure step decay method on core samples and permeability values are in the microdarcy range. The natural fracture network present in the pavement shows a connected network with dominant NS and EW strikes, where the NS fractures are the main fracture set with an orthogonal fracture set EW. Fracture spacing relations in the pavements show that the average distance to the nearest fracture varies between 7 cm (EW) and 14 cm (NS), where 90% of the matrix is 30 cm away from the nearest fracture. By making some assumptions like; fracture network at depth is similar to what is exposed in the current pavements and open to flow, fracture network is at hydrostatic pressure at 3 km depth, overpressure between matrix and fractures is 10% and a matrix permeability perpendicular to the bedding of 0.1 microdarcy, we have calculated the time it takes for a gas molecule to travel to the nearest fracture. These input values give travel times up to 8 days for a distance of 14 cm. If the permeability is changed to 1 nanodarcy or 10 microdarcy travel times change to 2.2 years or 2 hours respectively.

Specification of matrix cleanup goals in fractured porous media.

PubMed

Rodríguez, David J; Kueper, Bernard H

2013-01-01

Semianalytical transient solutions have been developed to evaluate what level of fractured porous media (e.g., bedrock or clay) matrix cleanup must be achieved in order to achieve compliance of fracture pore water concentrations within a specified time at specified locations of interest. The developed mathematical solutions account for forward and backward diffusion in a fractured porous medium where the initial condition comprises a spatially uniform, nonzero matrix concentration throughout the domain. Illustrative simulations incorporating the properties of mudstone fractured bedrock demonstrate that the time required to reach a desired fracture pore water concentration is a function of the distance between the point of compliance and the upgradient face of the domain where clean groundwater is inflowing. Shorter distances correspond to reduced times required to reach compliance, implying that shorter treatment zones will respond more favorably to remediation than longer treatment zones in which back-diffusion dominates the fracture pore water response. For a specified matrix cleanup goal, compliance of fracture pore water concentrations will be reached sooner for decreased fracture spacing, increased fracture aperture, higher matrix fraction organic carbon, lower matrix porosity, shorter aqueous phase decay half-life, and a higher hydraulic gradient. The parameters dominating the response of the system can be measured using standard field and laboratory techniques. © 2012, The Author(s). Ground Water © 2012, National Ground Water Association.

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation.

PubMed

Geerligs, Linda; Cam-Can; Henson, Richard N

2016-07-15

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on "distance correlation"; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps.

PubMed

Mendoza-Carranza, Manuel; Ejarque, Elisabet; Nagelkerke, Leopold A J

2018-01-01

Tropical small-scale fisheries are typical for providing complex multivariate data, due to their diversity in fishing techniques and highly diverse species composition. In this paper we used for the first time a supervised Self-Organizing Map (xyf-SOM), to recognize and understand the internal heterogeneity of a tropical marine small-scale fishery, using as model the fishery fleet of San Pedro port, Tabasco, Mexico. We used multivariate data from commercial logbooks, including the following four factors: fish species (47), gear types (bottom longline, vertical line+shark longline and vertical line), season (cold, warm), and inter-annual variation (2007-2012). The size of the xyf-SOM, a fundamental characteristic to improve its predictive quality, was optimized for the minimum distance between objects and the maximum prediction rate. The xyf-SOM successfully classified individual fishing trips in relation to the four factors included in the model. Prediction percentages were high (80-100%) for bottom longline and vertical line + shark longline, but lower prediction values were obtained for vertical line (51-74%) fishery. A confusion matrix indicated that classification errors occurred within the same fishing gear. Prediction rates were validated by generating confidence interval using bootstrap. The xyf-SOM showed that not all the fishing trips were targeting the most abundant species and the catch rates were not symmetrically distributed around the mean. Also, the species composition is not homogeneous among fishing trips. Despite the complexity of the data, the xyf-SOM proved to be an excellent tool to identify trends in complex scenarios, emphasizing the diverse and complex patterns that characterize tropical small scale-fishery fleets.

The Evolution of Interfacial Sliding Stresses During Cyclic Push-in Testing of C- and BN-Coated Hi-Nicalon Fiber-Reinforced CMCs

NASA Technical Reports Server (NTRS)

Eldridge, J. I.; Bansal, N. P.; Bhatt, R. T.

1998-01-01

Interfacial debond cracks and fiber/matrix sliding stresses in ceramic matrix composites (CMCs) can evolve under cyclic fatigue conditions as well as with changes in the environment, strongly affecting the crack growth behavior, and therefore, the useful service lifetime of the composite. In this study, room temperature cyclic fiber push-in testing was applied to monitor the evolution of frictional sliding stresses and fiber sliding distances with continued cycling in both C- and BN-coated Hi-Nicalon SiC fiber-reinforced CMCs. A SiC matrix composite reinforced with C-coated Hi-Nical on fibers as well as barium strontium aluminosilicate (BSAS) matrix composites reinforced with BN-coated (four different deposition processes compared) Hi-Nicalon fibers were examined. For failure at a C interface, test results indicated progressive increases in fiber sliding distances during cycling in room air but not in nitrogen. These results suggest the presence of moisture will promote crack growth when interfacial failure occurs at a C interface. While short-term testing environmental effects were not apparent for failure at the BN interfaces, long-term exposure of partially debonded BN-coated fibers to humid air resulted in large increases in fiber sliding distances and decreases in interfacial sliding stresses for all the BN coatings, presumably due to moisture attack. A wide variation was observed in debond and frictional sliding stresses among the different BN coatings.

Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles

NASA Astrophysics Data System (ADS)

Kato, Seiji; Sun-Mack, Sunny; Miller, Walter F.; Rose, Fred G.; Chen, Yan; Minnis, Patrick; Wielicki, Bruce A.

2010-01-01

A cloud frequency of occurrence matrix is generated using merged cloud vertical profiles derived from the satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud profiling radar. The matrix contains vertical profiles of cloud occurrence frequency as a function of the uppermost cloud top. It is shown that the cloud fraction and uppermost cloud top vertical profiles can be related by a cloud overlap matrix when the correlation length of cloud occurrence, which is interpreted as an effective cloud thickness, is introduced. The underlying assumption in establishing the above relation is that cloud overlap approaches random overlap with increasing distance separating cloud layers and that the probability of deviating from random overlap decreases exponentially with distance. One month of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data (July 2006) support these assumptions, although the correlation length sometimes increases with separation distance when the cloud top height is large. The data also show that the correlation length depends on cloud top hight and the maximum occurs when the cloud top height is 8 to 10 km. The cloud correlation length is equivalent to the decorrelation distance introduced by Hogan and Illingworth (2000) when cloud fractions of both layers in a two-cloud layer system are the same. The simple relationships derived in this study can be used to estimate the top-of-atmosphere irradiance difference caused by cloud fraction, uppermost cloud top, and cloud thickness vertical profile differences.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

PubMed Central

Liu, Jingxian; Wu, Kefeng

2017-01-01

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations. PMID:28777353

A time-series approach to dynamical systems from classical and quantum worlds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fossion, Ruben

2014-01-08

This contribution discusses some recent applications of time-series analysis in Random Matrix Theory (RMT), and applications of RMT in the statistial analysis of eigenspectra of correlation matrices of multivariate time series.

Multivariate Analysis of the Visual Information Processing of Numbers

ERIC Educational Resources Information Center

Levine, David M.

1977-01-01

Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)

Multivariate matrix model for source identification of inrush water: A case study from Renlou and Tongting coal mine in northern Anhui province, China

NASA Astrophysics Data System (ADS)

Zhang, Jun; Yao, Duoxi; Su, Yue

2018-02-01

Under the current situation of energy demand, coal is still one of the major energy sources in China for a certain period of time, so the task of coal mine safety production remains arduous. In order to identify the water source of the mine accurately, this article takes the example from Renlou and Tongting coal mines in the northern Anhui mining area. A total of 7 conventional water chemical indexes were selected, including Ca2+, Mg2+, Na++K+, Cl-, SO4 2-, HCO3 - and TDS, to establish a multivariate matrix model for the source identifying inrush water. The results show that the model is simple and is rarely limited by the quantity of water samples, and the recognition effect is ideal, which can be applied to the control and treatment for water inrush.

Multivariable frequency domain identification via 2-norm minimization

NASA Technical Reports Server (NTRS)

Bayard, David S.

1992-01-01

The author develops a computational approach to multivariable frequency domain identification, based on 2-norm minimization. In particular, a Gauss-Newton (GN) iteration is developed to minimize the 2-norm of the error between frequency domain data and a matrix fraction transfer function estimate. To improve the global performance of the optimization algorithm, the GN iteration is initialized using the solution to a particular sequentially reweighted least squares problem, denoted as the SK iteration. The least squares problems which arise from both the SK and GN iterations are shown to involve sparse matrices with identical block structure. A sparse matrix QR factorization method is developed to exploit the special block structure, and to efficiently compute the least squares solution. A numerical example involving the identification of a multiple-input multiple-output (MIMO) plant having 286 unknown parameters is given to illustrate the effectiveness of the algorithm.

Load cell having strain gauges of arbitrary location

DOEpatents

Spletzer, Barry [Albuquerque, NM

2007-03-13

A load cell utilizes a plurality of strain gauges mounted upon the load cell body such that there are six independent load-strain relations. Load is determined by applying the inverse of a load-strain sensitivity matrix to a measured strain vector. The sensitivity matrix is determined by performing a multivariate regression technique on a set of known loads correlated to the resulting strains. Temperature compensation is achieved by configuring the strain gauges as co-located orthogonal pairs.

Sexual dimorphism in the human face assessed by euclidean distance matrix analysis.

PubMed Central

Ferrario, V F; Sforza, C; Pizzini, G; Vogel, G; Miani, A

1993-01-01

The form of any object can be viewed as a combination of size and shape. A recently proposed method (euclidean distance matrix analysis) can differentiate between size and shape differences. It has been applied to analyse the sexual dimorphism in facial form in a sample of 108 healthy young adults (57 men, 51 women). The face was wider and longer in men than in women. A global shape difference was demonstrated, the male face being more rectangular and the female face more square. Gender variations involved especially the lower third of the face and, in particular, the position of the pogonion relative to the other structures. PMID:8300436

Evaluation of dissolution profile similarity - Comparison between the f2, the multivariate statistical distance and the f2 bootstrapping methods.

PubMed

Paixão, Paulo; Gouveia, Luís F; Silva, Nuno; Morais, José A G

2017-03-01

A simulation study is presented, evaluating the performance of the f 2 , the model-independent multivariate statistical distance and the f 2 bootstrap methods in the ability to conclude similarity between two dissolution profiles. Different dissolution profiles, based on the Noyes-Whitney equation and ranging from theoretical f 2 values between 100 and 40, were simulated. Variability was introduced in the dissolution model parameters in an increasing order, ranging from a situation complying with the European guidelines requirements for the use of the f 2 metric to several situations where the f 2 metric could not be used anymore. Results have shown that the f 2 is an acceptable metric when used according to the regulatory requirements, but loses its applicability when variability increases. The multivariate statistical distance presented contradictory results in several of the simulation scenarios, which makes it an unreliable metric for dissolution profile comparisons. The bootstrap f 2 , although conservative in its conclusions is an alternative suitable method. Overall, as variability increases, all of the discussed methods reveal problems that can only be solved by increasing the number of dosage form units used in the comparison, which is usually not practical or feasible. Additionally, experimental corrective measures may be undertaken in order to reduce the overall variability, particularly when it is shown that it is mainly due to the dissolution assessment instead of being intrinsic to the dosage form. Copyright © 2016. Published by Elsevier B.V.

A Forward Search Procedure for Identifying Influential Observations in the Estimation of a Covariance Matrix

ERIC Educational Resources Information Center

Poon, Wai-Yin; Wong, Yuen-Kwan

2004-01-01

This study uses a Cook's distance type diagnostic statistic to identify unusual observations in a data set that unduly influence the estimation of a covariance matrix. Similar to many other deletion-type diagnostic statistics, this proposed measure is susceptible to masking or swamping effect in the presence of several unusual observations. In…

Aluminum/alkaline earth metal composites and method for producing

DOEpatents

Russell, Alan M; Anderson, Iver E; Kim, Hyong J; Freichs, Andrew E

2014-02-11

A composite is provided having an electrically conducting Al matrix and elongated filaments comprising Ca and/or Sr and/or Ba disposed in the matrix and extending along a longitudinal axis of the composite. The filaments initially comprise Ca and/or Sr and/or Ba metal or allow and then may be reacted with the Al matrix to form a strengthening intermetallic compound comprising Al and Ca and/or Sr and/or Ba. The composite is useful as a long-distance, high voltage power transmission conductor.

Empirical performance of the multivariate normal universal portfolio

NASA Astrophysics Data System (ADS)

Tan, Choon Peng; Pang, Sook Theng

2013-09-01

Universal portfolios generated by the multivariate normal distribution are studied with emphasis on the case where variables are dependent, namely, the covariance matrix is not diagonal. The moving-order multivariate normal universal portfolio requires very long implementation time and large computer memory in its implementation. With the objective of reducing memory and implementation time, the finite-order universal portfolio is introduced. Some stock-price data sets are selected from the local stock exchange and the finite-order universal portfolio is run on the data sets, for small finite order. Empirically, it is shown that the portfolio can outperform the moving-order Dirichlet universal portfolio of Cover and Ordentlich[2] for certain parameters in the selected data sets.

Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China).

PubMed

Xue, Jian-long; Zhi, Yu-you; Yang, Li-ping; Shi, Jia-chun; Zeng, Ling-zao; Wu, Lao-sheng

2014-06-01

Chemical compositions of soil samples are multivariate in nature and provide datasets suitable for the application of multivariate factor analytical techniques. One of the analytical techniques, the positive matrix factorization (PMF), uses a weighted least square by fitting the data matrix to determine the weights of the sources based on the error estimates of each data point. In this research, PMF was employed to apportion the sources of heavy metals in 104 soil samples taken within a 1-km radius of a lead battery plant contaminated site in Changxing County, Zhejiang Province, China. The site is heavily contaminated with high concentrations of lead (Pb) and cadmium (Cd). PMF successfully partitioned the variances into sources related to soil background, agronomic practices, and the lead battery plants combined with a geostatistical approach. It was estimated that the lead battery plants and the agronomic practices contributed 55.37 and 29.28%, respectively, for soil Pb of the total source. Soil Cd mainly came from the lead battery plants (65.92%), followed by the agronomic practices (21.65%), and soil parent materials (12.43%). This research indicates that PMF combined with geostatistics is a useful tool for source identification and apportionment.

Factors Influencing Cognitive Function in Subjects With COPD.

PubMed

Dag, Ersel; Bulcun, Emel; Turkel, Yakup; Ekici, Aydanur; Ekici, Mehmet

2016-08-01

The aim of this study was to assess the association between cognitive function and age, pulmonary function, comorbidity index, and the 6-min walk distance in subjects with COPD as well as to compare the Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in terms of their ability to identify cognitive dysfunction in subjects with COPD. A total of 52 individuals with stable COPD were included in this study. Cognitive function was assessed using MMSE and MoCA. Age, body mass index, the Modified Cumulative Illness Rating Scale, 6-min walk distance, arterial blood gases, and pulmonary function tests were assessed and recorded. The range and SD of scores in subjects with COPD were larger with MoCA than with MMSE. MMSE and MoCA scores are associated with 6-min walk distance and comorbidity index in subjects with COPD. General cognitive function measured by MoCA was negatively correlated with the comorbidity index but was positively associated with 6-min walk distance in subjects with COPD after controlling for possible confounding factors in the multivariate model. However, general cognitive function measured by MMSE was not correlated with the comorbidity index and 6-min walk distance in subjects with COPD, after controlling for possible confounding factors in the multivariate model. MoCA may be a more reliable screening test than MMSE in detecting cognitive impairment in subjects with COPD. The addition of cognitive tests on assessment of subjects with COPD can provide further benefit. Copyright © 2016 by Daedalus Enterprises.

Long-distance dispersal of non-native pine bark beetles from host resources

Treesearch

Kevin Chase; Dave Kelly; Andrew M. Liebhold; Martin K.-F. Bader; Eckehard G. Brockerhoff

2017-01-01

Dispersal and host detection are behaviours promoting the spread of invading populations in a landscape matrix. In fragmented landscapes, the spatial arrangement of habitat structure affects the dispersal success of organisms. The aim of the present study was to determine the long distance dispersal capabilities of two non-native pine bark beetles (Hylurgus...

DTI segmentation by statistical surface evolution.

PubMed

Lenglet, Christophe; Rousson, Mikaël; Deriche, Rachid

2006-06-01

We address the problem of the segmentation of cerebral white matter structures from diffusion tensor images (DTI). A DTI produces, from a set of diffusion-weighted MR images, tensor-valued images where each voxel is assigned with a 3 x 3 symmetric, positive-definite matrix. This second order tensor is simply the covariance matrix of a local Gaussian process, with zero-mean, modeling the average motion of water molecules. As we will show in this paper, the definition of a dissimilarity measure and statistics between such quantities is a nontrivial task which must be tackled carefully. We claim and demonstrate that, by using the theoretically well-founded differential geometrical properties of the manifold of multivariate normal distributions, it is possible to improve the quality of the segmentation results obtained with other dissimilarity measures such as the Euclidean distance or the Kullback-Leibler divergence. The main goal of this paper is to prove that the choice of the probability metric, i.e., the dissimilarity measure, has a deep impact on the tensor statistics and, hence, on the achieved results. We introduce a variational formulation, in the level-set framework, to estimate the optimal segmentation of a DTI according to the following hypothesis: Diffusion tensors exhibit a Gaussian distribution in the different partitions. We must also respect the geometric constraints imposed by the interfaces existing among the cerebral structures and detected by the gradient of the DTI. We show how to express all the statistical quantities for the different probability metrics. We validate and compare the results obtained on various synthetic data-sets, a biological rat spinal cord phantom and human brain DTIs.

Eutectic equilibria in the quaternary system Fe-Cr-Mn-C

NASA Technical Reports Server (NTRS)

Nowotny, H.; Wayne, S.; Schuster, J. C.

1982-01-01

The constitution of the quaternary system, Fe-Cr-Mn-C and to a lesser extent of the quinary system, Fe-Cr-Mn-Al-C were examined for in situ composite alloy candidates. Multivariant eutectic compositions were determined from phase equilibria studies wherein M7C3 carbides (approximately 30% by volume) formed from the melt within gamma iron. An extended field of the hexagonal carbide, (Cr, Fe, Mn)7 C3, was found without undergoing transformation to the orthorhombic structure. Increasing stability for this carbide was found for higher ratios of Cr/Fe(+) Cr + Mn. Aluminum additions promoted a ferritic matrix while manganese favored the desired gamma austenitic matrix. In coexistence with the matrix phase, chromium enters preferentially the carbide phase while manganese distributes equally between the gamma matrix and the M7C3 carbide. The composition and lattice parameters of the carbide and matrix phases were determined to establish their respective stabilities.

Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data.

PubMed

Sáez, Carlos; Robles, Montserrat; García-Gómez, Juan Miguel

2013-01-01

Research biobanks are often composed by data from multiple sources. In some cases, these different subsets of data may present dissimilarities among their probability density functions (PDF) due to spatial shifts. This, may lead to wrong hypothesis when treating the data as a whole. Also, the overall quality of the data is diminished. With the purpose of developing a generic and comparable metric to assess the stability of multi-source datasets, we have studied the applicability and behaviour of several PDF distances over shifts on different conditions (such as uni- and multivariate, different types of variable, and multi-modality) which may appear in real biomedical data. From the studied distances, we found information-theoretic based and Earth Mover's Distance to be the most practical distances for most conditions. We discuss the properties and usefulness of each distance according to the possible requirements of a general stability metric.

Detection of Q-Matrix Misspecification Using Two Criteria for Validation of Cognitive Structures under the Least Squares Distance Model

ERIC Educational Resources Information Center

Romero, Sonia J.; Ordoñez, Xavier G.; Ponsoda, Vincente; Revuelta, Javier

2014-01-01

Cognitive Diagnostic Models (CDMs) aim to provide information about the degree to which individuals have mastered specific attributes that underlie the success of these individuals on test items. The Q-matrix is a key element in the application of CDMs, because contains links item-attributes representing the cognitive structure proposed for solve…

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Non-negative Matrix Factorization and Co-clustering: A Promising Tool for Multi-tasks Bearing Fault Diagnosis

NASA Astrophysics Data System (ADS)

Shen, Fei; Chen, Chao; Yan, Ruqiang

2017-05-01

Classical bearing fault diagnosis methods, being designed according to one specific task, always pay attention to the effectiveness of extracted features and the final diagnostic performance. However, most of these approaches suffer from inefficiency when multiple tasks exist, especially in a real-time diagnostic scenario. A fault diagnosis method based on Non-negative Matrix Factorization (NMF) and Co-clustering strategy is proposed to overcome this limitation. Firstly, some high-dimensional matrixes are constructed using the Short-Time Fourier Transform (STFT) features, where the dimension of each matrix equals to the number of target tasks. Then, the NMF algorithm is carried out to obtain different components in each dimension direction through optimized matching, such as Euclidean distance and divergence distance. Finally, a Co-clustering technique based on information entropy is utilized to realize classification of each component. To verity the effectiveness of the proposed approach, a series of bearing data sets were analysed in this research. The tests indicated that although the diagnostic performance of single task is comparable to traditional clustering methods such as K-mean algorithm and Guassian Mixture Model, the accuracy and computational efficiency in multi-tasks fault diagnosis are improved.

SOURCE APPORTIONMENT RESULTS, UNCERTAINTIES, AND MODELING TOOLS

EPA Science Inventory

Advanced multivariate receptor modeling tools are available from the U.S. Environmental Protection Agency (EPA) that use only speciated sample data to identify and quantify sources of air pollution. EPA has developed both EPA Unmix and EPA Positive Matrix Factorization (PMF) and ...

Stereoplotting Hominid Brain Endocasts : Some Preliminary Results

NASA Astrophysics Data System (ADS)

Holloway, Ralph L.

1980-07-01

To objectively and quantitatively demonstrate regional differences in brain endocast morphology, traditional anthropometric caliper measurements must be replaced by a system providing not only localness, but homology and reasonable freedom from allometric distortion. Stereoplotting the radial distances from endocast surface (the closest point to the once underlying brain cortex) to a homologous center every ten degrees provides some 300+ data points for each dorsal endocast surface, thus giving the requisite localness. These measurements provide a large matrix of data suitable for a number of multivariate statistical techniques, and the translation of such data and analyses to readily visualized maps, which can then be compared in relation to both taxonomic and functional knowledge about the cerebral surface. This paper descri-bes some preliminary results from using such methods on a sample of 64 undistorted endocasts composed of both pongids and fossil hominids. While sample sizes within taxonomic groups need to be augmented, the preliminary and tentative pilot studies conducted so far suggest that the method has excellent potential, and that two major areas of the brain endocast surface show the greatest shape changes : 1) the posterior association areas (inferior parietal lobule); 2) the anterior prefrontal areas.

Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase.

PubMed

Rojas, Cristian; Duchowicz, Pablo R; Tripaldi, Piercosimo; Pis Diez, Reinaldo

2015-11-27

A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randić-like index from reciprocal squared distance matrix has a high relevance for this purpose. Copyright © 2015 Elsevier B.V. All rights reserved.

Evaluation of respiratory parameters in finswimmers regarding gender, swimming style and distance.

PubMed

Stavrou, V; Vavougios, G; Karetsi, E; Adam, G; Daniil, Z; Gourgoulianis, K I

2018-04-13

The purpose of our study was to investigate the differences in the finswimmers' physiological characteristics, as far as gender, the swimming style and the different swimming distance are concerned. 52 finswimmers participated in our study (Age: 17.4 ± 2.1yrs, BMI: 21.8 ± 2.3, body fat: 12.2 ± 4.7%) and were allocated into groups [Gender: Female vs. Male, swimming style: Bifin vs. Surface, and swimming distance: <200 m vs. ≥200 m]. Anthropometric characteristics, handgrip, estimated strength of inspiratory muscles (PI max ) and pulmonary function parameters (FEV 1 , FVC and PEF) were measured. The Independent T-test was used for statistical comparisons between groups. Multivariate analyses were performed via binary logistic regression. The results showed differences between groups in gender in PEF (p < 0.05), PI max (p < 0.05) and handgrip (p < 0.001) in swimming style in handgrip (p < 0.05), FEV 1 (p < 0.05) and FVC (p < 0.05) and in swimming distance (p < 0.05) in hours/day spent at the gym (p < 0.05) and FVC (p < 0.05). In multivariate analyses handgrip remained an independent predictor of style (OR: 1.154; 95%CI: 1.022-1.303, p = .021), and hours/day spent at the gym was retained as an independent predictor of distance (OR: 131.607; 95%CI: 3.655-4739.441, p = .008). The data from the present study reveal that handgrip was associated with style, and hours per day spent at the gym were associated with distance. Copyright © 2018 Elsevier B.V. All rights reserved.

Evaluation of entropy and JM-distance criterions as features selection methods using spectral and spatial features derived from LANDSAT images

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Dutra, L. V.; Mascarenhas, N. D. A.; Mitsuo, Fernando Augusta, II

1984-01-01

A study area near Ribeirao Preto in Sao Paulo state was selected, with predominance in sugar cane. Eight features were extracted from the 4 original bands of LANDSAT image, using low-pass and high-pass filtering to obtain spatial features. There were 5 training sites in order to acquire the necessary parameters. Two groups of four channels were selected from 12 channels using JM-distance and entropy criterions. The number of selected channels was defined by physical restrictions of the image analyzer and computacional costs. The evaluation was performed by extracting the confusion matrix for training and tests areas, with a maximum likelihood classifier, and by defining performance indexes based on those matrixes for each group of channels. Results show that in spatial features and supervised classification, the entropy criterion is better in the sense that allows a more accurate and generalized definition of class signature. On the other hand, JM-distance criterion strongly reduces the misclassification within training areas.

Dissolution comparisons using a Multivariate Statistical Distance (MSD) test and a comparison of various approaches for calculating the measurements of dissolution profile comparison.

PubMed

Cardot, J-M; Roudier, B; Schütz, H

2017-07-01

The f 2 test is generally used for comparing dissolution profiles. In cases of high variability, the f 2 test is not applicable, and the Multivariate Statistical Distance (MSD) test is frequently proposed as an alternative by the FDA and EMA. The guidelines provide only general recommendations. MSD tests can be performed either on raw data with or without time as a variable or on parameters of models. In addition, data can be limited-as in the case of the f 2 test-to dissolutions of up to 85% or to all available data. In the context of the present paper, the recommended calculation included all raw dissolution data up to the first point greater than 85% as a variable-without the various times as parameters. The proposed MSD overcomes several drawbacks found in other methods.

EDENetworks: a user-friendly software to build and analyse networks in biogeography, ecology and population genetics.

PubMed

Kivelä, Mikko; Arnaud-Haond, Sophie; Saramäki, Jari

2015-01-01

The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data. © 2014 John Wiley & Sons Ltd.

Impact of geographic distance on appraisal delay for active TB treatment seeking in Uganda: a network analysis of the Kawempe Community Health Cohort Study.

PubMed

Fluegge, Kyle; Malone, LaShaunda L; Nsereko, Mary; Okware, Brenda; Wejse, Christian; Kisingo, Hussein; Mupere, Ezekiel; Boom, W Henry; Stein, Catherine M

2018-06-26

Appraisal delay is the time a patient takes to consider a symptom as not only noticeable, but a sign of illness. The study's objective was to determine the association between appraisal delay in seeking tuberculosis (TB) treatment and geographic distance measured by network travel (driving and pedestrian) time (in minutes) and distance (Euclidean and self-reported) (in kilometers) and to identify other risk factors from selected covariates and how they modify the core association between delay and distance. This was part of a longitudinal cohort study known as the Kawempe Community Health Study based in Kampala, Uganda. The study enrolled households from April 2002 to July 2012. Multivariable interval regression with multiplicative heteroscedasticity was used to assess the impact of time and distance on delay. The delay interval outcome was defined using a comprehensive set of 28 possible self-reported symptoms. The main independent variables were network travel time (in minutes) and Euclidean distance (in kilometers). Other covariates were organized according to the Andersen utilization conceptual framework. A total of 838 patients with both distance and delay data were included in the network analysis. Bivariate analyses did not reveal a significant association of any distance metric with the delay outcome. However, adjusting for patient characteristics and cavitary disease status, the multivariable model indicated that each minute of driving time to the clinic significantly (p = 0.02) and positively predicted 0.25 days' delay. At the median distance value of 47 min, this represented an additional delay of about 12 (95% CI: [3, 21]) days to the mean of 40 days (95% CI: [25, 56]). Increasing Euclidean distance significantly predicted (p = 0.02) reduced variance in the delay outcome, thereby increasing precision of the mean delay estimate. At the median Euclidean distance of 2.8 km, the variance in the delay was reduced by more than 25%. Of the four geographic distance measures, network travel driving time was a better and more robust predictor of mean delay in this setting. Including network travel driving time with other risk factors may be important in identifying populations especially vulnerable to delay.

Genetic diversity of popcorn genotypes using molecular analysis.

PubMed

Resh, F S; Scapim, C A; Mangolin, C A; Machado, M F P S; do Amaral, A T; Ramos, H C C; Vivas, M

2015-08-19

In this study, we analyzed dominant molecular markers to estimate the genetic divergence of 26 popcorn genotypes and evaluate whether using various dissimilarity coefficients with these dominant markers influences the results of cluster analysis. Fifteen random amplification of polymorphic DNA primers produced 157 amplified fragments, of which 65 were monomorphic and 92 were polymorphic. To calculate the genetic distances among the 26 genotypes, the complements of the Jaccard, Dice, and Rogers and Tanimoto similarity coefficients were used. A matrix of Dij values (dissimilarity matrix) was constructed, from which the genetic distances among genotypes were represented in a more simplified manner as a dendrogram generated using the unweighted pair-group method with arithmetic average. Clusters determined by molecular analysis generally did not group material from the same parental origin together. The largest genetic distance was between varieties 17 (UNB-2) and 18 (PA-091). In the identification of genotypes with the smallest genetic distance, the 3 coefficients showed no agreement. The 3 dissimilarity coefficients showed no major differences among their grouping patterns because agreement in determining the genotypes with large, medium, and small genetic distances was high. The largest genetic distances were observed for the Rogers and Tanimoto dissimilarity coefficient (0.74), followed by the Jaccard coefficient (0.65) and the Dice coefficient (0.48). The 3 coefficients showed similar estimations for the cophenetic correlation coefficient. Correlations among the matrices generated using the 3 coefficients were positive and had high magnitudes, reflecting strong agreement among the results obtained using the 3 evaluated dissimilarity coefficients.

Euclidean commute time distance embedding and its application to spectral anomaly detection

NASA Astrophysics Data System (ADS)

Albano, James A.; Messinger, David W.

2012-06-01

Spectral image analysis problems often begin by performing a preprocessing step composed of applying a transformation that generates an alternative representation of the spectral data. In this paper, a transformation based on a Markov-chain model of a random walk on a graph is introduced. More precisely, we quantify the random walk using a quantity known as the average commute time distance and find a nonlinear transformation that embeds the nodes of a graph in a Euclidean space where the separation between them is equal to the square root of this quantity. This has been referred to as the Commute Time Distance (CTD) transformation and it has the important characteristic of increasing when the number of paths between two nodes decreases and/or the lengths of those paths increase. Remarkably, a closed form solution exists for computing the average commute time distance that avoids running an iterative process and is found by simply performing an eigendecomposition on the graph Laplacian matrix. Contained in this paper is a discussion of the particular graph constructed on the spectral data for which the commute time distance is then calculated from, an introduction of some important properties of the graph Laplacian matrix, and a subspace projection that approximately preserves the maximal variance of the square root commute time distance. Finally, RX anomaly detection and Topological Anomaly Detection (TAD) algorithms will be applied to the CTD subspace followed by a discussion of their results.

Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size.

PubMed

Del Giudice, Marco

2017-01-01

The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution. I discuss the properties and limitations of the two coefficients and illustrate their use by reanalyzing some published findings from studies of gender differences.

A-TEEMTM, a new molecular fingerprinting technique: simultaneous absorbance-transmission and fluorescence excitation-emission matrix method

NASA Astrophysics Data System (ADS)

Quatela, Alessia; Gilmore, Adam M.; Steege Gall, Karen E.; Sandros, Marinella; Csatorday, Karoly; Siemiarczuk, Alex; (Ben Yang, Boqian; Camenen, Loïc

2018-04-01

We investigate the new simultaneous absorbance-transmission and fluorescence excitation-emission matrix method for rapid and effective characterization of the varying components from a mixture. The absorbance-transmission and fluorescence excitation-emission matrix method uniquely facilitates correction of fluorescence inner-filter effects to yield quantitative fluorescence spectral information that is largely independent of component concentration. This is significant because it allows one to effectively monitor quantitative component changes using multivariate methods and to generate and evaluate spectral libraries. We present the use of this novel instrument in different fields: i.e. tracking changes in complex mixtures including natural water, wine as well as monitoring stability and aggregation of hormones for biotherapeutics.

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

Combining Correlation Matrices: Simulation Analysis of Improved Fixed-Effects Methods

ERIC Educational Resources Information Center

Hafdahl, Adam R.

2007-01-01

The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better…

Solving matrix effects exploiting the second-order advantage in the resolution and determination of eight tetracycline antibiotics in effluent wastewater by modelling liquid chromatography data with multivariate curve resolution-alternating least squares and unfolded-partial least squares followed by residual bilinearization algorithms II. Prediction and figures of merit.

PubMed

García, M D Gil; Culzoni, M J; De Zan, M M; Valverde, R Santiago; Galera, M Martínez; Goicoechea, H C

2008-02-01

A new powerful algorithm (unfolded-partial least squares followed by residual bilinearization (U-PLS/RBL)) was applied for first time on second-order liquid chromatography with diode array detection (LC-DAD) data and compared with a well-known established method (multivariate curve resolution-alternating least squares (MCR-ALS)) for the simultaneous determination of eight tetracyclines (tetracycline, oxytetracycline, meclocycline, minocycline, metacycline, chlortetracycline, demeclocycline and doxycycline) in wastewaters. Tetracyclines were pre-concentrated using Oasis Max C18 cartridges and then separated on a Thermo Aquasil C18 (150 mm x 4.6mm, 5 microm) column. The whole method was validated using Milli-Q water samples and both univariate and multivariate analytical figures of merit were obtained. Additionally, two data pre-treatment were applied (baseline correction and piecewise direct standardization), which allowed to correct the effect of breakthrough and to reduce the total interferences retained after pre-concentration of wastewaters. The results showed that the eight tetracycline antibiotics can be successfully determined in wastewaters, the drawbacks due to matrix interferences being adequately handled and overcome by using U-PSL/RBL.

Environmental effects on the structure of the G-matrix.

PubMed

Wood, Corlett W; Brodie, Edmund D

2015-11-01

Genetic correlations between traits determine the multivariate response to selection in the short term, and thereby play a causal role in evolutionary change. Although individual studies have documented environmentally induced changes in genetic correlations, the nature and extent of environmental effects on multivariate genetic architecture across species and environments remain largely uncharacterized. We reviewed the literature for estimates of the genetic variance-covariance (G) matrix in multiple environments, and compared differences in G between environments to the divergence in G between conspecific populations (measured in a common garden). We found that the predicted evolutionary trajectory differed as strongly between environments as it did between populations. Between-environment differences in the underlying structure of G (total genetic variance and the relative magnitude and orientation of genetic correlations) were equal to or greater than between-population differences. Neither environmental novelty, nor the difference in mean phenotype predicted these differences in G. Our results suggest that environmental effects on multivariate genetic architecture may be comparable to the divergence that accumulates over dozens or hundreds of generations between populations. We outline avenues of future research to address the limitations of existing data and characterize the extent to which lability in genetic correlations shapes evolution in changing environments. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Multivariate analysis of matrix-assisted laser desorption/ionization mass spectrometric data related to glycoxidation products of human globins in nephropathic patients.

PubMed

Lapolla, Annunziata; Ragazzi, Eugenio; Andretta, Barbara; Fedele, Domenico; Tubaro, Michela; Seraglia, Roberta; Molin, Laura; Traldi, Pietro

2007-06-01

To clarify the possible pathogenetic role of oxidation products originated from the glycation of proteins, human globins from nephropathic patients have been studied by matrix-assisted laser desorption/ionization mass spectrometry (MALDI), revealing not only unglycated and monoglycated globins, but also a series of different species. For the last ones, structural assignments were tentatively done on the basis of observed masses and expectations for the Maillard reaction pattern. Consequently, they must be considered only propositive, and the discussion which will follow must be considered in this view. In our opinion this approach does not seem to compromise the intended diagnostic use of the data because distinctions are valid even if the assignments are uncertain. We studied nine healthy subjects and 19 nephropathic patients and processed the data obtained from the MALDI spectra using a multivariate analysis. Our results showed that multivariate analytical techniques enable differential aspects of the profile of molecular species to be identified in the blood of end stage nephropathic patients. A correct grouping can be achieved by principal component analysis (PCA) and the results suggest that several products involved in carbonyl stress exist in nephropathic patients. These compounds may have a relevant role as specific markers of the pathological state.

Developing the Fundamental Theorem of Calculus. Applications of Calculus to Work, Area, and Distance Problems. [and] Atmospheric Pressure in Relation to Height and Temperature. Applications of Calculus to Atmospheric Pressure. [and] The Gradient and Some of Its Applications. Applications of Multivariate Calculus to Physics. [and] Kepler's Laws and the Inverse Square Law. Applications of Calculus to Physics. UMAP Units 323, 426, 431, 473.

ERIC Educational Resources Information Center

Lindstrom, Peter A.; And Others

This document consists of four units. The first of these views calculus applications to work, area, and distance problems. It is designed to help students gain experience in: 1) computing limits of Riemann sums; 2) computing definite integrals; and 3) solving elementary area, distance, and work problems by integration. The second module views…

Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps

PubMed Central

Ejarque, Elisabet; Nagelkerke, Leopold A. J.

2018-01-01

Tropical small-scale fisheries are typical for providing complex multivariate data, due to their diversity in fishing techniques and highly diverse species composition. In this paper we used for the first time a supervised Self-Organizing Map (xyf-SOM), to recognize and understand the internal heterogeneity of a tropical marine small-scale fishery, using as model the fishery fleet of San Pedro port, Tabasco, Mexico. We used multivariate data from commercial logbooks, including the following four factors: fish species (47), gear types (bottom longline, vertical line+shark longline and vertical line), season (cold, warm), and inter-annual variation (2007–2012). The size of the xyf-SOM, a fundamental characteristic to improve its predictive quality, was optimized for the minimum distance between objects and the maximum prediction rate. The xyf-SOM successfully classified individual fishing trips in relation to the four factors included in the model. Prediction percentages were high (80–100%) for bottom longline and vertical line + shark longline, but lower prediction values were obtained for vertical line (51–74%) fishery. A confusion matrix indicated that classification errors occurred within the same fishing gear. Prediction rates were validated by generating confidence interval using bootstrap. The xyf-SOM showed that not all the fishing trips were targeting the most abundant species and the catch rates were not symmetrically distributed around the mean. Also, the species composition is not homogeneous among fishing trips. Despite the complexity of the data, the xyf-SOM proved to be an excellent tool to identify trends in complex scenarios, emphasizing the diverse and complex patterns that characterize tropical small scale-fishery fleets. PMID:29782501

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Graph distance for complex networks

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

2016-10-01

Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.

A Routing Protocol for Packet Radio Networks

DTIC Science & Technology

1995-01-01

table of node K is a matrix containing, for each destination L and each neighbor of K (say M ), the distance to L ( NEOPRQ ) and the predecessor ( S OP Q...identifier T The distance to the destination ( N OP ) T The predecessor of the shortest path chosen toward L ( S OP ) T The successor ( U OP ) of the shortest...P and the predecessor is updated as S OP À ¾ S Q P . Thus, a node can determine whether or not an update received from M affects its other distance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Acharya, Sahaja; Hsieh, Samantha; Michalski, Jeff M.

Purpose: Breast-conserving therapy (BCT) is a recommended alternative to mastectomy (MT) for early-stage breast cancer. Limited access to radiation therapy (RT) may result in higher rates of MT. We assessed the association between distance to the nearest RT facility and the use of MT, in a modern cohort of women. Methods and Materials: Women with stage 0-II breast cancer eligible for BCT diagnosed from 2004 to 2010 were identified from the Florida Cancer Data System (FCDS). Distances from patient census tracts to the nearest RT facility census tract were calculated. Multivariate logistic regression was used to identify explanatory variables thatmore » influenced MT use. Results: Of the 27,489 eligible women, 32.1% (n=8841) underwent MT, and 67.8% (n=18,648) underwent BCS. Thirty-two percent of patients lived in a census tract that was >5 miles from an RT facility. MT use increased with increasing distance to RT facility (31.1% at ≤5 miles, 33.8% at >5 to <15 miles, 34.9% at 15 to <40 miles, and 51% at ≥40 miles, P<.001). The likelihood was that MT was independently associated with increasing distance to RT facility on multivariate analysis (P<.001). Compared to patients living <5 miles away from an RT facility, patients living 15 to <40 miles away were 1.2 times more likely to be treated with MT (odds ratio [OR]: 1.19, 95% confidence interval [CI]: 1.05-1.35, P<.01), and those living ≥40 miles away were more than twice as likely to be treated with MT (OR: 2.17, 95% CI: 1.48-3.17, P<.001). However, in patients younger than 50 years (n=5179), MT use was not associated with distance to RT facility (P=.235). Conclusions: MT use in a modern cohort of women is independently associated with distance to RT facility. However, for young patients, distance to RT is not a significant explanatory variable for MT use.« less

Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes.

PubMed

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

2011-01-01

The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes. Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis. A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001) and in an Olympic distance triathlon (P < 0.0001) were the best variables related to Ironman race time. Ironman race time (minutes) might be partially predicted by the following equation: (r (2) = 0.65, standard error of estimate = 56.8) = 152.1 + 1.332 × (personal best time in a marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes). These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes.

Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes

PubMed Central

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

2011-01-01

Background The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes. Methods Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis. Results A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001) and in an Olympic distance triathlon (P < 0.0001) were the best variables related to Ironman race time. Ironman race time (minutes) might be partially predicted by the following equation: (r2 = 0.65, standard error of estimate = 56.8) = 152.1 + 1.332 × (personal best time in a marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes). Conclusion These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes. PMID:24198578

Residential distance to major roadways and cardiac structure in African Americans: cross-sectional results from the Jackson Heart Study.

PubMed

Weaver, Anne M; Wellenius, Gregory A; Wu, Wen-Chih; Hickson, DeMarc A; Kamalesh, Masoor; Wang, Yi

2017-03-08

Heart failure (HF) is a significant source of morbidity and mortality among African Americans. Ambient air pollution, including from traffic, is associated with HF, but the mechanisms remain unknown. The objectives of this study were to estimate the cross-sectional associations between residential distance to major roadways with markers of cardiac structure: left ventricular (LV) mass index, LV end-diastolic diameter, LV end-systolic diameter, and LV hypertrophy among African Americans. We studied baseline participants of the Jackson Heart Study (recruited 2000-2004), a prospective cohort of cardiovascular disease (CVD) among African Americans living in Jackson, Mississippi, USA. All cardiac measures were assessed from echocardiograms. We assessed the associations between residential distance to roads and cardiac structure indicators using multivariable linear regression or multivariable logistic regression, adjusting for potential confounders. Among 4826 participants, residential distance to road was <150 m for 103 participants, 150-299 m for 158, 300-999 for 1156, and ≥1000 m for 3409. Those who lived <150 m from a major road had mean 1.2 mm (95% CI 0.2, 2.1) greater LV diameter at end-systole compared to those who lived ≥1000 m. We did not observe statistically significant associations between distance to roads and LV mass index, LV end-diastolic diameter, or LV hypertrophy. Results did not materially change after additional adjustment for hypertension and diabetes or exclusion of those with CVD at baseline; results strengthened when modeling distance to A1 roads (such as interstate highways) as the exposure of interest. We found that residential distance to roads may be associated with LV end-systolic diameter, a marker of systolic dysfunction, in this cohort of African Americans, suggesting a potential mechanism by which exposure to traffic pollution increases the risk of HF.

Accessibility to surgical robot technology and prostate-cancer patient behavior for prostatectomy.

PubMed

Sugihara, Toru; Yasunaga, Hideo; Matsui, Hiroki; Nagao, Go; Ishikawa, Akira; Fujimura, Tetsuya; Fukuhara, Hiroshi; Fushimi, Kiyohide; Ohori, Makoto; Homma, Yukio

2017-07-01

To examine how surgical robot emergence affects prostate-cancer patient behavior in seeking radical prostatectomy focusing on geographical accessibility. In Japan, robotic surgery was approved in April 2012. Based on data in the Japanese Diagnosis Procedure Combination database between April 2012 and March 2014, distance to nearest surgical robot and interval days to radical prostatectomy (divided by mean interval in 2011: % interval days to radical prostatectomy) were calculated for individual radical prostatectomy cases at non-robotic hospitals. Caseload changes regarding distance to nearest surgical robot and robot introduction were investigated. Change in % interval days to radical prostatectomy was evaluated by multivariate analysis including distance to nearest surgical robot, age, comorbidity, hospital volume, operation type, hospital academic status, bed volume and temporal progress. % Interval days to radical prostatectomy became wider for distance to nearest surgical robot <30 km. When a surgical robot emerged within 30 and 10 km, the prostatectomy caseload in non-robot hospitals reduced by 13 and 18% within 6 months, respectively, while the robot hospitals gained +101% caseload (P < 0.01 for all) Multivariate analyses including 9759 open and 5052 non-robotic minimally invasive radical prostatectomies in 483 non-robot hospitals revealed a significant inverse association between distance to nearest surgical robot and % interval days to radical prostatectomy (B = -17.3% for distance to nearest surgical robot ≥30 km and -11.7% for 10-30 km versus distance to nearest surgical robot <10 km), while younger age, high-volume hospital, open-prostatectomy provider and temporal progress were other significant factors related to % interval days to radical prostatectomy widening (P < 0.05 for all). Robotic surgery accessibility within 30 km would make patients less likely select conventional surgery. The nearer a robot was, the faster the caseload reduction was. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.

PubMed

Sztepanacz, Jacqueline L; Blows, Mark W

2017-07-01

The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution. Here we show that genetic eigenvalues estimated using restricted maximum likelihood (REML) in a multivariate random effects model with an unconstrained genetic covariance structure will also conform to the TW distribution after empirical scaling and centering. However, where estimation procedures using either REML or MCMC impose boundary constraints, the resulting genetic eigenvalues tend not be TW distributed. We show how using confidence intervals from sampling distributions of genetic eigenvalues without reference to the TW distribution is insufficient protection against mistaking sampling error as genetic variance, particularly when eigenvalues are small. By scaling such sampling distributions to the appropriate TW distribution, the critical value of the TW statistic can be used to determine if the magnitude of a genetic eigenvalue exceeds the sampling error for each eigenvalue in the spectral distribution of a given genetic covariance matrix. Copyright © 2017 by the Genetics Society of America.

Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

ERIC Educational Resources Information Center

Raymond, Margaret; And Others

1983-01-01

Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…

Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains.

PubMed

Allefeld, Carsten; Bialonski, Stephan

2007-12-01

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.

Generalising Ward's Method for Use with Manhattan Distances.

PubMed

Strauss, Trudie; von Maltitz, Michael Johan

2017-01-01

The claim that Ward's linkage algorithm in hierarchical clustering is limited to use with Euclidean distances is investigated. In this paper, Ward's clustering algorithm is generalised to use with l1 norm or Manhattan distances. We argue that the generalisation of Ward's linkage method to incorporate Manhattan distances is theoretically sound and provide an example of where this method outperforms the method using Euclidean distances. As an application, we perform statistical analyses on languages using methods normally applied to biology and genetic classification. We aim to quantify differences in character traits between languages and use a statistical language signature based on relative bi-gram (sequence of two letters) frequencies to calculate a distance matrix between 32 Indo-European languages. We then use Ward's method of hierarchical clustering to classify the languages, using the Euclidean distance and the Manhattan distance. Results obtained from using the different distance metrics are compared to show that the Ward's algorithm characteristic of minimising intra-cluster variation and maximising inter-cluster variation is not violated when using the Manhattan metric.

Medicalising normality? Using a simulated dataset to assess the performance of different diagnostic criteria of HIV-associated cognitive impairment

PubMed Central

De Francesco, Davide; Leech, Robert; Sabin, Caroline A.; Winston, Alan

2018-01-01

Objective The reported prevalence of cognitive impairment remains similar to that reported in the pre-antiretroviral therapy era. This may be partially artefactual due to the methods used to diagnose impairment. In this study, we evaluated the diagnostic performance of the HIV-associated neurocognitive disorder (Frascati criteria) and global deficit score (GDS) methods in comparison to a new, multivariate method of diagnosis. Methods Using a simulated ‘normative’ dataset informed by real-world cognitive data from the observational Pharmacokinetic and Clinical Observations in PeoPle Over fiftY (POPPY) cohort study, we evaluated the apparent prevalence of cognitive impairment using the Frascati and GDS definitions, as well as a novel multivariate method based on the Mahalanobis distance. We then quantified the diagnostic properties (including positive and negative predictive values and accuracy) of each method, using bootstrapping with 10,000 replicates, with a separate ‘test’ dataset to which a pre-defined proportion of ‘impaired’ individuals had been added. Results The simulated normative dataset demonstrated that up to ~26% of a normative control population would be diagnosed with cognitive impairment with the Frascati criteria and ~20% with the GDS. In contrast, the multivariate Mahalanobis distance method identified impairment in ~5%. Using the test dataset, diagnostic accuracy [95% confidence intervals] and positive predictive value (PPV) was best for the multivariate method vs. Frascati and GDS (accuracy: 92.8% [90.3–95.2%] vs. 76.1% [72.1–80.0%] and 80.6% [76.6–84.5%] respectively; PPV: 61.2% [48.3–72.2%] vs. 29.4% [22.2–36.8%] and 33.9% [25.6–42.3%] respectively). Increasing the a priori false positive rate for the multivariate Mahalanobis distance method from 5% to 15% resulted in an increase in sensitivity from 77.4% (64.5–89.4%) to 92.2% (83.3–100%) at a cost of specificity from 94.5% (92.8–95.2%) to 85.0% (81.2–88.5%). Conclusion Our simulations suggest that the commonly used diagnostic criteria of HIV-associated cognitive impairment label a significant proportion of a normative reference population as cognitively impaired, which will likely lead to a substantial over-estimate of the true proportion in a study population, due to their lower than expected specificity. These findings have important implications for clinical research regarding cognitive health in people living with HIV. More accurate methods of diagnosis should be implemented, with multivariate techniques offering a promising solution. PMID:29641619

A dependence modelling study of extreme rainfall in Madeira Island

NASA Astrophysics Data System (ADS)

Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra

2016-08-01

The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.

Machine learning with quantum relative entropy

NASA Astrophysics Data System (ADS)

Tsuda, Koji

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Polynomial Supertree Methods Revisited

PubMed Central

Brinkmeyer, Malte; Griebel, Thasso; Böcker, Sebastian

2011-01-01

Supertree methods allow to reconstruct large phylogenetic trees by combining smaller trees with overlapping leaf sets into one, more comprehensive supertree. The most commonly used supertree method, matrix representation with parsimony (MRP), produces accurate supertrees but is rather slow due to the underlying hard optimization problem. In this paper, we present an extensive simulation study comparing the performance of MRP and the polynomial supertree methods MinCut Supertree, Modified MinCut Supertree, Build-with-distances, PhySIC, PhySIC_IST, and super distance matrix. We consider both quality and resolution of the reconstructed supertrees. Our findings illustrate the tradeoff between accuracy and running time in supertree construction, as well as the pros and cons of voting- and veto-based supertree approaches. Based on our results, we make some general suggestions for supertree methods yet to come. PMID:22229028

Effect of polarity and elongational flow on the morphology and properties of a new nanobiocomposite

NASA Astrophysics Data System (ADS)

Paolo, La Mantia Francesco; Manuela, Ceraulo; Chiara, Mistretta Maria; Fiorenza, Sutera; Laura, Ascione

2015-12-01

Nanobiocomposites are a new class of biodegradable polymer materials that shows very interesting properties and the biodegradability of the matrix. In this work the effect of the polarity of the organomodified montmorillonite and of the elongational flow on the morphology and the rheological and mechanical properties of a new nanobiocomposite having as a matrix a biodegradable copolyester based blend has been investigated. The mechanical properties increase in presence of the nanofiller and this increase is larger and larger with increasing the orientation. Moreover, a brittle-to-ductile transition is observed in the anisotropic sample and this effect is again larger for the nanocomposite. The increase of the interlayer distance is larger for the more polar montmorillonite, even if the two nanocomposites show about the same final interlayer distance.

Multispectral Palmprint Recognition Using a Quaternion Matrix

PubMed Central

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%. PMID:22666049

Multispectral palmprint recognition using a quaternion matrix.

PubMed

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

Clustering Tree-structured Data on Manifold

PubMed Central

Lu, Na; Miao, Hongyu

2016-01-01

Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of Euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorporate the structure constraints embedded in data into the non-negative matrix factorization method to determine meta-trees from the T-A matrix, and the signature vector of each single tree can then be extracted by meta-tree decomposition. The meta-tree space turns out to be a cone space, in which we explore the distance metric and implement the clustering algorithm based on the concepts like Fréchet mean. Finally, the T-A matrix based clustering (TAMBAC) framework is evaluated and compared using both simulated data and real retinal images to illus trate its efficiency and accuracy. PMID:26660696

Linear quadratic regulators with eigenvalue placement in a horizontal strip

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Dib, Hani M.; Ganesan, Sekar

1987-01-01

A method for optimally shifting the imaginary parts of the open-loop poles of a multivariable control system to the desirable closed-loop locations is presented. The optimal solution with respect to a quadratic performance index is obtained by solving a linear matrix Liapunov equation.

A LiDAR data-based camera self-calibration method

NASA Astrophysics Data System (ADS)

Xu, Lijun; Feng, Jing; Li, Xiaolu; Chen, Jianjun

2018-07-01

To find the intrinsic parameters of a camera, a LiDAR data-based camera self-calibration method is presented here. Parameters have been estimated using particle swarm optimization (PSO), enhancing the optimal solution of a multivariate cost function. The main procedure of camera intrinsic parameter estimation has three parts, which include extraction and fine matching of interest points in the images, establishment of cost function, based on Kruppa equations and optimization of PSO using LiDAR data as the initialization input. To improve the precision of matching pairs, a new method of maximal information coefficient (MIC) and maximum asymmetry score (MAS) was used to remove false matching pairs based on the RANSAC algorithm. Highly precise matching pairs were used to calculate the fundamental matrix so that the new cost function (deduced from Kruppa equations in terms of the fundamental matrix) was more accurate. The cost function involving four intrinsic parameters was minimized by PSO for the optimal solution. To overcome the issue of optimization pushed to a local optimum, LiDAR data was used to determine the scope of initialization, based on the solution to the P4P problem for camera focal length. To verify the accuracy and robustness of the proposed method, simulations and experiments were implemented and compared with two typical methods. Simulation results indicated that the intrinsic parameters estimated by the proposed method had absolute errors less than 1.0 pixel and relative errors smaller than 0.01%. Based on ground truth obtained from a meter ruler, the distance inversion accuracy in the experiments was smaller than 1.0 cm. Experimental and simulated results demonstrated that the proposed method was highly accurate and robust.

Concurrent generation of multivariate mixed data with variables of dissimilar types.

PubMed

Amatya, Anup; Demirtas, Hakan

2016-01-01

Data sets originating from wide range of research studies are composed of multiple variables that are correlated and of dissimilar types, primarily of count, binary/ordinal and continuous attributes. The present paper builds on the previous works on multivariate data generation and develops a framework for generating multivariate mixed data with a pre-specified correlation matrix. The generated data consist of components that are marginally count, binary, ordinal and continuous, where the count and continuous variables follow the generalized Poisson and normal distributions, respectively. The use of the generalized Poisson distribution provides a flexible mechanism which allows under- and over-dispersed count variables generally encountered in practice. A step-by-step algorithm is provided and its performance is evaluated using simulated and real-data scenarios.

Problems with small area surveys: lensing covariance of supernova distance measurements.

PubMed

Cooray, Asantha; Huterer, Dragan; Holz, Daniel E

2006-01-20

While luminosity distances from type Ia supernovae (SNe) are a powerful probe of cosmology, the accuracy with which these distances can be measured is limited by cosmic magnification due to gravitational lensing by the intervening large-scale structure. Spatial clustering of foreground mass leads to correlated errors in SNe distances. By including the full covariance matrix of SNe, we show that future wide-field surveys will remain largely unaffected by lensing correlations. However, "pencil beam" surveys, and those with narrow (but possibly long) fields of view, can be strongly affected. For a survey with 30 arcmin mean separation between SNe, lensing covariance leads to a approximately 45% increase in the expected errors in dark energy parameters.

A protein relational database and protein family knowledge bases to facilitate structure-based design analyses.

PubMed

Mobilio, Dominick; Walker, Gary; Brooijmans, Natasja; Nilakantan, Ramaswamy; Denny, R Aldrin; Dejoannis, Jason; Feyfant, Eric; Kowticwar, Rupesh K; Mankala, Jyoti; Palli, Satish; Punyamantula, Sairam; Tatipally, Maneesh; John, Reji K; Humblet, Christine

2010-08-01

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom-atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid-centroid and centroid-atom distances and angles have also been included permitting queries for pi-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.

Pasture succession in the Neotropics: extending the nucleation hypothesis into a matrix discontinuity hypothesis.

PubMed

Peterson, Chris J; Dosch, Jerald J; Carson, Walter P

2014-08-01

The nucleation hypothesis appears to explain widespread patterns of succession in tropical pastures, specifically the tendency for isolated trees to promote woody species recruitment. Still, the nucleation hypothesis has usually been tested explicitly for only short durations and in some cases isolated trees fail to promote woody recruitment. Moreover, at times, nucleation occurs in other key habitat patches. Thus, we propose an extension, the matrix discontinuity hypothesis: woody colonization will occur in focal patches that function to mitigate the herbaceous vegetation effects, thus providing safe sites or regeneration niches. We tested predictions of the classical nucleation hypothesis, the matrix discontinuity hypothesis, and a distance from forest edge hypothesis, in five abandoned pastures in Costa Rica, across the first 11 years of succession. Our findings confirmed the matrix discontinuity hypothesis: specifically, rotting logs and steep slopes significantly enhanced woody colonization. Surprisingly, isolated trees did not consistently significantly enhance recruitment; only larger trees did so. Finally, woody recruitment consistently decreased with distance from forest. Our results as well as results from others suggest that the nucleation hypothesis needs to be broadened beyond its historical focus on isolated trees or patches; the matrix discontinuity hypothesis focuses attention on a suite of key patch types or microsites that promote woody species recruitment. We argue that any habitat discontinuities that ameliorate the inhibition by dense graminoid layers will be foci for recruitment. Such patches could easily be manipulated to speed the transition of pastures to closed canopy forests.

Research and simulation of the decoupling transformation in AC motor vector control

NASA Astrophysics Data System (ADS)

He, Jiaojiao; Zhao, Zhongjie; Liu, Ken; Zhang, Yongping; Yao, Tuozhong

2018-04-01

Permanent magnet synchronous motor (PMSM) is a nonlinear, strong coupling, multivariable complex object, and transformation decoupling can solve the coupling problem of permanent magnet synchronous motor. This paper gives a permanent magnet synchronous motor (PMSM) mathematical model, introduces the permanent magnet synchronous motor vector control coordinate transformation in the process of modal matrix inductance matrix transform through the matrix related knowledge of different coordinates of diagonalization, which makes the coupling between the independent, realize the control of motor current and excitation the torque current coupling separation, and derived the coordinate transformation matrix, the thought to solve the coupling problem of AC motor. Finally, in the Matlab/Simulink environment, through the establishment and combination between the PMSM ontology, coordinate conversion module, built the simulation model of permanent magnet synchronous motor vector control, introduces the model of each part, and analyzed the simulation results.

Spatial compression algorithm for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation

NASA Astrophysics Data System (ADS)

Suryowati, K.; Bekti, R. D.; Faradila, A.

2018-04-01

Spatial autocorrelation is one of spatial analysis to identify patterns of relationship or correlation between locations. This method is very important to get information on the dispersal patterns characteristic of a region and linkages between locations. In this study, it applied on the incidence of Dengue Hemorrhagic Fever (DHF) in 17 sub districts in Sleman, Daerah Istimewa Yogyakarta Province. The link among location indicated by a spatial weight matrix. It describe the structure of neighbouring and reflects the spatial influence. According to the spatial data, type of weighting matrix can be divided into two types: point type (distance) and the neighbourhood area (contiguity). Selection weighting function is one determinant of the results of the spatial analysis. This study use queen contiguity based on first order neighbour weights, queen contiguity based on second order neighbour weights, and inverse distance weights. Queen contiguity first order and inverse distance weights shows that there is the significance spatial autocorrelation in DHF, but not by queen contiguity second order. Queen contiguity first and second order compute 68 and 86 neighbour list

Analysis of geological materials containing uranium using laser-induced breakdown spectroscopy (LIBS)

NASA Astrophysics Data System (ADS)

Barefield, James E.; Judge, Elizabeth J.; Campbell, Keri R.; Colgan, James P.; Kilcrease, David P.; Johns, Heather M.; Wiens, Roger C.; McInroy, Rhonda E.; Martinez, Ronald K.; Clegg, Samuel M.

2016-06-01

Laser induced breakdown spectroscopy (LIBS) is a rapid atomic emission spectroscopy technique that can be configured for a variety of applications including space, forensics, and industry. LIBS can also be configured for stand-off distances or in-situ, under vacuum, high pressure, atmospheric or different gas environments, and with different resolving-power spectrometers. The detection of uranium in a complex geological matrix under different measurement schemes is explored in this paper. Although many investigations have been completed in an attempt to detect and quantify uranium in different matrices at in-situ and standoff distances, this work detects and quantifies uranium in a complex matrix under Martian and ambient air conditions. Investigation of uranium detection using a low resolving-power LIBS system at stand-off distances (1.6 m) is also reported. The results are compared to an in-situ LIBS system with medium resolving power and under ambient air conditions. Uranium has many thousands of emission lines in the 200-800 nm spectral region. In the presence of other matrix elements and at lower concentrations, the limit of detection of uranium is significantly reduced. The two measurement methods (low and high resolving-power spectrometers) are compared for limit of detection (LOD). Of the twenty-one potential diagnostic uranium emission lines, seven (409, 424, 434, 435, 436, 591, and 682 nm) have been used to determine the LOD for pitchblende in a dunite matrix using the ChemCam test bed LIBS system. The LOD values determined for uranium transitions in air are 409.013 nm (24,700 ppm), 424.167 nm (23,780 ppm), 434.169 nm (24,390 ppm), 435.574 nm (35,880 ppm), 436.205 nm (19,340 ppm), 591.539 nm (47,310 ppm), and 682.692 nm (18,580 ppm). The corresponding LOD values determined for uranium transitions in 7 Torr CO2 are 424.167 nm (25,760 ppm), 434.169 nm (40,800 ppm), 436.205 nm (32,050 ppm), 591.539 nm (15,340 ppm), and 682.692 nm (29,080 ppm). The LOD values determine for uranium emission lines using the medium resolving power (10,000 λ/Δλ) LIBS system for the dunite matrix in air are 409.013 nm (6120 ppm), 424.167 nm (5356 ppm), 434.169 nm (5693 ppm), 435.574 nm (6329 ppm), 436.205 nm (2142 ppm), and 682.692 nm (10,741 ppm). The corresponding LOD values determined for uranium transitions in a SiO2 matrix are 409.013 nm (272 ppm), 424.167 nm (268 ppm), 434.169 nm (402 ppm), 435.574 nm (1067 ppm), 436.205 nm (482 ppm), and 682.692 nm (720 ppm). The impact of spectral resolution, atmospheric conditions, matrix elements, and measurement distances on LOD is discussed. The measurements will assist one in selecting the proper system components based upon the application and the required analytical performance.

Single-Isocenter Multiple-Target Stereotactic Radiosurgery: Risk of Compromised Coverage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roper, Justin, E-mail: justin.roper@emory.edu; Department of Biostatistics and Bioinformatics, Winship Cancer Institute of Emory University, Atlanta, Georgia; Chanyavanich, Vorakarn

2015-11-01

Purpose: To determine the dosimetric effects of rotational errors on target coverage using volumetric modulated arc therapy (VMAT) for multitarget stereotactic radiosurgery (SRS). Methods and Materials: This retrospective study included 50 SRS cases, each with 2 intracranial planning target volumes (PTVs). Both PTVs were planned for simultaneous treatment to 21 Gy using a single-isocenter, noncoplanar VMAT SRS technique. Rotational errors of 0.5°, 1.0°, and 2.0° were simulated about all axes. The dose to 95% of the PTV (D95) and the volume covered by 95% of the prescribed dose (V95) were evaluated using multivariate analysis to determine how PTV coverage was relatedmore » to PTV volume, PTV separation, and rotational error. Results: At 0.5° rotational error, D95 values and V95 coverage rates were ≥95% in all cases. For rotational errors of 1.0°, 7% of targets had D95 and V95 values <95%. Coverage worsened substantially when the rotational error increased to 2.0°: D95 and V95 values were >95% for only 63% of the targets. Multivariate analysis showed that PTV volume and distance to isocenter were strong predictors of target coverage. Conclusions: The effects of rotational errors on target coverage were studied across a broad range of SRS cases. In general, the risk of compromised coverage increased with decreasing target volume, increasing rotational error and increasing distance between targets. Multivariate regression models from this study may be used to quantify the dosimetric effects of rotational errors on target coverage given patient-specific input parameters of PTV volume and distance to isocenter.« less

Changes in chemical composition of bone matrix in ovariectomized (OVX) rats detected by Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Oshima, Yusuke; Iimura, Tadahiro; Saitou, Takashi; Imamura, Takeshi

2015-02-01

Osteoporosis is a major bone disease that connotes the risk of fragility fractures resulting from alterations to bone quantity and/or quality to mechanical competence. Bone strength arises from both bone quantity and quality. Assessment of bone quality and bone quantity is important for prediction of fracture risk. In spite of the two factors contribute to maintain the bone strength, only one factor, bone mineral density is used to determine the bone strength in the current diagnosis of osteoporosis. On the other hand, there is no practical method to measure chemical composition of bone tissue including hydroxyapatite and collagen non-invasively. Raman spectroscopy is a powerful technique to analyze chemical composition and material properties of bone matrix non-invasively. Here we demonstrated Raman spectroscopic analysis of the bone matrix in osteoporosis model rat. Ovariectomized (OVX) rat was made and the decalcified sections of tibias were analyzed by a Raman microscope. In the results, Raman bands of typical collagen appeared in the obtained spectra. Although the typical mineral bands at 960 cm-1 (Phosphate) was absent due to decalcified processing, we found that Raman peak intensities of amide I and C-C stretching bands were significantly different between OVX and sham-operated specimens. These differences on the Raman spectra were statistically compared by multivariate analyses, principal component analysis (PCA) and liner discrimination analysis (LDA). Our analyses suggest that amide I and C-C stretching bands can be related to stability of bone matrix which reflects bone quality.

Visual Impairment Is Associated With Depressive Symptoms-Results From the Nationwide German DEGS1 Study.

PubMed

Schuster, Alexander K; Tesarz, Jonas; Rezapour, Jasmin; Beutel, Manfred E; Bertram, Bernd; Pfeiffer, Norbert

2018-01-01

Visual impairment (VI) is associated with a variety of comorbidities including physical and mental health in industrial countries. Our aim is to examine associations between self-reported impairment and depressive symptoms in the German population. The point prevalence of self-reported VI in Germany was computed using data from the German Health Interview and Examination Survey for adults from 2008 to 2011 ( N  = 7.783, 50.5% female, age range 18-79 years). VI was surveyed by two questions, one for seeing faces at a distance of 4 m and one for reading newspapers. Depressive symptoms were evaluated with the Patient Health Questionnaire-9 questionnaire and 2-week prevalence was computed with weighted data. Depressive symptoms were defined by a value of ≥10. Logistic regression analysis was performed to analyze an association between self-reported VI and depressive symptoms. Multivariable analysis including adjustment for age, gender, socioeconomic status, and chronic diseases were carried out with weighted data. The 2-week prevalence of depressive symptoms was 20.8% (95% CI: 16.6-25.7%) for some difficulties in distance vision and 14.4% (95% CI: 7.5-25.9%) for severe difficulties in distance vision, while 17.0% (95% CI: 13.3-21.4%), respectively, 16.7% (95% CI: 10.7-25.1%) for near vision. Analysis revealed that depressive symptoms were associated with self-reported VI for reading, respectively, with low VI for distance vision. Multivariable regression analysis including potential confounders confirmed these findings. Depressive symptoms are a frequent finding in subjects with difficulties in distance and near vision with a prevalence of up to 24%. Depressive comorbidity should therefore be evaluated in subjects reporting VI.

Robustness results in LQG based multivariable control designs

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.; Sandell, N. R., Jr.; Athans, M.

1980-01-01

The robustness of control systems with respect to model uncertainty is considered using simple frequency domain criteria. Results are derived under a common framework in which the minimum singular value of the return difference transfer matrix is the key quantity. In particular, the LQ and LQG robustness results are discussed.

A multivariant study of the absorption properties of poly(glutaric-acid-glycerol) films

USDA-ARS?s Scientific Manuscript database

The solvent absorption into the matrix of poly(glutaric acid-glycerol) films made with or without either iminodiacetic acid, sugarcane bagasse, pectin, corn fiber gum or microcrystalline cellulose have been evaluated. The films were incubated in various solvent systems for 24h. The amounts of solve...

Inference on the Ranks of the Canonical Correlation Matrices for Elliptically Symmetric Populations.

DTIC Science & Technology

1985-05-01

robust estimates of the covariance matrix, the reader is referred to Devlin, Gnanadesikan and Kettenring (1975) and Maronna (1976). Murihead and...contoured distributions. J. Multivariate Anal. 11, 368-385. 6. DEVLIN, S.J. GNANADESIKAN , R. and KETTENRING, J. (1975). Robust estima- tion and outlier

Source Apportionment of Primary and Secondary Organic Aerosol Using Positive Matrix Factorization (PMF) of Molecular Markers

EPA Science Inventory

Monthly average ambient concentrations of more than eighty particle-phase organic compounds, as well as total organic carbon (OC) and elemental carbon (EC), were measured from March 2004 through February 2005 in five cities in the Midwestern United States. A multi-variant source...

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution.

PubMed

Han, Fang; Liu, Han

2017-02-01

Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix. Although Pearson's sample correlation matrix enjoys various good properties under Gaussian models, its not an effective estimator when facing heavy-tail distributions with possible outliers. As a robust alternative, Han and Liu (2013b) advocated the use of a transformed version of the Kendall's tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall's tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) for estimating the population Kendall's tau correlation matrix and the latent Pearson's correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of "effective rank" in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a "sign subgaussian condition" which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence. In both cases, we do not need any moment condition.

Distance-Based Tear Lactoferrin Assay on Microfluidic Paper Device Using Interfacial Interactions on Surface-Modified Cellulose.

PubMed

Yamada, Kentaro; Henares, Terence G; Suzuki, Koji; Citterio, Daniel

2015-11-11

"Distance-based" detection motifs on microfluidic paper-based analytical devices (μPADs) allow quantitative analysis without using signal readout instruments in a similar manner to classical analogue thermometers. To realize a cost-effective and calibration-free distance-based assay of lactoferrin in human tear fluid on a μPAD not relying on antibodies or enzymes, we investigated the fluidic mobilities of the target protein and Tb(3+) cations used as the fluorescent detection reagent on surface-modified cellulosic filter papers. Chromatographic elution experiments in a tear-like sample matrix containing electrolytes and proteins revealed a collapse of attractive electrostatic interactions between lactoferrin or Tb(3+) and the cellulosic substrate, which was overcome by the modification of the paper surface with the sulfated polysaccharide ι-carrageenan. The resulting μPAD based on the fluorescence emission distance successfully analyzed 0-4 mg mL(-1) of lactoferrin in complex human tear matrix with a lower limit of detection of 0.1 mg mL(-1) by simple visual inspection. Assay results of 18 human tear samples including ocular disease patients and healthy volunteers showed good correlation to the reference ELISA method with a slope of 0.997 and a regression coefficient of 0.948. The distance-based quantitative signal and the good batch-to-batch fabrication reproducibility relying on printing methods enable quantitative analysis by simply reading out "concentration scale marks" printed on the μPAD without performing any calibration and using any signal readout instrument.

Gradient-based stochastic estimation of the density matrix

NASA Astrophysics Data System (ADS)

Wang, Zhentao; Chern, Gia-Wei; Batista, Cristian D.; Barros, Kipton

2018-03-01

Fast estimation of the single-particle density matrix is key to many applications in quantum chemistry and condensed matter physics. The best numerical methods leverage the fact that the density matrix elements f(H)ij decay rapidly with distance rij between orbitals. This decay is usually exponential. However, for the special case of metals at zero temperature, algebraic decay of the density matrix appears and poses a significant numerical challenge. We introduce a gradient-based probing method to estimate all local density matrix elements at a computational cost that scales linearly with system size. For zero-temperature metals, the stochastic error scales like S-(d+2)/2d, where d is the dimension and S is a prefactor to the computational cost. The convergence becomes exponential if the system is at finite temperature or is insulating.

Distribution of organic matrix in calcium oxalate renal calculi.

PubMed

Warpehoski, M A; Buscemi, P J; Osborn, D C; Finlayson, B; Goldberg, E P

1981-01-01

The quantity of protein and carbohydrate comprising the matrix of calcium oxalate monohydrate (COM) renal stones was found to decrease with distance from the surface of the stone. The average organic concentration of stones 3 to 30 mm in diameter ranged from 5.7% at the surface to 2.7% at the core. This concentration gradient suggests matrix involvement in a "growth front" on stone surfaces with migration of organic material from the "older" interior. The matrix distribution was not readily correlated with density variations or with the presence of hydroxyapatite or calcium oxalate dihydrate. Surface matrix concentrations were greater than amounts predicted by physical adsorption. Electron microscopy confirmed the presence of the organic-rich surface layer and also suggested that increase in stone size occurs predominantly by crystal growth with microcrystal aggregates as growth centers.

Screening effect in matrix graphene / SiC planar field emmiters

NASA Astrophysics Data System (ADS)

Jityaev, I. L.; Svetlichnyi, A. M.; Kolomiytsev, A. S.; Ageev, O. A.

2017-11-01

The paper describes simulation of matrix field emission nanostructures on the basis of graphene on a semi-insulating silicon carbide. The planar spike-type field emission cathodes were measured. The electric field distribution in an interelectrode gap of the emission structure was obtained. The models take into account the distance between cathode tops. Screening effect condition was detected in planar field emission structure and a way of eliminating was proposed.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

PubMed

Li, Xin; Bai, Yanqin; Peng, Yaxin; Du, Shaoyi; Ying, Shihui

2018-03-01

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.

A Case-Based Reasoning Method with Rank Aggregation

NASA Astrophysics Data System (ADS)

Sun, Jinhua; Du, Jiao; Hu, Jian

2018-03-01

In order to improve the accuracy of case-based reasoning (CBR), this paper addresses a new CBR framework with the basic principle of rank aggregation. First, the ranking methods are put forward in each attribute subspace of case. The ordering relation between cases on each attribute is got between cases. Then, a sorting matrix is got. Second, the similar case retrieval process from ranking matrix is transformed into a rank aggregation optimal problem, which uses the Kemeny optimal. On the basis, a rank aggregation case-based reasoning algorithm, named RA-CBR, is designed. The experiment result on UCI data sets shows that case retrieval accuracy of RA-CBR algorithm is higher than euclidean distance CBR and mahalanobis distance CBR testing.So we can get the conclusion that RA-CBR method can increase the performance and efficiency of CBR.

Pathloss Calculation Using the Transmission Line Matrix and Finite Difference Time Domain Methods With Coarse Grids

DOE PAGES

Nutaro, James; Kuruganti, Teja

2017-02-24

Numerical simulations of the wave equation that are intended to provide accurate time domain solutions require a computational mesh with grid points separated by a distance less than the wavelength of the source term and initial data. However, calculations of radio signal pathloss generally do not require accurate time domain solutions. This paper describes an approach for calculating pathloss by using the finite difference time domain and transmission line matrix models of wave propagation on a grid with points separated by distances much greater than the signal wavelength. The calculated pathloss can be kept close to the true value formore » freespace propagation with an appropriate selection of initial conditions. This method can also simulate diffraction with an error governed by the ratio of the signal wavelength to the grid spacing.« less

Self-assembly of an electronically conductive network through microporous scaffolds.

PubMed

Sebastian, H Bri; Bryant, Steven L

2017-06-15

Electron transfer spanning significant distances through a microporous structure was established via the self-assembly of an electronically conductive iridium oxide nanowire matrix enveloping the pore walls. Microporous formations were simulated using two scaffold materials of varying physical and chemical properties; paraffin wax beads, and agar gel. Following infiltration into the micropores, iridium nanoparticles self-assembled at the pore wall/ethanol interface. Subsequently, cyclic voltammetry was employed to electrochemically crosslink the metal, erecting an interconnected, and electronically conductive metal oxide nanowire matrix. Electrochemical and spectral characterization techniques confirmed the formation of oxide nanowire matrices encompassing lengths of at least 1.6mm, 400× distances previously achieved using iridium nanoparticles. Nanowire matrices were engaged as biofuel cell anodes, where electrons were donated to the nanowires by a glucose oxidizing enzyme. Copyright © 2017 Elsevier Inc. All rights reserved.

Structure-performance relationships of phenyl cinnamic acid derivatives as MALDI-MS matrices for sulfatide detection.

PubMed

Tambe, Suparna; Blott, Henning; Fülöp, Annabelle; Spang, Nils; Flottmann, Dirk; Bräse, Stefan; Hopf, Carsten; Junker, Hans-Dieter

2017-02-01

A key aspect for the further development of matrix-assisted laser desorption ionization (MALDI)-mass spectrometry (MS) is a better understanding of the working principles of MALDI matrices. To address this issue, a chemical compound library of 59 structurally related cinnamic acid derivatives was synthesized. Potential MALDI matrices were evaluated with sulfatides, a class of anionic lipids which are abundant in complex brain lipid mixtures. For each matrix relative mean S/N ratios of sulfatides were determined against 9-aminoacridine as a reference matrix using negative ion mass spectrometry with 355 and 337 nm laser systems. The comparison of matrix features with their corresponding relative mean S/N ratios for sulfatide detection identified correlations between matrix substitution patterns, their chemical functionality, and their MALDI-MS performance. Crystal structures of six selected matrices provided structural insight in hydrogen bond interactions in the solid state. Principal component analysis allowed the additional identification of correlation trends between structural and physical matrix properties like number of exchangeable protons at the head group, MW, logP, UV-Vis, and sulfatide detection sensitivity. Graphical abstract Design, synthesis and mass spectrometric evaluation of MALDI-MS matrix compound libraries allows the identification of matrix structure - MALDI-MS performance relationships using multivariate statistics as a tool.

Basic principles of Hasse diagram technique in chemistry.

PubMed

Brüggemann, Rainer; Voigt, Kristina

2008-11-01

Principles of partial order applied to ranking are explained. The Hasse diagram technique (HDT) is the application of partial order theory based on a data matrix. In this paper, HDT is introduced in a stepwise procedure, and some elementary theorems are exemplified. The focus is to show how the multivariate character of a data matrix is realized by HDT and in which cases one should apply other mathematical or statistical methods. Many simple examples illustrate the basic theoretical ideas. Finally, it is shown that HDT is a useful alternative for the evaluation of antifouling agents, which was originally performed by amoeba diagrams.

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

Distance-constrained orthogonal Latin squares for brain-computer interface.

PubMed

Luo, Gang; Min, Wanli

2012-02-01

The P300 brain-computer interface (BCI) using electroencephalogram (EEG) signals can allow amyotrophic lateral sclerosis (ALS) patients to instruct computers to perform tasks. To strengthen the P300 response and increase classification accuracy, we proposed an experimental design where characters are intensified according to orthogonal Latin square pairs. These orthogonal Latin square pairs satisfy certain distance constraint so that neighboring characters are not intensified simultaneously. However, it is unknown whether such distance-constrained, orthogonal Latin square pairs actually exist. In this paper, we show that for every matrix size commonly used in P300 BCI, thousands to millions of such distance-constrained, orthogonal Latin square pairs can be systematically and efficiently constructed and are sufficient for the purpose of being used in P300 BCI.

Relationship of biomarkers of extracellular matrix with myocardial function in Type 2 diabetes mellitus.

PubMed

Liu, Ju-Hua; Chen, Yan; Zhen, Zhe; Ho, Lai-Ming; Tsang, Anita; Yuen, Michele; Lam, Karen; Tse, Hung-Fat; Yiu, Kai-Hang

2017-07-01

The study evaluated the relationship of extracellular matrix and renin angiotensin system with myocardial dysfunction in Type 2 diabetes mellitus. All patients underwent resting and exercise echocardiography, including conventional parameters, E/E' ratio, global longitudinal strain and diastolic function reserve index. Plasma matrix metalloproteinase-1, TIMP-1, amino-terminal propeptide of type I and type III procollagen and renin angiotensin system activity were measured. As patients with diastolic dysfunction had a higher plasma level of TIMP-1 and propeptide of type III procollagen than those with no diastolic dysfunction. After multivariate adjustment, TIMP-1 associated with E/E' (both at rest and stress) and diastolic function reserve index. TIMP-1 is independently associated with myocardial diastolic dysfunction in patients with Type 2 diabetes mellitus.

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

Surnames in Honduras: A study of the population of Honduras through isonymy.

PubMed

Herrera Paz, Edwin Francisco; Scapoli, Chiara; Mamolini, Elisabetta; Sandri, Massimo; Carrieri, Alberto; Rodriguez-Larralde, Alvaro; Barrai, Italo

2014-05-01

In this work, we investigated surname distribution in 4,348,021 Honduran electors with the aim of detecting population structure through the study of isonymy in three administrative levels: the whole nation, the 18 departments, and the 298 municipalities. For each administrative level, we studied the surname effective number, α, the total inbreeding, FIT , the random inbreeding, FST , and the local inbreeding, FIS . Principal components analysis, multidimensional scaling, and cluster analysis were performed on Lasker's distance matrix to detect the direction of surname diffusion and for a graphic representation of the surname relationship between different locations. The values of FIT , FST , and FIS display a variation of random inbreeding between the administrative levels in the Honduras population, which is attributed to the "Prefecture effect." Multivariate analyses of department data identified two main clusters, one south-western and the second north-eastern, with the Bay Islands and the eastern Gracias a Dios out of the main clusters. The results suggest that currently the population structure of this country is the result of the joint action of short-range directional migration and drift, with drift dominating over migration, and that population diffusion may have taken place mainly in the NW-SE direction. © 2014 John Wiley & Sons Ltd/University College London.

Ornaments Reveal Resistance of North European Cultures to the Spread of Farming

PubMed Central

Rigaud, Solange; d'Errico, Francesco; Vanhaeren, Marian

2015-01-01

The transition to farming is the process by which human groups switched from hunting and gathering wild resources to food production. Understanding how and to what extent the spreading of farming communities from the Near East had an impact on indigenous foraging populations in Europe has been the subject of lively debates for decades. Ethnographic and archaeological studies have shown that population replacement and admixture, trade, and long distance diffusion of cultural traits lead to detectable changes in symbolic codes expressed by associations of ornaments on the human body. Here we use personal ornaments to document changes in cultural geography during the Mesolithic-Neolithic transition. We submitted a binary matrix of 224 bead-types found at 212 European Mesolithic and 222 Early Neolithic stratigraphic units to a series of spatial and multivariate analyses. Our results reveal consistent diachronic and geographical trends in the use of personal ornaments during the Neolithisation. Adoption of novel bead-types combined with selective appropriation of old attires by incoming farmers is identified in Southern and Central Europe while cultural resistance leading to the nearly exclusive persistence of indigenous personal ornaments characterizes Northern Europe. We argue that this pattern reflects two distinct cultural trajectories with different potential for gene flow. PMID:25853888

Dynamics of aquatic ecosystems and models under toxicant stress: State space analysis, covariance structure, and ecological risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, A.R.; Bartell, S.M.

1988-06-01

The state of an ecosystem at any time t may be characterized by a multidimensional state vector x(t). Changes in state are represented by the trajectory traced out by x(t) over time. The effects of toxicant stress are summarized by the displacement of a perturbed state vector, x/sub p/(t), relative to an appropriate control, x/sub c/(t). Within a multivariate statistical framework, the response of an ecosystem to perturbation is conveniently quantified by the distance separating x/sub p/(t) from x/sub c/(t) as measured by a Mahalanobis metric. Use of the Mahalanobis metric requires that the covariance matrix associated with the controlmore » state vector be estimated. State space displacement analysis was applied to data on the response of aquatic microcosms and outdoor ponds to alkylphenols. Dose-response relationships were derived using calculated state space separations as integrated measures of the ecological effects of toxicant exposure. Inspection of the data also revealed that the covariance structure varied both with time and with toxicant exposure, suggesting that analysis of such changes might be a useful tool for probing control mechanisms underlying ecosystem dynamics. 90 refs., 53 figs., 9 tabs.« less

Comparison of clast and matrix dispersal in till: Charlo-Atholville area, north-central New Brunswick

USGS Publications Warehouse

Dickson, M.L.; Broster, B.E.; Parkhill, M.A.

2004-01-01

Striations and dispersal patterns for till clasts and matrix geochemistry are used to define flow directions of glacial transport across an area of about 800km2 in the Charlo-Atholville area of north-central New Brunswick. A total of 170 clast samples and 328 till matrix samples collected for geochemical analysis across the region, were analyzed for a total of 39 elements. Major lithologic contacts used here to delineate till clast provenance were based on recent bedrock mapping. Eleven known mineral occurrences and a gossan are used to define point source targets for matrix geochemical dispersal trains and to estimate probable distance and direction of transport from unknown sources. Clast trains are traceable for distances of approximately 10 km, whereas till geochemical dispersal patterns are commonly lost within 5 km of transport. Most dispersal patterns reflect more than a single direction of glacial transport. These data indicate that a single till sheet, 1-4 m thick, was deposited as the dominant ice-flow direction fluctuated between southeastward, eastward, and northward over the study area. Directions of early flow represent changes in ice sheet dominance, first from the northwest and then from the west. Locally, eastward and northward flow represent the maximum erosive phases. The last directions of flow are likely due to late glacial ice sheet drawdown towards the valley outlet at Baie des Chaleurs.

Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

ERIC Educational Resources Information Center

Klauer, Karl Christoph

2010-01-01

Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…

Bootstrapping Cox’s Regression Model.

DTIC Science & Technology

1985-11-01

crucial points a multivariate martingale central limit theorem. Involved in this is a p x p covariance matrix Z with elements T j2= f {2(s8 ) - s(l)( s ,8o...1980). The statistical analaysis of failure time data. Wiley, New York. Meyer, P.-A. (1971). Square integrable martingales, a survey. Lecture Notes

SOURCE APPORTIONMENT OF PM2.5 AT AN URBAN IMPROVE SITE IN SEATTLE, WA

EPA Science Inventory

The multivariate receptor models Positive Matrix Factorization (PMF) and Unmix were used along with EPA's Chemical Mass Balance model to deduce the sources of PM2.5 at a centrally located urban site in Seattle, Washington. A total of 289 filter samples were obtained with an IM...

Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

NASA Astrophysics Data System (ADS)

Gürgey, K.; Canbolat, S.

2017-11-01

Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Does silvoagropecuary landscape fragmentation affect the genetic diversity of the sigmodontine rodent Oligoryzomys longicaudatus?

PubMed Central

Lazo-Cancino, Daniela; Musleh, Selim S.; Hernandez, Cristian E.; Palma, Eduardo

2017-01-01

Background Fragmentation of native forests is a highly visible result of human land-use throughout the world. In this study, we evaluated the effects of landscape fragmentation and matrix features on the genetic diversity and structure of Oligoryzomys longicaudatus, the natural reservoir of Hantavirus in southern South America. We focused our work in the Valdivian Rainforest where human activities have produced strong change of natural habitats, with an important number of human cases of Hantavirus. Methods We sampled specimens of O. longicaudatus from five native forest patches surrounded by silvoagropecuary matrix from Panguipulli, Los Rios Region, Chile. Using the hypervariable domain I (mtDNA), we characterized the genetic diversity and evaluated the effect of fragmentation and landscape matrix on the genetic structure of O. longicaudatus. For the latter, we used three approaches: (i) Isolation by Distance (IBD) as null model, (ii) Least-cost Path (LCP) where genetic distances between patch pairs increase with cost-weighted distances, and (iii) Isolation by Resistance (IBR) where the resistance distance is the average number of steps that is needed to commute between the patches during a random walk. Results We found low values of nucleotide diversity (π) for the five patches surveyed, ranging from 0.012 to 0.015, revealing that the 73 sampled specimens of this study belong to two populations but with low values of genetic distance (γST) ranging from 0.022 to 0.099. Likewise, we found that there are no significant associations between genetic distance and geographic distance for IBD and IBR. However, we found for the LCP approach, a significant positive relationship (r = 0.737, p = 0.05), with shortest least-cost paths traced through native forest and arborescent shrublands. Discussion In this work we found that, at this reduced geographical scale, Oligoryzomys longicaudatus shows genetic signs of fragmentation. In addition, we found that connectivity between full growth native forest remnants is mediated by the presence of dense shrublands and native forest corridors. In this sense, our results are important because they show how native forest patches and associated routes act as source of vector species in silvoagropecuary landscape, increasing the infection risk on human population. This study is the first approach to understand the epidemiological spatial context of silvoagropecuary risk of Hantavirus emergence. Further studies are needed to elucidate the effects of landscape fragmentation in order to generate new predictive models based on vector intrinsic attributes and landscape features. PMID:28975057

Does silvoagropecuary landscape fragmentation affect the genetic diversity of the sigmodontine rodent Oligoryzomys longicaudatus?

PubMed

Lazo-Cancino, Daniela; Musleh, Selim S; Hernandez, Cristian E; Palma, Eduardo; Rodriguez-Serrano, Enrique

2017-01-01

Fragmentation of native forests is a highly visible result of human land-use throughout the world. In this study, we evaluated the effects of landscape fragmentation and matrix features on the genetic diversity and structure of Oligoryzomys longicaudatus, the natural reservoir of Hantavirus in southern South America. We focused our work in the Valdivian Rainforest where human activities have produced strong change of natural habitats, with an important number of human cases of Hantavirus. We sampled specimens of O. longicaudatus from five native forest patches surrounded by silvoagropecuary matrix from Panguipulli, Los Rios Region, Chile. Using the hypervariable domain I (mtDNA), we characterized the genetic diversity and evaluated the effect of fragmentation and landscape matrix on the genetic structure of O. longicaudatus . For the latter, we used three approaches: (i) Isolation by Distance (IBD) as null model, (ii) Least-cost Path (LCP) where genetic distances between patch pairs increase with cost-weighted distances, and (iii) Isolation by Resistance (IBR) where the resistance distance is the average number of steps that is needed to commute between the patches during a random walk. We found low values of nucleotide diversity ( π ) for the five patches surveyed, ranging from 0.012 to 0.015, revealing that the 73 sampled specimens of this study belong to two populations but with low values of genetic distance ( γ ST ) ranging from 0.022 to 0.099. Likewise, we found that there are no significant associations between genetic distance and geographic distance for IBD and IBR. However, we found for the LCP approach, a significant positive relationship ( r  = 0.737, p  = 0.05), with shortest least-cost paths traced through native forest and arborescent shrublands. In this work we found that, at this reduced geographical scale , Oligoryzomys longicaudatus shows genetic signs of fragmentation. In addition, we found that connectivity between full growth native forest remnants is mediated by the presence of dense shrublands and native forest corridors. In this sense, our results are important because they show how native forest patches and associated routes act as source of vector species in silvoagropecuary landscape, increasing the infection risk on human population. This study is the first approach to understand the epidemiological spatial context of silvoagropecuary risk of Hantavirus emergence. Further studies are needed to elucidate the effects of landscape fragmentation in order to generate new predictive models based on vector intrinsic attributes and landscape features.

A comparison of phenotypic variation and covariation patterns and the role of phylogeny, ecology, and ontogeny during cranial evolution of new world monkeys.

PubMed

Marroig, G; Cheverud, J M

2001-12-01

Similarity of genetic and phenotypic variation patterns among populations is important for making quantitative inferences about past evolutionary forces acting to differentiate populations and for evaluating the evolution of relationships among traits in response to new functional and developmental relationships. Here, phenotypic co variance and correlation structure is compared among Platyrrhine Neotropical primates. Comparisons range from among species within a genus to the superfamily level. Matrix correlation followed by Mantel's test and vector correlation among responses to random natural selection vectors (random skewers) were used to compare correlation and variance/covariance matrices of 39 skull traits. Sampling errors involved in matrix estimates were taken into account in comparisons using matrix repeatability to set upper limits for each pairwise comparison. Results indicate that covariance structure is not strictly constant but that the amount of variance pattern divergence observed among taxa is generally low and not associated with taxonomic distance. Specific instances of divergence are identified. There is no correlation between the amount of divergence in covariance patterns among the 16 genera and their phylogenetic distance derived from a conjoint analysis of four already published nuclear gene datasets. In contrast, there is a significant correlation between phylogenetic distance and morphological distance (Mahalanobis distance among genus centroids). This result indicates that while the phenotypic means were evolving during the last 30 millions years of New World monkey evolution, phenotypic covariance structures of Neotropical primate skulls have remained relatively consistent. Neotropical primates can be divided into four major groups based on their feeding habits (fruit-leaves, seed-fruits, insect-fruits, and gum-insect-fruits). Differences in phenotypic covariance structure are correlated with differences in feeding habits, indicating that to some extent changes in interrelationships among skull traits are associated with changes in feeding habits. Finally, common patterns and levels of morphological integration are found among Platyrrhine primates, suggesting that functional/developmental integration could be one major factor keeping covariance structure relatively stable during evolutionary diversification of South American monkeys.

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information.

PubMed

Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S

2017-02-01

The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information. Copyright © 2016. Published by Elsevier Inc.

Establishing Benchmarks for Outcome Indicators: A Statistical Approach to Developing Performance Standards.

ERIC Educational Resources Information Center

Henry, Gary T.; And Others

1992-01-01

A statistical technique is presented for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit (a school district in the example), a unique comparison group is selected. (SLD)

An iterative technique to stabilize a linear time invariant multivariable system with output feedback

NASA Technical Reports Server (NTRS)

Sankaran, V.

1974-01-01

An iterative procedure for determining the constant gain matrix that will stabilize a linear constant multivariable system using output feedback is described. The use of this procedure avoids the transformation of variables which is required in other procedures. For the case in which the product of the output and input vector dimensions is greater than the number of states of the plant, general solution is given. In the case in which the states exceed the product of input and output vector dimensions, a least square solution which may not be stable in all cases is presented. The results are illustrated with examples.

Use of the Wii Gaming System for Balance Rehabilitation: Establishing Parameters for Healthy Individuals.

PubMed

Burns, Melissa K; Andeway, Kathleen; Eppenstein, Paula; Ruroede, Kathleen

2014-06-01

This study was designed to establish balance parameters for the Nintendo(®) (Redmond, WA) "Wii Fit™" Balance Board system with three common games, in a sample of healthy adults, and to evaluate the balance measurement reproducibility with separation by age. This was a prospective, multivariate analysis of variance, cohort study design. Seventy-five participants who satisfied all inclusion criteria and completed an informed consent were enrolled. Participants were grouped into age ranges: 21-35 years (n=24), 36-50 years (n=24), and 51-65 years (n=27). Each participant completed the following games three consecutive times, in a randomized order, during one session: "Balance Bubble" (BB) for distance and duration, "Tight Rope" (TR) for distance and duration, and "Center of Balance" (COB) on the left and right sides. COB distributed weight was fairly symmetrical across all subjects and trials; therefore, no influence was assumed on or interaction with other "Wii Fit" measurements. Homogeneity of variance statistics indicated the assumption of distribution normality of the dependent variables (rates) were tenable. The multivariate analysis of variance included dependent variables BB and TR rates (distance divided by duration to complete) with age group and trials as the independent variables. The BB rate was statistically significant (F=4.725, P<0.005), but not the TR rate. The youngest group's BB rate was significantly larger than those of the other two groups. "Wii Fit" can discriminate among age groups across trials. The results show promise as a viable tool to measure balance and distance across time (speed) and center of balance distribution.

Prevalence, Correlates, and Impact of Uncorrected Presbyopia in a Multiethnic Asian Population.

PubMed

Kidd Man, Ryan Eyn; Fenwick, Eva Katie; Sabanayagam, Charumathi; Li, Ling-Jun; Gupta, Preeti; Tham, Yih-Chung; Wong, Tien Yin; Cheng, Ching-Yu; Lamoureux, Ecosse Luc

2016-08-01

To examine the prevalence, correlates, and impact of uncorrected presbyopia on vision-specific functioning (VF) in a multiethnic Asian population. Population-based cross-sectional study. We included 7890 presbyopic subjects (3909 female; age range, 40-86 years) of Malay, Indian, and Chinese ethnicities from the Singapore Epidemiology of Eye Disease study. Presbyopia was classified as corrected and uncorrected based on self-reported near correction use. VF was assessed with the VF-11 questionnaire validated using Rasch analysis. Multivariable logistic and linear regression models were used to investigate the associations of sociodemographic and clinical parameters with uncorrected presbyopia, and its impact on VF, respectively. As myopia may mitigate the impact of noncorrection, we performed a subgroup analysis on myopic subjects only (n = 2742). In total, 2678 of 7890 subjects (33.9%) had uncorrected presbyopia. In multivariable models, younger age, male sex, Malay and Indian ethnicities, presenting distance visual impairment (any eye), and lower education and income levels were associated with higher odds of uncorrected presbyopia (all P < .05). Compared with corrected presbyopia, noncorrection was associated with worse overall VF and reduced ability to perform individual near and distance vision-specific tasks even after adjusting for distance VA and other confounders (all P < .05). Results were very similar for myopic individuals. One-third of presbyopic Singaporean adults did not have near correction. Given its detrimental impact on both near and distance VF, public health strategies to increase uptake of presbyopic correction in younger individuals, male individuals, and those of Malay and Indian ethnicities are needed. Copyright © 2016 Elsevier Inc. All rights reserved.

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution

PubMed Central

Han, Fang; Liu, Han

2016-01-01

Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson’s sample correlation matrix. Although Pearson’s sample correlation matrix enjoys various good properties under Gaussian models, its not an effective estimator when facing heavy-tail distributions with possible outliers. As a robust alternative, Han and Liu (2013b) advocated the use of a transformed version of the Kendall’s tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall’s tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) for estimating the population Kendall’s tau correlation matrix and the latent Pearson’s correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of “effective rank” in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a “sign subgaussian condition” which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence. In both cases, we do not need any moment condition. PMID:28337068

Turbine bucket for use in gas turbine engines and methods for fabricating the same

DOEpatents

Garcia-Crespo, Andres

2014-06-03

A turbine bucket for use with a turbine engine. The turbine bucket includes an airfoil that extends between a root end and a tip end. The airfoil includes an outer wall that defines a cavity that extends from the root end to the tip end. The outer wall includes a first ceramic matrix composite (CMC) substrate that extends a first distance from the root end to the tip end. An inner wall is positioned within the cavity. The inner wall includes a second CMC substrate that extends a second distance from the root end towards the tip end that is different than the first distance.

Almost all quantum channels are equidistant

NASA Astrophysics Data System (ADS)

Nechita, Ion; Puchała, Zbigniew; Pawela, Łukasz; Życzkowski, Karol

2018-05-01

In this work, we analyze properties of generic quantum channels in the case of large system size. We use random matrix theory and free probability to show that the distance between two independent random channels converges to a constant value as the dimension of the system grows larger. As a measure of the distance we use the diamond norm. In the case of a flat Hilbert-Schmidt distribution on quantum channels, we obtain that the distance converges to 1/2 +2/π , giving also an estimate for the maximum success probability for distinguishing the channels. We also consider the problem of distinguishing two random unitary rotations.

TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION

PubMed Central

Allen, Genevera I.; Tibshirani, Robert

2015-01-01

Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal, in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility. PMID:26877823

TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.

PubMed

Allen, Genevera I; Tibshirani, Robert

2010-06-01

Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable , meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal , in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.

Suture, synthetic, or biologic in contaminated ventral hernia repair.

PubMed

Bondre, Ioana L; Holihan, Julie L; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-02-01

Data are lacking to support the choice between suture, synthetic mesh, or biologic matrix in contaminated ventral hernia repair (VHR). We hypothesize that in contaminated VHR, suture repair is associated with the lowest rate of surgical site infection (SSI). A multicenter database of all open VHR performed at from 2010-2011 was reviewed. All patients with follow-up of 1 mo and longer were included. The primary outcome was SSI as defined by the Centers for Disease Control and Prevention. The secondary outcome was hernia recurrence (assessed clinically or radiographically). Multivariate analysis (stepwise regression for SSI and Cox proportional hazard model for recurrence) was performed. A total of 761 VHR were reviewed for a median (range) follow-up of 15 (1-50) mo: there were 291(38%) suture, 303 (40%) low-density and/or mid-density synthetic mesh, and 167(22%) biologic matrix repair. On univariate analysis, there were differences in the three groups including ethnicity, ASA, body mass index, institution, diabetes, primary versus incisional hernia, wound class, hernia size, prior VHR, fascial release, skin flaps, and acute repair. The unadjusted outcomes for SSI (15.1%; 17.8%; 21.0%; P = 0.280) and recurrence (17.8%; 13.5%; 21.5%; P = 0.074) were not statistically different between groups. On multivariate analysis, biologic matrix was associated with a nonsignificant reduction in both SSI and recurrences, whereas synthetic mesh associated with fewer recurrences compared to suture (hazard ratio = 0.60; P = 0.015) and nonsignificant increase in SSI. Interval estimates favored biologic matrix repair in contaminated VHR; however, these results were not statistically significant. In the absence of higher level evidence, surgeons should carefully balance risk, cost, and benefits in managing contaminated ventral hernia repair. Copyright © 2016 Elsevier Inc. All rights reserved.

Second-order standard addition for deconvolution and quantification of fatty acids of fish oil using GC-MS.

PubMed

Vosough, Maryam; Salemi, Amir

2007-08-15

In the present work two second-order calibration methods, generalized rank annihilation method (GRAM) and multivariate curve resolution-alternating least square (MCR-ALS) have been applied on standard addition data matrices obtained by gas chromatography-mass spectrometry (GC-MS) to characterize and quantify four unsaturated fatty acids cis-9-hexadecenoic acid (C16:1omega7c), cis-9-octadecenoic acid (C18:1omega9c), cis-11-eicosenoic acid (C20:1omega9) and cis-13-docosenoic acid (C22:1omega9) in fish oil considering matrix interferences. With these methods, the area does not need to be directly measured and predictions are more accurate. Because of non-trilinear conditions of GC-MS data matrices, at first MCR-ALS and GRAM have been used on uncorrected data matrices. In comparison to MCR-ALS, biased and imprecise concentrations (%R.S.D.=27.3) were obtained using GRAM without correcting the retention time-shift. As trilinearity is the essential requirement for implementing GRAM, the data need to be corrected. Multivariate rank alignment objectively corrects the run-to-run retention time variations between sample GC-MS data matrix and a standard addition GC-MS data matrix. Then, two second-order algorithms have been compared with each other. The above algorithms provided similar mean predictions, pure concentrations and spectral profiles. The results validated using standard mass spectra of target compounds. In addition, some of the quantification results were compared with the concentration values obtained using the selected mass chromatograms. As in the case of strong peak-overlap and the matrix effect, the classical univariate method of determination of the area of the peaks of the analytes will fail, the "second-order advantage" has solved this problem successfully.

Learning object correspondences with the observed transport shape measure.

PubMed

Pitiot, Alain; Delingette, Hervé; Toga, Arthur W; Thompson, Paul M

2003-07-01

We propose a learning method which introduces explicit knowledge to the object correspondence problem. Our approach uses an a priori learning set to compute a dense correspondence field between two objects, where the characteristics of the field bear close resemblance to those in the learning set. We introduce a new local shape measure we call the "observed transport measure", whose properties make it particularly amenable to the matching problem. From the values of our measure obtained at every point of the objects to be matched, we compute a distance matrix which embeds the correspondence problem in a highly expressive and redundant construct and facilitates its manipulation. We present two learning strategies that rely on the distance matrix and discuss their applications to the matching of a variety of 1-D, 2-D and 3-D objects, including the corpus callosum and ventricular surfaces.

Density-matrix simulation of small surface codes under current and projected experimental noise

NASA Astrophysics Data System (ADS)

O'Brien, T. E.; Tarasinski, B.; DiCarlo, L.

2017-09-01

We present a density-matrix simulation of the quantum memory and computing performance of the distance-3 logical qubit Surface-17, following a recently proposed quantum circuit and using experimental error parameters for transmon qubits in a planar circuit QED architecture. We use this simulation to optimize components of the QEC scheme (e.g., trading off stabilizer measurement infidelity for reduced cycle time) and to investigate the benefits of feedback harnessing the fundamental asymmetry of relaxation-dominated error in the constituent transmons. A lower-order approximate calculation extends these predictions to the distance-5 Surface-49. These results clearly indicate error rates below the fault-tolerance threshold of the surface code, and the potential for Surface-17 to perform beyond the break-even point of quantum memory. However, Surface-49 is required to surpass the break-even point of computation at state-of-the-art qubit relaxation times and readout speeds.

Low energy sign illumination system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minogue, R.W.

A low energy sign contruction is illustrated for illumination of signs of the type having translucent illuminated faces. An opaque sign border is bridged by a reflector extending generally parallel to the illuminated face and having a truncated sawtooth profile. For single sided signs, one set of sawtooth points is truncated; for dual sided signs, both set of sawtooth points are truncated. Bayonet mounted lighting sockets are mounted at apertures in the respective truncations and utilize the metallic reflective surface as one side of a low voltage (10.5-volt) ac circuit. The reflector forms a cooled heat sink mounting the bulbsmore » as well as a supporting matrix. The lamps, as mounted to this supporting matrix, are typically spaced at distances which do not exceed twice the distance of the lamp filament to the translucent face. By the expedient of using 14-V lamps, prolonged lamp life with low energy illumination results.« less

Oscillation properties of active and sterile neutrinos and neutrino anomalies at short distances

NASA Astrophysics Data System (ADS)

Khruschov, V. V.; Fomichev, S. V.; Titov, O. A.

2016-09-01

A generalized phenomenological (3 + 2 + 1) model featuring three active and three sterile neutrinos that is intended for calculating oscillation properties of neutrinos for the case of a normal activeneutrino mass hierarchy and a large splitting between the mass of one sterile neutrino and the masses of the other two sterile neutrinos is considered. A new parametrization and a specific form of the general mixing matrix are proposed for active and sterile neutrinos with allowance for possible CP violation in the lepton sector, and test values are chosen for the neutrino masses and mixing parameters. The probabilities for the transitions between different neutrino flavors are calculated, and graphs representing the probabilities for the disappearance of muon neutrinos/antineutrinos and the appearance of electron neutrinos/antineutrinos in a beam of muon neutrinos/antineutrinos versus the distance from the neutrino source for various values of admissible model parameters at neutrino energies not higher than 50 MeV, as well as versus the ratio of this distance to the neutrino energy, are plotted. It is shown that the short-distance accelerator anomaly in neutrino data (LNSD anomaly) can be explained in the case of a specific mixing matrix for active and sterile neutrinos (which belongs to the a 2 type) at the chosen parameter values. The same applies to the short-distance reactor and gallium anomalies. The theoretical results obtained in the present study can be used to interpret and predict the results of ground-based neutrino experiments aimed at searches for sterile neutrinos, as well as to analyze some astrophysical observational data.

Synergistic interactions between edge and area effects in a heavily fragmented landscape.

PubMed

Ewers, Robert M; Thorpe, Stephen; Didham, Raphael K

2007-01-01

Both area and edge effects have a strong influence on ecological processes in fragmented landscapes, but there is little understanding of how these two factors might interact to exacerbate local species declines. To test for synergistic interactions between area and edge effects, we sampled a diverse beetle community in a heavily fragmented landscape in New Zealand. More than 35,000 beetles of approximately 900 species were sampled over large gradients in habitat area (10(-2) 10(6) ha) and distance from patch edge (2(0)-2(10) m from the forest edge into both the forest and adjacent matrix). Using a new approach to partition variance following an ordination analysis, we found that a synergistic interaction between habitat area and distance to edge was a more important determinant of patterns in beetle community composition than direct edge or area effects alone. The strength of edge effects in beetle-species composition increased nonlinearly with increasing fragment area. One important consequence of the synergy is that the slopes of species area (SA) curves constructed from habitat islands depend sensitively on the distance from edge at which sampling is conducted. Surprisingly, we found negative SA curves for communities sampled at intermediate distances from habitat edges, caused by differential edge responses of matrix- vs. forest-specialist species in fragments of increasing area. Our data indicate that distance to habitat edge has a consistently greater impact on beetle community composition than habitat area and that variation in the strength of edge effects may underlie many patterns that are superficially related to habitat area.

Is Hidden Crossings Theory a New MOCC Method?

NASA Astrophysics Data System (ADS)

Krstić, Predrag; Schultz, David

1998-05-01

We find un unitary transformation of the scaled adiabatic Hamiltonian of a two-center, one-electron collision system which yields a new representation for the matrix elements of nonadiabatic radial coupling, valid for low-to-intermediate collision velocities. These are given in analytic form once the topology of the branch points of the adiabatic Hamiltonian in the plane of complex internuclear distance R is known. The matrix elements do not depend on origin of electronic coordinates and properly vanish at large internuclear distances. The role of the rotational couplings in the new representation is also discussed. The aproach is appropriately extended and compared with the PSS treatment in the fully quantal description of the collision. We apply new radial and rotational matrix elements in the standard Molecular Orbital Close Coupling (MOCC) approach to describe excitation and ionization in collisions of antiprotons with He^+ and of alpha-particles with hydrogen(P.S. Krstić et al, J. Phys. B. 31, in press (1998).). The results are compared with those obtained from the standard MOCC method and from the direct solutions of the Schrödinger equation on lattice (LTDSE)(D.R. Schultz et al, Phys. Rev. A 56, 3710 (1997)).

Method of locating related items in a geometric space for data mining

DOEpatents

Hendrickson, B.A.

1999-07-27

A method for locating related items in a geometric space transforms relationships among items to geometric locations. The method locates items in the geometric space so that the distance between items corresponds to the degree of relatedness. The method facilitates communication of the structure of the relationships among the items. The method is especially beneficial for communicating databases with many items, and with non-regular relationship patterns. Examples of such databases include databases containing items such as scientific papers or patents, related by citations or keywords. A computer system adapted for practice of the present invention can include a processor, a storage subsystem, a display device, and computer software to direct the location and display of the entities. The method comprises assigning numeric values as a measure of similarity between each pairing of items. A matrix is constructed, based on the numeric values. The eigenvectors and eigenvalues of the matrix are determined. Each item is located in the geometric space at coordinates determined from the eigenvectors and eigenvalues. Proper construction of the matrix and proper determination of coordinates from eigenvectors can ensure that distance between items in the geometric space is representative of the numeric value measure of the items' similarity. 12 figs.

Method of locating related items in a geometric space for data mining

DOEpatents

Hendrickson, Bruce A.

1999-01-01

A method for locating related items in a geometric space transforms relationships among items to geometric locations. The method locates items in the geometric space so that the distance between items corresponds to the degree of relatedness. The method facilitates communication of the structure of the relationships among the items. The method is especially beneficial for communicating databases with many items, and with non-regular relationship patterns. Examples of such databases include databases containing items such as scientific papers or patents, related by citations or keywords. A computer system adapted for practice of the present invention can include a processor, a storage subsystem, a display device, and computer software to direct the location and display of the entities. The method comprises assigning numeric values as a measure of similarity between each pairing of items. A matrix is constructed, based on the numeric values. The eigenvectors and eigenvalues of the matrix are determined. Each item is located in the geometric space at coordinates determined from the eigenvectors and eigenvalues. Proper construction of the matrix and proper determination of coordinates from eigenvectors can ensure that distance between items in the geometric space is representative of the numeric value measure of the items' similarity.

Discriminant projective non-negative matrix factorization.

PubMed

Guan, Naiyang; Zhang, Xiang; Luo, Zhigang; Tao, Dacheng; Yang, Xuejun

2013-01-01

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms.

Discriminant Projective Non-Negative Matrix Factorization

PubMed Central

Guan, Naiyang; Zhang, Xiang; Luo, Zhigang; Tao, Dacheng; Yang, Xuejun

2013-01-01

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers WT X as their coefficients, i.e., X≈WWT X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms. PMID:24376680

Molecular analysis of genetic diversity among vine accessions using DNA markers.

PubMed

da Costa, A F; Teodoro, P E; Bhering, L L; Tardin, F D; Daher, R F; Campos, W F; Viana, A P; Pereira, M G

2017-04-13

Viticulture presents a number of economic and social advantages, such as increasing employment levels and fixing the labor force in rural areas. With the aim of initiating a program of genetic improvement in grapevine from the State University of the state of Rio de Janeiro North Darcy Ribeiro, genetic diversity between 40 genotypes (varieties, rootstock, and species of different subgenera) was evaluated using Random amplified polymorphic DNA (RAPD) molecular markers. We built a matrix of binary data, whereby the presence of a band was assigned as "1" and the absence of a band was assigned as "0." The genetic distance was calculated between pairs of genotypes based on the arithmetic complement from the Jaccard Index. The results revealed the presence of considerable variability in the collection. Analysis of the genetic dissimilarity matrix revealed that the most dissimilar genotypes were Rupestris du Lot and Vitis rotundifolia because they were the most genetically distant (0.5972). The most similar were genotypes 31 (unidentified) and Rupestris du lot, which showed zero distance, confirming the results of field observations. A duplicate was confirmed, consistent with field observations, and a short distance was found between the variety 'Italy' and its mutation, 'Ruby'. The grouping methods used were somewhat concordant.

Survey of Quantification and Distance Functions Used for Internet-based Weak-link Sociological Phenomena

DTIC Science & Technology

2016-03-01

well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology literature for the relevant...Google matrix, PageRank as well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology...The PI studied all mathematical literature he can find related to the Google search engine, Google matrix, PageRank as well as the Yahoo search

CD-Based Indices for Link Prediction in Complex Network.

PubMed

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

2016-01-01

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.

CD-Based Indices for Link Prediction in Complex Network

PubMed Central

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

2016-01-01

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks. PMID:26752405

Geometrical eigen-subspace framework based molecular conformation representation for efficient structure recognition and comparison

NASA Astrophysics Data System (ADS)

Li, Xiao-Tian; Yang, Xiao-Bao; Zhao, Yu-Jun

2017-04-01

We have developed an extended distance matrix approach to study the molecular geometric configuration through spectral decomposition. It is shown that the positions of all atoms in the eigen-space can be specified precisely by their eigen-coordinates, while the refined atomic eigen-subspace projection array adopted in our approach is demonstrated to be a competent invariant in structure comparison. Furthermore, a visual eigen-subspace projection function (EPF) is derived to characterize the surrounding configuration of an atom naturally. A complete set of atomic EPFs constitute an intrinsic representation of molecular conformation, based on which the interatomic EPF distance and intermolecular EPF distance can be reasonably defined. Exemplified with a few cases, the intermolecular EPF distance shows exceptional rationality and efficiency in structure recognition and comparison.

Optimal Frequency-Domain System Realization with Weighting

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Maghami, Peiman G.

1999-01-01

Several approaches are presented to identify an experimental system model directly from frequency response data. The formulation uses a matrix-fraction description as the model structure. Frequency weighting such as exponential weighting is introduced to solve a weighted least-squares problem to obtain the coefficient matrices for the matrix-fraction description. A multi-variable state-space model can then be formed using the coefficient matrices of the matrix-fraction description. Three different approaches are introduced to fine-tune the model using nonlinear programming methods to minimize the desired cost function. The first method uses an eigenvalue assignment technique to reassign a subset of system poles to improve the identified model. The second method deals with the model in the real Schur or modal form, reassigns a subset of system poles, and adjusts the columns (rows) of the input (output) influence matrix using a nonlinear optimizer. The third method also optimizes a subset of poles, but the input and output influence matrices are refined at every optimization step through least-squares procedures.

Multivariate curve resolution-alternating least squares and kinetic modeling applied to near-infrared data from curing reactions of epoxy resins: mechanistic approach and estimation of kinetic rate constants.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2006-02-01

This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Investigating the sex-related geometric variation of the human cranium.

PubMed

Bertsatos, Andreas; Papageorgopoulou, Christina; Valakos, Efstratios; Chovalopoulou, Maria-Eleni

2018-01-29

Accurate sexing methods are of great importance in forensic anthropology since sex assessment is among the principal tasks when examining human skeletal remains. The present study explores a novel approach in assessing the most accurate metric traits of the human cranium for sex estimation based on 80 ectocranial landmarks from 176 modern individuals of known age and sex from the Athens Collection. The purpose of the study is to identify those distance and angle measurements that can be most effectively used in sex assessment. Three-dimensional landmark coordinates were digitized with a Microscribe 3DX and analyzed in GNU Octave. An iterative linear discriminant analysis of all possible combinations of landmarks was performed for each unique set of the 3160 distances and 246,480 angles. Cross-validated correct classification as well as multivariate DFA on top performing variables reported 13 craniometric distances with over 85% classification accuracy, 7 angles over 78%, as well as certain multivariate combinations yielding over 95%. Linear regression of these variables with the centroid size was used to assess their relation to the size of the cranium. In contrast to the use of generalized procrustes analysis (GPA) and principal component analysis (PCA), which constitute the common analytical work flow for such data, our method, although computational intensive, produced easily applicable discriminant functions of high accuracy, while at the same time explored the maximum of cranial variability.

Vectors, Change of Basis and Matrix Representation: Onto-Semiotic Approach in the Analysis of Creating Meaning

ERIC Educational Resources Information Center

Montiel, Mariana; Wilhelmi, Miguel R.; Vidakovic, Draga; Elstak, Iwan

2012-01-01

In a previous study, the onto-semiotic approach was employed to analyse the mathematical notion of different coordinate systems, as well as some situations and university students' actions related to these coordinate systems in the context of multivariate calculus. This study approaches different coordinate systems through the process of change of…

On the Evaluation of Certain Multivariate Normal Probabilities.

DTIC Science & Technology

1982-08-12

a "single- factor matrix" in one context of factor analysis. In this case, we have (Pk 111 n 0# td i-i 2./- jwk+lI. where the sumation is taken over...is an equicorrelation matrix with p > 0, Pk Q k .j * ~sP (t)dt For c O, we have p(n) T, tn % td Pn = tht () 1 1 -pad (3)1 it is well known that pM2 CO...ZI,*.*.,±Zn_ 1 ± Zn) and proceed as above. Another situation for which combinatorial arguments are known are for t with a j = JAJ Vij. This t is

Fluid-driven Fractures and Backflow in a Multilayered Elastic Matrix

NASA Astrophysics Data System (ADS)

Smiddy, Samuel; Lai, Ching-Yao; Stone, Howard

2016-11-01

We study the dynamics when pressurized fluid is injected at a constant flow rate into a multi-layered elastic matrix. In particular, we report experiments of such crack propagation as a function of orientation and distance from the contact of the layers. Subsequently we study the shape and propagation of the fluid along the contact of layers as well as volume of fluid remaining in the matrix once the injection pressure is released and "flowback" occurs. The experiments presented here may mimic the interaction between hydraulic fractures and pre-existing fractures and the dynamics of flowback in hydraulic fracturing. Study made possible by the Andlinger Center for Energy and the Environment and the Fred Fox Fund.

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

PubMed

Zhang, Yilong; Han, Sung Won; Cox, Laura M; Li, Huilin

2017-12-01

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code. © 2017 WILEY PERIODICALS, INC.

An information-based network approach for protein classification

PubMed Central

Wan, Xiaogeng; Zhao, Xin; Yau, Stephen S. T.

2017-01-01

Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. PMID:28350835

Decisive role of magnetism in the interaction of chromium and nickel solute atoms with 1/2$$\\langle$$111$$\\rangle$$-screw dislocation core in body-centered cubic iron

DOE PAGES

Odbadrakh, Kh.; Samolyuk, G.; Nicholson, D.; ...

2016-09-13

Resistance to swelling under irradiation and a low rate of corrosion in high temperature environments make Fe-Cr and Fe-Cr-Ni alloys promising structural materials for energy technologies. In this paper we report the results obtained using a combination of density functional theory (DFT) techniques: plane wave basis set solutions for pseudo-potentials and multiple scattering solutions for all electron potentials. We have found a very strong role of magnetism in the stability of screw dislocation cores in pure Fe and their interaction with Cr and Ni magnetic impurities. In particular, the screw dislocation quadrupole in Fe is stabilized only in the presencemore » of ferromagnetism. In addition, Ni atoms, who's magnetic moment is oriented along the magnetization direction of the Fe matrix, prefer to occupy in core positions whereas Cr atoms, which couple anti-ferromagnetically with the Fe matrix, prefer out of the dislocation core positions. In effect, Ni impurities are attracted to, while Cr impurities are repelled by the dislocation core. Moreover, we demonstrate that this contrasting behavior can be explained only by the nature of magnetic coupling of the impurities to the Fe matrix. In addition, Cr interaction with the dislocation core mirrors that of Ni if the Cr magnetic moment is constrained to be along the direction of Fe matrix magnetization. In addition, we have shown that the magnetic contribution can affect the impurity-impurity interaction at distances up to a few Burgers vectors. In particular, the distance between Cr atoms in Fe matrix should be at least 3–4 lattice parameters in order to eliminate finite size effects.« less

Geometrical analysis of Cys-Cys bridges in proteins and their prediction from incomplete structural information

NASA Technical Reports Server (NTRS)

Goldblum, A.; Rein, R.

1987-01-01

Analysis of C-alpha atom positions from cysteines involved in disulphide bridges in protein crystals shows that their geometric characteristics are unique with respect to other Cys-Cys, non-bridging pairs. They may be used for predicting disulphide connections in incompletely determined protein structures, such as low resolution crystallography or theoretical folding experiments. The basic unit for analysis and prediction is the 3 x 3 distance matrix for Cx positions of residues (i - 1), Cys(i), (i +1) with (j - 1), Cys(j), (j + 1). In each of its columns, row and diagonal vector--outer distances are larger than the central distance. This analysis is compared with some analytical models.

Compensator improvement for multivariable control systems

NASA Technical Reports Server (NTRS)

Mitchell, J. R.; Mcdaniel, W. L., Jr.; Gresham, L. L.

1977-01-01

A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.

Cyclic Fiber Push-In Test Monitors Evolution of Interfacial Behavior in Ceramic Matrix Composites

NASA Technical Reports Server (NTRS)

Eldridge, Jeffrey I.

1998-01-01

SiC fiber-reinforced ceramic matrix composites are being developed for high-temperature advanced jet engine applications. Obtaining a strong, tough composite material depends critically on optimizing the mechanical coupling between the reinforcing fibers and the surrounding matrix material. This has usually been accomplished by applying a thin C or BN coating onto the surface of the reinforcing fibers. The performance of these fiber coatings, however, may degrade under cyclic loading conditions or exposure to different environments. Degradation of the coating-controlled interfacial behavior will strongly affect the useful service lifetime of the composite material. Cyclic fiber push-in testing was applied to monitor the evolution of fiber sliding behavior in both C- and BN-coated small-diameter (15-mm) SiC-fiber-reinforced ceramic matrix composites. The cyclic fiber push-in tests were performed using a desktop fiber push-out apparatus. At the beginning of each test, the fiber to be tested was aligned underneath a 10- mm-diameter diamond punch; then, the applied load was cycled between selected maximum and minimum loads. From the measured response, the fiber sliding distance and frictional sliding stresses were determined for each cycle. Tests were performed in both room air and nitrogen. Cyclic fiber push-in tests of C-coated, SiC-fiber-reinforced SiC showed progressive increases in fiber sliding distances along with decreases in frictional sliding stresses for continued cycling in room air. This rapid degradation in interfacial response was not observed for cycling in nitrogen, indicating that moisture exposure had a large effect in immediately lowering the frictional sliding stresses of C-coated fibers. These results indicate that matrix cracks bridged by C-coated fibers will not be stable, but will rapidly grow in moisture-containing environments. In contrast, cyclic fiber push-in tests of both BN-coated, SiC-fiber-reinforced SiC and BNcoated, SiC-fiber-reinforced barium strontium aluminosilicate showed no significant changes in fiber sliding behavior with continued short-term cycling in either room air or nitrogen. Although the composites with BN-coated fibers showed stable short-term cycling behavior in both environments, long-term (several-week) exposure of debonded fibers to room air resulted in dramatically increased fiber sliding distances and decreased frictional sliding stresses. These results indicate that although matrix cracks bridged by BNcoated fibers will show short-term stability, such cracks will show substantial growth with long-term exposure to moisture-containing environments. Newly formulated BN coatings, with higher moisture resistance, will be tested in the near future.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

PubMed

Rongrong Ji; Hong Liu; Liujuan Cao; Di Liu; Yongjian Wu; Feiyue Huang

2017-11-01

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

[Prevalence of myopia and influencing factors among primary and middle school students in 6 provinces of China].

PubMed

Zhou, Jia; Ma, Yinghua; Ma, Jun; Zou, Zhiyong; Meng, Xiangkun; Tao, Fangbiao; Luo, Chunyan; Jing, Jin; Pan, Dehong; Luo, Jiayou; Zhang, Xin; Wang, Hong; Zhao, Haiping

2016-01-01

To understand the prevalence of myopia in primary and middle school students in 6 provinces and the possible influencing factors. Primary and middle school students were selected through multistage cluster sampling in 60 primary and middle schools in 6 provinces in China. The questionnaire survey and eyesight test were conducted among all the students selected according to the national student's physique and health survey protocol. Pearson chi-square test and binary multivariate logistic regression analysis were done to identify the influencing factors for myopia in students. The prevalence of myopia among primary and middle school students surveyed was 55.7%, the gender specific difference was statistically significant (59.7% for girls, 51.9% for boys) (P<0.01). The prevalence of myopia increased with age obviously. The prevalence was 35.8% in age group 6-8 years, 58.9% in age group 10-12 years, 73.4% in age group 13-15 years and 81.2% in age group 16-18 years, the differences were statistically significant (P<0.001). Single factor and multivariate analysis showed that parents' myopia, distance between computer screen and eyes, distance less than 30 cm between eyes and book while reading, distance less than 10 cm between chest and the table edge while studying, distance less than 3 cm between fingers and pen tip, sleep time, average outdoor activity time during last week, school sport activities in the afternoon, the size of television set at home, time spent on watching TV and playing computer were the influencing factors for myopia. The prevalence of myopia is till high in primary and middle school students. Myopia is associated with both genetic factors and individual eye health related behaviors.

Oscillation properties of active and sterile neutrinos and neutrino anomalies at short distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khruschov, V. V., E-mail: khruschov-vv@nrcki.ru; Fomichev, S. V., E-mail: fomichev-sv@nrcki.ru; Titov, O. A., E-mail: titov-oa@nrcki.ru

2016-09-15

A generalized phenomenological (3 + 2 + 1) model featuring three active and three sterile neutrinos that is intended for calculating oscillation properties of neutrinos for the case of a normal active neutrino mass hierarchy and a large splitting between the mass of one sterile neutrino and the masses of the other two sterile neutrinos is considered. A new parametrization and a specific form of the general mixing matrix are proposed for active and sterile neutrinos with allowance for possible CP violation in the lepton sector, and test values are chosen for the neutrino masses and mixing parameters. The probabilitiesmore » for the transitions between different neutrino flavors are calculated, and graphs representing the probabilities for the disappearance of muon neutrinos/antineutrinos and the appearance of electron neutrinos/antineutrinos in a beam of muon neutrinos/antineutrinos versus the distance from the neutrino source for various values of admissible model parameters at neutrino energies not higher than 50 MeV, as well as versus the ratio of this distance to the neutrino energy, are plotted. It is shown that the short-distance accelerator anomaly in neutrino data (LNSD anomaly) can be explained in the case of a specific mixing matrix for active and sterile neutrinos (which belongs to the a{sub 2} type) at the chosen parameter values. The same applies to the short-distance reactor and gallium anomalies. The theoretical results obtained in the present study can be used to interpret and predict the results of ground-based neutrino experiments aimed at searches for sterile neutrinos, as well as to analyze some astrophysical observational data.« less

Optimization and Analysis of Laser Beam Machining Parameters for Al7075-TiB2 In-situ Composite

NASA Astrophysics Data System (ADS)

Manjoth, S.; Keshavamurthy, R.; Pradeep Kumar, G. S.

2016-09-01

The paper focuses on laser beam machining (LBM) of In-situ synthesized Al7075-TiB2 metal matrix composite. Optimization and influence of laser machining process parameters on surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy of composites were studied. Al7075-TiB2 metal matrix composite was synthesized by in-situ reaction technique using stir casting process. Taguchi's L9 orthogonal array was used to design experimental trials. Standoff distance (SOD) (0.3 - 0.5mm), Cutting Speed (1000 - 1200 m/hr) and Gas pressure (0.5 - 0.7 bar) were considered as variable input parameters at three different levels, while power and nozzle diameter were maintained constant with air as assisting gas. Optimized process parameters for surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy were calculated by generating the main effects plot for signal noise ratio (S/N ratio) for surface roughness, VMRR and dimensional error using Minitab software (version 16). The Significant of standoff distance (SOD), cutting speed and gas pressure on surface roughness, volumetric material removal rate (VMRR) and dimensional error were calculated using analysis of variance (ANOVA) method. Results indicate that, for surface roughness, cutting speed (56.38%) is most significant parameter followed by standoff distance (41.03%) and gas pressure (2.6%). For volumetric material removal (VMRR), gas pressure (42.32%) is most significant parameter followed by cutting speed (33.60%) and standoff distance (24.06%). For dimensional error, Standoff distance (53.34%) is most significant parameter followed by cutting speed (34.12%) and gas pressure (12.53%). Further, verification experiments were carried out to confirm performance of optimized process parameters.

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Unraveling the Tangles of Language Evolution

NASA Astrophysics Data System (ADS)

Petroni, F.; Serva, M.; Volchenkov, D.

2012-07-01

The relationships between languages molded by extremely complex social, cultural and political factors are assessed by an automated method, in which the distance between languages is estimated by the average normalized Levenshtein distance between words from the list of 200 meanings maximally resistant to change. A sequential process of language classification described by random walks on the matrix of lexical distances allows to represent complex relationships between languages geometrically, in terms of distances and angles. We have tested the method on a sample of 50 Indo-European and 50 Austronesian languages. The geometric representations of language taxonomy allows for making accurate interfaces on the most significant events of human history by tracing changes in language families through time. The Anatolian and Kurgan hypothesis of the Indo-European origin and the "express train" model of the Polynesian origin are thoroughly discussed.

Cooperative Activated Transport of Dilute Penetrants in Viscous Molecular and Polymer Liquids

NASA Astrophysics Data System (ADS)

Schweizer, Kenneth; Zhang, Rui

We generalize the force-level Elastically Collective Nonlinear Langevin Equation theory of activated relaxation in one-component supercooled liquids to treat the hopping transport of a dilute penetrant in a dense hard sphere fluid. The new idea is to explicitly account for the coupling between penetrant displacement and a local matrix cage re-arrangement which facilitates its hopping. A temporal casuality condition is employed to self-consistently determine a dimensionless degree of matrix distortion relative to the penetrant jump distance using the dynamic free energy concept. Penetrant diffusion becomes increasingly coupled to the correlated matrix displacements for larger penetrant to matrix particle size ratio (R) and/or attraction strength (physical bonds), but depends weakly on matrix packing fraction. In the absence of attractions, a nearly exponential dependence of penetrant diffusivity on R is predicted in the intermediate range of 0.2

Wear study of Al-SiC metal matrix composites processed through microwave energy

NASA Astrophysics Data System (ADS)

Honnaiah, C.; Srinath, M. S.; Prasad, S. L. Ajit

2018-04-01

Particulate reinforced metal matrix composites are finding wider acceptance in many industrial applications due to their isotropic properties and ease of manufacture. Uniform distribution of reinforcement particulates and good bonding between matrix and reinforcement phases are essential features in order to obtain metal matrix composites with improved properties. Conventional powder metallurgy technique can successfully overcome the limitation of stir casting techniques, but it is time consuming and not cost effective. Use of microwave technology for processing particulate reinforced metal matrix composites through powder metallurgy technique is being increasingly explored in recent times because of its cost effectiveness and speed of processing. The present work is an attempt to process Al-SiC metal matrix composites using microwaves irradiated at 2.45 GHz frequency and 900 W power for 10 minutes. Further, dry sliding wear studies were conducted at different loads at constant velocity of 2 m/s for various sliding distances using pin-on-disc equipment. Analysis of the obtained results show that the microwave processed Al-SiC composite material shows around 34 % of resistance to wear than the aluminium alloy.

A matrix-inversion method for gamma-source mapping from gamma-count data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adsley, Ian; Burgess, Claire; Bull, Richard K

In a previous paper it was proposed that a simple matrix inversion method could be used to extract source distributions from gamma-count maps, using simple models to calculate the response matrix. The method was tested using numerically generated count maps. In the present work a 100 kBq Co{sup 60} source has been placed on a gridded surface and the count rate measured using a NaI scintillation detector. The resulting map of gamma counts was used as input to the matrix inversion procedure and the source position recovered. A multi-source array was simulated by superposition of several single-source count maps andmore » the source distribution was again recovered using matrix inversion. The measurements were performed for several detector heights. The effects of uncertainties in source-detector distances on the matrix inversion method are also examined. The results from this work give confidence in the application of the method to practical applications, such as the segregation of highly active objects amongst fuel-element debris. (authors)« less

IR-MALDESI MASS SPECTROMETRY IMAGING OF BIOLOGICAL TISSUE SECTIONS USING ICE AS A MATRIX

PubMed Central

Robichaud, Guillaume; Barry, Jeremy A.; Muddiman, David C.

2014-01-01

Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) Mass Spectrometry imaging of biological tissue sections using a layer of deposited ice as an energy absorbing matrix was investigated. Dynamics of plume ablation were first explored using a nanosecond exposure shadowgraphy system designed to simultaneously collect pictures of the plume with a camera and collect the FT-ICR mass spectrum corresponding to that same ablation event. Ablation of fresh tissue analyzed with and without using ice as a matrix were both compared using this technique. Effect of spot-to-spot distance, number of laser shots per pixel and tissue condition (matrix) on ion abundance was also investigated for 50 µm thick tissue sections. Finally, the statistical method called design of experiments was used to compare source parameters and determine the optimal conditions for IR-MALDESI of tissue sections using deposited ice as a matrix. With a better understanding of the fundamentals of ablation dynamics and a systematic approach to explore the experimental space, it was possible to improve ion abundance by nearly one order of magnitude. PMID:24385399

Socio Cultural and Geographical Determinants of Child Immunisation in Borno State, Nigeria

PubMed Central

2013-01-01

Immunisation has been an important strategy for disease prevention globally. Despite proven successes in other settings, child immunisation has continued to be problematic in developing countries including Nigeria. In addressing the problems, policy in Nigeria is largely directed at overcoming socio cultural issues surrounding parents’ rejection of vaccines. However, determinants of immunisation have geographical implications as well. A cross sectional survey was used to select 484 mothers/caregivers through a multi stage cluster sampling technique from the three senatorial districts of Borno State, Nigeria. Mothers or caregivers of children 12–23 months were interviewed using a structured questionnaire adapted from the Nigeria Demographic and Health Survey (2008). Socio cultural factors measured include mother’s education, religion, husband’s permission and sex of child while spatial variables include location i.e. whether rural or urban, and distance measured in terms of physical distance, cost and perception of physical distance. Descriptive statistics, univariate and multivariate logistic regressions were used to analyse the results. Data indicate that only 10.5% of children were fully immunised. Though immunisation uptake differed between the senatorial districts, this was not significant (P=0.1). In the bivariate analysis, mothers living in urban areas, <1 km to immunisation centre, their perception of travel distance and travel cost were the spatial predictors of immunisation while literacy and husband’s permission were the socio-cultural factors of significance. However, in the multivariate regression only two geographical factors i.e. living in an urban area [odds ratio (OR) 3.42, confidence interval (CI) 1.40–8.33] and mothers’ perception of distance (OR 4.52, CI 2.14–9.55) were protective against under immunisation while mother’s education was the only socio cultural variable of significance (OR 0.10, CI 0.03–0.41). It was concluded that while it is important to address socio cultural issues, policies directed at overcoming the friction of distance especially mobile clinics in rural areas are required to significantly improve immunisation uptake in the state. PMID:28299099

Does Travel Distance Affect Readmission Rates after Cardiac Surgery?

PubMed

Juo, Yen-Yi; Woods, Alexis; Ou, Ryan; Ramos, Gianna; Shemin, Richard; Benharash, Peyman

2017-10-01

With emphasis on value-based health care, empiric models are used to estimate expected readmission rates for individual institutions. The aim of this study was to determine the relationship between distance traveled to seek surgical care and likelihood of readmission in adult patients undergoing cardiac operations at a single medical center. All adults undergoing major cardiac surgeries from 2008 to 2015 were included. Patients were stratified by travel distance into regional and distant travel groups. Multivariable logistic regression models were developed to assess the impact of distance traveled on odds of readmission. Of the 4232 patients analyzed, 29 per cent were in the regional group and 71 per cent in the distant. Baseline characteristics between the two groups were comparable except mean age (62 vs 61 years, P < 0.01) and Caucasian race (59 vs 73%, P < 0.01). Distant travel was associated with a significantly longer hospital length of stay (11.8 vs 10.5 days, P < 0.01) and lower risk of readmission (9.5 vs 13.4%, P < 0.01). Odds of readmission was inversely associated with logarithm of distance traveled (odds ratio 0.75). Travel distance in patients undergoing major cardiac surgeries was inversely associated with odds of readmission.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glicken, H.

Large volcanic debris avalanches are among the world's largest mass movements. The rockslide-debris avalanche of the May 18, 1980, eruption of Mount St. Helens produced a 2.8 km/sup 3/ deposit and is the largest historic mass movement. A Pleistocene debris avalanche at Mount Shasta produced a 26 km/sup 3/ deposit that may be the largest Quaternary mass movement. The hummocky deposits at both volcanoes consist of rubble divided into (1) block facies that comprises unconsolidated pieces of the old edifice transported relatively intact, and (2) matrix facies that comprises a mixture of rocks from the old mountain and material pickedmore » up from the surrounding terrain. At Mount St. Helens, the juvenile dacite is found in the matrix facies, indicating that matrix facies formed from explosions of the erupting magma as well as from disaggregation and mixing of blocks. The block facies forms both hummocks and interhummock areas in the proximal part of the St. Helens avalanche deposit. At Mount St. Helens, the density of the old cone is 21% greater than the density of the avalanche deposit. Block size decreases with distance. Clast size, measured in the field and by sieving, coverages about a mean with distance, which suggests that blocks disaggregated and mixed together during transport.« less

Interpreting Gas Production Decline Curves By Combining Geometry and Topology

NASA Astrophysics Data System (ADS)

Ewing, R. P.; Hu, Q.

2014-12-01

Shale gas production forms an increasing fraction of domestic US energy supplies, but individual gas production wells show steep production declines. Better understanding of this production decline would allow better economic forecasting; better understanding of the reasons behind the decline would allow better production management. Yet despite these incentives, production declines curves remain poorly understood, and current analyses range from Arps' purely empirical equation to new sophisticated approaches requiring multiple unavailable parameters. Models often fail to capture salient features: for example, in log-log space many wells decline with an exponent markedly different from the -0.5 expected from diffusion, and often show a transition from one decline mode to another. We propose a new approach based on the assumption that the rate-limiting step is gas movement from the matrix to the induced fracture network. The matrix is represented as an assemblage of equivalent spheres (geometry), with low matrix pore connectivity (topology) that results in a distance-dependent accessible porosity profile given by percolation theory. The basic theory has just 2 parameters: the sphere size distribution (geometry), and the crossover distance (topology) that characterizes the porosity distribution. The theory is readily extended to include e.g. alternative geometries and bi-modal size distributions. Comparisons with historical data are promising.

Towards a formal genealogical classification of the Lezgian languages (North Caucasus): testing various phylogenetic methods on lexical data.

PubMed

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies.

Towards a Formal Genealogical Classification of the Lezgian Languages (North Caucasus): Testing Various Phylogenetic Methods on Lexical Data

PubMed Central

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies. PMID:25719456

A rough set approach for determining weights of decision makers in group decision making.

PubMed

Yang, Qiang; Du, Ping-An; Wang, Yong; Liang, Bin

2017-01-01

This study aims to present a novel approach for determining the weights of decision makers (DMs) based on rough group decision in multiple attribute group decision-making (MAGDM) problems. First, we construct a rough group decision matrix from all DMs' decision matrixes on the basis of rough set theory. After that, we derive a positive ideal solution (PIS) founded on the average matrix of rough group decision, and negative ideal solutions (NISs) founded on the lower and upper limit matrixes of rough group decision. Then, we obtain the weight of each group member and priority order of alternatives by using relative closeness method, which depends on the distances from each individual group member' decision to the PIS and NISs. Through comparisons with existing methods and an on-line business manager selection example, the proposed method show that it can provide more insights into the subjectivity and vagueness of DMs' evaluations and selections.

Kinetic-energy matrix elements for atomic Hylleraas-CI wave functions.

PubMed

Harris, Frank E

2016-05-28

Hylleraas-CI is a superposition-of-configurations method in which each configuration is constructed from a Slater-type orbital (STO) product to which is appended (linearly) at most one interelectron distance rij. Computations of the kinetic energy for atoms by this method have been difficult due to the lack of formulas expressing these matrix elements for general angular momentum in terms of overlap and potential-energy integrals. It is shown here that a strategic application of angular-momentum theory, including the use of vector spherical harmonics, enables the reduction of all atomic kinetic-energy integrals to overlap and potential-energy matrix elements. The new formulas are validated by showing that they yield correct results for a large number of integrals published by other investigators.

Matrix theory for baryons: an overview of holographic QCD for nuclear physics.

PubMed

Aoki, Sinya; Hashimoto, Koji; Iizuka, Norihiro

2013-10-01

We provide, for non-experts, a brief overview of holographic QCD (quantum chromodynamics) and a review of the recent proposal (Hashimoto et al 2010 (arXiv:1003.4988[hep-th])) of a matrix-like description of multi-baryon systems in holographic QCD. Based on the matrix model, we derive the baryon interaction at short distances in multi-flavor holographic QCD. We show that there is a very universal repulsive core of inter-baryon forces for a generic number of flavors. This is consistent with a recent lattice QCD analysis for Nf = 2, 3 where the repulsive core looks universal. We also provide a comparison of our results with the lattice QCD and the operator product expansion analysis.

Estimating gene function with least squares nonnegative matrix factorization.

PubMed

Wang, Guoli; Ochs, Michael F

2007-01-01

Nonnegative matrix factorization is a machine learning algorithm that has extracted information from data in a number of fields, including imaging and spectral analysis, text mining, and microarray data analysis. One limitation with the method for linking genes through microarray data in order to estimate gene function is the high variance observed in transcription levels between different genes. Least squares nonnegative matrix factorization uses estimates of the uncertainties on the mRNA levels for each gene in each condition, to guide the algorithm to a local minimum in normalized chi2, rather than a Euclidean distance or divergence between the reconstructed data and the data itself. Herein, application of this method to microarray data is demonstrated in order to predict gene function.

A model for compression-weakening materials and the elastic fields due to contractile cells

NASA Astrophysics Data System (ADS)

Rosakis, Phoebus; Notbohm, Jacob; Ravichandran, Guruswami

2015-12-01

We construct a homogeneous, nonlinear elastic constitutive law that models aspects of the mechanical behavior of inhomogeneous fibrin networks. Fibers in such networks buckle when in compression. We model this as a loss of stiffness in compression in the stress-strain relations of the homogeneous constitutive model. Problems that model a contracting biological cell in a finite matrix are solved. It is found that matrix displacements and stresses induced by cell contraction decay slower (with distance from the cell) in a compression weakening material than linear elasticity would predict. This points toward a mechanism for long-range cell mechanosensing. In contrast, an expanding cell would induce displacements that decay faster than in a linear elastic matrix.

Derivation of stiffness matrix in constitutive modeling of magnetorheological elastomer

NASA Astrophysics Data System (ADS)

Leng, D.; Sun, L.; Sun, J.; Lin, Y.

2013-02-01

Magnetorheological elastomers (MREs) are a class of smart materials whose mechanical properties change instantly by the application of a magnetic field. Based on the specially orthotropic, transversely isotropic stress-strain relationships and effective permeability model, the stiffness matrix of constitutive equations for deformable chain-like MRE is considered. To valid the components of shear modulus in this stiffness matrix, the magnetic-structural simulations with finite element method (FEM) are presented. An acceptable agreement is illustrated between analytical equations and numerical simulations. For the specified magnetic field, sphere particle radius, distance between adjacent particles in chains and volume fractions of ferrous particles, this constitutive equation is effective to engineering application to estimate the elastic behaviour of chain-like MRE in an external magnetic field.

Regulation of Hematopoietic Stem Cell Behavior by the Nanostructured Presentation of Extracellular Matrix Components

PubMed Central

Muth, Christine Anna; Steinl, Carolin; Klein, Gerd; Lee-Thedieck, Cornelia

2013-01-01

Hematopoietic stem cells (HSCs) are maintained in stem cell niches, which regulate stem cell fate. Extracellular matrix (ECM) molecules, which are an essential part of these niches, can actively modulate cell functions. However, only little is known on the impact of ECM ligands on HSCs in a biomimetic environment defined on the nanometer-scale level. Here, we show that human hematopoietic stem and progenitor cell (HSPC) adhesion depends on the type of ligand, i.e., the type of ECM molecule, and the lateral, nanometer-scaled distance between the ligands (while the ligand type influenced the dependency on the latter). For small fibronectin (FN)–derived peptide ligands such as RGD and LDV the critical adhesive interligand distance for HSPCs was below 45 nm. FN-derived (FN type III 7–10) and osteopontin-derived protein domains also supported cell adhesion at greater distances. We found that the expression of the ECM protein thrombospondin-2 (THBS2) in HSPCs depends on the presence of the ligand type and its nanostructured presentation. Functionally, THBS2 proved to mediate adhesion of HSPCs. In conclusion, the present study shows that HSPCs are sensitive to the nanostructure of their microenvironment and that they are able to actively modulate their environment by secreting ECM factors. PMID:23405094

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ladd-Lively, Jennifer L

2014-01-01

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 inmore » 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.« less

Determinants of 6-minute walk distance in patients with idiopathic pulmonary fibrosis undergoing lung transplant evaluation.

PubMed

Porteous, Mary K; Rivera-Lebron, Belinda N; Kreider, Maryl; Lee, James; Kawut, Steven M

2016-03-01

Little is known about the physiologic determinants of 6-minute walk distance in idiopathic pulmonary fibrosis. We investigated the demographic, pulmonary function, echocardiographic, and hemodynamic determinants of 6-minute walk distance in patients with idiopathic pulmonary fibrosis evaluated for lung transplantation. We performed a cross-sectional analysis of 130 patients with idiopathic pulmonary fibrosis who completed a lung transplantation evaluation at the Hospital of the University of Pennsylvania between 2005 and 2010. Multivariable linear regression analysis was used to generate an explanatory model for 6-minute walk distance. After adjustment for age, sex, race, height, and weight, the presence of right ventricular dilation was associated with a decrease of 50.9 m (95% confidence interval [CI], 8.4-93.3) in 6-minute walk distance ([Formula: see text]). For each 200-mL reduction in forced vital capacity, the walk distance decreased by 15.0 m (95% CI, 9.0-21.1; [Formula: see text]). For every increase of 1 Wood unit in pulmonary vascular resistance, the walk distance decreased by 17.3 m (95% CI, 5.1-29.5; [Formula: see text]). Six-minute walk distance in idiopathic pulmonary fibrosis depends in part on circulatory impairment and the degree of restrictive lung disease. Future trials that target right ventricular morphology, pulmonary vascular resistance, and forced vital capacity may potentially improve exercise capacity in patients with idiopathic pulmonary fibrosis.

Distance dependence in photo-induced intramolecular electron transfer

NASA Astrophysics Data System (ADS)

Larsson, Sven; Volosov, Andrey

1986-09-01

The distance dependence of the rate of photo-induced electron transfer reactions is studied. A quantum mechanical method CNDO/S is applied to a series of molecules recently investigated by Hush et al. experimentally. The calculations show a large interaction through the saturated bridge which connects the two chromophores. The electronic matrix element HAB decreases a factor 10 in about 4 Å. There is also a decrease of the rate due to less exothermicity for the longer molecule. The results are in fair agreement with the experimental results.

Source apportionment of atmospheric bulk deposition in the Belgrade urban area using Positive Matrix factorization

NASA Astrophysics Data System (ADS)

Tasić, M.; Mijić, Z.; Rajšić, S.; Stojić, A.; Radenković, M.; Joksić, J.

2009-04-01

The primary objective of the present study was to assess anthropogenic impacts of heavy metals to the environment by determination of total atmospheric deposition of heavy metals. Atmospheric depositions (wet + dry) were collected monthly, from June 2002 to December 2006, at three urban locations in Belgrade, using bulk deposition samplers. Concentrations of Fe, Al, Pb, Zn, Cu, Ni, Mn, Cr, V, As and Cd were analyzed using atomic absorption spectrometry. Based upon these results, the study attempted to examine elemental associations in atmospheric deposition and to elucidate the potential sources of heavy metal contaminants in the region by the use of multivariate receptor model Positive Matrix Factorization (PMF).

Partial Least Squares Regression Models for the Analysis of Kinase Signaling.

PubMed

Bourgeois, Danielle L; Kreeger, Pamela K

2017-01-01

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Some rules for polydimensional squeezing

NASA Technical Reports Server (NTRS)

Manko, Vladimir I.

1994-01-01

The review of the following results is presented: For mixed state light of N-mode electromagnetic field described by Wigner function which has generic Gaussian form, the photon distribution function is obtained and expressed explicitly in terms of Hermite polynomials of 2N-variables. The momenta of this distribution are calculated and expressed as functions of matrix invariants of the dispersion matrix. The role of new uncertainty relation depending on photon state mixing parameter is elucidated. New sum rules for Hermite polynomials of several variables are found. The photon statistics of polymode even and odd coherent light and squeezed polymode Schroedinger cat light is given explicitly. Photon distribution for polymode squeezed number states expressed in terms of multivariable Hermite polynomials is discussed.

A flexible new method for 3D measurement based on multi-view image sequences

NASA Astrophysics Data System (ADS)

Cui, Haihua; Zhao, Zhimin; Cheng, Xiaosheng; Guo, Changye; Jia, Huayu

2016-11-01

Three-dimensional measurement is the base part for reverse engineering. The paper developed a new flexible and fast optical measurement method based on multi-view geometry theory. At first, feature points are detected and matched with improved SIFT algorithm. The Hellinger Kernel is used to estimate the histogram distance instead of traditional Euclidean distance, which is immunity to the weak texture image; then a new filter three-principle for filtering the calculation of essential matrix is designed, the essential matrix is calculated using the improved a Contrario Ransac filter method. One view point cloud is constructed accurately with two view images; after this, the overlapped features are used to eliminate the accumulated errors caused by added view images, which improved the camera's position precision. At last, the method is verified with the application of dental restoration CAD/CAM, experiment results show that the proposed method is fast, accurate and flexible for tooth 3D measurement.

Kirchhoff index of linear hexagonal chains

NASA Astrophysics Data System (ADS)

Yang, Yujun; Zhang, Heping

The resistance distance rij between vertices i and j of a connected (molecular) graph G is computed as the effective resistance between nodes i and j in the corresponding network constructed from G by replacing each edge of G with a unit resistor. The Kirchhoff index Kf(G) is the sum of resistance distances between all pairs of vertices. In this work, according to the decomposition theorem of Laplacian polynomial, we obtain that the Laplacian spectrum of linear hexagonal chain Ln consists of the Laplacian spectrum of path P2n+1 and eigenvalues of a symmetric tridiagonal matrix of order 2n + 1. By applying the relationship between roots and coefficients of the characteristic polynomial of the above matrix, explicit closed-form formula for Kirchhoff index of Ln is derived in terms of Laplacian spectrum. To our surprise, the Krichhoff index of Ln is approximately to one half of its Wiener index. Finally, we show that holds for all graphs G in a class of graphs including Ln.0

Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.

PubMed

Satterthwaite, T D; Cook, P A; Bruce, S E; Conway, C; Mikkelsen, E; Satchell, E; Vandekar, S N; Durbin, T; Shinohara, R T; Sheline, Y I

2016-07-01

Depressive symptoms are common in multiple psychiatric disorders and are frequent sequelae of trauma. A dimensional conceptualization of depression suggests that symptoms should be associated with a continuum of deficits in specific neural circuits. However, most prior investigations of abnormalities in functional connectivity have typically focused on a single diagnostic category using hypothesis-driven seed-based analyses. Here, using a sample of 105 adult female participants from three diagnostic groups (healthy controls, n=17; major depression, n=38; and post-traumatic stress disorder, n=50), we examine the dimensional relationship between resting-state functional dysconnectivity and severity of depressive symptoms across diagnostic categories using a data-driven analysis (multivariate distance-based matrix regression). This connectome-wide analysis identified foci of dysconnectivity associated with depression severity in the bilateral amygdala. Follow-up seed analyses using subject-specific amygdala segmentations revealed that depression severity was associated with amygdalo-frontal hypo-connectivity in a network of regions including bilateral dorsolateral prefrontal cortex, anterior cingulate and anterior insula. In contrast, anxiety was associated with elevated connectivity between the amygdala and the ventromedial prefrontal cortex. Taken together, these results emphasize the centrality of the amygdala in the pathophysiology of depressive symptoms, and suggest that dissociable patterns of amygdalo-frontal dysconnectivity are a critical neurobiological feature across clinical diagnostic categories.

PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances.

PubMed

Tang, Zheng-Zheng; Chen, Guanhua; Alekseyenko, Alexander V

2016-09-01

Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings. We derive presence-weighted UniFrac to complement the existing UniFrac distances for more powerful detection of the variation in species richness. We develop PERMANOVA-S, a new distance-based method that tests the association of microbiome composition with any covariates of interest. PERMANOVA-S improves the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexible confounder adjustments and ensembling multiple distances. We conducted extensive simulation studies to evaluate the performance of different distances under various patterns of association. Our simulation studies demonstrate that the power of the test relies on how well the selected distance captures the nature of the association. The PERMANOVA-S unified test combines multiple distances and achieves good power regardless of the patterns of the underlying association. We demonstrate the usefulness of our approach by reanalyzing several real microbiome datasets. miProfile software is freely available at https://medschool.vanderbilt.edu/tang-lab/software/miProfile z.tang@vanderbilt.edu or g.chen@vanderbilt.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances

PubMed Central

Tang, Zheng-Zheng; Chen, Guanhua; Alekseyenko, Alexander V.

2016-01-01

Motivation: Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings. Results: We derive presence-weighted UniFrac to complement the existing UniFrac distances for more powerful detection of the variation in species richness. We develop PERMANOVA-S, a new distance-based method that tests the association of microbiome composition with any covariates of interest. PERMANOVA-S improves the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexible confounder adjustments and ensembling multiple distances. We conducted extensive simulation studies to evaluate the performance of different distances under various patterns of association. Our simulation studies demonstrate that the power of the test relies on how well the selected distance captures the nature of the association. The PERMANOVA-S unified test combines multiple distances and achieves good power regardless of the patterns of the underlying association. We demonstrate the usefulness of our approach by reanalyzing several real microbiome datasets. Availability and Implementation: miProfile software is freely available at https://medschool.vanderbilt.edu/tang-lab/software/miProfile. Contact: z.tang@vanderbilt.edu or g.chen@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27197815

Distance-dependent magnetic resonance tuning as a versatile MRI sensing platform for biological targets

NASA Astrophysics Data System (ADS)

Choi, Jin-Sil; Kim, Soojin; Yoo, Dongwon; Shin, Tae-Hyun; Kim, Hoyoung; Gomes, Muller D.; Kim, Sun Hee; Pines, Alexander; Cheon, Jinwoo

2017-05-01

Nanoscale distance-dependent phenomena, such as Förster resonance energy transfer, are important interactions for use in sensing and imaging, but their versatility for bioimaging can be limited by undesirable photon interactions with the surrounding biological matrix, especially in in vivo systems. Here, we report a new type of magnetism-based nanoscale distance-dependent phenomenon that can quantitatively and reversibly sense and image intra-/intermolecular interactions of biologically important targets. We introduce distance-dependent magnetic resonance tuning (MRET), which occurs between a paramagnetic `enhancer' and a superparamagnetic `quencher', where the T1 magnetic resonance imaging (MRI) signal is tuned ON or OFF depending on the separation distance between the quencher and the enhancer. With MRET, we demonstrate the principle of an MRI-based ruler for nanometre-scale distance measurement and the successful detection of both molecular interactions (for example, cleavage, binding, folding and unfolding) and biological targets in in vitro and in vivo systems. MRET can serve as a novel sensing principle to augment the exploration of a wide range of biological systems.

Environmental heterogeneity explains the genetic structure of Continental and Mediterranean populations of Fraxinus angustifolia Vahl.

PubMed

Temunović, Martina; Franjić, Jozo; Satovic, Zlatko; Grgurev, Marin; Frascaria-Lacoste, Nathalie; Fernández-Manjarrés, Juan F

2012-01-01

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations.

Environmental Heterogeneity Explains the Genetic Structure of Continental and Mediterranean Populations of Fraxinus angustifolia Vahl

PubMed Central

Temunović, Martina; Franjić, Jozo; Satovic, Zlatko; Grgurev, Marin; Frascaria-Lacoste, Nathalie; Fernández-Manjarrés, Juan F.

2012-01-01

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations. PMID:22905171

Can a simple test of functional capacity add to the clinical assessment of diabetes?

PubMed

Stewart, T; Caffrey, D G; Gilman, R H; Mathai, S C; Lerner, A; Hernandez, A; Pinto, M E; Huaylinos, Y; Cabrera, L; Wise, R A; Miranda, J J; Checkley, W

2016-08-01

To identify impairment in functional capacity associated with complicated and non-complicated diabetes using the 6-min walk distance test. We enrolled 111 adults, aged ≥40 years, with Type 2 diabetes from a hospital facility and 150 healthy control subjects of similar age and sex from a community site in Lima, Peru. All participants completed a 6-min walk test. The mean age of the 261 participants was 58.3 years, and 43.3% were male. Among those with diabetes, 67 (60%) had non-complicated diabetes and 44 (40%) had complications such as peripheral neuropathy, retinopathy or nephropathy. The mean unadjusted 6-min walk distances were 376 m and 394 m in adults with and without diabetes complications, respectively, vs 469 m in control subjects (P<0.001). In multivariable regression, the subjects with diabetes complications walked 84 m less far (95% CI -104 to -63 m) and those without complications walked 60 m less far (-77 to -42 m) than did control subjects. When using HbA1c level as a covariate in multivariable regression, participants walked 13 m less far (-16.9 to -9.9 m) for each % increase in HbA1c . The subjects with diabetes had lower functional capacity compared with healthy control subjects with similar characteristics. Differences in 6-min walk distance were even apparent in the subjects without diabetes complications. Potential mechanisms that could explain this finding are early cardiovascular disease or deconditioning. © 2015 Diabetes UK.

Can a simple test of functional capacity add to the clinical assessment of diabetes?

PubMed Central

Stewart, T.; Caffrey, D. G.; Gilman, R. H.; Mathai, S. C.; Lerner, A.; Hernandez, A.; Pinto, M. E.; Huaylinos, Y.; Cabrera, L.; Wise, R. A.; Miranda, J. J.; Checkley, W.

2016-01-01

Aim To identify impairment in functional capacity associated with complicated and non-complicated diabetes using the 6-min walk distance test. Methods We enrolled 111 adults, aged ≥40 years, with Type 2 diabetes from a hospital facility and 150 healthy control subjects of similar age and sex from a community site in Lima, Peru. All participants completed a 6-min walk test. Results The mean age of the 261 participants was 58.3 years, and 43.3% were male. Among those with diabetes, 67 (60%) had non-complicated diabetes and 44 (40%) had complications such as peripheral neuropathy, retinopathy or nephropathy. The mean unadjusted 6-min walk distances were 376 m and 394 m in adults with and without diabetes complications, respectively, vs 469 m in control subjects (P<0.001). In multivariable regression, the subjects with diabetes complications walked 84 m less far (95% CI -104 to -63 m) and those without complications walked 60 m less far (-77 to -42 m) than did control subjects. When using HbA1c level as a covariate in multivariable regression, participants walked 13 m less far (-16.9 to -9.9 m) for each % increase in HbA1c. Conclusions The subjects with diabetes had lower functional capacity compared with healthy control subjects with similar characteristics. Differences in 6-min walk distance were even apparent in the subjects without diabetes complications. Potential mechanisms that could explain this finding are early cardiovascular disease or deconditioning. PMID:26599981

Discrimination of lichen genera and species using element concentrations

USGS Publications Warehouse

Bennett, James P.

2008-01-01

The importance of organic chemistry in the classification of lichens is well established, but inorganic chemistry has been largely overlooked. Six lichen species were studied over a period of 23 years that were growing in 11 protected areas of the northern Great Lakes ecoregion, which were not greatly influenced by anthropogenic particulates or gaseous air pollutants. The elemental data from these studies were aggregated in order to test the hypothesis that differences among species in tissue element concentrations were large enough to discriminate between taxa faithfully. Concentrations of 16 chemical elements that were found in tissue samples from Cladonia rangiferina, Evernia mesomorpha, Flavopunctelia flaventior, Hypogymnia physodes, Parmelia sulcata, and Punctelia rudecta were analyzed statistically using multivariate discriminant functions and CART analyses, as well as t-tests. Genera and species were clearly separated in element space, and elemental discriminant functions were able to classify 91-100 of the samples correctly into species. At the broadest level, a Zn concentration of 51 ppm in tissues of four of the lichen species effectively discriminated foliose from fruticose species. Similarly, a S concentration of 680 ppm discriminated C. rangiferina and E. mesomorpha, and a Ca concentration of 10 436 ppm discriminated H. physodes from P. sulcata. For the three parmelioid species, a Ca concentration >32 837 ppm discriminated Punctelia rudecta from the other two species, while a Zn concentration of 56 ppm discriminated Parmelia sulcata from F. flaventior. Foliose species also had higher concentrations than did fruticose species of all elements except Na. Elemental signatures for each of the six species were developed using standardized means. Twenty-four mechanisms explaining the differences among species are summarized. Finally, the relationships of four species based on element concentrations, using additive-trees clustering of a Euclidean-distance matrix, produced identical relationships as did analyses based on secondary product chemistry that used additive-trees clustering of a Jaccard similarity matrix. At least for these six species, element composition has taxonomic significance, and may be useful for discriminating other taxa.

Automated classification of immunostaining patterns in breast tissue from the human protein atlas.

PubMed

Swamidoss, Issac Niwas; Kårsnäs, Andreas; Uhlmann, Virginie; Ponnusamy, Palanisamy; Kampf, Caroline; Simonsson, Martin; Wählby, Carolina; Strand, Robin

2013-01-01

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

One Hundred Ways to be Non-Fickian - A Rigorous Multi-Variate Statistical Analysis of Pore-Scale Transport

NASA Astrophysics Data System (ADS)

Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

2015-04-01

Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where the validity of yet-unexplored non-Gaussian-but-Markovian random walks start. 2) The distance where bivariate statistical dependence simplifies to a multi-Gaussian dependence based on simple linear correlation (validity of correlated PTRW/CTRW). 3) The distance of complete statistical independence (validity of classical PTRW/CTRW). The second objective is to reveal characteristic dependencies influencing transport the most. Those dependencies can be very complex. Copulas are highly capable of representing linear dependence as well as non-linear dependence. With that tool we are able to detect persistent characteristics dominating transport even across different scales. The results derived from our experimental data set suggest that there are many more non-Fickian aspects of pore-scale transport than the univariate statistics of longitudinal displacements. Non-Fickianity can also be found in transverse displacements, and in the relations between increments at different time steps. Also, the found dependence is non-linear (i.e. beyond simple correlation) and persists over long distances. Thus, our results strongly support the further refinement of techniques like correlated PTRW or correlated CTRW towards non-linear statistical relations.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Diagonal dominance for the multivariable Nyquist array using function minimization

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1977-01-01

A new technique for the design of multivariable control systems using the multivariable Nyquist array method was developed. A conjugate direction function minimization algorithm is utilized to achieve a diagonal dominant condition over the extended frequency range of the control system. The minimization is performed on the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of either the inverse or direct open loop transfer function matrix. Several new feedback design concepts were also developed, including: (1) dominance control parameters for each control loop; (2) compensator normalization to evaluate open loop conditions for alternative design configurations; and (3) an interaction index to determine the degree and type of system interaction when all feedback loops are closed simultaneously. This new design capability was implemented on an IBM 360/75 in a batch mode but can be easily adapted to an interactive computer facility. The method was applied to the Pratt and Whitney F100 turbofan engine.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Liquid chromatography with diode array detection and multivariate curve resolution for the selective and sensitive quantification of estrogens in natural waters.

PubMed

Pérez, Rocío L; Escandar, Graciela M

2014-07-04

Following the green analytical chemistry principles, an efficient strategy involving second-order data provided by liquid chromatography (LC) with diode array detection (DAD) was applied for the simultaneous determination of estriol, 17β-estradiol, 17α-ethinylestradiol and estrone in natural water samples. After a simple pre-concentration step, LC-DAD matrix data were rapidly obtained (in less than 5 min) with a chromatographic system operating isocratically. Applying a second-order calibration algorithm based on multivariate curve resolution with alternating least-squares (MCR-ALS), successful resolution was achieved in the presence of sample constituents that strongly coelute with the analytes. The flexibility of this multivariate model allowed the quantification of the four estrogens in tap, mineral, underground and river water samples. Limits of detection in the range between 3 and 13 ng L(-1), and relative prediction errors from 2 to 11% were achieved. Copyright © 2014 Elsevier B.V. All rights reserved.

Linear, multivariable robust control with a mu perspective

NASA Technical Reports Server (NTRS)

Packard, Andy; Doyle, John; Balas, Gary

1993-01-01

The structured singular value is a linear algebra tool developed to study a particular class of matrix perturbation problems arising in robust feedback control of multivariable systems. These perturbations are called linear fractional, and are a natural way to model many types of uncertainty in linear systems, including state-space parameter uncertainty, multiplicative and additive unmodeled dynamics uncertainty, and coprime factor and gap metric uncertainty. The structured singular value theory provides a natural extension of classical SISO robustness measures and concepts to MIMO systems. The structured singular value analysis, coupled with approximate synthesis methods, make it possible to study the tradeoff between performance and uncertainty that occurs in all feedback systems. In MIMO systems, the complexity of the spatial interactions in the loop gains make it difficult to heuristically quantify the tradeoffs that must occur. This paper examines the role played by the structured singular value (and its computable bounds) in answering these questions, as well as its role in the general robust, multivariable control analysis and design problem.

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

A Comparison of Accuracy of Matrix Impression System with Putty Reline Technique and Multiple Mix Technique: An In Vitro Study.

PubMed

Kumar, M Praveen; Patil, Suneel G; Dheeraj, Bhandari; Reddy, Keshav; Goel, Dinker; Krishna, Gopi

2015-06-01

The difficulty in obtaining an acceptable impression increases exponentially as the number of abutments increases. Accuracy of the impression material and the use of a suitable impression technique are of utmost importance in the fabrication of a fixed partial denture. This study compared the accuracy of the matrix impression system with conventional putty reline and multiple mix technique for individual dies by comparing the inter-abutment distance in the casts obtained from the impressions. Three groups, 10 impressions each with three impression techniques (matrix impression system, putty reline technique and multiple mix technique) were made of a master die. Typodont teeth were embedded in a maxillary frasaco model base. The left first premolar was removed to create a three-unit fixed partial denture situation and the left canine and second premolar were prepared conservatively, and hatch marks were made on the abutment teeth. The final casts obtained from the impressions were examined under a profile projector and the inter-abutment distance was calculated for all the casts and compared. The results from this study showed that in the mesiodistal dimensions the percentage deviation from master model in Group I was 0.1 and 0.2, in Group II was 0.9 and 0.3, and Group III was 1.6 and 1.5, respectively. In the labio-palatal dimensions the percentage deviation from master model in Group I was 0.01 and 0.4, Group II was 1.9 and 1.3, and Group III was 2.2 and 2.0, respectively. In the cervico-incisal dimensions the percentage deviation from the master model in Group I was 1.1 and 0.2, Group II was 3.9 and 1.7, and Group III was 1.9 and 3.0, respectively. In the inter-abutment dimension of dies, percentage deviation from master model in Group I was 0.1, Group II was 0.6, and Group III was 1.0. The matrix impression system showed more accuracy of reproduction for individual dies when compared with putty reline technique and multiple mix technique in all the three directions, as well as the inter-abutment distance.

Size-dependent characterization of embedded Ge nanocrystals: Structural and thermal properties

NASA Astrophysics Data System (ADS)

Araujo, L. L.; Giulian, R.; Sprouster, D. J.; Schnohr, C. S.; Llewellyn, D. J.; Kluth, P.; Cookson, D. J.; Foran, G. J.; Ridgway, M. C.

2008-09-01

A combination of conventional and synchrotron-based techniques has been used to characterize the size-dependent structural and thermal properties of Ge nanocrystals (NCs) embedded in a silica (a-SiO2) matrix. Ge NC size distributions with four different diameters ranging from 4.0 to 9.0 nm were produced by ion implantation and thermal annealing as characterized with small-angle x-ray scattering and transmission electron microscopy. The NCs were well represented by the superposition of bulklike crystalline and amorphous environments, suggesting the formation of an amorphous layer separating the crystalline NC core and the a-SiO2 matrix. The amorphous fraction was quantified with x-ray-absorption near-edge spectroscopy and increased as the NC diameter decreased, consistent with the increase in surface-to-volume ratio. The structural parameters of the first three nearest-neighbor shells were determined with extended x-ray-absorption fine-structure (EXAFS) spectroscopy and evolved linearly with inverse NC diameter. Specifically, increases in total disorder, interatomic distance, and the asymmetry in the distribution of distances were observed as the NC size decreased, demonstrating that finite-size effects govern the structural properties of embedded Ge NCs. Temperature-dependent EXAFS measurements in the range of 15-300 K were employed to probe the mean vibrational frequency and the variation of the interatomic distance distribution (mean value, variance, and asymmetry) with temperature for all NC distributions. A clear trend of increased stiffness (higher vibrational frequency) and decreased thermal expansion with decreasing NC size was evident, confirming the close relationship between the variation of structural and thermal/vibrational properties with size for embedded Ge NCs. The increase in surface-to-volume ratio and the presence of an amorphous Ge layer separating the matrix and crystalline NC core are identified as the main factors responsible for the observed behavior, with the surrounding a-SiO2 matrix also contributing to a lesser extent. Such results are compared to previous reports and discussed in terms of the influence of the surface-to-volume ratio in objects of nanometer dimensions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fast, J; Zhang, Q; Tilp, A

Significantly improved returns in their aerosol chemistry data can be achieved via the development of a value-added product (VAP) of deriving OA components, called Organic Aerosol Components (OACOMP). OACOMP is primarily based on multivariate analysis of the measured organic mass spectral matrix. The key outputs of OACOMP are the concentration time series and the mass spectra of OA factors that are associated with distinct sources, formation and evolution processes, and physicochemical properties.

A Multivariate Randomization Text of Association Applied to Cognitive Test Results

NASA Technical Reports Server (NTRS)

Ahumada, Albert; Beard, Bettina

2009-01-01

Randomization tests provide a conceptually simple, distribution-free way to implement significance testing. We have applied this method to the problem of evaluating the significance of the association among a number (k) of variables. The randomization method was the random re-ordering of k-1 of the variables. The criterion variable was the value of the largest eigenvalue of the correlation matrix.

[Study on preparation of laser micropore porcine acellular dermal matrix combined with split-thickness autograft and its application in wound transplantation].

PubMed

Liang, Li-Ming; Chai, Ji-Ke; Yang, Hong-Ming; Feng, Rui; Yin, Hui-Nan; Li, Feng-Yu; Sun, Qiang

2007-04-01

To prepare a porcine acellular dermal matrix (PADM), and to optimize the interpore distance between PADM and co-grafted split-thickness autologous skin. Porcine skin was treated with trypsin/Triton X-100 to prepare an acellular dermal matrix. Micropores were produced on the PADM with a laser punch. The distance between micropores varied as 0.8 mm, 1.0 mm, 1.2 mm and 1.5 mm. Full-thickness defect wounds were created on the back of 144 SD rats. The rats were randomly divided into 6 groups as follows, with 24 rats in each group. Micropore groups I -IV: the wounds were grafted with PADM with micropores in four different intervals respectively, and covered with split-thickness autologous skin graft. Mesh group: the wounds were grafted with meshed PADM and split-thickness autograft. with simple split-thickness autografting. The gross observation of wound healing and histological observation were performed at 2, 4, 6 weeks after surgery. The wound healing rate and contraction rate were calculated. Two and four weeks after surgery, the wound healing rate in micropore groups I and II was lower than that in control group (P < 0.05), but no obvious difference was between micropore groups I , II and mesh group (P > 0.05) until 6 weeks after grafting( P <0.05). The wound contraction rate in micropore groups I and II ([(16.0 +/- 2.6)%, (15.1 +/- 2.4)%] was remarkably lower than that in control group 4 and 6 weeks after grafting (P < 0.05), and it was significantly lower than that in mesh group [(19.3 +/- 2.4)%] 6 weeks after surgery (P <0.05). Histological examination showed good epithelization, regularly arranged collagenous fibers, and integral structure of basement membrane. Laser micropore PADM (0.8 mm or 1.0 mm in distance) grafting in combination with split-thickness autografting can improve the quality of wound healing. PADM with laser micropores in 1.0 mm distance is the best choice among them.

Preparation of laser micropore porcine acellular dermal matrix for skin graft: an experimental study.

PubMed

Chai, Jia-Ke; Liang, Li-Ming; Yang, Hong-Ming; Feng, Rui; Yin, Hui-Nan; Li, Feng-Yu; Sheng, Zhi-Yong

2007-09-01

In our previous study, we used composite grafts consisting of meshed porcine acellular dermal matrix (PADM) and thin split-thickness autologous epidermis to cover full thickness burn wounds in clinical practice. However, a certain degree of contraction might occur because the distribution of dermal matrix was not uniform in burn wound. In this study, we prepare a composite skin graft consisting of PADM with the aid of laser to improve the quality of healing of burn wound. PADM was prepared by the trypsin/Triton X-100 method. Micropores were produced on the PADM with a laser punch. The distance between micropores varied from 0.8, 1.0, 1.2 to 1.5mm. Full thickness defect wounds were created on the back of 144 SD rats. The rats were randomly divided into six groups: micropore groups I-IV in which the wound were grafted with PADM with micropores, in four different distances, respectively and split-thickness autograft; mesh group rats received meshed PADM graft and split-thickness autograft; control group received simple split-thickness autografting. The status of wound healing was histologically observed at regular time points after surgery. The wound healing rate and contraction rate were calculated. The wound healing rate in micropore groups I and II was not statistically different from that in control group, but was significantly higher than that in mesh group 6 weeks after grafting. The wound healing rate in micropore groups III and IV was lower than that in mesh and control groups 4 and 6 weeks after grafting. The wound contraction rate in micropore groups I and II was remarkably lower than that in control group 4 and 6 weeks after surgery and it was significantly much lower than that in mesh group 6 weeks after surgery. Histological examination revealed good epithelization, regularly arranged collagenous fibers and integral structure of basement membrane. Laser micropore PADM (0.8 or 1.0mm in distance) grafting in combination with split-thickness autografting can improve wound healing. The PADM with laser micropores in 1.0mm distance is the better choice.

Surname distribution in France: a distance analysis by a distorted geographical map.

PubMed

Mourrieras, B; Darlu, P; Hochez, J; Hazout, S

1995-01-01

The distribution of surnames in 90 distinct regions in France during two successive periods, 1889-1915 and 1916-1940, is analysed from the civil birth registers of the 36,500 administrative units in France. A new approach, called 'Mobile Site Method' (MSM), is developed to allow representation of a surname distance matrix by a distorted geographical map. A surname distance matrix between the various regions in France is first calculated, then a distorted geographical map called the 'surname similarity map' is built up from the surname distances between regions. To interpret this map we draw (a) successive map contours obtained during the step-by-step distortion process, revealing zones of high surname dissimilarity, and (b) maps in grey levels representing the displacement magnitude, and allowing the segmentation of the geographical and surname maps into 'homogeneous surname zones'. By integrating geography and surname information in the same analysis, and by comparing results obtained for the two successive periods, the MSM approach produces convenient maps showing: (a) 'regionalism' of some peripheral populations such as Pays Basque, Alsace, Corsica and Brittany; (b) the presence of preferential axes of communications (Rhodanian corridor, Garonne valley); (c) barriers such as the Central Massif, Vosges; (d) the weak modifications of the distorted maps associated with the two periods studied suggest an extension (but limited) of the tendency of surname uniformity in France. These results are interpreted, in the nineteenth- and twentieth century context, as the consequences of a slow process of local migrations occurring over a long period of time.

Multivariate Qst–Fst Comparisons: A Neutrality Test for the Evolution of the G Matrix in Structured Populations

PubMed Central

Martin, Guillaume; Chapuis, Elodie; Goudet, Jérôme

2008-01-01

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Qst–Fst) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2Fst/(1 − Fst)G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2Fst/(1 − Fst)] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Qst–Fst comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions. PMID:18245845

A novel statistical approach shows evidence for multi-system physiological dysregulation during aging.

PubMed

Cohen, Alan A; Milot, Emmanuel; Yong, Jian; Seplaki, Christopher L; Fülöp, Tamàs; Bandeen-Roche, Karen; Fried, Linda P

2013-03-01

Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

BANYAN. XI. The BANYAN Σ Multivariate Bayesian Algorithm to Identify Members of Young Associations with 150 pc

NASA Astrophysics Data System (ADS)

Gagné, Jonathan; Mamajek, Eric E.; Malo, Lison; Riedel, Adric; Rodriguez, David; Lafrenière, David; Faherty, Jacqueline K.; Roy-Loubier, Olivier; Pueyo, Laurent; Robin, Annie C.; Doyon, René

2018-03-01

BANYAN Σ is a new Bayesian algorithm to identify members of young stellar associations within 150 pc of the Sun. It includes 27 young associations with ages in the range ∼1–800 Myr, modeled with multivariate Gaussians in six-dimensional (6D) XYZUVW space. It is the first such multi-association classification tool to include the nearest sub-groups of the Sco-Cen OB star-forming region, the IC 2602, IC 2391, Pleiades and Platais 8 clusters, and the ρ Ophiuchi, Corona Australis, and Taurus star formation regions. A model of field stars is built from a mixture of multivariate Gaussians based on the Besançon Galactic model. The algorithm can derive membership probabilities for objects with only sky coordinates and proper motion, but can also include parallax and radial velocity measurements, as well as spectrophotometric distance constraints from sequences in color–magnitude or spectral type–magnitude diagrams. BANYAN Σ benefits from an analytical solution to the Bayesian marginalization integrals over unknown radial velocities and distances that makes it more accurate and significantly faster than its predecessor BANYAN II. A contamination versus hit rate analysis is presented and demonstrates that BANYAN Σ achieves a better classification performance than other moving group tools available in the literature, especially in terms of cross-contamination between young associations. An updated list of bona fide members in the 27 young associations, augmented by the Gaia-DR1 release, as well as all parameters for the 6D multivariate Gaussian models for each association and the Galactic field neighborhood within 300 pc are presented. This new tool will make it possible to analyze large data sets such as the upcoming Gaia-DR2 to identify new young stars. IDL and Python versions of BANYAN Σ are made available with this publication, and a more limited online web tool is available at http://www.exoplanetes.umontreal.ca/banyan/banyansigma.php.

Big geo data surface approximation using radial basis functions: A comparative study

NASA Astrophysics Data System (ADS)

Majdisova, Zuzana; Skala, Vaclav

2017-12-01

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.

Kinetic-energy matrix elements for atomic Hylleraas-CI wave functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, Frank E., E-mail: harris@qtp.ufl.edu

Hylleraas-CI is a superposition-of-configurations method in which each configuration is constructed from a Slater-type orbital (STO) product to which is appended (linearly) at most one interelectron distance r{sub ij}. Computations of the kinetic energy for atoms by this method have been difficult due to the lack of formulas expressing these matrix elements for general angular momentum in terms of overlap and potential-energy integrals. It is shown here that a strategic application of angular-momentum theory, including the use of vector spherical harmonics, enables the reduction of all atomic kinetic-energy integrals to overlap and potential-energy matrix elements. The new formulas are validatedmore » by showing that they yield correct results for a large number of integrals published by other investigators.« less

A preliminary characterization of the tensile and fatigue behavior of tungsten-fiber/Waspaloy-matrix composite

NASA Technical Reports Server (NTRS)

Corner, Ralph E.; Lerch, Brad A.

1992-01-01

A microstructural study and a preliminary characterization of the room temperature tensile and fatigue behavior of a continuous, tungsten fiber, Waspaloy-matrix composite was conducted. A heat treatment was chosen that would allow visibility of planar slip if it occurred during deformation, but would not allow growth of the reaction zone. Tensile and fatigue tests showed that the failed specimens contained transverse cracks in the fibers. The cracks that occurred in the tensile specimen were observed at the fracture surface and up to approximately 4.0 mm below the fracture surface. The crack spacing remained constant along the entire length of the cracked fibers. Conversely, the cracks that occurred in the fatigue specimen were only observed in the vicinity of the fracture surface. In instances where two fiber cracks occurred in the same plane, the matrix often necked between the two cracked fibers. Large groups of slip bands were generated in the matrix near the fiber cracks. Slip bands in the matrix of the tensile specimen were also observed in areas where there were no fiber cracks, at distances greater than 4 mm from the fracture surface. This suggests that the matrix plastically flows before fiber cracking occurs.

Mining Diagnostic Assessment Data for Concept Similarity

ERIC Educational Resources Information Center

Madhyastha, Tara; Hunt, Earl

2009-01-01

This paper introduces a method for mining multiple-choice assessment data for similarity of the concepts represented by the multiple choice responses. The resulting similarity matrix can be used to visualize the distance between concepts in a lower-dimensional space. This gives an instructor a visualization of the relative difficulty of concepts…

Clustering of the human skeletal muscle fibers using linear programming and angular Hilbertian metrics.

PubMed

Neji, Radhouène; Besbes, Ahmed; Komodakis, Nikos; Deux, Jean-François; Maatouk, Mezri; Rahmouni, Alain; Bassez, Guillaume; Fleury, Gilles; Paragios, Nikos

2009-01-01

In this paper, we present a manifold clustering method fo the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.

Electromagnetic Scattering by an Exponentially Distributed Rough Surface with the Introduction of a Rough Surface Generation Technique

DTIC Science & Technology

1987-12-01

d integer corrow, corcol , refrow, refcol C Create lower triangle of corr. matrix (symetric matrix) do 33 i~l,n2 C calculate the row point (i) is in...reference Fig.(21)) corrow = (((i-l)/n)+1) C claculate the column point (i) is in corcol = i-(corrow-l)*n) write(6,*) i do 31 jl,i C calculate the row...refrow)*space C the horizontal distance (b) b = ( corcol -refcol)*space 14 d = sqrt(a**2+b**2) S coeff(i,j) = e%-P(-d**2) 31 ]i<ontiinue .3 crnt inue

Forecasts of non-Gaussian parameter spaces using Box-Cox transformations

NASA Astrophysics Data System (ADS)

Joachimi, B.; Taylor, A. N.

2011-09-01

Forecasts of statistical constraints on model parameters using the Fisher matrix abound in many fields of astrophysics. The Fisher matrix formalism involves the assumption of Gaussianity in parameter space and hence fails to predict complex features of posterior probability distributions. Combining the standard Fisher matrix with Box-Cox transformations, we propose a novel method that accurately predicts arbitrary posterior shapes. The Box-Cox transformations are applied to parameter space to render it approximately multivariate Gaussian, performing the Fisher matrix calculation on the transformed parameters. We demonstrate that, after the Box-Cox parameters have been determined from an initial likelihood evaluation, the method correctly predicts changes in the posterior when varying various parameters of the experimental setup and the data analysis, with marginally higher computational cost than a standard Fisher matrix calculation. We apply the Box-Cox-Fisher formalism to forecast cosmological parameter constraints by future weak gravitational lensing surveys. The characteristic non-linear degeneracy between matter density parameter and normalization of matter density fluctuations is reproduced for several cases, and the capabilities of breaking this degeneracy by weak-lensing three-point statistics is investigated. Possible applications of Box-Cox transformations of posterior distributions are discussed, including the prospects for performing statistical data analysis steps in the transformed Gaussianized parameter space.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

A possible biochemical missing link among archaebacteria

NASA Technical Reports Server (NTRS)

Achenbach-Richter, Laurie; Woese, Carl R.; Stetter, Karl O.

1987-01-01

The characteristics of the newly discovered strain of archaebacteria, VC-16, the only archaebacterium known to reduce sulfate, suggest that VC-16 might represent a transitional form between an anaerobic thermophilic sulfur-based type of metabolism and methanogenesis. It is shown here, using a matrix of evolutionary distances derived from an alignment of various archaebacterial 16S rRNAs and the phylogenetic tree derived from these evolutionary distances, that the lineage represented by strain VC-16 arises from the archaebacterial tree precisely where such an interpretation would predict that it would, between the Methanococcus lineage and that of Thermococcus.

Approximate string matching algorithms for limited-vocabulary OCR output correction

NASA Astrophysics Data System (ADS)

Lasko, Thomas A.; Hauser, Susan E.

2000-12-01

Five methods for matching words mistranslated by optical character recognition to their most likely match in a reference dictionary were tested on data from the archives of the National Library of Medicine. The methods, including an adaptation of the cross correlation algorithm, the generic edit distance algorithm, the edit distance algorithm with a probabilistic substitution matrix, Bayesian analysis, and Bayesian analysis on an actively thinned reference dictionary were implemented and their accuracy rates compared. Of the five, the Bayesian algorithm produced the most correct matches (87%), and had the advantage of producing scores that have a useful and practical interpretation.

Application of Image Analysis for Characterization of Spatial Arrangements of Features in Microstructure

NASA Technical Reports Server (NTRS)

Louis, Pascal; Gokhale, Arun M.

1995-01-01

A number of microstructural processes are sensitive to the spatial arrangements of features in microstructure. However, very little attention has been given in the past to the experimental measurements of the descriptors of microstructural distance distributions due to the lack of practically feasible methods. We present a digital image analysis procedure to estimate the micro-structural distance distributions. The application of the technique is demonstrated via estimation of K function, radial distribution function, and nearest-neighbor distribution function of hollow spherical carbon particulates in a polymer matrix composite, observed in a metallographic section.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Theiler, James; Grosklos, Guen

We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors, we can trace the problem to a projection in feature space, which arises when a pseudoinverse is used on the covariance matrix in that feature space. Here, we show that a regularized variant of KRX overcomes this difficulty and achieves superior performance over a widemore » range of bandwidths.« less

Structural model of dioxouranium(VI) with hydrazono ligands.

PubMed

Mubarak, Ahmed T

2005-04-01

Synthesis and characterization of several new coordination compounds of dioxouranium(VI) heterochelates with bidentate hydrazono compounds derived from 1-phenyl-3-methyl-5-pyrazolone are described. The ligands and uranayl complexes have been characterized by various physico-chemical techniques. The bond lengths and the force constant have been calculated from asymmetric stretching frequency of OUO groups. The infrared spectral studies showed a monobasic bidentate behaviour with the oxygen and hydrazo nitrogen donor system. The effect of Hammett's constant on the bond distances and the force constants were also discussed and drawn. Wilson's matrix method, Badger's formula, Jones and El-Sonbati equations were used to determine the stretching and interaction force constant from which the UO bond distances were calculated. The bond distances of these complexes were also investigated.

Structural model of dioxouranium(VI) with hydrazono ligands

NASA Astrophysics Data System (ADS)

Mubarak, Ahmed T.

2005-04-01

Synthesis and characterization of several new coordination compounds of dioxouranium(VI) heterochelates with bidentate hydrazono compounds derived from 1-phenyl-3-methyl-5-pyrazolone are described. The ligands and uranayl complexes have been characterized by various physico-chemical techniques. The bond lengths and the force constant have been calculated from asymmetric stretching frequency of O sbnd U sbnd O groups. The infrared spectral studies showed a monobasic bidentate behaviour with the oxygen and hydrazo nitrogen donor system. The effect of Hammett's constant on the bond distances and the force constants were also discussed and drawn. Wilson's matrix method, Badger's formula, Jones and El-Sonbati equations were used to determine the stretching and interaction force constant from which the U sbnd O bond distances were calculated. The bond distances of these complexes were also investigated.

Short-distance matrix elements for D 0 -meson mixing from N f = 2 + 1 lattice QCD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bazavov, A.; Bernard, C.; Bouchard, C. M.

We calculate in three-flavor lattice QCD the short-distance hadronic matrix elements of all five ΔC=2 four-fermion operators that contribute to neutral D-meson mixing both in and beyond the Standard Model. We use the MILC Collaboration’s N f=2+1 lattice gauge-field configurations generated with asqtad-improved staggered sea quarks. We also employ the asqtad action for the valence light quarks and use the clover action with the Fermilab interpretation for the charm quark. We analyze a large set of ensembles with pions as light as M π≈180 MeV and lattice spacings as fine as a≈0.045 fm, thereby enabling good control over the extrapolation to the physical pion mass and continuum limit. We obtain for the matrix elements in themore » $$\\overline{MS}$$-NDR scheme using the choice of evanescent operators proposed by Beneke et al., evaluated at 3 GeV, $$\\langle$$D 0|O i|$$\\bar{D}$$ 0 $$\\rangle$$={0.0805(55)(16),-0.1561(70)(31),0.0464(31)(9),0.2747(129)(55),0.1035(71)(21)} GeV 4 (i=1–5). The errors shown are from statistics and lattice systematics, and the omission of charmed sea quarks, respectively. To illustrate the utility of our matrix-element results, we place bounds on the scale of CP-violating new physics in D 0 mixing, finding lower limits of about 10–50×10 3 TeV for couplings of O(1). To enable our results to be employed in more sophisticated or model-specific phenomenological studies, we provide the correlations among our matrix-element results. For convenience, we also present numerical results in the other commonly used scheme of Buras, Misiak, and Urban.« less

Cytochrome C in a dry trehalose matrix: structural and dynamical effects probed by x-ray absorption spectroscopy.

PubMed

Giachini, Lisa; Francia, Francesco; Cordone, Lorenzo; Boscherini, Federico; Venturoli, Giovanni

2007-02-15

We report on the structure and dynamics of the Fe ligand cluster of reduced horse heart cytochrome c in solution, in a dried polyvinyl alcohol (PVA) film, and in two trehalose matrices characterized by different contents of residual water. The effect of the solvent/matrix environment was studied at room temperature using Fe K-edge x-ray absorption fine structure (XAFS) spectroscopy. XAFS data were analyzed by combining ab initio simulations and multi-parameter fitting in an attempt to disentangle structural from disorder parameters. Essentially the same structural and disorder parameters account adequately for the XAFS spectra measured in solution, both in the absence and in the presence of glycerol, and in the PVA film, showing that this polymer interacts weakly with the embedded protein. Instead, incorporation in trehalose leads to severe structural changes, more prominent in the more dried matrix, consisting of 1), an increase up to 0.2 A of the distance between Fe and the imidazole N atom of the coordinating histidine residue and 2), an elongation up to 0.16 A of the distance between Fe and the fourth-shell C atoms of the heme pyrrolic units. These structural distortions are accompanied by a substantial decrease of the relative mean-square displacements of the first ligands. In the extensively dried trehalose matrix, extremely low values of the Debye Waller factors are obtained for the pyrrolic and for the imidazole N atoms. This finding is interpreted as reflecting a drastic hindering in the relative motions of the Fe ligand cluster atoms and an impressive decrease in the static disorder of the local Fe structure. It appears, therefore, that the dried trehalose matrix dramatically perturbs the energy landscape of cytochrome c, giving rise, at the level of local structure, to well-resolved structural distortions and restricting the ensemble of accessible conformational substates.

Short-distance matrix elements for D 0 -meson mixing from N f = 2 + 1 lattice QCD

DOE PAGES

Bazavov, A.; Bernard, C.; Bouchard, C. M.; ...

2018-02-28

We calculate in three-flavor lattice QCD the short-distance hadronic matrix elements of all five ΔC=2 four-fermion operators that contribute to neutral D-meson mixing both in and beyond the Standard Model. We use the MILC Collaboration’s N f=2+1 lattice gauge-field configurations generated with asqtad-improved staggered sea quarks. We also employ the asqtad action for the valence light quarks and use the clover action with the Fermilab interpretation for the charm quark. We analyze a large set of ensembles with pions as light as M π≈180 MeV and lattice spacings as fine as a≈0.045 fm, thereby enabling good control over the extrapolation to the physical pion mass and continuum limit. We obtain for the matrix elements in themore » $$\\overline{MS}$$-NDR scheme using the choice of evanescent operators proposed by Beneke et al., evaluated at 3 GeV, $$\\langle$$D 0|O i|$$\\bar{D}$$ 0 $$\\rangle$$={0.0805(55)(16),-0.1561(70)(31),0.0464(31)(9),0.2747(129)(55),0.1035(71)(21)} GeV 4 (i=1–5). The errors shown are from statistics and lattice systematics, and the omission of charmed sea quarks, respectively. To illustrate the utility of our matrix-element results, we place bounds on the scale of CP-violating new physics in D 0 mixing, finding lower limits of about 10–50×10 3 TeV for couplings of O(1). To enable our results to be employed in more sophisticated or model-specific phenomenological studies, we provide the correlations among our matrix-element results. For convenience, we also present numerical results in the other commonly used scheme of Buras, Misiak, and Urban.« less

A Method for Comparing Multivariate Time Series with Different Dimensions

PubMed Central

Tapinos, Avraam; Mendes, Pedro

2013-01-01

In many situations it is desirable to compare dynamical systems based on their behavior. Similarity of behavior often implies similarity of internal mechanisms or dependency on common extrinsic factors. While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. A semi-metric is a distance function that has the properties of non-negativity, symmetry and reflexivity, but not sub-additivity. Here we develop a semi-metric – SMETS – that can be used for comparing groups of time series that may have different dimensions. To demonstrate its utility, the method is applied to dynamic models of biochemical networks and to portfolios of shares. The former is an example of a case where the dependencies between system variables are known, while in the latter the system is treated (and behaves) as a black box. PMID:23393554

Movement behaviour within and beyond perceptual ranges in three small mammals: effects of matrix type and body mass.

PubMed

Prevedello, Jayme Augusto; Forero-Medina, Germán; Vieira, Marcus Vinícius

2010-11-01

1. For animal species inhabiting heterogeneous landscapes, the tortuosity of the dispersal path is a key determinant of the success in locating habitat patches. Path tortuosity within and beyond perceptual range must differ, and may be differently affected by intrinsic attributes of individuals and extrinsic environmental factors. Understanding how these factors interact to determine path tortuosity allows more accurate inference of successful movements between habitat patches. 2. We experimentally determined the effects of intrinsic (body mass and species identity) and extrinsic factors (distance to nearest forest fragment and matrix type) on the tortuosity of movements of three forest-dwelling didelphid marsupials, in a fragmented landscape of the Atlantic Forest, Brazil. 3. A total of 202 individuals were captured in forest fragments and released in three unsuitable matrix types (mowed pasture, abandoned pasture and manioc plantation), carrying spool-and-line devices. 4. Twenty-four models were formulated representing a priori hypotheses of major determinants of path tortuosity, grouped in three scenarios (only intrinsic factors, only extrinsic factors and models with combinations of both), and compared using a model selection approach. Models were tested separately for individuals released within the perceptual range of the species, and for individuals released beyond the perceptual range. 5. Matrix type strongly affected path tortuosity, with more obstructed matrix types hampering displacement of animals. Body mass was more important than species identity to determine path tortuosity, with larger animals moving more linearly. Increased distance to the fragment resulted in more tortuous paths, but actually reflects a threshold in perceptual range: linear paths within perceptual range, tortuous paths beyond. 6. The variables tested explained successfully path tortuosity, but only for animals released within the perceptual range. Other factors, such as wind intensity and direction of plantation rows, may be more important for individuals beyond their perceptual range. 7. Simplistic scenarios considering only intrinsic or extrinsic factors are inadequate to predict path tortuosity, and to infer dispersal success in heterogeneous landscapes. Perceptual range represents a fundamental threshold where the effects of matrix type, body mass and individual behaviour change drastically. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices.

PubMed

Tsafrir, D; Tsafrir, I; Ein-Dor, L; Zuk, O; Notterman, D A; Domany, E

2005-05-15

We introduce a novel unsupervised approach for the organization and visualization of multidimensional data. At the heart of the method is a presentation of the full pairwise distance matrix of the data points, viewed in pseudocolor. The ordering of points is iteratively permuted in search of a linear ordering, which can be used to study embedded shapes. Several examples indicate how the shapes of certain structures in the data (elongated, circular and compact) manifest themselves visually in our permuted distance matrix. It is important to identify the elongated objects since they are often associated with a set of hidden variables, underlying continuous variation in the data. The problem of determining an optimal linear ordering is shown to be NP-Complete, and therefore an iterative search algorithm with O(n3) step-complexity is suggested. By using sorting points into neighborhoods, i.e. SPIN to analyze colon cancer expression data we were able to address the serious problem of sample heterogeneity, which hinders identification of metastasis related genes in our data. Our methodology brings to light the continuous variation of heterogeneity--starting with homogeneous tumor samples and gradually increasing the amount of another tissue. Ordering the samples according to their degree of contamination by unrelated tissue allows the separation of genes associated with irrelevant contamination from those related to cancer progression. Software package will be available for academic users upon request.

Cartilage degradation biomarkers predict efficacy of a novel, highly selective matrix metalloproteinase 13 inhibitor in a dog model of osteoarthritis: confirmation by multivariate analysis that modulation of type II collagen and aggrecan degradation peptides parallels pathologic changes.

PubMed

Settle, Steven; Vickery, Lillian; Nemirovskiy, Olga; Vidmar, Tom; Bendele, Alison; Messing, Dean; Ruminski, Peter; Schnute, Mark; Sunyer, Teresa

2010-10-01

To demonstrate that the novel highly selective matrix metalloproteinase 13 (MMP-13) inhibitor PF152 reduces joint lesions in adult dogs with osteoarthritis (OA) and decreases biomarkers of cartilage degradation. The potency and selectivity of PF152 were evaluated in vitro using 16 MMPs, TACE, and ADAMTS-4 and ADAMTS-5, as well as ex vivo in human cartilage explants. In vivo effects were evaluated at 3 concentrations in mature beagles with partial medial meniscectomy. Gross and histologic changes in the femorotibial joints were evaluated using various measures of cartilage degeneration. Biomarkers of cartilage turnover were examined in serum, urine, or synovial fluid. Results were analyzed individually and in combination using multivariate analysis. The potent and selective MMP-13 inhibitor PF152 decreased human cartilage degradation ex vivo in a dose-dependent manner. PF152 treatment of dogs with OA reduced cartilage lesions and decreased biomarkers of type II collagen (type II collagen neoepitope) and aggrecan (peptides ending in ARGN or AGEG) degradation. The dose required for significant inhibition varied with the measure used, but multivariate analysis of 6 gross and histologic measures indicated that all doses differed significantly from vehicle but not from each other. Combined analysis of cartilage degradation markers showed similar results. This highly selective MMP-13 inhibitor exhibits chondroprotective effects in mature animals. Biomarkers of cartilage degradation, when evaluated in combination, parallel the joint structural changes induced by the MMP-13 inhibitor. These data support the potential therapeutic value of selective MMP-13 inhibitors and the use of a set of appropriate biomarkers to predict efficacy in OA clinical trials.

Analysis of some metallic elements and metalloids composition and relationships in parasol mushroom Macrolepiota procera.

PubMed

Falandysz, Jerzy; Sapkota, Atindra; Dryżałowska, Anna; Mędyk, Małgorzata; Feng, Xinbin

2017-06-01

The aim of the study was to characterise the multi-elemental composition and associations between a group of 32 elements and 16 rare earth elements collected by mycelium from growing substrates and accumulated in fruiting bodies of Macrolepiota procera from 16 sites from the lowland areas of Poland. The elements were quantified by inductively coupled plasma quadrupole mass spectrometry using validated method. The correlation matrix obtained from a possible 48 × 16 data matrix has been used to examine if any association exits between 48 elements in mushrooms foraged from 16 sampling localizations by multivariate approach using principal component (PC) analysis. The model could explain up to 93% variability by eight factors for which an eigenvalue value was ≥1. Absolute values of the correlation coefficient were above 0.72 (significance at p < 0.05) for 43 elements. From a point of view by consumer, the absolute content of Cd, Hg, Pb in caps of M. procera collected from background (unpolluted) areas could be considered elevated while sporadic/occasional ingestion of this mushroom is considered safe. The multivariate functional analysis revealed on associated accumulation of many elements in this mushroom. M. procera seem to possess some features of a bio-indicative species for anthropogenic Pb but also for some geogenic metals.

Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution.

PubMed

Novikova, Anna; Carstensen, Jens M; Rades, Thomas; Leopold, Prof Dr Claudia S

2016-12-30

In the present study the applicability of multispectral UV imaging in combination with multivariate image analysis for surface evaluation of MUPS tablets was investigated with respect to the differentiation of the API pellets from the excipients matrix, estimation of the drug content as well as pellet distribution, and influence of the coating material and tablet thickness on the predictive model. Different formulations consisting of coated drug pellets with two coating polymers (Aquacoat ® ECD and Eudragit ® NE 30 D) at three coating levels each were compressed to MUPS tablets with various amounts of coated pellets and different tablet thicknesses. The coated drug pellets were clearly distinguishable from the excipients matrix using a partial least squares approach regardless of the coating layer thickness and coating material used. Furthermore, the number of the detected drug pellets on the tablet surface allowed an estimation of the true drug content in the respective MUPS tablet. In addition, the pellet distribution in the MUPS formulations could be estimated by UV image analysis of the tablet surface. In conclusion, this study revealed that UV imaging in combination with multivariate image analysis is a promising approach for the automatic quality control of MUPS tablets during the manufacturing process. Copyright © 2016 Elsevier B.V. All rights reserved.

Simultaneous chemometric determination of pyridoxine hydrochloride and isoniazid in tablets by multivariate regression methods.

PubMed

Dinç, Erdal; Ustündağ, Ozgür; Baleanu, Dumitru

2010-08-01

The sole use of pyridoxine hydrochloride during treatment of tuberculosis gives rise to pyridoxine deficiency. Therefore, a combination of pyridoxine hydrochloride and isoniazid is used in pharmaceutical dosage form in tuberculosis treatment to reduce this side effect. In this study, two chemometric methods, partial least squares (PLS) and principal component regression (PCR), were applied to the simultaneous determination of pyridoxine (PYR) and isoniazid (ISO) in their tablets. A concentration training set comprising binary mixtures of PYR and ISO consisting of 20 different combinations were randomly prepared in 0.1 M HCl. Both multivariate calibration models were constructed using the relationships between the concentration data set (concentration data matrix) and absorbance data matrix in the spectral region 200-330 nm. The accuracy and the precision of the proposed chemometric methods were validated by analyzing synthetic mixtures containing the investigated drugs. The recovery results obtained by applying PCR and PLS calibrations to the artificial mixtures were found between 100.0 and 100.7%. Satisfactory results obtained by applying the PLS and PCR methods to both artificial and commercial samples were obtained. The results obtained in this manuscript strongly encourage us to use them for the quality control and the routine analysis of the marketing tablets containing PYR and ISO drugs. Copyright © 2010 John Wiley & Sons, Ltd.

Application of distance correction to ChemCam laser-induced breakdown spectroscopy measurements

DOE PAGES

Mezzacappa, A.; Melikechi, N.; Cousin, A.; ...

2016-04-04

Laser-induced breakdown spectroscopy (LIBS) provides chemical information from atomic, ionic, and molecular emissions from which geochemical composition can be deciphered. Analysis of LIBS spectra in cases where targets are observed at different distances, as is the case for the ChemCam instrument on the Mars rover Curiosity, which performs analyses at distances between 2 and 7.4 m is not a simple task. Previously, we showed that spectral distance correction based on a proxy spectroscopic standard created from first-shot dust observations on Mars targets ameliorates the distance bias in multivariate-based elemental-composition predictions of laboratory data. In this work, we correct an expandedmore » set of neutral and ionic spectral emissions for distance bias in the ChemCam data set. By using and testing different selection criteria to generate multiple proxy standards, we find a correction that minimizes the difference in spectral intensity measured at two different distances and increases spectral reproducibility. When the quantitative performance of distance correction is assessed, there is improvement for SiO 2, Al 2O 3, CaO, FeOT, Na 2O, K 2O, that is, for most of the major rock forming elements, and for the total major-element weight percent predicted. But, for MgO the method does not provide improvements while for TiO 2, it yields inconsistent results. Additionally, we observed that many emission lines do not behave consistently with distance, evidenced from laboratory analogue measurements and ChemCam data. This limits the effectiveness of the method.« less

A rough set approach for determining weights of decision makers in group decision making

PubMed Central

Yang, Qiang; Du, Ping-an; Wang, Yong; Liang, Bin

2017-01-01

This study aims to present a novel approach for determining the weights of decision makers (DMs) based on rough group decision in multiple attribute group decision-making (MAGDM) problems. First, we construct a rough group decision matrix from all DMs’ decision matrixes on the basis of rough set theory. After that, we derive a positive ideal solution (PIS) founded on the average matrix of rough group decision, and negative ideal solutions (NISs) founded on the lower and upper limit matrixes of rough group decision. Then, we obtain the weight of each group member and priority order of alternatives by using relative closeness method, which depends on the distances from each individual group member’ decision to the PIS and NISs. Through comparisons with existing methods and an on-line business manager selection example, the proposed method show that it can provide more insights into the subjectivity and vagueness of DMs’ evaluations and selections. PMID:28234974

Quantum confinement of nanocrystals within amorphous matrices

NASA Astrophysics Data System (ADS)

Lusk, Mark T.; Collins, Reuben T.; Nourbakhsh, Zahra; Akbarzadeh, Hadi

2014-02-01

Nanocrystals encapsulated within an amorphous matrix are computationally analyzed to quantify the degree to which the matrix modifies the nature of their quantum-confinement power—i.e., the relationship between nanocrystal size and the gap between valence- and conduction-band edges. A special geometry allows exactly the same amorphous matrix to be applied to nanocrystals of increasing size to precisely quantify changes in confinement without the noise typically associated with encapsulating structures that are different for each nanocrystal. The results both explain and quantify the degree to which amorphous matrices redshift the character of quantum confinement. The character of this confinement depends on both the type of encapsulating material and the separation distance between the nanocrystals within it. Surprisingly, the analysis also identifies a critical nanocrystal threshold below which quantum confinement is not possible—a feature unique to amorphous encapsulation. Although applied to silicon nanocrystals within an amorphous silicon matrix, the methodology can be used to accurately analyze the confinement softening of other amorphous systems as well.

Differential computation method used to calibrate the angle-centroid relationship in coaxial reverse Hartmann test

NASA Astrophysics Data System (ADS)

Li, Xinji; Hui, Mei; Zhao, Zhu; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-05-01

A differential computation method is presented to improve the precision of calibration for coaxial reverse Hartmann test (RHT). In the calibration, the accuracy of the distance measurement greatly influences the surface shape test, as demonstrated in the mathematical analyses. However, high-precision absolute distance measurement is difficult in the calibration. Thus, a differential computation method that only requires the relative distance was developed. In the proposed method, a liquid crystal display screen successively displayed two regular dot matrix patterns with different dot spacing. In a special case, images on the detector exhibited similar centroid distributions during the reflector translation. Thus, the critical value of the relative displacement distance and the centroid distributions of the dots on the detector were utilized to establish the relationship between the rays at certain angles and the detector coordinates. Experiments revealed the approximately linear behavior of the centroid variation with the relative displacement distance. With the differential computation method, we increased the precision of traditional calibration 10-5 rad root mean square. The precision of the RHT was increased by approximately 100 nm.

Treating Sample Covariances for Use in Strongly Coupled Atmosphere-Ocean Data Assimilation

NASA Astrophysics Data System (ADS)

Smith, Polly J.; Lawless, Amos S.; Nichols, Nancy K.

2018-01-01

Strongly coupled data assimilation requires cross-domain forecast error covariances; information from ensembles can be used, but limited sampling means that ensemble derived error covariances are routinely rank deficient and/or ill-conditioned and marred by noise. Thus, they require modification before they can be incorporated into a standard assimilation framework. Here we compare methods for improving the rank and conditioning of multivariate sample error covariance matrices for coupled atmosphere-ocean data assimilation. The first method, reconditioning, alters the matrix eigenvalues directly; this preserves the correlation structures but does not remove sampling noise. We show that it is better to recondition the correlation matrix rather than the covariance matrix as this prevents small but dynamically important modes from being lost. The second method, model state-space localization via the Schur product, effectively removes sample noise but can dampen small cross-correlation signals. A combination that exploits the merits of each is found to offer an effective alternative.

Pu239 Cross-Section Variations Based on Experimental Uncertainties and Covariances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sigeti, David Edward; Williams, Brian J.; Parsons, D. Kent

2016-10-18

Algorithms and software have been developed for producing variations in plutonium-239 neutron cross sections based on experimental uncertainties and covariances. The varied cross-section sets may be produced as random samples from the multi-variate normal distribution defined by an experimental mean vector and covariance matrix, or they may be produced as Latin-Hypercube/Orthogonal-Array samples (based on the same means and covariances) for use in parametrized studies. The variations obey two classes of constraints that are obligatory for cross-section sets and which put related constraints on the mean vector and covariance matrix that detemine the sampling. Because the experimental means and covariances domore » not obey some of these constraints to sufficient precision, imposing the constraints requires modifying the experimental mean vector and covariance matrix. Modification is done with an algorithm based on linear algebra that minimizes changes to the means and covariances while insuring that the operations that impose the different constraints do not conflict with each other.« less

Pharmaceutical analysis in solids using front face fluorescence spectroscopy and multivariate calibration with matrix correction by piecewise direct standardization

NASA Astrophysics Data System (ADS)

Alves, Julio Cesar L.; Poppi, Ronei J.

2013-02-01

This paper reports the application of piecewise direct standardization (PDS) for matrix correction in front face fluorescence spectroscopy of solids when different excipients are used in a pharmaceutical preparation based on a mixture of acetylsalicylic acid (ASA), paracetamol (acetaminophen) and caffeine. As verified in earlier studies, the use of different excipients and their ratio can cause a displacement, change in fluorescence intensity or band profile. To overcome this important drawback, a standardization strategy was adopted to convert all the excitation-emission fluorescence spectra into those used for model development. An excitation-emission matrix (EEM) for which excitation and emission wavelengths ranging from 265 to 405 nm and 300 to 480 nm, respectively, was used. Excellent results were obtained using unfolded partial least squares (U-PLS), with RMSEP values of 8.2 mg/g, 10.9 mg/g and 2.7 mg/g for ASA, paracetamol and caffeine, respectively, and with relative errors lesser than 5% for the three analytes.

Theory of activated penetrant diffusion in viscous fluids and colloidal suspensions

NASA Astrophysics Data System (ADS)

Zhang, Rui; Schweizer, Kenneth S.

2015-10-01

We heuristically formulate a microscopic, force level, self-consistent nonlinear Langevin equation theory for activated barrier hopping and non-hydrodynamic diffusion of a hard sphere penetrant in very dense hard sphere fluid matrices. Penetrant dynamics is controlled by a rich competition between force relaxation due to penetrant self-motion and collective matrix structural (alpha) relaxation. In the absence of penetrant-matrix attraction, three activated dynamical regimes are predicted as a function of penetrant-matrix size ratio which are physically distinguished by penetrant jump distance and the nature of matrix motion required to facilitate its hopping. The penetrant diffusion constant decreases the fastest with size ratio for relatively small penetrants where the matrix effectively acts as a vibrating amorphous solid. Increasing penetrant-matrix attraction strength reduces penetrant diffusivity due to physical bonding. For size ratios approaching unity, a distinct dynamical regime emerges associated with strong slaving of penetrant hopping to matrix structural relaxation. A crossover regime at intermediate penetrant-matrix size ratio connects the two limiting behaviors for hard penetrants, but essentially disappears if there are strong attractions with the matrix. Activated penetrant diffusivity decreases strongly with matrix volume fraction in a manner that intensifies as the size ratio increases. We propose and implement a quasi-universal approach for activated diffusion of a rigid atomic/molecular penetrant in a supercooled liquid based on a mapping between the hard sphere system and thermal liquids. Calculations for specific systems agree reasonably well with experiments over a wide range of temperature, covering more than 10 orders of magnitude of variation of the penetrant diffusion constant.

Theory of activated penetrant diffusion in viscous fluids and colloidal suspensions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Rui; Schweizer, Kenneth S., E-mail: kschweiz@illinois.edu

2015-10-14

We heuristically formulate a microscopic, force level, self-consistent nonlinear Langevin equation theory for activated barrier hopping and non-hydrodynamic diffusion of a hard sphere penetrant in very dense hard sphere fluid matrices. Penetrant dynamics is controlled by a rich competition between force relaxation due to penetrant self-motion and collective matrix structural (alpha) relaxation. In the absence of penetrant-matrix attraction, three activated dynamical regimes are predicted as a function of penetrant-matrix size ratio which are physically distinguished by penetrant jump distance and the nature of matrix motion required to facilitate its hopping. The penetrant diffusion constant decreases the fastest with size ratiomore » for relatively small penetrants where the matrix effectively acts as a vibrating amorphous solid. Increasing penetrant-matrix attraction strength reduces penetrant diffusivity due to physical bonding. For size ratios approaching unity, a distinct dynamical regime emerges associated with strong slaving of penetrant hopping to matrix structural relaxation. A crossover regime at intermediate penetrant-matrix size ratio connects the two limiting behaviors for hard penetrants, but essentially disappears if there are strong attractions with the matrix. Activated penetrant diffusivity decreases strongly with matrix volume fraction in a manner that intensifies as the size ratio increases. We propose and implement a quasi-universal approach for activated diffusion of a rigid atomic/molecular penetrant in a supercooled liquid based on a mapping between the hard sphere system and thermal liquids. Calculations for specific systems agree reasonably well with experiments over a wide range of temperature, covering more than 10 orders of magnitude of variation of the penetrant diffusion constant.« less

Eigenvalue assignment by minimal state-feedback gain in LTI multivariable systems

NASA Astrophysics Data System (ADS)

Ataei, Mohammad; Enshaee, Ali

2011-12-01

In this article, an improved method for eigenvalue assignment via state feedback in the linear time-invariant multivariable systems is proposed. This method is based on elementary similarity operations, and involves mainly utilisation of vector companion forms, and thus is very simple and easy to implement on a digital computer. In addition to the controllable systems, the proposed method can be applied for the stabilisable ones and also systems with linearly dependent inputs. Moreover, two types of state-feedback gain matrices can be achieved by this method: (1) the numerical one, which is unique, and (2) the parametric one, in which its parameters are determined in order to achieve a gain matrix with minimum Frobenius norm. The numerical examples are presented to demonstrate the advantages of the proposed method.

Development of an in-situ multi-component reinforced Al-based metal matrix composite by direct metal laser sintering technique — Optimization of process parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosh, Subrata Kumar, E-mail: subratagh82@gmail.com; Bandyopadhyay, Kaushik; Saha, Partha

2014-07-01

In the present investigation, an in-situ multi-component reinforced aluminum based metal matrix composite was fabricated by the combination of self-propagating high-temperature synthesis and direct metal laser sintering process. The different mixtures of Al, TiO{sub 2} and B{sub 4}C powders were used to initiate and maintain the self-propagating high-temperature synthesis by laser during the sintering process. It was found from the X-ray diffraction analysis and scanning electron microscopy that the reinforcements like Al{sub 2}O{sub 3}, TiC, and TiB{sub 2} were formed in the composite. The scanning electron microscopy revealed the distribution of the reinforcement phases in the composite and phase identities.more » The variable parameters such as powder layer thickness, laser power, scanning speed, hatching distance and composition of the powder mixture were optimized for higher density, lower porosity and higher microhardness using Taguchi method. Experimental investigation shows that the density of the specimen mainly depends upon the hatching distance, composition and layer thickness. On the other hand, hatching distance, layer thickness and laser power are the significant parameters which influence the porosity. The composition, laser power and layer thickness are the key influencing parameters for microhardness. - Highlights: • The reinforcements such as Al{sub 2}O{sub 3}, TiC, and TiB{sub 2} were produced in Al-MMC through SHS. • The density is mainly influenced by the material composition and hatching distance. • Hatching distance is the major influencing parameter on porosity. • The material composition is the significant parameter to enhance the microhardness. • The SEM micrographs reveal the distribution of TiC, TiB{sub 2} and Al{sub 2}O{sub 3} in the composite.« less

Bayesian statistics and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Koch, K. R.

2018-03-01

The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes' theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. TheMonte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum.

Gene Expression Data to Mouse Atlas Registration Using a Nonlinear Elasticity Smoother and Landmark Points Constraints

PubMed Central

Lin, Tungyou; Guyader, Carole Le; Dinov, Ivo; Thompson, Paul; Toga, Arthur; Vese, Luminita

2013-01-01

This paper proposes a numerical algorithm for image registration using energy minimization and nonlinear elasticity regularization. Application to the registration of gene expression data to a neuroanatomical mouse atlas in two dimensions is shown. We apply a nonlinear elasticity regularization to allow larger and smoother deformations, and further enforce optimality constraints on the landmark points distance for better feature matching. To overcome the difficulty of minimizing the nonlinear elasticity functional due to the nonlinearity in the derivatives of the displacement vector field, we introduce a matrix variable to approximate the Jacobian matrix and solve for the simplified Euler-Lagrange equations. By comparison with image registration using linear regularization, experimental results show that the proposed nonlinear elasticity model also needs fewer numerical corrections such as regridding steps for binary image registration, it renders better ground truth, and produces larger mutual information; most importantly, the landmark points distance and L2 dissimilarity measure between the gene expression data and corresponding mouse atlas are smaller compared with the registration model with biharmonic regularization. PMID:24273381

Optical drift effects in general relativity

NASA Astrophysics Data System (ADS)

Korzyński, Mikołaj; Kopiński, Jarosław

2018-03-01

We consider the question of determining the optical drift effects in general relativity, i.e. the rate of change of the apparent position, redshift, Jacobi matrix, angular distance and luminosity distance of a distant object as registered by an observer in an arbitrary spacetime. We present a fully relativistic and covariant approach, in which the problem is reduced to a hierarchy of ODE's solved along the line of sight. The 4-velocities and 4-accelerations of the observer and the emitter and the geometry of the spacetime along the line of sight constitute the input data. We build on the standard relativistic geometric optics formalism and extend it to include the time derivatives of the observables. In the process we obtain two general, non-perturbative relations: the first one between the gravitational lensing, represented by the Jacobi matrix, and the apparent position drift, also called the cosmic parallax, and the second one between the apparent position drift and the redshift drift. The applications of the results include the theoretical study of the drift effects of cosmological origin (so-called real-time cosmology) in numerical or exact Universe models.

Morphological and Wear behaviour of new Al-SiCmicro-SiCnano hybrid nanocomposites fabricated through powder metallurgy

NASA Astrophysics Data System (ADS)

Arif, Sajjad; Tanwir Alam, Md; Aziz, Tariq; Ansari, Akhter H.

2018-04-01

In the present work, aluminium matrix composites reinforced with 10 wt% SiC micro particles along with x% SiC nano particles (x = 0, 1, 3, 5 and 7 wt%) were fabricated through powder metallurgy. The fabricated hybrid composites were characterized by x-ray diffractometer (XRD), scanning electron microscope (SEM), energy dispersive spectrum (EDS) and elemental mapping. The relative density, hardness and wear behaviour of all hybrid nanocomposites were studied. The influence of various control factors like SiC reinforcement, sliding distance (300, 600, 900 and 1200 m) and applied load (20, 30 and 40 N) were explored using pin-on-disc wear apparatus. The uniform distribution of micro and nano SiC particles in aluminium matrix is confirmed by elemental maps. The hardness and wear test results showed that properties of the hybrid composite containing 5 wt% nano SiC was better than other hybrid composites. Additionally, the wear loss of all hybrid nanocomposites increases with increasing sliding distance and applied load. The identification of wear phenomenon were studied through the SEM images of worn surface.

Quarkonium polarization and the long distance matrix elements hierarchies using jet substructure

NASA Astrophysics Data System (ADS)

Dai, Lin; Shrivastava, Prashant

2017-08-01

We investigate the quarkonium production mechanisms in jets at the LHC, using the fragmenting jet functions (FJF) approach. Specifically, we discuss the jet energy dependence of the J /ψ production cross section at the LHC. By comparing the cross sections for the different NRQCD production channels (1S0[8], 3S1[8], 3PJ[8], and 3cripts>S1[1]), we find that at fixed values of energy fraction z carried by the J /ψ , if the normalized cross section is a decreasing function of the jet energy, in particular for z >0.5 , then the depolarizing 1S0[8] must be the dominant channel. This makes the prediction made in [Baumgart et al., J. High Energy Phys. 11 (2014) 003, 10.1007/JHEP11(2014)003] for the FJF's also true for the cross section. We also make comparisons between the long distance matrix elements extracted by various groups. This analysis could potentially shed light on the polarization properties of the J /ψ production in high pT region.

Phylogeny of metabolic networks: a spectral graph theoretical approach.

PubMed

Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

2015-10-01

Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

Invalid-point removal based on epipolar constraint in the structured-light method

NASA Astrophysics Data System (ADS)

Qi, Zhaoshuai; Wang, Zhao; Huang, Junhui; Xing, Chao; Gao, Jianmin

2018-06-01

In structured-light measurement, there unavoidably exist many invalid points caused by shadows, image noise and ambient light. According to the property of the epipolar constraint, because the retrieved phase of the invalid point is inaccurate, the corresponding projector image coordinate (PIC) will not satisfy the epipolar constraint. Based on this fact, a new invalid-point removal method based on the epipolar constraint is proposed in this paper. First, the fundamental matrix of the measurement system is calculated, which will be used for calculating the epipolar line. Then, according to the retrieved phase map of the captured fringes, the PICs of each pixel are retrieved. Subsequently, the epipolar line in the projector image plane of each pixel is obtained using the fundamental matrix. The distance between the corresponding PIC and the epipolar line of a pixel is defined as the invalidation criterion, which quantifies the satisfaction degree of the epipolar constraint. Finally, all pixels with a distance larger than a certain threshold are removed as invalid points. Experiments verified that the method is easy to implement and demonstrates better performance than state-of-the-art measurement systems.

Association between US features of primary tumor and axillary lymph node metastasis in patients with clinical T1-T2N0 breast cancer.

PubMed

Bae, Min Sun; Shin, Sung Ui; Song, Sung Eun; Ryu, Han Suk; Han, Wonshik; Moon, Woo Kyung

2018-04-01

Background Most patients with early-stage breast cancer have clinically negative lymph nodes (LNs). However, 15-20% of patients have axillary nodal metastasis based on the sentinel LN biopsy. Purpose To assess whether ultrasound (US) features of a primary tumor are associated with axillary LN metastasis in patients with clinical T1-T2N0 breast cancer. Material and Methods This retrospective study included 138 consecutive patients (median age = 51 years; age range = 27-78 years) who underwent breast surgery with axillary LN evaluation for clinically node-negative T1-T2 breast cancer. Three radiologists blinded to the axillary surgery results independently reviewed the US images. Tumor distance from the skin and distance from the nipple were determined based on the US report. Association between US features of a breast tumor and axillary LN metastasis was assessed using a multivariate logistic regression model after controlling for clinicopathologic variables. Results Of the 138 patients, 28 (20.3%) had nodal metastasis. At univariate analysis, tumor distance from the skin ( P = 0.019), tumor size on US ( P = 0.023), calcifications ( P = 0.036), architectural distortion ( P = 0.001), and lymphovascular invasion ( P = 0.049) were associated with axillary LN metastasis. At multivariate analysis, shorter skin-to-tumor distance (odds ratio [OR] = 4.15; 95% confidence interval [CI] = 1.01-16.19; P = 0.040) and masses with associated architectural distortion (OR = 3.80; 95% CI = 1.57-9.19; P = 0.003) were independent predictors of axillary LN metastasis. Conclusion US features of breast cancer can be promising factors associated with axillary LN metastasis in patients with clinically node-negative early-stage breast cancer.

Influence of rurality, deprivation and distance from clinic on uptake in men invited for abdominal aortic aneurysm screening.

PubMed

Crilly, M A; Mundie, A; Bachoo, P; Nimmo, F

2015-07-01

Effective abdominal aortic aneurysm (AAA) screening requires high uptake. The aim was to assess the independent association of screening uptake with rurality, social deprivation, clinic type, distance to clinic and season. Screening across Grampian was undertaken by trained nurses in six community and three hospital clinics. Men aged 65 years were invited for screening by post (with 2 further reminders for non-responders). AAA screening data are stored on a national call-recall database. The Scottish postcode directory was used to allocate to all invited men a deprivation index (Scottish Index of Multiple Deprivation), a Scottish urban/rural category and distance to clinic. Multivariable analysis was undertaken. The cohort included 5645 men invited for screening over 12 months (October 2012 to October 2013); 42·6 per cent lived in urban areas, 38·9 per cent in rural areas and 18·5 per cent in small towns (uptake 87·0, 89·3 and 90·8 per cent respectively). Overall uptake was 88·6 per cent with 76 new AAAs detected: 15·2 (95 per cent c.i. 11·8 to 18·6) per 1000 men screened. Aberdeen city (large urban area) had the lowest uptake (86·1 per cent). Uptake declined with increasing deprivation, with the steepest decline in urban areas. On multivariable analysis, a 1-point increase in deprivation deciles was associated with a 0·08 (95 per cent c.i. 0·06 to 0·11) reduction in the odds of being screened (P < 0·001). Clinic type (community versus hospital), distance to clinic and season were not associated independently with uptake. Both urban residence and social deprivation were associated independently with uptake among men invited for AAA screening. © 2015 BJS Society Ltd Published by John Wiley & Sons Ltd.

Institutional and matrix support and its relationship with primary healthcare

PubMed Central

dos Santos, Alaneir de Fátima; Machado, Antônio Thomaz Gonzaga da Matta; dos Reis, Clarice Magalhães Rodrigues; Abreu, Daisy Maria Xavier; de Araújo, Lucas Henrique Lobato; Rodrigues, Simone Cristina; de Lima, Ângela Maria de Lourdes Dayrell; Jorge, Alzira de Oliveira; Fonseca, Délcio

2015-01-01

OBJECTIVE To analyze whether the level of institutional and matrix support is associated with better certification of primary healthcare teams. METHODS In this cross-sectional study, we evaluated two kinds of primary healthcare support – 14,489 teams received institutional support and 14,306 teams received matrix support. Logistic regression models were applied. In the institutional support model, the independent variable was “level of support” (as calculated by the sum of supporting activities for both modalities). In the matrix support model, in turn, the independent variables were the supporting activities. The multivariate analysis has considered variables with p < 0.20. The model was adjusted by the Hosmer-Lemeshow test. RESULTS The teams had institutional and matrix supporting activities (84.0% and 85.0%), respectively, with 55.0% of them performing between six and eight activities. For the institutional support, we have observed 1.96 and 3.77 chances for teams who had medium and high levels of support to have very good or good certification, respectively. For the matrix support, the chances of their having very good or good certification were 1.79 and 3.29, respectively. Regarding to the association between institutional support activities and the certification, the very good or good certification was positively associated with self-assessment (OR = 1.95), permanent education (OR = 1.43), shared evaluation (OR = 1.40), and supervision and evaluation of indicators (OR = 1.37). In regards to the matrix support, the very good or good certification was positively associated with permanent education (OR = 1.50), interventions in the territory (OR = 1.30), and discussion in the work processes (OR = 1.23). CONCLUSIONS In Brazil, supporting activities are being incorporated in primary healthcare, and there is an association between the level of support, both matrix and institutional, and the certification result. PMID:26274872

Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging.

PubMed

Yoon, Hai-Jeon; Kim, Yemi; Chung, Jin; Kim, Bom Sahn

2018-03-30

Predicting response to neo-adjuvant chemotherapy (NAC) and survival in locally advanced breast cancer (LABC) is important. This study investigated the prognostic value of tumor heterogeneity evaluated with textural analysis through F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and diffusion-weighted imaging (DWI). We enrolled 83 patients with LABC who had completed NAC and curative surgery. Tumor texture indices from pretreatment FDG PET and DWI were extracted from histogram analysis and 7 different parent matrices: co-occurrence matrix, the voxel-alignment matrix, neighborhood intensity difference matrix, intensity size-zone matrix (ISZM), normalized gray-level co-occurrence matrix (NGLCM), neighboring gray-level dependence matrix (NGLDM), and texture spectrum matrix. The predictive values of textural features were tested regarding both pathologic NAC response and progression-free survival. Among 83 patients, 46 were pathologic responders, while 37 were nonresponders. The PET texture indices from 7 parent matrices, DWI texture indices from histogram, and 1 parent matrix (NGLCM) showed significant differences according to NAC response. On multivariable analysis, number nonuniformity of PET extracted from the NGLDM was an independent predictor of pathologic response (P = .009). During a median follow-up period of 17.3 months, 14 patients experienced recurrence. High-intensity zone emphasis (HIZE) and high-intensity short-zone emphasis (HISZE) from PET extracted from ISZM were significant textural predictors (P = .011 and P = .033). On Cox regression analysis, only HIZE was a significant predictor of recurrence (P = .027), while HISZE showed borderline significance (P = .107). Tumor texture indices are useful for NAC response prediction in LABC. Moreover, PET texture indices can help to predict disease recurrence. © 2018 Wiley Periodicals, Inc.

Robustness of Thirty Meter Telescope primary mirror control

NASA Astrophysics Data System (ADS)

Macmynowski, Douglas G.; Thompson, Peter M.; Shelton, Chris; Roberts, Lewis C., Jr.

2010-07-01

The primary mirror control system for the Thirty Meter Telescope (TMT) maintains the alignment of the 492 segments in the presence of both quasi-static (gravity and thermal) and dynamic disturbances due to unsteady wind loads. The latter results in a desired control bandwidth of 1Hz at high spatial frequencies. The achievable bandwidth is limited by robustness to (i) uncertain telescope structural dynamics (control-structure interaction) and (ii) small perturbations in the ill-conditioned influence matrix that relates segment edge sensor response to actuator commands. Both of these effects are considered herein using models of TMT. The former is explored through multivariable sensitivity analysis on a reduced-order Zernike-basis representation of the structural dynamics. The interaction matrix ("A-matrix") uncertainty has been analyzed theoretically elsewhere, and is examined here for realistic amplitude perturbations due to segment and sensor installation errors, and gravity and thermal induced segment motion. The primary influence of A-matrix uncertainty is on the control of "focusmode"; this is the least observable mode, measurable only through the edge-sensor (gap-dependent) sensitivity to the dihedral angle between segments. Accurately estimating focus-mode will require updating the A-matrix as a function of the measured gap. A-matrix uncertainty also results in a higher gain-margin requirement for focus-mode, and hence the A-matrix and CSI robustness need to be understood simultaneously. Based on the robustness analysis, the desired 1 Hz bandwidth is achievable in the presence of uncertainty for all except the lowest spatial-frequency response patterns of the primary mirror.

A multivariate analysis of genetic constraints to life history evolution in a wild population of red deer.

PubMed

Walling, Craig A; Morrissey, Michael B; Foerster, Katharina; Clutton-Brock, Tim H; Pemberton, Josephine M; Kruuk, Loeske E B

2014-12-01

Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance-covariance matrix ( G: ) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G: on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. Copyright © 2014 Walling et al.

A Multivariate Analysis of Genetic Constraints to Life History Evolution in a Wild Population of Red Deer

PubMed Central

Walling, Craig A.; Morrissey, Michael B.; Foerster, Katharina; Clutton-Brock, Tim H.; Pemberton, Josephine M.; Kruuk, Loeske E. B.

2014-01-01

Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance–covariance matrix (G) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. PMID:25278555

Matching pollution with adaptive changes in mangrove plants by multivariate statistics. A case study, Rhizophora mangle from four neotropical mangroves in Brazil.

PubMed

Souza, Iara da Costa; Morozesk, Mariana; Duarte, Ian Drumond; Bonomo, Marina Marques; Rocha, Lívia Dorsch; Furlan, Larissa Maria; Arrivabene, Hiulana Pereira; Monferrán, Magdalena Victoria; Matsumoto, Silvia Tamie; Milanez, Camilla Rozindo Dias; Wunderlin, Daniel Alberto; Fernandes, Marisa Narciso

2014-08-01

Roots of mangrove trees have an important role in depurating water and sediments by retaining metals that may accumulate in different plant tissues, affecting physiological processes and anatomy. The present study aimed to evaluate adaptive changes in root of Rhizophora mangle in response to different levels of chemical elements (metals/metalloids) in interstitial water and sediments from four neotropical mangroves in Brazil. What sets this study apart from other studies is that we not only investigate adaptive modifications in R. mangle but also changes in environments where this plant grows, evaluating correspondence between physical, chemical and biological issues by a combined set of multivariate statistical methods (pattern recognition). Thus, we looked to match changes in the environment with adaptations in plants. Multivariate statistics highlighted that the lignified periderm and the air gaps are directly related to the environmental contamination. Current results provide new evidences of root anatomical strategies to deal with contaminated environments. Multivariate statistics greatly contributes to extrapolate results from complex data matrixes obtained when analyzing environmental issues, pointing out parameters involved in environmental changes and also evidencing the adaptive response of the exposed biota. Copyright © 2014 Elsevier Ltd. All rights reserved.

Application of agglomerative clustering for analyzing phylogenetically on bacterium of saliva

NASA Astrophysics Data System (ADS)

Bustamam, A.; Fitria, I.; Umam, K.

2017-07-01

Analyzing population of Streptococcus bacteria is important since these species can cause dental caries, periodontal, halitosis (bad breath) and more problems. This paper will discuss the phylogenetically relation between the bacterium Streptococcus in saliva using a phylogenetic tree of agglomerative clustering methods. Starting with the bacterium Streptococcus DNA sequence obtained from the GenBank, then performed characteristic extraction of DNA sequences. The characteristic extraction result is matrix form, then performed normalization using min-max normalization and calculate genetic distance using Manhattan distance. Agglomerative clustering technique consisting of single linkage, complete linkage and average linkage. In this agglomerative algorithm number of group is started with the number of individual species. The most similar species is grouped until the similarity decreases and then formed a single group. Results of grouping is a phylogenetic tree and branches that join an established level of distance, that the smaller the distance the more the similarity of the larger species implementation is using R, an open source program.

The applicability of ordinary least squares to consistently short distances between taxa in phylogenetic tree construction and the normal distribution test consequences.

PubMed

Roux, C Z

2009-05-01

Short phylogenetic distances between taxa occur, for example, in studies on ribosomal RNA-genes with slow substitution rates. For consistently short distances, it is proved that in the completely singular limit of the covariance matrix ordinary least squares (OLS) estimates are minimum variance or best linear unbiased (BLU) estimates of phylogenetic tree branch lengths. Although OLS estimates are in this situation equal to generalized least squares (GLS) estimates, the GLS chi-square likelihood ratio test will be inapplicable as it is associated with zero degrees of freedom. Consequently, an OLS normal distribution test or an analogous bootstrap approach will provide optimal branch length tests of significance for consistently short phylogenetic distances. As the asymptotic covariances between branch lengths will be equal to zero, it follows that the product rule can be used in tree evaluation to calculate an approximate simultaneous confidence probability that all interior branches are positive.

The ability of individuals to assess population density influences the evolution of emigration propensity and dispersal distance.

PubMed

Poethke, Hans Joachim; Gros, Andreas; Hovestadt, Thomas

2011-08-07

We analyze the simultaneous evolution of emigration and settlement decisions for actively dispersing species differing in their ability to assess population density. Using an individual-based model we simulate dispersal as a multi-step (patch to patch) movement in a world consisting of habitat patches surrounded by a hostile matrix. Each such step is associated with the same mortality risk. Our simulations show that individuals following an informed strategy, where emigration (and settlement) probability depends on local population density, evolve a lower (natal) emigration propensity but disperse over significantly larger distances - i.e. postpone settlement longer - than individuals performing density-independent emigration. This holds especially when variation in environmental conditions is spatially correlated. Both effects can be traced to the informed individuals' ability to better exploit existing heterogeneity in reproductive chances. Yet, already moderate distance-dependent dispersal costs prevent the evolution of multi-step (long-distance) dispersal, irrespective of the dispersal strategy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Continuous fiber ceramic matrix composites for heat engine components

NASA Technical Reports Server (NTRS)

Tripp, David E.

1988-01-01

High strength at elevated temperatures, low density, resistance to wear, and abundance of nonstrategic raw materials make structural ceramics attractive for advanced heat engine applications. Unfortunately, ceramics have a low fracture toughness and fail catastrophically because of overload, impact, and contact stresses. Ceramic matrix composites provide the means to achieve improved fracture toughness while retaining desirable characteristics, such as high strength and low density. Materials scientists and engineers are trying to develop the ideal fibers and matrices to achieve the optimum ceramic matrix composite properties. A need exists for the development of failure models for the design of ceramic matrix composite heat engine components. Phenomenological failure models are currently the most frequently used in industry, but they are deterministic and do not adequately describe ceramic matrix composite behavior. Semi-empirical models were proposed, which relate the failure of notched composite laminates to the stress a characteristic distance away from the notch. Shear lag models describe composite failure modes at the micromechanics level. The enhanced matrix cracking stress occurs at the same applied stress level predicted by the two models of steady state cracking. Finally, statistical models take into consideration the distribution in composite failure strength. The intent is to develop these models into computer algorithms for the failure analysis of ceramic matrix composites under monotonically increasing loads. The algorithms will be included in a postprocessor to general purpose finite element programs.

Directionally solidified eutectic gamma plus beta nickel-base superalloys

NASA Technical Reports Server (NTRS)

Jackson, M. R. (Inventor)

1977-01-01

A directionally solidified multivariant eutectic gamma + beta nickel-base superalloy casting having improved high temperature strength and oxidation resistance properties is provided. This comprises a two phase eutectic structure containing, on a weight percent basis, 5.0-15.0 tungsten, 8.5-14.5 aluminum, 0.0-35.0 cobalt and the balance being nickel. Embedded within the gamma phase nickel-base matrix are aligned eutectic beta phase (primarily (NiCo)Al reinforcing lamellae.

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

Palmprint verification using Lagrangian decomposition and invariant interest points

NASA Astrophysics Data System (ADS)

Gupta, P.; Rattani, A.; Kisku, D. R.; Hwang, C. J.; Sing, J. K.

2011-06-01

This paper presents a palmprint based verification system using SIFT features and Lagrangian network graph technique. We employ SIFT for feature extraction from palmprint images whereas the region of interest (ROI) which has been extracted from wide palm texture at the preprocessing stage, is considered for invariant points extraction. Finally, identity is established by finding permutation matrix for a pair of reference and probe palm graphs drawn on extracted SIFT features. Permutation matrix is used to minimize the distance between two graphs. The propsed system has been tested on CASIA and IITK palmprint databases and experimental results reveal the effectiveness and robustness of the system.

A comparison of human and porcine acellularized dermis: interactions with human fibroblasts in vitro.

PubMed

Armour, Alexis D; Fish, Joel S; Woodhouse, Kimberly A; Semple, John L

2006-03-01

Dermal substitutes derived from xenograft materials require elaborate processing at a considerable cost. Acellularized porcine dermis is a readily available material associated with minimal immunogenicity. The objective of this study was to evaluate acellularized pig dermis as a scaffold for human fibroblasts. In vitro methods were used to evaluate fibroblast adherence, proliferation, and migration on pig acellularized dermal matrix. Acellular human dermis was used as a control. Pig acellularized dermal matrix was found to be inferior to human acellularized dermal matrix as a scaffold for human fibroblasts. Significantly more samples of human acellularized dermal matrix (83 percent, n = 24; p < 0.05) demonstrated fibroblast infiltration below the cell-seeded surface than pig acellularized dermal matrix (31 percent, n = 49). Significantly more (p < 0.05) fibroblasts infiltrated below the surface of human acellularized dermal matrix (mean, 1072 +/- 80 cells per section; n = 16 samples) than pig acellularized dermal matrix (mean, 301 +/- 48 cells per section; n = 16 samples). Fibroblasts migrated significantly less (p < 0.05) distance from the cell-seeded pig acellularized dermal matrix surface than in the human acellularized dermal matrix (78.8 percent versus 38.3 percent cells within 150 mum from the surface, respectively; n = 5). Fibroblasts proliferated more rapidly (p < 0.05) on pig acellularized dermal matrix (n = 9) than on the human acellularized dermal matrix (7.4-fold increase in cell number versus 1.8-fold increase, respectively; n = 9 for human acellularized dermal matrix). There was no difference between the two materials with respect to fibroblast adherence (8120 versus 7436 average adherent cells per section, for pig and human acellularized dermal matrix, respectively; n = 20 in each group; p > 0.05). Preliminary findings suggest that substantial differences may exist between human fibroblast behavior in cell-matrix interactions of porcine and human acellularized dermis.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Application of a multivariate normal distribution methodology to the dissociation of doubly ionized molecules: The DMDS (CH3 -SS-CH3 ) case.

PubMed

Varas, Lautaro R; Pontes, F C; Santos, A C F; Coutinho, L H; de Souza, G G B

2015-09-15

The ion-ion-coincidence mass spectroscopy technique brings useful information about the fragmentation dynamics of doubly and multiply charged ionic species. We advocate the use of a matrix-parameter methodology in order to represent and interpret the entire ion-ion spectra associated with the ionic dissociation of doubly charged molecules. This method makes it possible, among other things, to infer fragmentation processes and to extract information about overlapped ion-ion coincidences. This important piece of information is difficult to obtain from other previously described methodologies. A Wiley-McLaren time-of-flight mass spectrometer was used to discriminate the positively charged fragment ions resulting from the sample ionization by a pulsed 800 eV electron beam. We exemplify the application of this methodology by analyzing the fragmentation and ionic dissociation of the dimethyl disulfide (DMDS) molecule as induced by fast electrons. The doubly charged dissociation was analyzed using the Multivariate Normal Distribution. The ion-ion spectrum of the DMDS molecule was obtained at an incident electron energy of 800 eV and was matrix represented using the Multivariate Distribution theory. The proposed methodology allows us to distinguish information among [CH n SH n ] + /[CH 3 ] + (n = 1-3) fragment ions in the ion-ion coincidence spectra using ion-ion coincidence data. Using the momenta balance methodology for the inferred parameters, a secondary decay mechanism is proposed for the [CHS] + ion formation. As an additional check on the methodology, previously published data on the SiF 4 molecule was re-analyzed with the present methodology and the results were shown to be statistically equivalent. The use of a Multivariate Normal Distribution allows for the representation of the whole ion-ion mass spectrum of doubly or multiply ionized molecules as a combination of parameters and the extraction of information among overlapped data. We have successfully applied this methodology to the analysis of the fragmentation of the DMDS molecule. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Molecular phylogeny of the hominoid primates as indicated by two-dimensional protein electrophoresis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldman, D.; Giri, P.R.; O'Brien, J.O.

1987-05-01

A molecular phylogeny for the hominoid primates was constructed by using genetic distances from a survey of 383 radiolabeled fibroblast polypeptides resolved by two-dimensional electrophoresis (2DE). An internally consistent matrix of Nei genetic distances was generated on the basis of variants in electrophoretic position. The derived phylogenetic tree indicated a branching sequence, from oldest to most recent, of cercopithecoids (Macaca fascicularis), gibbon-siamang, orangutan, gorilla, and human-chimpanzee. A cladistic analysis of 240 electrophoretic characters that varied between ape species produced an identical tree. Genetic distance measures obtained by 2DE are largely consistent with those generated by other molecular procedures. In addition,more » the 2DE data set appears to resolve the human-chimpanzee-gorilla trichotomy in favor of a more recent association of chimpanzees and humans.« less

Genetic code, hamming distance and stochastic matrices.

PubMed

He, Matthew X; Petoukhov, Sergei V; Ricci, Paolo E

2004-09-01

In this paper we use the Gray code representation of the genetic code C=00, U=10, G=11 and A=01 (C pairs with G, A pairs with U) to generate a sequence of genetic code-based matrices. In connection with these code-based matrices, we use the Hamming distance to generate a sequence of numerical matrices. We then further investigate the properties of the numerical matrices and show that they are doubly stochastic and symmetric. We determine the frequency distributions of the Hamming distances, building blocks of the matrices, decomposition and iterations of matrices. We present an explicit decomposition formula for the genetic code-based matrix in terms of permutation matrices, which provides a hypercube representation of the genetic code. It is also observed that there is a Hamiltonian cycle in a genetic code-based hypercube.

An Upper Bound on Orbital Debris Collision Probability When Only One Object has Position Uncertainty Information

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2015-01-01

Upper bounds on high speed satellite collision probability, P (sub c), have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum P (sub c). If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but useful P (sub c) upper bound. There are various avenues along which an upper bound on the high speed satellite collision probability has been pursued. Typically, for the collision plane representation of the high speed collision probability problem, the predicted miss position in the collision plane is assumed fixed. Then the shape (aspect ratio of ellipse), the size (scaling of standard deviations) or the orientation (rotation of ellipse principal axes) of the combined position error ellipse is varied to obtain a maximum P (sub c). Regardless as to the exact details of the approach, previously presented methods all assume that an individual position error covariance matrix is available for each object and the two are combined into a single, relative position error covariance matrix. This combined position error covariance matrix is then modified according to the chosen scheme to arrive at a maximum P (sub c). But what if error covariance information for one of the two objects is not available? When error covariance information for one of the objects is not available the analyst has commonly defaulted to the situation in which only the relative miss position and velocity are known without any corresponding state error covariance information. The various usual methods of finding a maximum P (sub c) do no good because the analyst defaults to no knowledge of the combined, relative position error covariance matrix. It is reasonable to think, given an assumption of no covariance information, an analyst might still attempt to determine the error covariance matrix that results in an upper bound on the P (sub c). Without some guidance on limits to the shape, size and orientation of the unknown covariance matrix, the limiting case is a degenerate ellipse lying along the relative miss vector in the collision plane. Unless the miss position is exceptionally large or the at-risk object is exceptionally small, this method results in a maximum P (sub c) too large to be of practical use. For example, assuming that the miss distance is equal to the current ISS alert volume along-track (+ or -) distance of 25 kilometers and that the at-risk area has a 70 meter radius. The maximum (degenerate ellipse) P (sub c) is about 0.00136. At 40 kilometers, the maximum P (sub c) would be 0.00085 which is still almost an order of magnitude larger than the ISS maneuver threshold of 0.0001. In fact, a miss distance of almost 340 kilometers is necessary to reduce the maximum P (sub c) associated with this degenerate ellipse to the ISS maneuver threshold value. Such a result is frequently of no practical value to the analyst. Some improvement may be made with respect to this problem by realizing that while the position error covariance matrix of one of the objects (usually the debris object) may not be known the position error covariance matrix of the other object (usually the asset) is almost always available. Making use of the position error covariance information for the one object provides an improvement in finding a maximum P (sub c) which, in some cases, may offer real utility. The equations to be used are presented and their use discussed.

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

PubMed Central

Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.

2016-01-01

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times. PMID:27563373

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

Searching for New Biomarkers and the Use of Multivariate Analysis in Gastric Cancer Diagnostics.

PubMed

Kucera, Radek; Smid, David; Topolcan, Ondrej; Karlikova, Marie; Fiala, Ondrej; Slouka, David; Skalicky, Tomas; Treska, Vladislav; Kulda, Vlastimil; Simanek, Vaclav; Safanda, Martin; Pesta, Martin

2016-04-01

The first aim of this study was to search for new biomarkers to be used in gastric cancer diagnostics. The second aim was to verify the findings presented in literature on a sample of the local population and investigate the risk of gastric cancer in that population using a multivariant statistical analysis. We assessed a group of 36 patients with gastric cancer and 69 healthy individuals. We determined carcinoembryonic antigen, cancer antigen 19-9, cancer antigen 72-4, matrix metalloproteinases (-1, -2, -7, -8 and -9), osteoprotegerin, osteopontin, prothrombin induced by vitamin K absence-II, pepsinogen I, pepsinogen II, gastrin and Helicobacter pylori for each sample. The multivariate stepwise logistic regression identified the following biomarkers as the best gastric cancer predictors: CEA, CA72-4, pepsinogen I, Helicobacter pylori presence and MMP7. CEA and CA72-4 remain the best markers for gastric cancer diagnostics. We suggest a mathematical model for the assessment of risk of gastric cancer. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Valuing the visual impact of wind farms: A calculus method for synthesizing choice experiments studies.

PubMed

Wen, Cheng; Dallimer, Martin; Carver, Steve; Ziv, Guy

2018-05-06

Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions. Copyright © 2018. Published by Elsevier B.V.

Morphoagronomic and molecular profiling of Capsicum spp from southwest Mato Grosso, Brazil.

PubMed

Campos, A L; Marostega, T N; Cabral, N S S; Araújo, K L; Serafim, M E; Seabra-Júnior, S; Sudré, C P; Rodrigues, R; Neves, L G

2016-07-15

The genus Capsicum ranks as the second most exported vegetable in Brazil, which is also considered to be a center of diversity for this genus. The aim of this study was to rescue genetic variability in the genus Capsicum in the southwest region of Mato Grosso, and to characterize and estimate the genetic diversity of accessions based on morphoagronomic descriptors and inter-simple sequence repeat molecular markers. Data were obtained following the criteria of the International Plant Genetic Resources Institute, renamed Bioversity International for Capsicum. Data were analyzed using different multivariate statistical techniques. An array of binary data was used to analyze molecular data, and the arithmetic complement of the Jaccard index was used to estimate the genetic dissimilarity among accessions. Six well-defined groups were formed based on the morphological characterization. The most divergent accessions were 142 and 126, with 125 and 126 being the most similar. The groups formed following agronomic characterization differed from those formed by morphological characterization, and there was a need to subdivide the groups for better distinction of accessions. Based on molecular analysis, accessions were divided into two groups, and there was also a need to subdivide the groups. Based on joint analysis (morphological + agronomic + molecular), six groups were formed with no duplicates. For all groups, the cophenetic correlation coefficient was higher than 0.8. These results provide useful information for the better management of the work collection. All correlations between the combined distance matrix were significant by the Mantel test.

A Multivariate Analysis of Unilateral Cleft Lip and Palate Facial Skeletal Morphology.

PubMed

Starbuck, John M; Ghoneima, Ahmed; Kula, Katherine

2015-07-01

Unilateral cleft lip and palate (UCLP) occurs when the maxillary and nasal facial prominences fail to fuse correctly during development, resulting in a palatal cleft and clefted soft and hard tissues of the dentoalveolus. The UCLP deformity may compromise an individual's ability to eat, chew, and speak. In this retrospective cross-sectional study, cone beam computed tomography (CBCT) images of 7-17-year-old individuals born with UCLP (n = 24) and age- and sex-matched controls (n = 24) were assessed. Coordinate values of three-dimensional anatomical landmarks (n = 32) were recorded from each CBCT image. Data were evaluated using principal coordinates analysis (PCOORD) and Euclidean distance matrix analysis (EDMA). Approximately 40% of morphometric variation is captured by PCOORD axes 1-3, and the negative and positive ends of each axis are associated with specific patterns of morphological differences. Approximately 36% of facial skeletal measures significantly differ by confidence interval testing (α = 0.10) between samples. Although significant form differences occur across the facial skeleton, strong patterns of morphological differences were localized to the lateral and superioinferior aspects of the nasal aperture, particularly on the clefted side of the face. The UCLP deformity strongly influences facial skeletal morphology of the midface and oronasal facial regions, and to a lesser extent the upper and lower facial skeletons. The pattern of strong morphological differences in the oronasal region combined with differences across the facial complex suggests that craniofacial bones are integrated and covary, despite influences from the congenital cleft.

Reduced rank regression via adaptive nuclear norm penalization

PubMed Central

Chen, Kun; Dong, Hongbo; Chan, Kung-Sik

2014-01-01

Summary We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally non-convex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed non-convex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy. PMID:25045172

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.

Metabolomic Analysis of Oxidative and Glycolytic Skeletal Muscles by Matrix-Assisted Laser Desorption/IonizationMass Spectrometric Imaging (MALDI MSI)

NASA Astrophysics Data System (ADS)

Tsai, Yu-Hsuan; Garrett, Timothy J.; Carter, Christy S.; Yost, Richard A.

2015-06-01

Skeletal muscles are composed of heterogeneous muscle fibers that have different physiological, morphological, biochemical, and histological characteristics. In this work, skeletal muscles extensor digitorum longus, soleus, and whole gastrocnemius were analyzed by matrix-assisted laser desorption/ionization mass spectrometry to characterize small molecule metabolites of oxidative and glycolytic muscle fiber types as well as to visualize biomarker localization. Multivariate data analysis such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to extract significant features. Different metabolic fingerprints were observed from oxidative and glycolytic fibers. Higher abundances of biomolecules such as antioxidant anserine as well as acylcarnitines were observed in the glycolytic fibers, whereas taurine and some nucleotides were found to be localized in the oxidative fibers.

ADVANCED SURVEILLANCE OF ENVIROMENTAL RADIATION IN AUTOMATIC NETWORKS.

PubMed

Benito, G; Sáez, J C; Blázquez, J B; Quiñones, J

2018-06-01

The objective of this study is the verification of the operation of a radiation monitoring network conformed by several sensors. The malfunction of a surveillance network has security and economic consequences, which derive from its maintenance and could be avoided with an early detection. The proposed method is based on a kind of multivariate distance, and the verification for the methodology has been tested at CIEMAT's local radiological early warning network.

Synthetic gene frequency maps of man and selective effects of climate

PubMed Central

Piazza, A.; Menozzi, P.; Cavalli-Sforza, L. L.

1981-01-01

The world distribution of 39 independent gene frequencies in human populations is analyzed by multivariate techniques and synthetic geographic maps. Most genetic variation is associated with longitude, with South Asia showing a tendency to be central. Also latitude and, more particularly, distance from the equator play a significant role in a way that suggests that climatic factors exercise selective pressures, especially for certain genes. Images PMID:6941316

Subset Selection Procedures: A Review and an Assessment

DTIC Science & Technology

1984-02-01

distance function (Alam and Rizvi, 1966; Gupta, 1966; Gupta and Studden, 1970), generalized variance ( Gnanadesikan and Gupta, 1970), and multiple... Gnanadesikan (1966) considered a location type procedure based on sample component means. Except in the case of bivariate normal, only a lower bound of the...Frischtak, 1973; Gnanadesikan , 1966) for ranking multivariate normal populations but the results in these cases are very limited in scope or are asymptotic

Accessibility and distribution of the Norwegian National Air Emergency Service: 1988-1998.

PubMed

Heggestad, Torhild; Børsheim, Knut Yngve

2002-01-01

To evaluate the accessibility and distribution of the Norwegian National Air Emergency Service in the 10-year period from 1988 to 1998. The primary material was annual standardized activity data that included all helicopter missions. A multivariate model of determinants for use of the helicopter service was computed by linear regression. Accessibility was measured as the percentage of the population reached in different flying times, and we evaluated the service using a simulation of alternative locations for the helicopter bases. The helicopter service (HEMS) has short access times, with a mean reaction time of 8 minutes and a mean response time of 26 minutes for acute missions. Nearly all patients (98%) are reached within 1 hour. A simulation that tested alternative locations of the helicopter bases compared with current locations showed no increase in accessibility. The use of the service shows large regional differences. Multivariate analyses showed that the distances of the patients from the nearest helicopter base and the nearest hospital are significant determinants for the use of HEMS. Establishment of a national service has given the Norwegian population better access to highly qualified prehospital emergency services. Furthermore, the HEMS has a compensating effect in adjusting for differences in traveling distances to a hospital. Safety, cost-containment, and gatekeeper functions remain challenges.

Distribution of black-tailed jackrabbit habitat determined by GIS in southwestern Idaho

USGS Publications Warehouse

Knick, Steven T.; Dyer, D.L.

1997-01-01

We developed a multivariate description of black-tailed jackrabbit (Lepus californicus) habitat associations from Geographical Information Systems (GIS) signatures surrounding known jackrabbit locations in the Snake River Birds of Prey National Conservation Area (NCA), in southwestern Idaho. Habitat associations were determined for characteristics within a 1-km radius (approx home range size) of jackrabbits sighted on night spotlight surveys conducted from 1987 through 1995. Predictive habitat variables were number of shrub, agriculture, and hydrography cells, mean and standard deviation of shrub patch size, habitat richness, and a measure of spatial heterogeneity. In winter, jackrabbits used smaller and less variable sizes of shrub patches and areas of higher spatial heterogeneity when compared to summer observations (P 0.05), differed significantly between high and low population phase. We used the Mahalanobis distance statistic to rank all 50-m cells in a 440,000-ha region relative to the multivariate mean habitat vector. On verification surveys to test predicted models, we sighted jackrabbits in areas ranked close to the mean habitat vector. Areas burned by large-scale fires between 1980 and 1992 or in an area repeatedly burned by military training activities had greater Mahalanobis distances from the mean habitat vector than unburned areas and were less likely to contain habitats used by jackrabbits.

Predictive model to describe water migration in cellular solid foods during storage.

PubMed

Voogt, Juliën A; Hirte, Anita; Meinders, Marcel B J

2011-11-01

Water migration in cellular solid foods during storage causes loss of crispness. To improve crispness retention, physical understanding of this process is needed. Mathematical models are suitable tools to gain this physical knowledge. Water migration in cellular solid foods involves migration through both the air cells and the solid matrix. For systems in which the water migration distance is large compared with the cell wall thickness of the solid matrix, the overall water flux through the system is dominated by the flux through the air. For these systems, water migration can be approximated well by a Fickian diffusion model. The effective diffusion coefficient can be expressed in terms of the material properties of the solid matrix (i.e. the density, sorption isotherm and diffusion coefficient of water in the solid matrix) and the morphological properties of the cellular structure (i.e. water vapour permeability and volume fraction of the solid matrix). The water vapour permeability is estimated from finite element method modelling using a simplified model for the cellular structure. It is shown that experimentally observed dynamical water profiles of bread rolls that differ in crust permeability are predicted well by the Fickian diffusion model. Copyright © 2011 Society of Chemical Industry.

A novel chaos-based image encryption algorithm using DNA sequence operations

NASA Astrophysics Data System (ADS)

Chai, Xiuli; Chen, Yiran; Broyde, Lucie

2017-01-01

An image encryption algorithm based on chaotic system and deoxyribonucleic acid (DNA) sequence operations is proposed in this paper. First, the plain image is encoded into a DNA matrix, and then a new wave-based permutation scheme is performed on it. The chaotic sequences produced by 2D Logistic chaotic map are employed for row circular permutation (RCP) and column circular permutation (CCP). Initial values and parameters of the chaotic system are calculated by the SHA 256 hash of the plain image and the given values. Then, a row-by-row image diffusion method at DNA level is applied. A key matrix generated from the chaotic map is used to fuse the confused DNA matrix; also the initial values and system parameters of the chaotic system are renewed by the hamming distance of the plain image. Finally, after decoding the diffused DNA matrix, we obtain the cipher image. The DNA encoding/decoding rules of the plain image and the key matrix are determined by the plain image. Experimental results and security analyses both confirm that the proposed algorithm has not only an excellent encryption result but also resists various typical attacks.

Walking Behavior of Zoo Elephants: Associations between GPS-Measured Daily Walking Distances and Environmental Factors, Social Factors, and Welfare Indicators

PubMed Central

Holdgate, Matthew R.; Meehan, Cheryl L.; Hogan, Jennifer N.; Miller, Lance J.; Soltis, Joseph; Andrews, Jeff; Shepherdson, David J.

2016-01-01

Research with humans and other animals suggests that walking benefits physical health. Perhaps because these links have been demonstrated in other species, it has been suggested that walking is important to elephant welfare, and that zoo elephant exhibits should be designed to allow for more walking. Our study is the first to address this suggestion empirically by measuring the mean daily walking distance of elephants in North American zoos, determining the factors that are associated with variations in walking distance, and testing for associations between walking and welfare indicators. We used anklets equipped with GPS data loggers to measure outdoor daily walking distance in 56 adult female African (n = 33) and Asian (n = 23) elephants housed in 30 North American zoos. We collected 259 days of data and determined associations between distance walked and social, housing, management, and demographic factors. Elephants walked an average of 5.3 km/day with no significant difference between species. In our multivariable model, more diverse feeding regimens were correlated with increased walking, and elephants who were fed on a temporally unpredictable feeding schedule walked 1.29 km/day more than elephants fed on a predictable schedule. Distance walked was also positively correlated with an increase in the number of social groupings and negatively correlated with age. We found a small but significant negative correlation between distance walked and nighttime Space Experience, but no other associations between walking distances and exhibit size were found. Finally, distance walked was not related to health or behavioral outcomes including foot health, joint health, body condition, and the performance of stereotypic behavior, suggesting that more research is necessary to determine explicitly how differences in walking may impact elephant welfare. PMID:27414411

Walking Behavior of Zoo Elephants: Associations between GPS-Measured Daily Walking Distances and Environmental Factors, Social Factors, and Welfare Indicators.

PubMed

Holdgate, Matthew R; Meehan, Cheryl L; Hogan, Jennifer N; Miller, Lance J; Soltis, Joseph; Andrews, Jeff; Shepherdson, David J

2016-01-01

Research with humans and other animals suggests that walking benefits physical health. Perhaps because these links have been demonstrated in other species, it has been suggested that walking is important to elephant welfare, and that zoo elephant exhibits should be designed to allow for more walking. Our study is the first to address this suggestion empirically by measuring the mean daily walking distance of elephants in North American zoos, determining the factors that are associated with variations in walking distance, and testing for associations between walking and welfare indicators. We used anklets equipped with GPS data loggers to measure outdoor daily walking distance in 56 adult female African (n = 33) and Asian (n = 23) elephants housed in 30 North American zoos. We collected 259 days of data and determined associations between distance walked and social, housing, management, and demographic factors. Elephants walked an average of 5.3 km/day with no significant difference between species. In our multivariable model, more diverse feeding regimens were correlated with increased walking, and elephants who were fed on a temporally unpredictable feeding schedule walked 1.29 km/day more than elephants fed on a predictable schedule. Distance walked was also positively correlated with an increase in the number of social groupings and negatively correlated with age. We found a small but significant negative correlation between distance walked and nighttime Space Experience, but no other associations between walking distances and exhibit size were found. Finally, distance walked was not related to health or behavioral outcomes including foot health, joint health, body condition, and the performance of stereotypic behavior, suggesting that more research is necessary to determine explicitly how differences in walking may impact elephant welfare.

Entanglement Entropy in Two-Dimensional String Theory.

PubMed

Hartnoll, Sean A; Mazenc, Edward A

2015-09-18

To understand an emergent spacetime is to understand the emergence of locality. Entanglement entropy is a powerful diagnostic of locality, because locality leads to a large amount of short distance entanglement. Two-dimensional string theory is among the very simplest instances of an emergent spatial dimension. We compute the entanglement entropy in the large-N matrix quantum mechanics dual to two-dimensional string theory in the semiclassical limit of weak string coupling. We isolate a logarithmically large, but finite, contribution that corresponds to the short distance entanglement of the tachyon field in the emergent spacetime. From the spacetime point of view, the entanglement is regulated by a nonperturbative "graininess" of space.

Trailing Vortex-Induced Loads During Close Encounters in Cruise

NASA Technical Reports Server (NTRS)

Mendenhall, Michael R.; Lesieutre, Daniel J; Kelly, Michael J.

2015-01-01

The trailing vortex induced aerodynamic loads on a Falcon 20G business jet flying in the wake of a DC-8 are predicted to provide a preflight estimate of safe trail distances during flight test measurements in the wake. Static and dynamic loads on the airframe flying in the near wake are shown at a matrix of locations, and the dynamic motion of the Falcon 20G during traverses of the DC-8 primary trailing vortex is simulated. Safe trailing distances for the test flights are determined, and optimum vortex traverse schemes are identified to moderate the motion of the trailing aircraft during close encounters with the vortex wake.

(Non-)Arguments in Long-Distance Extractions.

PubMed

Nyvad, Anne Mette; Kizach, Johannes; Christensen, Ken Ramshøj

2015-10-01

Previous research has shown that in fully grammatical sentences, response time increases and acceptability decreases when the filler in a long-distance extraction is incompatible with the matrix verb. This effect could potentially be due to a difference between argument and adjunct extraction. In this paper we investigate the effect of long extraction of arguments and adjuncts where incompatibility is kept constant. Based on the results from two offline surveys and an online experiment, we argue that the argument/adjunct asymmetry in terms of acceptability is due to differences in processing difficulty, but that both types of extraction involve the same intermediate attachment sites in the online processing.

A Simple Method for Computing Resistance Distance

NASA Astrophysics Data System (ADS)

Bapat, Ravindra B.; Gutmana, Ivan; Xiao, Wenjun

2003-10-01

The resistance distance ri j between two vertices vi and vj of a (connected, molecular) graph G is equal to the effective resistance between the respective two points of an electrical network, constructed so as to correspond to G, such that the resistance of any edge is unity. We show how rij can be computed from the Laplacian matrix L of the graph G: Let L(i) and L(i, j) be obtained from L by deleting its i-th row and column, and by deleting its i-th and j-th rows and columns, respectively. Then rij = detL(i, j)/detL(i).

A Comparison of Accuracy of Matrix Impression System with Putty Reline Technique and Multiple Mix Technique: An In Vitro Study

PubMed Central

Kumar, M Praveen; Patil, Suneel G; Dheeraj, Bhandari; Reddy, Keshav; Goel, Dinker; Krishna, Gopi

2015-01-01

Background: The difficulty in obtaining an acceptable impression increases exponentially as the number of abutments increases. Accuracy of the impression material and the use of a suitable impression technique are of utmost importance in the fabrication of a fixed partial denture. This study compared the accuracy of the matrix impression system with conventional putty reline and multiple mix technique for individual dies by comparing the inter-abutment distance in the casts obtained from the impressions. Materials and Methods: Three groups, 10 impressions each with three impression techniques (matrix impression system, putty reline technique and multiple mix technique) were made of a master die. Typodont teeth were embedded in a maxillary frasaco model base. The left first premolar was removed to create a three-unit fixed partial denture situation and the left canine and second premolar were prepared conservatively, and hatch marks were made on the abutment teeth. The final casts obtained from the impressions were examined under a profile projector and the inter-abutment distance was calculated for all the casts and compared. Results: The results from this study showed that in the mesiodistal dimensions the percentage deviation from master model in Group I was 0.1 and 0.2, in Group II was 0.9 and 0.3, and Group III was 1.6 and 1.5, respectively. In the labio-palatal dimensions the percentage deviation from master model in Group I was 0.01 and 0.4, Group II was 1.9 and 1.3, and Group III was 2.2 and 2.0, respectively. In the cervico-incisal dimensions the percentage deviation from the master model in Group I was 1.1 and 0.2, Group II was 3.9 and 1.7, and Group III was 1.9 and 3.0, respectively. In the inter-abutment dimension of dies, percentage deviation from master model in Group I was 0.1, Group II was 0.6, and Group III was 1.0. Conclusion: The matrix impression system showed more accuracy of reproduction for individual dies when compared with putty reline technique and multiple mix technique in all the three directions, as well as the inter-abutment distance. PMID:26124599

Limitations to mapping habitat-use areas in changing landscapes using the Mahalanobis distance statistic

USGS Publications Warehouse

Knick, Steven T.; Rotenberry, J.T.

1998-01-01

We tested the potential of a GIS mapping technique, using a resource selection model developed for black-tailed jackrabbits (Lepus californicus) and based on the Mahalanobis distance statistic, to track changes in shrubsteppe habitats in southwestern Idaho. If successful, the technique could be used to predict animal use areas, or those undergoing change, in different regions from the same selection function and variables without additional sampling. We determined the multivariate mean vector of 7 GIS variables that described habitats used by jackrabbits. We then ranked the similarity of all cells in the GIS coverage from their Mahalanobis distance to the mean habitat vector. The resulting map accurately depicted areas where we sighted jackrabbits on verification surveys. We then simulated an increase in shrublands (which are important habitats). Contrary to expectation, the new configurations were classified as lower similarity relative to the original mean habitat vector. Because the selection function is based on a unimodal mean, any deviation, even if biologically positive, creates larger Malanobis distances and lower similarity values. We recommend the Mahalanobis distance technique for mapping animal use areas when animals are distributed optimally, the landscape is well-sampled to determine the mean habitat vector, and distributions of the habitat variables does not change.

Travel distance influences readmissions in colorectal cancer patients-what the primary operative team needs to know.

PubMed

Kelley, Katherine A; Young, J Isaac; Bassale, Solange; Herzig, Daniel O; Martindale, Robert G; Sheppard, Brett C; Lu, Kim C; Tsikitis, V Liana

2018-07-01

Many colorectal cancer patients receive complex surgical care remotely. We hypothesized that their readmission rates would be adversely affected after accounting for differences in travel distance from primary/index hospital and correlate with mortality. We identified 48,481 colorectal cancer patients in the Surveillance, Epidemiology and End Results (SEER)-Medicare database. Travel distance was calculated, using Google Maps, and SAS. Multivariate negative binomial regression was used to identify factors associated with readmission rates. Overall survival was analyzed, using Kaplan-Meier and Cox proportional hazard. Thirty-day readmissions occurred in 14.9% of the cohort, 27.5% of which were to a nonindex hospital. In the colon and rectal cancer cohorts, readmissions were 14.5% and 16.5%, respectively. Rectal cancer patients had an increase in readmission by 13% (incidence rate ratios [IRR] 1.13; 95% confidence interval [CI] 1.05-1.21). Factors associated with readmission were male gender, advanced disease, length of stay (LOS), discharge disposition, hospital volume, Charlson score, and poverty level (P < 0.05). Greater distance traveled increased the likelihood of readmission but did not affect mortality. Travel distance influences readmission rates but not mortality. Discharge readiness to decrease readmissions is essential for colorectal cancer patients discharged from index hospitals. Copyright © 2018 Elsevier Inc. All rights reserved.

Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

NASA Astrophysics Data System (ADS)

Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

2014-06-01

Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

Optimizing the well pumping rate and its distance from a stream

NASA Astrophysics Data System (ADS)

Abdel-Hafez, M. H.; Ogden, F. L.

2008-12-01

Both ground water and surface water are very important component of the water resources. Since they are coupled systems in riparian areas, management strategies that neglect interactions between them penalize senior surface water rights to the benefit of junior ground water rights holders in the prior appropriation rights system. Water rights managers face a problem in deciding which wells need to be shut down and when, in the case of depleted stream flow. A simulation model representing a combined hypothetical aquifer and stream has been developed using MODFLOW 2000 to capture parameter sensitivity, test management strategies and guide field data collection campaigns to support modeling. An optimization approach has been applied to optimize both the well distance from the stream and the maximum pumping rate that does not affect the stream discharge downstream the pumping wells. Conjunctive management can be modeled by coupling the numerical simulation model with the optimization techniques using the response matrix technique. The response matrix can be obtained by calculating the response coefficient for each well and stream. The main assumption of the response matrix technique is that the amount of water out of the stream to the aquifer is linearly proportional to the well pumping rate (Barlow et al. 2003). The results are presented in dimensionless form, which can be used by the water managers to solve conflicts between surface water and ground water holders by making the appropriate decision to choose which well need to be shut down first.

Reactive solute transport in an asymmetrical fracture-rock matrix system

NASA Astrophysics Data System (ADS)

Zhou, Renjie; Zhan, Hongbin

2018-02-01

The understanding of reactive solute transport in a single fracture-rock matrix system is the foundation of studying transport behavior in the complex fractured porous media. When transport properties are asymmetrically distributed in the adjacent rock matrixes, reactive solute transport has to be considered as a coupled three-domain problem, which is more complex than the symmetric case with identical transport properties in the adjacent rock matrixes. This study deals with the transport problem in a single fracture-rock matrix system with asymmetrical distribution of transport properties in the rock matrixes. Mathematical models are developed for such a problem under the first-type and the third-type boundary conditions to analyze the spatio-temporal concentration and mass distribution in the fracture and rock matrix with the help of Laplace transform technique and de Hoog numerical inverse Laplace algorithm. The newly acquired solutions are then tested extensively against previous analytical and numerical solutions and are proven to be robust and accurate. Furthermore, a water flushing phase is imposed on the left boundary of system after a certain time. The diffusive mass exchange along the fracture/rock matrixes interfaces and the relative masses stored in each of three domains (fracture, upper rock matrix, and lower rock matrix) after the water flushing provide great insights of transport with asymmetric distribution of transport properties. This study has the following findings: 1) Asymmetric distribution of transport properties imposes greater controls on solute transport in the rock matrixes. However, transport in the fracture is mildly influenced. 2) The mass stored in the fracture responses quickly to water flushing, while the mass stored in the rock matrix is much less sensitive to the water flushing. 3) The diffusive mass exchange during the water flushing phase has similar patterns under symmetric and asymmetric cases. 4) The characteristic distance which refers to the zero diffusion between the fracture and the rock matrix during the water flushing phase is closely associated with dispersive process in the fracture.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Basis for substrate recognition and distinction by matrix metalloproteinases

PubMed Central

Ratnikov, Boris I.; Cieplak, Piotr; Gramatikoff, Kosi; Pierce, James; Eroshkin, Alexey; Igarashi, Yoshinobu; Kazanov, Marat; Sun, Qing; Godzik, Adam; Osterman, Andrei; Stec, Boguslaw; Strongin, Alex; Smith, Jeffrey W.

2014-01-01

Genomic sequencing and structural genomics produced a vast amount of sequence and structural data, creating an opportunity for structure–function analysis in silico [Radivojac P, et al. (2013) Nat Methods 10(3):221–227]. Unfortunately, only a few large experimental datasets exist to serve as benchmarks for function-related predictions. Furthermore, currently there are no reliable means to predict the extent of functional similarity among proteins. Here, we quantify structure–function relationships among three phylogenetic branches of the matrix metalloproteinase (MMP) family by comparing their cleavage efficiencies toward an extended set of phage peptide substrates that were selected from ∼64 million peptide sequences (i.e., a large unbiased representation of substrate space). The observed second-order rate constants [k(obs)] across the substrate space provide a distance measure of functional similarity among the MMPs. These functional distances directly correlate with MMP phylogenetic distance. There is also a remarkable and near-perfect correlation between the MMP substrate preference and sequence identity of 50–57 discontinuous residues surrounding the catalytic groove. We conclude that these residues represent the specificity-determining positions (SDPs) that allowed for the expansion of MMP proteolytic function during evolution. A transmutation of only a few selected SDPs proximal to the bound substrate peptide, and contributing the most to selectivity among the MMPs, is sufficient to enact a global change in the substrate preference of one MMP to that of another, indicating the potential for the rational and focused redesign of cleavage specificity in MMPs. PMID:25246591

A chemogenomic analysis of the human proteome: application to enzyme families.

PubMed

Bernasconi, Paul; Chen, Min; Galasinski, Scott; Popa-Burke, Ioana; Bobasheva, Anna; Coudurier, Louis; Birkos, Steve; Hallam, Rhonda; Janzen, William P

2007-10-01

Sequence-based phylogenies (SBP) are well-established tools for describing relationships between proteins. They have been used extensively to predict the behavior and sensitivity toward inhibitors of enzymes within a family. The utility of this approach diminishes when comparing proteins with little sequence homology. Even within an enzyme family, SBPs must be complemented by an orthogonal method that is independent of sequence to better predict enzymatic behavior. A chemogenomic approach is demonstrated here that uses the inhibition profile of a 130,000 diverse molecule library to uncover relationships within a set of enzymes. The profile is used to construct a semimetric additive distance matrix. This matrix, in turn, defines a sequence-independent phylogeny (SIP). The method was applied to 97 enzymes (kinases, proteases, and phosphatases). SIP does not use structural information from the molecules used for establishing the profile, thus providing a more heuristic method than the current approaches, which require knowledge of the specific inhibitor's structure. Within enzyme families, SIP shows a good overall correlation with SBP. More interestingly, SIP uncovers distances within families that are not recognizable by sequence-based methods. In addition, SIP allows the determination of distance between enzymes with no sequence homology, thus uncovering novel relationships not predicted by SBP. This chemogenomic approach, used in conjunction with SBP, should prove to be a powerful tool for choosing target combinations for drug discovery programs as well as for guiding the selection of profiling and liability targets.

Choice of commuting mode among employees: Do home neighborhood environment, worksite neighborhood environment, and worksite policy and supports matter?

PubMed Central

Yang, Lin; Hipp, J. Aaron; Adlakha, Deepti; Marx, Christine M.; Tabak, Rachel G.; Brownson, Ross C.

2015-01-01

Background Promoting the use of public transit and active transport (walking and cycling) instead of car driving is an appealing strategy to increase overall physical activity. Purpose To quantify the combined associations between self-reported home and worksite neighborhood environments, worksite support and policies, and employees’ commuting modes. Method Between 2012 and 2013, participants residing in four Missouri metropolitan areas were interviewed via telephone (n = 1,338) and provided information on socio-demographic characteristics, home and worksite neighborhoods, and worksite support and policies. Commuting mode was self-reported and categorized into car driving, public transit, and active commuting. Commuting distance was calculated using geographic information systems. Commuters providing completed data were included in the analysis. Multivariate logistic regression models were used to examine the correlates of using public transit and active commuting. Result The majority of participants reported commuting by driving (88.9%); only 4.9% used public transit and 6.2% used active modes. After multivariate adjustment, having transit stops within 10-15 minutes walking distance from home (p=0.05) and using worksite incentive for public transit (p<0.001) were associated with commuting by public transit. Commuting distance (p<0.001) was negatively associated with active commuting. Having free or low cost recreation facilities around the worksite (p=0.04) and using bike facilities to lock bikes at the worksite (p<0.001) were associated with active commuting. Conclusion Both environment features and worksite supports and policies are associated with the choice of commuting mode. Future studies should use longitudinal designs to investigate the potential of promoting alternative commuting modes through worksite efforts that support sustainable commuting behaviors as well as the potential of built environment improvements. PMID:26085979

Missed opportunities to improve the health of postpartum women: high rates of untreated hypertension in rural Tanzania

PubMed Central

Larson, Elysia; Rabkin, Miriam; Mbaruku, Godfrey M.; Mbatia, Redempta; Kruk, Margaret E.

2017-01-01

Objectives To assess the prevalence of high blood pressure amongst postpartum women in rural Tanzania, and to explore factors associated with hypertension prevalence, awareness of their own hypertension, treatment, and control. Methods We conducted a cross-sectional study of 1,849 women in Tanzania’s Pwani Region who delivered a child in the prior year. We measured blood pressure, administered a structured questionnaire, and assessed factors associated with hypertension (HTN) prevalence, women’s awareness of their own HTN, treatment, and control of HTN using bivariable and multivariable logistic regressions. Findings 26.7% of women had high blood pressure and/or were taking antihypertensive medication. Women were on average 27.5 years old (range 15–54). Nearly all women (99.5%) reported contact with the health system during their pregnancy and delivery, with 97.0% reporting at least one antenatal care visit, 81.4% reporting facility delivery, and an overall average of 5.2 visits for their own care in the past year. Only 23.5% of those with HTN were aware of their diagnosis, 17.4% were taking medication, and only 10.5% had controlled blood pressure. In multivariable analysis, facility delivery, health insurance, and increased distance from a hospital were associated with increased likelihood of HTN awareness; facility delivery and hospital distance were associated with current hypertensive treatment; younger age and increased hospital distance were associated with control of HTN. Conclusion The prevalence of high blood pressure in this postpartum population was high, and despite frequent recent contacts with the health system, awareness, treatment and control of HTN were low. These findings highlight an important missed opportunity to improve women’s health during antenatal and postnatal care. PMID:28120288

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

DOE PAGES

Tibi, Rigobert; Koper, Keith D.; Pankow, Kristine L.; ...

2018-03-20

Most of the commonly used seismic discrimination approaches are designed for regional data. Relatively little attention has focused on discriminants for local distances (< 200 km), the range at which the smallest events are recorded. We take advantage of the variety of seismic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events, and tectonic earthquakes.more » We achieved a limited success. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category, pairwise classification, seven out of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and mining-induced events. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4–14% in misclassification rates compared to Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74%, compared to the rate of about 86% for two-category, pairwise classification.« less

Anthropometric and training variables related to half-marathon running performance in recreational female runners.

PubMed

Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Rosemann, Thomas

2011-05-01

The relationship between skin-fold thickness and running has been investigated in distances ranging from 100 m to the marathon distance (42.195 km), with the exclusion of the half-marathon distance (21.0975 km). We investigated the association between anthropometric variables, prerace experience, and training variables with race time in 42 recreational, nonprofessional, female half-marathon runners using bi- and multivariate analysis. Body weight (r, 0.60); body mass index (r, 0.48); body fat percentage (r, 0.56); pectoral (r, 0.61), mid-axilla (r, 0.69), triceps (r, 0.49), subscapular (r, 0.61), abdominal (r, 0.59), suprailiac (r, 0.55), and medial calf (r, 0.53) skin-fold thickness; mean speed of the training sessions (r, -0.68); and personal best time in a half-marathon (r, 0.69) correlated with race time after bivariate analysis. Body weight (P = 0.0054), pectoral skin-fold thickness (P = 0.0068), and mean speed of the training sessions (P = 0.0041) remained significant after multivariate analysis. Mean running speed during training was related to mid-axilla (r, -0.31), subscapular (r, -0.38), abdominal (r, -0.44), and suprailiac (r, -0.41) skin-fold thickness, the sum of 8 skin-fold thicknesses (r, -0.36); and percent body fat (r, -0.31). It was determined that variables of both anthropometry and training were related to half-marathon race time, and that skin-fold thicknesses were associated with running speed during training. For practical applications, high running speed during training (as opposed to extensive training) may both reduce upper-body skin-fold thicknesses and improve race performance in recreational female half-marathon runners.

Relationship between water quality and macro-scale parameters (land use, erosion, geology, and population density) in the Siminehrood River Basin.

PubMed

Bostanmaneshrad, Farshid; Partani, Sadegh; Noori, Roohollah; Nachtnebel, Hans-Peter; Berndtsson, Ronny; Adamowski, Jan Franklin

2018-10-15

To date, few studies have investigated the simultaneous effects of macro-scale parameters (MSPs) such as land use, population density, geology, and erosion layers on micro-scale water quality variables (MSWQVs). This research focused on an evaluation of the relationship between MSPs and MSWQVs in the Siminehrood River Basin, Iran. In addition, we investigated the importance of water particle travel time (hydrological distance) on this relationship. The MSWQVs included 13 physicochemical and biochemical parameters observed at 15 stations during three seasons. Primary screening was performed by utilizing three multivariate statistical analyses (Pearson's correlation, cluster and discriminant analyses) in seven series of observed data. These series included three separate seasonal data, three two-season data, and aggregated three-season data for investigation of relationships between MSPs and MSWQVs. Coupled data (pairs of MSWQVs and MSPs) repeated in at least two out of three statistical analyses were selected for final screening. The primary screening results demonstrated significant relationships between land use and phosphorus, total solids and turbidity, erosion levels and electrical conductivity, and erosion and total solids. Furthermore, water particle travel time effects were considered through three geographical pattern definitions of distance for each MSP by using two weighting methods. To find effective MSP factors on MSWQVs, a multivariate linear regression analysis was employed. Then, preliminary equations that estimated MSWQVs were developed. The preliminary equations were modified to adaptive equations to obtain the final models. The final models indicated that a new metric, referred to as hydrological distance, provided better MSWQV estimation and water quality prediction compared to the National Sanitation Foundation Water Quality Index. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Depth Discrimination Using Rg-to-Sg Spectral Amplitude Ratios for Seismic Events in Utah Recorded at Local Distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tibi, Rigobert; Koper, Keith D.; Pankow, Kristine L.

Most of the commonly used seismic discrimination approaches are designed for regional data. Relatively little attention has focused on discriminants for local distances (< 200 km), the range at which the smallest events are recorded. We take advantage of the variety of seismic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events, and tectonic earthquakes.more » We achieved a limited success. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category, pairwise classification, seven out of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and mining-induced events. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4–14% in misclassification rates compared to Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74%, compared to the rate of about 86% for two-category, pairwise classification.« less

Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy.

PubMed

Neal, Sharon L

2018-01-01

The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process, the results are consistent with the evolution of mixed phase bicelles (nanodisks) and small amounts of worm-like DMPC/DHPC aggregates, and perhaps DHPC micelles, at low temperature to suspensions of branched and entangled worm-like aggregates above the DMPC gel phase transition and perforated multi-lamellar aggregates at high temperature.

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT.

PubMed

Nardone, Valerio; Tini, Paolo; Nioche, Christophe; Mazzei, Maria Antonietta; Carfagno, Tommaso; Battaglia, Giuseppe; Pastina, Pierpaolo; Grassi, Roberta; Sebaste, Lucio; Pirtoli, Luigi

2018-06-01

Image texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers. We performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia. Seventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664). Xerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.

Distance and utilisation of out-of-hours services in a Norwegian urban/rural district: an ecological study

PubMed Central

2013-01-01

Background Long travel distances limit the utilisation of health services. We wanted to examine the relationship between the utilisation of a Norwegian out-of-hours service and the distance from the municipality population centroid to the associated casualty clinic. Methods All first contacts from ten municipalities in Arendal out-of-hours district were registered from 2007 through 2011. The main outcomes were contact and consultation rates for each municipality for each year. The associations between main outcomes and distance from the population centroid of the participating municipalities to the casualty clinic and were examined by linear regression. Demographic and socioeconomic factors were included in multivariate linear regression. Secondary endpoints include association between distance and rates of different first actions taken and priority grades assessed by triage nurses. Age and gender specific subgroup analyses were performed. Results 141 342 contacts were included in the analyses. Increasing distance was associated with marked lower rates of all contact types except telephone consultations by doctor. Moving 43 kilometres away from the casualty clinic led to a 50 per cent drop in the rate of face-to-face consultations with a doctor. Availability of primary care doctors and education level contributed to a limited extent to the variance in consultation rate. The rates of all priority grades decreased significantly with increasing distance. The rate of acute events was reduced by 22 per cent when moving 50 kilometres away. The proportion of patients above 66 years increased with increasing distance, while the proportion of 13- to 19 year olds decreased. The proportion of female patients decreased with increasing distance. Conclusions The results confirm that increasing distance is associated with lower utilisation of out-of-hours services, even for the most acute cases. Extremely long distances might compromise patient safety. This must be taken into consideration when organising future out-of-hours districts. PMID:23773207

A computer program to find the kernel of a polynomial operator

NASA Technical Reports Server (NTRS)

Gejji, R. R.

1976-01-01

This paper presents a FORTRAN program written to solve for the kernel of a matrix of polynomials with real coefficients. It is an implementation of Sain's free modular algorithm for solving the minimal design problem of linear multivariable systems. The structure of the program is discussed, together with some features as they relate to questions of implementing the above method. An example of the use of the program to solve a design problem is included.

On Generalizations of Cochran’s Theorem and Projection Matrices.

DTIC Science & Technology

1980-08-01

Definiteness of the Estimated Dispersion Matrix in a Multivariate Linear Model ," F. Pukelsheim and George P.H. Styan, May 1978. TECHNICAL REPORTS...with applications to the analysis of covariance," Proc. Cambridge Philos. Soc., 30, pp. 178-191. Graybill , F. A. and Marsaglia, G. (1957...34Idempotent matrices and quad- ratic forms in the general linear hypothesis," Ann. Math. Statist., 28, pp. 678-686. Greub, W. (1975). Linear Algebra (4th ed

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

PubMed

Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z

2015-11-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

PubMed Central

Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.

2015-01-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872

Kantorovich-Wasserstein Distance for Identifying the Dynamic of Some Compartmental Models in Biology

NASA Astrophysics Data System (ADS)

Pousin, Jérôme

2008-09-01

Determining the influence of a biological species to the evolution of an other one strongly depends on the choice of mathematical models in biology. In this work we consider the case of distribution of lipids (docosahexaenoic acid (DHA)) in two compartments of the plasma, the platelets and the erythrocytes, and we compare three different mathematical approaches. The first one, consists of a system of differential equations the coefficients of which are identified through a least square procedure. The second one is made of a system of differential equations on a graph, the adjacency matrix of which represents the interplay between the species. The third one consists of mapping the provider curves to the target curves. Thus we have a distance between two families of curves, the curves of providers and the curves of targets, and by comparing the distances, we are able to decide which provider delivers preferentially to a target according to cumulative species mass curves. Numerical results are presented, and we show that the ordinary differential least square model provides qualitatively the same result as the Kantorovich-Wasserstein distance strategy. Finally, we discuss the potential ability of the presented Kantorovich-Wasserstein distance to perform the biological properties of a system.

Locating sources within a dense sensor array using graph clustering

NASA Astrophysics Data System (ADS)

Gerstoft, P.; Riahi, N.

2017-12-01

We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. In a dense network, well-separated sources induce clusters in this graph. The geographic spread of these clusters can serve to localize the sources. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with a robust phase-only coherence test statistic combined with a physical distance criterion. The latter criterion ensures graph sparsity and thus prevents clusters from forming by chance. We verify the approach and quantify its reliability on a simulated dataset. The method is then applied to data from a dense 5200 element geophone array that blanketed of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.

Eigenvector dynamics: General theory and some applications

NASA Astrophysics Data System (ADS)

Allez, Romain; Bouchaud, Jean-Philippe

2012-10-01

We propose a general framework to study the stability of the subspace spanned by P consecutive eigenvectors of a generic symmetric matrix H0 when a small perturbation is added. This problem is relevant in various contexts, including quantum dissipation (H0 is then the Hamiltonian) and financial risk control (in which case H0 is the assets' return covariance matrix). We argue that the problem can be formulated in terms of the singular values of an overlap matrix, which allows one to define an overlap distance. We specialize our results for the case of a Gaussian orthogonal H0, for which the full spectrum of singular values can be explicitly computed. We also consider the case when H0 is a covariance matrix and illustrate the usefulness of our results using financial data. The special case where the top eigenvalue is much larger than all the other ones can be investigated in full detail. In particular, the dynamics of the angle made by the top eigenvector and its true direction defines an interesting class of random processes.

An application of corelap algoritm to improve the utilization space of the classroom

NASA Astrophysics Data System (ADS)

Sembiring, A. C.; Budiman, I.; Mardhatillah, A.; Tarigan, U. P.; Jawira, A.

2018-04-01

The high demand of the room due to the increasing number of students requires the addition of the room The limited number of rooms, the price of land and the cost of building expensive infrastructure requires effective and efficient use of the space. The facility layout redesign is done using the Computerized Relationship Planning (CORELAP) algorithm based on total closeness rating (TCR). By calculating the square distance between the departments based on the coordinates of the central point of the department. The distance obtained is multiplied by the material current from the From-to chart matrix. The analysis is done by comparing the total distance between the initial layout and the proposed layout and then viewing the activities performed in each room. The results of CORELAP algorithm processing gives an increase of room usage efficiency equal to 14, 98% from previous activity.

Phylogenetic Trees and Networks Reduce to Phylogenies on Binary States: Does It Furnish an Explanation to the Robustness of Phylogenetic Trees against Lateral Transfers.

PubMed

Thuillard, Marc; Fraix-Burnet, Didier

2015-01-01

This article presents an innovative approach to phylogenies based on the reduction of multistate characters to binary-state characters. We show that the reduction to binary characters' approach can be applied to both character- and distance-based phylogenies and provides a unifying framework to explain simply and intuitively the similarities and differences between distance- and character-based phylogenies. Building on these results, this article gives a possible explanation on why phylogenetic trees obtained from a distance matrix or a set of characters are often quite reasonable despite lateral transfers of genetic material between taxa. In the presence of lateral transfers, outer planar networks furnish a better description of evolution than phylogenetic trees. We present a polynomial-time reconstruction algorithm for perfect outer planar networks with a fixed number of states, characters, and lateral transfers.

The kinetic energy operator for distance-dependent effective nuclear masses: Derivation for a triatomic molecule.

PubMed

Khoma, Mykhaylo; Jaquet, Ralph

2017-09-21

The kinetic energy operator for triatomic molecules with coordinate or distance-dependent nuclear masses has been derived. By combination of the chain rule method and the analysis of infinitesimal variations of molecular coordinates, a simple and general technique for the construction of the kinetic energy operator has been proposed. The asymptotic properties of the Hamiltonian have been investigated with respect to the ratio of the electron and proton mass. We have demonstrated that an ad hoc introduction of distance (and direction) dependent nuclear masses in Cartesian coordinates preserves the total rotational invariance of the problem. With the help of Wigner rotation functions, an effective Hamiltonian for nuclear motion can be derived. In the derivation, we have focused on the effective trinuclear Hamiltonian. All necessary matrix elements are given in closed analytical form. Preliminary results for the influence of non-adiabaticity on vibrational band origins are presented for H 3 + .

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Technical Reports Prepared Under Contract N00014-76-C-0475.

DTIC Science & Technology

1987-05-29

264 Approximations to Densities in Geometric H. Solomon 10/27/78 Probability M.A. Stephens 3. Technical Relort No. Title Author Date 265 Sequential ...Certain Multivariate S. Iyengar 8/12/82 Normal Probabilities 323 EDF Statistics for Testing for the Gamma M.A. Stephens 8/13/82 Distribution with...20-85 Nets 360 Random Sequential Coding By Hamming Distance Yoshiaki Itoh 07-11-85 Herbert Solomon 361 Transforming Censored Samples And Testing Fit

Matrix density alters zyxin phosphorylation, which limits peripheral process formation and extension in endothelial cells invading 3D collagen matrices.

PubMed

Abbey, Colette A; Bayless, Kayla J

2014-09-01

This study was designed to determine the optimal conditions required for known pro-angiogenic stimuli to elicit successful endothelial sprouting responses. We used an established, quantifiable model of endothelial cell (EC) sprout initiation where ECs were tested for invasion in low (1 mg/mL) and high density (5 mg/mL) 3D collagen matrices. Sphingosine 1-phosphate (S1P) alone, or S1P combined with stromal derived factor-1α (SDF) and phorbol ester (TPA), elicited robust sprouting responses. The ability of these factors to stimulate sprouting was more effective in higher density collagen matrices. S1P stimulation resulted in a significant increase in invasion distance, and with the exception of treatment groups containing phorbol ester, invasion distance was longer in 1mg/mL compared to 5mg/mL collagen matrices. Closer examination of cell morphology revealed that increasing matrix density and supplementing with SDF and TPA enhanced the formation of multicellular structures more closely resembling capillaries. TPA enhanced the frequency and size of lumen formation and correlated with a robust increase in phosphorylation of p42/p44 Erk kinase, while S1P and SDF did not. Also, a higher number of significantly longer extended processes formed in 5mg/mL compared to 1mg/mL collagen matrices. Because collagen matrices at higher density have been reported to be stiffer, we tested for changes in the mechanosensitive protein, zyxin. Interestingly, zyxin phosphorylation levels inversely correlated with matrix density, while levels of total zyxin did not change significantly. Immunofluorescence and localization studies revealed that total zyxin was distributed evenly throughout invading structures, while phosphorylated zyxin was slightly more intense in extended peripheral processes. Silencing zyxin expression increased extended process length and number of processes, while increasing zyxin levels decreased extended process length. Altogether these data indicate that ECs integrate signals from multiple exogenous factors, including changes in matrix density, to accomplish successful sprouting responses. We show here for the first time that zyxin limited the formation and extension of fine peripheral processes used by ECs for matrix interrogation, providing a molecular explanation for altered EC responses to high and low density collagen matrices. Copyright © 2014 International Society of Matrix Biology. Published by Elsevier B.V. All rights reserved.

Robust Image Regression Based on the Extended Matrix Variate Power Exponential Distribution of Dependent Noise.

PubMed

Luo, Lei; Yang, Jian; Qian, Jianjun; Tai, Ying; Lu, Gui-Fu

2017-09-01

Dealing with partial occlusion or illumination is one of the most challenging problems in image representation and classification. In this problem, the characterization of the representation error plays a crucial role. In most current approaches, the error matrix needs to be stretched into a vector and each element is assumed to be independently corrupted. This ignores the dependence between the elements of error. In this paper, it is assumed that the error image caused by partial occlusion or illumination changes is a random matrix variate and follows the extended matrix variate power exponential distribution. This has the heavy tailed regions and can be used to describe a matrix pattern of l×m dimensional observations that are not independent. This paper reveals the essence of the proposed distribution: it actually alleviates the correlations between pixels in an error matrix E and makes E approximately Gaussian. On the basis of this distribution, we derive a Schatten p -norm-based matrix regression model with L q regularization. Alternating direction method of multipliers is applied to solve this model. To get a closed-form solution in each step of the algorithm, two singular value function thresholding operators are introduced. In addition, the extended Schatten p -norm is utilized to characterize the distance between the test samples and classes in the design of the classifier. Extensive experimental results for image reconstruction and classification with structural noise demonstrate that the proposed algorithm works much more robustly than some existing regression-based methods.

Relationship Between Medication Adherence and Distance to Dispensing Pharmacies and Prescribers Among an Urban Medicaid Population with Diabetes Mellitus.

PubMed

Syed, Samina T; Sharp, Lisa K; Kim, Yoonsang; Jentleson, Adam; Lora, Claudia M; Touchette, Daniel R; Berbaum, Michael L; Suda, Katie J; Gerber, Ben S

2016-06-01

To determine whether a relationship exists between medication adherence to angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) and distance to dispensing pharmacies and prescribers among an urban public aid population with diabetes mellitus. Retrospective cohort study using claims data. Illinois Department of Healthcare and Family Services database. A total of 6532 patients aged 18-64 years with diabetes who had at least one prescription fill for an ACEI or ARB and had continuous Medicaid coverage in the greater Chicago area in 2009. We assessed medication adherence, defined as proportion of days covered (PDC) of 0.8 or higher, to ACEIs and ARBs and its association with distances between patients and their pharmacies and prescribers. Of the 6532 patients included in the analyses, 2930 (45%) had PDC levels of 0.8 or higher. No significant differences were observed between patients who were adherent versus those who were nonadherent in distance to pharmacy (median 1.39 vs 1.35 miles, p=0.15) or distance to prescriber (median 4.39 vs 4.48 miles, p=0.80). In a multivariate regression model including age, sex, race/ethnicity, number of pharmacies, number of prescribers, distance to pharmacy, and distance to prescriber, a greater number of prescribers was associated with higher adherence (two prescribers vs one prescriber: odds ratio [OR] 1.396, 95% confidence interval [CI] 1.233-1.580; three or more prescribers vs one prescriber: OR 2.208, 95% CI 1.787-2.727). ACEI or ARB adherence was not associated with distances to pharmacies and prescribers. © 2016 Pharmacotherapy Publications, Inc.

Near Work Related Parameters and Myopia in Chinese Children: the Anyang Childhood Eye Study

PubMed Central

Li, Shi-Ming; Li, Si-Yuan; Kang, Meng-Tian; Zhou, Yuehua; Liu, Luo-Ru; Li, He; Wang, Yi-Peng; Zhan, Si-Yan; Gopinath, Bamini; Mitchell, Paul; Wang, Ningli

2015-01-01

Purpose To examine the associations of near work related parameters with spherical equivalent refraction and axial length in Chinese children. Methods A total of 1770 grade 7 students with mean age of 12.7 years were examined with cycloplegic autorefraction and axial length. Questions were asked regarding time spent in near work and outdoors per day, and near work related parameters. Results Multivariate models revealed the following associations with greater odds of myopia: continuous reading (> 45min), odds ratio [OR], 1.4; 95% confidence interval [CI], 1.1-1.8; close television viewing distance (≤ 3m), OR, 1.7; 95% CI, 1.2-2.3; head tilt when writing, OR, 1.3; 95% CI, 1.1-1.7, and desk lighting using fluorescent vs. incandescent lamp, OR, 1.5; 95% CI, 1.2-2.0. These factors, together with close reading distance and close nib-to-fingertip distance were significantly associated with greater myopia (P<0.01). Among near work activities, only reading more books for pleasure was significantly associated with greater myopia (P=0.03). Television viewing distance (≤ 3 m), fluorescent desk light, close reading distance (≤20 cm) and close nib-to-fingertip distance (≤ 2 cm) were significantly associated with longer axial length (P<0.01). Reading distance, desk light, and reading books for pleasure had significant interaction effects with parental myopia. Conclusions Continuous reading, close distances of reading, television viewing and nib-to-fingertip, head tilt when writing, reading more books for pleasure and use of fluorescent desk light were significantly associated with myopia in 12-year-old Chinese children, which indicates that visual behaviors and environments may be important factors mediating the effects of near work on myopia. PMID:26244865

General transfer matrix formalism to calculate DNA-protein-drug binding in gene regulation: application to OR operator of phage lambda.

PubMed

Teif, Vladimir B

2007-01-01

The transfer matrix methodology is proposed as a systematic tool for the statistical-mechanical description of DNA-protein-drug binding involved in gene regulation. We show that a genetic system of several cis-regulatory modules is calculable using this method, considering explicitly the site-overlapping, competitive, cooperative binding of regulatory proteins, their multilayer assembly and DNA looping. In the methodological section, the matrix models are solved for the basic types of short- and long-range interactions between DNA-bound proteins, drugs and nucleosomes. We apply the matrix method to gene regulation at the O(R) operator of phage lambda. The transfer matrix formalism allowed the description of the lambda-switch at a single-nucleotide resolution, taking into account the effects of a range of inter-protein distances. Our calculations confirm previously established roles of the contact CI-Cro-RNAP interactions. Concerning long-range interactions, we show that while the DNA loop between the O(R) and O(L) operators is important at the lysogenic CI concentrations, the interference between the adjacent promoters P(R) and P(RM) becomes more important at small CI concentrations. A large change in the expression pattern may arise in this regime due to anticooperative interactions between DNA-bound RNA polymerases. The applicability of the matrix method to more complex systems is discussed.

General transfer matrix formalism to calculate DNA–protein–drug binding in gene regulation: application to OR operator of phage λ

PubMed Central

Teif, Vladimir B.

2007-01-01

The transfer matrix methodology is proposed as a systematic tool for the statistical–mechanical description of DNA–protein–drug binding involved in gene regulation. We show that a genetic system of several cis-regulatory modules is calculable using this method, considering explicitly the site-overlapping, competitive, cooperative binding of regulatory proteins, their multilayer assembly and DNA looping. In the methodological section, the matrix models are solved for the basic types of short- and long-range interactions between DNA-bound proteins, drugs and nucleosomes. We apply the matrix method to gene regulation at the OR operator of phage λ. The transfer matrix formalism allowed the description of the λ-switch at a single-nucleotide resolution, taking into account the effects of a range of inter-protein distances. Our calculations confirm previously established roles of the contact CI–Cro–RNAP interactions. Concerning long-range interactions, we show that while the DNA loop between the OR and OL operators is important at the lysogenic CI concentrations, the interference between the adjacent promoters PR and PRM becomes more important at small CI concentrations. A large change in the expression pattern may arise in this regime due to anticooperative interactions between DNA-bound RNA polymerases. The applicability of the matrix method to more complex systems is discussed. PMID:17526526

Convergence of the standard RLS method and UDUT factorisation of covariance matrix for solving the algebraic Riccati equation of the DLQR via heuristic approximate dynamic programming

NASA Astrophysics Data System (ADS)

Moraes Rêgo, Patrícia Helena; Viana da Fonseca Neto, João; Ferreira, Ernesto M.

2015-08-01

The main focus of this article is to present a proposal to solve, via UDUT factorisation, the convergence and numerical stability problems that are related to the covariance matrix ill-conditioning of the recursive least squares (RLS) approach for online approximations of the algebraic Riccati equation (ARE) solution associated with the discrete linear quadratic regulator (DLQR) problem formulated in the actor-critic reinforcement learning and approximate dynamic programming context. The parameterisations of the Bellman equation, utility function and dynamic system as well as the algebra of Kronecker product assemble a framework for the solution of the DLQR problem. The condition number and the positivity parameter of the covariance matrix are associated with statistical metrics for evaluating the approximation performance of the ARE solution via RLS-based estimators. The performance of RLS approximators is also evaluated in terms of consistence and polarisation when associated with reinforcement learning methods. The used methodology contemplates realisations of online designs for DLQR controllers that is evaluated in a multivariable dynamic system model.

A methodology for formulating a minimal uncertainty model for robust control system design and analysis

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Chang, B.-C.; Fischl, Robert

1989-01-01

In the design and analysis of robust control systems for uncertain plants, the technique of formulating what is termed an M-delta model has become widely accepted and applied in the robust control literature. The M represents the transfer function matrix M(s) of the nominal system, and delta represents an uncertainty matrix acting on M(s). The uncertainty can arise from various sources, such as structured uncertainty from parameter variations or multiple unstructured uncertainties from unmodeled dynamics and other neglected phenomena. In general, delta is a block diagonal matrix, and for real parameter variations the diagonal elements are real. As stated in the literature, this structure can always be formed for any linear interconnection of inputs, outputs, transfer functions, parameter variations, and perturbations. However, very little of the literature addresses methods for obtaining this structure, and none of this literature addresses a general methodology for obtaining a minimal M-delta model for a wide class of uncertainty. Since have a delta matrix of minimum order would improve the efficiency of structured singular value (or multivariable stability margin) computations, a method of obtaining a minimal M-delta model would be useful. A generalized method of obtaining a minimal M-delta structure for systems with real parameter variations is given.

Multivariate Tensor-based Morphometry on Surfaces: Application to Mapping Ventricular Abnormalities in HIV/AIDS

PubMed Central

Wang, Yalin; Zhang, Jie; Gutman, Boris; Chan, Tony F.; Becker, James T.; Aizenstein, Howard J.; Lopez, Oscar L.; Tamburo, Robert J.; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics - these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. PMID:19900560

Travel distance and use of salvage palliative chemotherapy in patients with metastatic colorectal cancer.

PubMed

Ahmed, Shahid; Iqbal, Mahjabeen; Le, Duc; Iqbal, Nayyer; Pahwa, Punam

2018-04-01

Salvage palliative chemotherapy in metastatic colorectal cancer has been associated with significant improvement in survival. However, not all patients receive all available therapies. Travel burden can affect patient access and use of future therapy. The present study aims to determine relationship between travel distance (TD) and salvage palliative chemotherapy in patients with metastatic colorectal cancer. A patient cohort diagnosed with metastatic colorectal cancer during 2006-2010 in the province of Saskatchewan, Canada was studied. Logistic regression analyses were performed to assess relationship between travel distance and subsequent line therapies. The median age of 264 eligible patients was 62 years [interquartile range (IQR): 53-72]. The patients who received salvage systemic therapy had a median distance to travel of 60.0 km (IQR: 4.7-144) compared with 88.1 km (IQR: 4.8-189) if they did not receive second- or third-line therapy (P=0.06). In multivariate analysis distance to the cancer center <100 km, odds ratio (OR) 1.69 (95% CI: 1.003-2.84), no metastasectomy, OR 1.89 (95% CI: 1.03-3.46), and absence of comorbid illness as per Charlson comorbid index, OR 1.45 (95% CI: 1.19-1.77) were correlated with the use of second- and subsequent line therapies. Our result revealed that travel distance to the cancer center greater than 100 km was associated less frequent use of second or subsequent line therapies in patients with metastatic colorectal cancer.

The evolution of multivariate maternal effects.

PubMed

Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

2014-04-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

The Evolution of Multivariate Maternal Effects

PubMed Central

Kuijper, Bram; Johnstone, Rufus A.; Townley, Stuart

2014-01-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations. PMID:24722346

Automatic Configuration of Programmable Logic Controller Emulators

DTIC Science & Technology

2015-03-01

25 11 Example tree generated using UPGMA [Edw13] . . . . . . . . . . . . . . . . . . . . 33 12 Example sequence alignment for two... UPGMA Unweighted Pair Group Method with Arithmetic Mean URL uniform resource locator VM virtual machine XML Extensible Markup Language xx List of...appearance in the ses- sion, and then they are clustered again using Unweighted Pair Group Method with Arithmetic Mean ( UPGMA ) with a distance matrix based

Resonant electronic excitation energy transfer by exchange mechanism in the quantum dot system

NASA Astrophysics Data System (ADS)

Chikalova-Luzina, O. P.; Samosvat, D. M.; Vyatkin, V. M.; Zegrya, G. G.

2017-11-01

A microscopic theory of nonradiative resonance energy transfer between spherical A3B5 semiconductor quantum dots by the exchange mechanism is suggested. The interdot Coulomb interaction is taken into consideration. It is assumed that the quantum dot-donor and the quantum dot-acceptor are made from the same A3B5 compound and are embedded in the matrix of another material that produces potential barriers for electrons and holes. The dependences of the energy transfer rate on the quantum-dot system parameters are found in the frame of the Kane model that provides the most adequate description of the real spectra of A3B5 semiconductors. The analytical treatment is carried out with using the density matrix method, which enabled us to perform an energy transfer analysis both in the weak-interaction approximation and in the strong-interaction approximation. The numerical calculations showed the saturation of the energy transfer rate at the distances between the donor and the acceptor approaching the contact one. The contributions of the exchange and direct Coulomb intractions can be of the same order at the small distances and can have the same value in the saturation range.

On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle

PubMed Central

Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos

2015-01-01

For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489

The extreme mobility of debris avalanches: A new model of transport mechanism

NASA Astrophysics Data System (ADS)

Perinotto, Hélène; Schneider, Jean-Luc; Bachèlery, Patrick; Le Bourdonnec, François-Xavier; Famin, Vincent; Michon, Laurent

2015-12-01

Large rockslide-debris avalanches, resulting from flank collapses that shape volcanoes and mountains on Earth and other object of the solar system, are rapid and dangerous gravity-driven granular flows that travel abnormal distances. During the last 50 years, numerous physical models have been put forward to explain their extreme mobility. The principal models are based on fluidization, lubrication, or dynamic disintegration. However, these processes remain poorly constrained. To identify precisely the transport mechanisms during debris avalanches, we examined morphometric (fractal dimension and circularity), grain size, and exoscopic characteristics of the various types of particles (clasts and matrix) from volcanic debris avalanche deposits of La Réunion Island (Indian Ocean). From these data we demonstrate for the first time that syn-transport dynamic disintegration continuously operates with the increasing runout distance from the source down to a grinding limit of 500 µm. Below this limit, the particle size reduction exclusively results from their attrition by frictional interactions. Consequently, the exceptional mobility of debris avalanches may be explained by the combined effect of elastic energy release during the dynamic disintegration of the larger clasts and frictional reduction within the matrix due to interactions between the finer particles.

Problematic projection to the in-sample subspace for a kernelized anomaly detector

DOE PAGES

Theiler, James; Grosklos, Guen

2016-03-07

We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors, we can trace the problem to a projection in feature space, which arises when a pseudoinverse is used on the covariance matrix in that feature space. Here, we show that a regularized variant of KRX overcomes this difficulty and achieves superior performance over a widemore » range of bandwidths.« less

Phylogenetics beyond biology.

PubMed

Retzlaff, Nancy; Stadler, Peter F

2018-06-21

Evolutionary processes have been described not only in biology but also for a wide range of human cultural activities including languages and law. In contrast to the evolution of DNA or protein sequences, the detailed mechanisms giving rise to the observed evolution-like processes are not or only partially known. The absence of a mechanistic model of evolution implies that it remains unknown how the distances between different taxa have to be quantified. Considering distortions of metric distances, we first show that poor choices of the distance measure can lead to incorrect phylogenetic trees. Based on the well-known fact that phylogenetic inference requires additive metrics, we then show that the correct phylogeny can be computed from a distance matrix [Formula: see text] if there is a monotonic, subadditive function [Formula: see text] such that [Formula: see text] is additive. The required metric-preserving transformation [Formula: see text] can be computed as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process remains elusive.

Time-Distance Helioseismology: Noise Estimation

NASA Astrophysics Data System (ADS)

Gizon, L.; Birch, A. C.

2004-10-01

As in global helioseismology, the dominant source of noise in time-distance helioseismology measurements is realization noise due to the stochastic nature of the excitation mechanism of solar oscillations. Characterizing noise is important for the interpretation and inversion of time-distance measurements. In this paper we introduce a robust definition of travel time that can be applied to very noisy data. We then derive a simple model for the full covariance matrix of the travel-time measurements. This model depends only on the expectation value of the filtered power spectrum and assumes that solar oscillations are stationary and homogeneous on the solar surface. The validity of the model is confirmed through comparison with SOHO MDI measurements in a quiet-Sun region. We show that the correlation length of the noise in the travel times is about half the dominant wavelength of the filtered power spectrum. We also show that the signal-to-noise ratio in quiet-Sun travel-time maps increases roughly as the square root of the observation time and is at maximum for a distance near half the length scale of supergranulation.

Discrimination of lichen genera and species using element concentrations

USGS Publications Warehouse

Bennett, J.P.

2008-01-01

The importance of organic chemistry in the classification of lichens is well established, but inorganic chemistry has been largely overlooked. Six lichen species were studied over a period of 23 years that were growing in 11 protected areas of the northern Great Lakes ecoregion, which were not greatly influenced by anthropogenic particulates or gaseous air pollutants. The elemental data from these studies were aggregated in order to test the hypothesis that differences among species in tissue element concentrations were large enough to discriminate between taxa faithfully. Concentrations of 16 chemical elements that were found in tissue samples from Cladonia rangiferina, Evernia mesomorpha, Flavopunctelia flaventior, Hypogymnia physodes, Parmelia sulcata, and Punctelia rudecta were analyzed statistically using multivariate discriminant functions and CART analyses, as well as t-tests. Genera and species were clearly separated in element space, and elemental discriminant functions were able to classify 91-100 of the samples correctly into species. At the broadest level, a Zn concentration of 51 ppm in tissues of four of the lichen species effectively discriminated foliose from fruticose species. Similarly, a S concentration of 680 ppm discriminated C. rangiferina and E. mesomorpha, and a Ca concentration of 10 436 ppm discriminated H. physodes from P. sulcata. For the three parmelioid species, a Ca concentration >32 837 ppm discriminated Punctelia rudecta from the other two species, while a Zn concentration of 56 ppm discriminated Parmelia sulcata from F. flaventior. Foliose species also had higher concentrations than did fruticose species of all elements except Na. Elemental signatures for each of the six species were developed using standardized means. Twenty-four mechanisms explaining the differences among species are summarized. Finally, the relationships of four species based on element concentrations, using additive-trees clustering of a Euclidean-distance matrix, produced identical relationships as did analyses based on secondary product chemistry that used additive-trees clustering of a Jaccard similarity matrix. At least for these six species, element composition has taxonomic significance, and may be useful for discriminating other taxa. ?? 2008 British Lichen Society.