Development of an Efficient Binaural Simulation for the Analysis of Structural Acoustic Data

NASA Technical Reports Server (NTRS)

Lalime, Aimee L.; Johnson, Marty E.; Rizzi, Stephen A. (Technical Monitor)

2002-01-01

Binaural or "virtual acoustic" representation has been proposed as a method of analyzing acoustic and vibroacoustic data. Unfortunately, this binaural representation can require extensive computer power to apply the Head Related Transfer Functions (HRTFs) to a large number of sources, as with a vibrating structure. This work focuses on reducing the number of real-time computations required in this binaural analysis through the use of Singular Value Decomposition (SVD) and Equivalent Source Reduction (ESR). The SVD method reduces the complexity of the HRTF computations by breaking the HRTFs into dominant singular values (and vectors). The ESR method reduces the number of sources to be analyzed in real-time computation by replacing sources on the scale of a structural wavelength with sources on the scale of an acoustic wavelength. It is shown that the effectiveness of the SVD and ESR methods improves as the complexity of the source increases. In addition, preliminary auralization tests have shown that the results from both the SVD and ESR methods are indistinguishable from the results found with the exhaustive method.

Two Dimensional Finite Element Based Magnetotelluric Inversion using Singular Value Decomposition Method on Transverse Electric Mode

NASA Astrophysics Data System (ADS)

Tjong, Tiffany; Yihaa’ Roodhiyah, Lisa; Nurhasan; Sutarno, Doddy

2018-04-01

In this work, an inversion scheme was performed using a vector finite element (VFE) based 2-D magnetotelluric (MT) forward modelling. We use an inversion scheme with Singular value decomposition (SVD) method toimprove the accuracy of MT inversion.The inversion scheme was applied to transverse electric (TE) mode of MT. SVD method was used in this inversion to decompose the Jacobian matrices. Singular values which obtained from the decomposition process were analyzed. This enabled us to determine the importance of data and therefore to define a threshold for truncation process. The truncation of singular value in inversion processcould improve the resulted model.

Singular value decomposition approach to the yttrium occurrence in mineral maps of rare earth element ores using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Romppanen, Sari; Häkkänen, Heikki; Kaski, Saara

2017-08-01

Laser-induced breakdown spectroscopy (LIBS) has been used in analysis of rare earth element (REE) ores from the geological formation of Norra Kärr Alkaline Complex in southern Sweden. Yttrium has been detected in eudialyte (Na15 Ca6(Fe,Mn)3 Zr3Si(Si25O73)(O,OH,H2O)3 (OH,Cl)2) and catapleiite (Ca/Na2ZrSi3O9·2H2O). Singular value decomposition (SVD) has been employed in classification of the minerals in the rock samples and maps representing the mineralogy in the sampled area have been constructed. Based on the SVD classification the percentage of the yttrium-bearing ore minerals can be calculated even in fine-grained rock samples.

Reduced Order Model Basis Vector Generation: Generates Basis Vectors fro ROMs

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

Arrighi, Bill

2016-03-03

libROM is a library that implements order reduction via singular value decomposition (SVD) of sampled state vectors. It implements 2 parallel, incremental SVD algorithms and one serial, non-incremental algorithm. It also provides a mechanism for adaptive sampling of basis vectors.

Deconvolutions based on singular value decomposition and the pseudoinverse: a guide for beginners.

PubMed

Hendler, R W; Shrager, R I

1994-01-01

Singular value decomposition (SVD) is deeply rooted in the theory of linear algebra, and because of this is not readily understood by a large group of researchers who could profit from its application. In this paper, we discuss the subject on a level that should be understandable to scientists who are not well versed in linear algebra. However, because it is necessary that certain key concepts in linear algebra be appreciated in order to comprehend what is accomplished by SVD, we present the section, 'Bare basics of linear algebra'. This is followed by a discussion of the theory of SVD. Next we present step-by-step examples to illustrate how SVD is applied to deconvolute a titration involving a mixture of three pH indicators. One noiseless case is presented as well as two cases where either a fixed or varying noise level is present. Finally, we discuss additional deconvolutions of mixed spectra based on the use of the pseudoinverse.

Singular value decomposition for collaborative filtering on a GPU

NASA Astrophysics Data System (ADS)

Kato, Kimikazu; Hosino, Tikara

2010-06-01

A collaborative filtering predicts customers' unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an effective algorithm to compute SVD toward a solution of an open competition called "Netflix Prize". The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be efficient by an experiment.

Singular value decomposition based feature extraction technique for physiological signal analysis.

PubMed

Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C

2012-06-01

Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

Nonlinear QR code based optical image encryption using spiral phase transform, equal modulus decomposition and singular value decomposition

NASA Astrophysics Data System (ADS)

Kumar, Ravi; Bhaduri, Basanta; Nishchal, Naveen K.

2018-01-01

In this study, we propose a quick response (QR) code based nonlinear optical image encryption technique using spiral phase transform (SPT), equal modulus decomposition (EMD) and singular value decomposition (SVD). First, the primary image is converted into a QR code and then multiplied with a spiral phase mask (SPM). Next, the product is spiral phase transformed with particular spiral phase function, and further, the EMD is performed on the output of SPT, which results into two complex images, Z 1 and Z 2. Among these, Z 1 is further Fresnel propagated with distance d, and Z 2 is reserved as a decryption key. Afterwards, SVD is performed on Fresnel propagated output to get three decomposed matrices i.e. one diagonal matrix and two unitary matrices. The two unitary matrices are modulated with two different SPMs and then, the inverse SVD is performed using the diagonal matrix and modulated unitary matrices to get the final encrypted image. Numerical simulation results confirm the validity and effectiveness of the proposed technique. The proposed technique is robust against noise attack, specific attack, and brutal force attack. Simulation results are presented in support of the proposed idea.

A robust indicator based on singular value decomposition for flaw feature detection from noisy ultrasonic signals

NASA Astrophysics Data System (ADS)

Cui, Ximing; Wang, Zhe; Kang, Yihua; Pu, Haiming; Deng, Zhiyang

2018-05-01

Singular value decomposition (SVD) has been proven to be an effective de-noising tool for flaw echo signal feature detection in ultrasonic non-destructive evaluation (NDE). However, the uncertainty in the arbitrary manner of the selection of an effective singular value weakens the robustness of this technique. Improper selection of effective singular values will lead to bad performance of SVD de-noising. What is more, the computational complexity of SVD is too large for it to be applied in real-time applications. In this paper, to eliminate the uncertainty in SVD de-noising, a novel flaw indicator, named the maximum singular value indicator (MSI), based on short-time SVD (STSVD), is proposed for flaw feature detection from a measured signal in ultrasonic NDE. In this technique, the measured signal is first truncated into overlapping short-time data segments to put feature information of a transient flaw echo signal in local field, and then the MSI can be obtained from the SVD of each short-time data segment. Research shows that this indicator can clearly indicate the location of ultrasonic flaw signals, and the computational complexity of this STSVD-based indicator is significantly reduced with the algorithm proposed in this paper. Both simulation and experiments show that this technique is very efficient for real-time application in flaw detection from noisy data.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure.

PubMed

Zhang, Wen; Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure

PubMed Central

Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods. PMID:27579031

Singular Value Decomposition Method to Determine Distance Distributions in Pulsed Dipolar Electron Spin Resonance.

PubMed

Srivastava, Madhur; Freed, Jack H

2017-11-16

Regularization is often utilized to elicit the desired physical results from experimental data. The recent development of a denoising procedure yielding about 2 orders of magnitude in improvement in SNR obviates the need for regularization, which achieves a compromise between canceling effects of noise and obtaining an estimate of the desired physical results. We show how singular value decomposition (SVD) can be employed directly on the denoised data, using pulse dipolar electron spin resonance experiments as an example. Such experiments are useful in measuring distances and their distributions, P(r) between spin labels on proteins. In noise-free model cases exact results are obtained, but even a small amount of noise (e.g., SNR = 850 after denoising) corrupts the solution. We develop criteria that precisely determine an optimum approximate solution, which can readily be automated. This method is applicable to any signal that is currently processed with regularization of its SVD analysis.

Using dynamic mode decomposition for real-time background/foreground separation in video

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

Kutz, Jose Nathan; Grosek, Jacob; Brunton, Steven

The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

Rapid surface defect detection based on singular value decomposition using steel strips as an example

NASA Astrophysics Data System (ADS)

Sun, Qianlai; Wang, Yin; Sun, Zhiyi

2018-05-01

For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.

Efficient subtle motion detection from high-speed video for sound recovery and vibration analysis using singular value decomposition-based approach

NASA Astrophysics Data System (ADS)

Zhang, Dashan; Guo, Jie; Jin, Yi; Zhu, Chang'an

2017-09-01

High-speed cameras provide full field measurement of structure motions and have been applied in nondestructive testing and noncontact structure monitoring. Recently, a phase-based method has been proposed to extract sound-induced vibrations from phase variations in videos, and this method provides insights into the study of remote sound surveillance and material analysis. An efficient singular value decomposition (SVD)-based approach is introduced to detect sound-induced subtle motions from pixel intensities in silent high-speed videos. A high-speed camera is initially applied to capture a video of the vibrating objects stimulated by sound fluctuations. Then, subimages collected from a small region on the captured video are reshaped into vectors and reconstructed to form a matrix. Orthonormal image bases (OIBs) are obtained from the SVD of the matrix; available vibration signal can then be obtained by projecting subsequent subimages onto specific OIBs. A simulation test is initiated to validate the effectiveness and efficiency of the proposed method. Two experiments are conducted to demonstrate the potential applications in sound recovery and material analysis. Results show that the proposed method efficiently detects subtle motions from the video.

Asymmetric color image encryption based on singular value decomposition

NASA Astrophysics Data System (ADS)

Yao, Lili; Yuan, Caojin; Qiang, Junjie; Feng, Shaotong; Nie, Shouping

2017-02-01

A novel asymmetric color image encryption approach by using singular value decomposition (SVD) is proposed. The original color image is encrypted into a ciphertext shown as an indexed image by using the proposed method. The red, green and blue components of the color image are subsequently encoded into a complex function which is then separated into U, S and V parts by SVD. The data matrix of the ciphertext is obtained by multiplying orthogonal matrices U and V while implementing phase-truncation. Diagonal entries of the three diagonal matrices of the SVD results are abstracted and scrambling combined to construct the colormap of the ciphertext. Thus, the encrypted indexed image covers less space than the original image. For decryption, the original color image cannot be recovered without private keys which are obtained from phase-truncation and the orthogonality of V. Computer simulations are presented to evaluate the performance of the proposed algorithm. We also analyze the security of the proposed system.

Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

NASA Astrophysics Data System (ADS)

Akhbardeh, Alireza; Junnila, Sakari; Koivuluoma, Mikko; Koivistoinen, Teemu; Värri, Alpo

2006-12-01

As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an [InlineEquation not available: see fulltext.]-by-1 or 1-by- [InlineEquation not available: see fulltext.] array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD.

PubMed

Bullinaria, John A; Levy, Joseph P

2012-09-01

In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis.

PubMed

Till, Kevin; Jones, Ben L; Cobley, Stephen; Morley, David; O'Hara, John; Chapman, Chris; Cooke, Carlton; Beggs, Clive B

2016-01-01

Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.

Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis

PubMed Central

Till, Kevin; Jones, Ben L.; Cobley, Stephen; Morley, David; O'Hara, John; Chapman, Chris; Cooke, Carlton; Beggs, Clive B.

2016-01-01

Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification. PMID:27224653

Local sensitivity analysis for inverse problems solved by singular value decomposition

USGS Publications Warehouse

Hill, M.C.; Nolan, B.T.

2010-01-01

Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by regression based on the range of singular values. Identifiability statistic results varied based on the number of SVD parameters included. Identifiability statistics calculated for four SVD parameters indicate the same three most important process-model parameters as CSS/PCC (WFC1, WFC2, and BD2), but the order differed. Additionally, the identifiability statistic showed that BD1 was almost as dominant as WFC1. The CSS/PCC analysis showed that this results from its high correlation with WCF1 (-0.94), and not its individual sensitivity. Such distinctions, combined with analysis of how high correlations and(or) sensitivities result from the constructed model, can produce important insights into, for example, the use of sensitivity analysis to design monitoring networks. In conclusion, the statistics considered identified similar important parameters. They differ because (1) with CSS/PCC can be more awkward because sensitivity and interdependence are considered separately and (2) identifiability requires consideration of how many SVD parameters to include. A continuing challenge is to understand how these computationally efficient methods compare with computationally demanding global methods like Markov-Chain Monte Carlo given common nonlinear processes and the often even more nonlinear models.

A robust watermarking scheme using lifting wavelet transform and singular value decomposition

NASA Astrophysics Data System (ADS)

Bhardwaj, Anuj; Verma, Deval; Verma, Vivek Singh

2017-01-01

The present paper proposes a robust image watermarking scheme using lifting wavelet transform (LWT) and singular value decomposition (SVD). Second level LWT is applied on host/cover image to decompose into different subbands. SVD is used to obtain singular values of watermark image and then these singular values are updated with the singular values of LH2 subband. The algorithm is tested on a number of benchmark images and it is found that the present algorithm is robust against different geometric and image processing operations. A comparison of the proposed scheme is performed with other existing schemes and observed that the present scheme is better not only in terms of robustness but also in terms of imperceptibility.

Three dimensional empirical mode decomposition analysis apparatus, method and article manufacture

NASA Technical Reports Server (NTRS)

Gloersen, Per (Inventor)

2004-01-01

An apparatus and method of analysis for three-dimensional (3D) physical phenomena. The physical phenomena may include any varying 3D phenomena such as time varying polar ice flows. A repesentation of the 3D phenomena is passed through a Hilbert transform to convert the data into complex form. A spatial variable is separated from the complex representation by producing a time based covariance matrix. The temporal parts of the principal components are produced by applying Singular Value Decomposition (SVD). Based on the rapidity with which the eigenvalues decay, the first 3-10 complex principal components (CPC) are selected for Empirical Mode Decomposition into intrinsic modes. The intrinsic modes produced are filtered in order to reconstruct the spatial part of the CPC. Finally, a filtered time series may be reconstructed from the first 3-10 filtered complex principal components.

Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.

A Feasibility Study on a Parallel Mechanism for Examining the Space Shuttle Orbiter Payload Bay Radiators

NASA Technical Reports Server (NTRS)

Roberts, Rodney G.; LopezdelCastillo, Eduardo

1996-01-01

The goal of the project was to develop the necessary analysis tools for a feasibility study of a cable suspended robot system for examining the space shuttle orbiter payload bay radiators These tools were developed to address design issues such as workspace size, tension requirements on the cable, the necessary accuracy and resolution requirements and the stiffness and movement requirements of the system. This report describes the mathematical models for studying the inverse kinematics, statics, and stiffness of the robot. Each model is described by a matrix. The manipulator Jacobian was also related to the stiffness matrix, which characterized the stiffness of the system. Analysis tools were then developed based on the singular value decomposition (SVD) of the corresponding matrices. It was demonstrated how the SVD can be used to quantify the robot's performance and to provide insight into different design issues.

Reduced-rank approximations to the far-field transform in the gridded fast multipole method

NASA Astrophysics Data System (ADS)

Hesford, Andrew J.; Waag, Robert C.

2011-05-01

The fast multipole method (FMM) has been shown to have a reduced computational dependence on the size of finest-level groups of elements when the elements are positioned on a regular grid and FFT convolution is used to represent neighboring interactions. However, transformations between plane-wave expansions used for FMM interactions and pressure distributions used for neighboring interactions remain significant contributors to the cost of FMM computations when finest-level groups are large. The transformation operators, which are forward and inverse Fourier transforms with the wave space confined to the unit sphere, are smooth and well approximated using reduced-rank decompositions that further reduce the computational dependence of the FMM on finest-level group size. The adaptive cross approximation (ACA) is selected to represent the forward and adjoint far-field transformation operators required by the FMM. However, the actual error of the ACA is found to be greater than that predicted using traditional estimates, and the ACA generally performs worse than the approximation resulting from a truncated singular-value decomposition (SVD). To overcome these issues while avoiding the cost of a full-scale SVD, the ACA is employed with more stringent accuracy demands and recompressed using a reduced, truncated SVD. The results show a greatly reduced approximation error that performs comparably to the full-scale truncated SVD without degrading the asymptotic computational efficiency associated with ACA matrix assembly.

Reduced-Rank Approximations to the Far-Field Transform in the Gridded Fast Multipole Method.

PubMed

Hesford, Andrew J; Waag, Robert C

2011-05-10

The fast multipole method (FMM) has been shown to have a reduced computational dependence on the size of finest-level groups of elements when the elements are positioned on a regular grid and FFT convolution is used to represent neighboring interactions. However, transformations between plane-wave expansions used for FMM interactions and pressure distributions used for neighboring interactions remain significant contributors to the cost of FMM computations when finest-level groups are large. The transformation operators, which are forward and inverse Fourier transforms with the wave space confined to the unit sphere, are smooth and well approximated using reduced-rank decompositions that further reduce the computational dependence of the FMM on finest-level group size. The adaptive cross approximation (ACA) is selected to represent the forward and adjoint far-field transformation operators required by the FMM. However, the actual error of the ACA is found to be greater than that predicted using traditional estimates, and the ACA generally performs worse than the approximation resulting from a truncated singular-value decomposition (SVD). To overcome these issues while avoiding the cost of a full-scale SVD, the ACA is employed with more stringent accuracy demands and recompressed using a reduced, truncated SVD. The results show a greatly reduced approximation error that performs comparably to the full-scale truncated SVD without degrading the asymptotic computational efficiency associated with ACA matrix assembly.

Reduced-Rank Approximations to the Far-Field Transform in the Gridded Fast Multipole Method

PubMed Central

Hesford, Andrew J.; Waag, Robert C.

2011-01-01

The fast multipole method (FMM) has been shown to have a reduced computational dependence on the size of finest-level groups of elements when the elements are positioned on a regular grid and FFT convolution is used to represent neighboring interactions. However, transformations between plane-wave expansions used for FMM interactions and pressure distributions used for neighboring interactions remain significant contributors to the cost of FMM computations when finest-level groups are large. The transformation operators, which are forward and inverse Fourier transforms with the wave space confined to the unit sphere, are smooth and well approximated using reduced-rank decompositions that further reduce the computational dependence of the FMM on finest-level group size. The adaptive cross approximation (ACA) is selected to represent the forward and adjoint far-field transformation operators required by the FMM. However, the actual error of the ACA is found to be greater than that predicted using traditional estimates, and the ACA generally performs worse than the approximation resulting from a truncated singular-value decomposition (SVD). To overcome these issues while avoiding the cost of a full-scale SVD, the ACA is employed with more stringent accuracy demands and recompressed using a reduced, truncated SVD. The results show a greatly reduced approximation error that performs comparably to the full-scale truncated SVD without degrading the asymptotic computational efficiency associated with ACA matrix assembly. PMID:21552350

Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis

PubMed Central

Ritchie, Matthew; Ash, Matthew; Chen, Qingchao; Chetty, Kevin

2016-01-01

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques. PMID:27589760

Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis.

PubMed

Ritchie, Matthew; Ash, Matthew; Chen, Qingchao; Chetty, Kevin

2016-08-31

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline

NASA Astrophysics Data System (ADS)

Yu, Xuchao; Liang, Wei; Zhang, Laibin; Jin, Hao; Qiu, Jingwei

2016-05-01

During the last decades, leak detection for natural gas pipeline has become one of the paramount concerns of pipeline operators and researchers across the globe. However, acoustic wave method has been proved to be an effective way to identify and localize leakage for gas pipeline. Considering the fact that noises inevitably exist in the acoustic signals collected, noise reduction should be enforced on the signals for subsequent data mining and analysis. Thus, an integrated acoustic noise reduction method based on DTCWT and SVD is proposed in this study. The method is put forward based on the idea that noise reduction strategy should match the characteristics of the noisy signal. According to previous studies, it is known that the energy of acoustic signals collected under leaking condition is mainly concentrated in low-frequency portion (0-100 Hz). And ultralow-frequency component (0-5 Hz), which is taken as the characteristic frequency band in this study, can propagate a relatively longer distance and be captured by sensors. Therefore, in order to filter the noises and to reserve the characteristic frequency band, DTCWT is taken as the core to conduct multilevel decomposition and refining for acoustic signals and SVD is employed to eliminate noises in non-characteristic bands. Both simulation and field experiments show that DTCWT-SVD is an excellent method for acoustic noise reduction. At the end of this study, application in leakage localization shows that it becomes much easier and a little more accurate to estimate the location of leak hole after noise reduction by DTCWT-SVD.

A novel image watermarking method based on singular value decomposition and digital holography

NASA Astrophysics Data System (ADS)

Cai, Zhishan

2016-10-01

According to the information optics theory, a novel watermarking method based on Fourier-transformed digital holography and singular value decomposition (SVD) is proposed in this paper. First of all, a watermark image is converted to a digital hologram using the Fourier transform. After that, the original image is divided into many non-overlapping blocks. All the blocks and the hologram are decomposed using SVD. The singular value components of the hologram are then embedded into the singular value components of each block using an addition principle. Finally, SVD inverse transformation is carried out on the blocks and hologram to generate the watermarked image. The watermark information embedded in each block is extracted at first when the watermark is extracted. After that, an averaging operation is carried out on the extracted information to generate the final watermark information. Finally, the algorithm is simulated. Furthermore, to test the encrypted image's resistance performance against attacks, various attack tests are carried out. The results show that the proposed algorithm has very good robustness against noise interference, image cut, compression, brightness stretching, etc. In particular, when the image is rotated by a large angle, the watermark information can still be extracted correctly.

A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Ming; Jia, Xiaodong

2017-09-01

Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.

Note: Sound recovery from video using SVD-based information extraction

NASA Astrophysics Data System (ADS)

Zhang, Dashan; Guo, Jie; Lei, Xiujun; Zhu, Chang'an

2016-08-01

This note reports an efficient singular value decomposition (SVD)-based vibration extraction approach that recovers sound information in silent high-speed video. A high-speed camera of which frame rates are in the range of 2 kHz-10 kHz is applied to film the vibrating objects. Sub-images cut from video frames are transformed into column vectors and then reconstructed to a new matrix. The SVD of the new matrix produces orthonormal image bases (OIBs) and image projections onto specific OIB can be recovered as understandable acoustical signals. Standard frequencies of 256 Hz and 512 Hz tuning forks are extracted offline from their vibrating surfaces and a 3.35 s speech signal is recovered online from a piece of paper that is stimulated by sound waves within 1 min.

Image processing enhancement of high-resolution TEM micrographs of nanometer-size metal particles

NASA Technical Reports Server (NTRS)

Artal, P.; Avalos-Borja, M.; Soria, F.; Poppa, H.; Heinemann, K.

1989-01-01

The high-resolution TEM detectability of lattice fringes from metal particles supported on substrates is impeded by the substrate itself. Single value decomposition (SVD) and Fourier filtering (FFT) methods were applied to standard high resolution micrographs to enhance lattice resolution from particles as well as from crystalline substrates. SVD produced good results for one direction of fringes, and it can be implemented as a real-time process. Fourier methods are independent of azimuthal directions and allow separation of particle lattice planes from those pertaining to the substrate, which makes it feasible to detect possible substrate distortions produced by the supported particle. This method, on the other hand, is more elaborate, requires more computer time than SVD and is, therefore, less likely to be used in real-time image processing applications.

Explosion Source Similarity Analysis via SVD

NASA Astrophysics Data System (ADS)

Yedlin, Matthew; Ben Horin, Yochai; Margrave, Gary

2016-04-01

An important seismological ingredient for establishing a regional seismic nuclear discriminant is the similarity analysis of a sequence of explosion sources. To investigate source similarity, we are fortunate to have access to a sequence of 1805 three-component recordings of quarry blasts, shot from March 2002 to January 2015. The centroid of these blasts has an estimated location 36.3E and 29.9N. All blasts were detonated by JPMC (Jordan Phosphate Mines Co.) All data were recorded at the Israeli NDC, HFRI, located at 30.03N and 35.03E. Data were first winnowed based on the distribution of maximum amplitudes in the neighborhood of the P-wave arrival. The winnowed data were then detrended using the algorithm of Cleveland et al (1990). The detrended data were bandpass filtered between .1 to 12 Hz using an eighth order Butterworth filter. Finally, data were sorted based on maximum trace amplitude. Two similarity analysis approaches were used. First, for each component, the entire suite of traces was decomposed into its eigenvector representation, by employing singular-valued decomposition (SVD). The data were then reconstructed using 10 percent of the singular values, with the resulting enhancement of the S-wave and surface wave arrivals. The results of this first method are then compared to the second analysis method based on the eigenface decomposition analysis of Turk and Pentland (1991). While both methods yield similar results in enhancement of data arrivals and reduction of data redundancy, more analysis is required to calibrate the recorded data to charge size, a quantity that was not available for the current study. References Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I., Stl: A seasonal-trend decomposition procedure based on loess, Journal of Official Statistics, 6, No. 1, 3-73, 1990. Turk, M. and Pentland, A., Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1), 71-86, 1991.

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model.

PubMed

Laamiri, Imen; Khouaja, Anis; Messaoud, Hassani

2015-03-01

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS). Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Real-time Automatic Detectors of P and S Waves Using Singular Values Decomposition

NASA Astrophysics Data System (ADS)

Kurzon, I.; Vernon, F.; Rosenberger, A.; Ben-Zion, Y.

2013-12-01

We implement a new method for the automatic detection of the primary P and S phases using Singular Value Decomposition (SVD) analysis. The method is based on a real-time iteration algorithm of Rosenberger (2010) for the SVD of three component seismograms. Rosenberger's algorithm identifies the incidence angle by applying SVD and separates the waveforms into their P and S components. We have been using the same algorithm with the modification that we filter the waveforms prior to the SVD, and then apply SNR (Signal-to-Noise Ratio) detectors for picking the P and S arrivals, on the new filtered+SVD-separated channels. A recent deployment in San Jacinto Fault Zone area provides a very dense seismic network that allows us to test the detection algorithm in diverse setting, such as: events with different source mechanisms, stations with different site characteristics, and ray paths that diverge from the SVD approximation used in the algorithm, (e.g., rays propagating within the fault and recorded on linear arrays, crossing the fault). We have found that a Butterworth band-pass filter of 2-30Hz, with four poles at each of the corner frequencies, shows the best performance in a large variety of events and stations within the SJFZ. Using the SVD detectors we obtain a similar number of P and S picks, which is a rare thing to see in ordinary SNR detectors. Also for the actual real-time operation of the ANZA and SJFZ real-time seismic networks, the above filter (2-30Hz) shows a very impressive performance, tested on many events and several aftershock sequences in the region from the MW 5.2 of June 2005, through the MW 5.4 of July 2010, to MW 4.7 of March 2013. Here we show the results of testing the detectors on the most complex and intense aftershock sequence, the MW 5.2 of June 2005, in which in the very first hour there were ~4 events a minute. This aftershock sequence was thoroughly reviewed by several analysts, identifying 294 events in the first hour, located in a condensed cluster around the main shock. We used this hour of events to fine-tune the automatic SVD detection, association and location of the real-time system, reaching a 37% automatic identification and location of events, with a minimum of 10 stations per event, all events fall within the same condensed cluster and there are no false events or large offsets of their locations. An ordinary SNR detector did not exceed the 11% success with a minimum of 8 stations per event, 2 false events and a wider spread of events (not within the reviewed cluster). One of the main advantages of the SVD detectors for real-time operations is the actual separation between the P and S components, by that significantly reducing the noise of picks detected by ordinary SNR detectors. The new method has been applied for a significant amount of events within the SJFZ in the past 8 years, and is now in the final stage of real-time implementation in UCSD for the ANZA and SJFZ networks, tuned for automatic detection and location of local events.

Incorporation of perceptually adaptive QIM with singular value decomposition for blind audio watermarking

NASA Astrophysics Data System (ADS)

Hu, Hwai-Tsu; Chou, Hsien-Hsin; Yu, Chu; Hsu, Ling-Yuan

2014-12-01

This paper presents a novel approach for blind audio watermarking. The proposed scheme utilizes the flexibility of discrete wavelet packet transformation (DWPT) to approximate the critical bands and adaptively determines suitable embedding strengths for carrying out quantization index modulation (QIM). The singular value decomposition (SVD) is employed to analyze the matrix formed by the DWPT coefficients and embed watermark bits by manipulating singular values subject to perceptual criteria. To achieve even better performance, two auxiliary enhancement measures are attached to the developed scheme. Performance evaluation and comparison are demonstrated with the presence of common digital signal processing attacks. Experimental results confirm that the combination of the DWPT, SVD, and adaptive QIM achieves imperceptible data hiding with satisfying robustness and payload capacity. Moreover, the inclusion of self-synchronization capability allows the developed watermarking system to withstand time-shifting and cropping attacks.

Glove-based approach to online signature verification.

PubMed

Kamel, Nidal S; Sayeed, Shohel; Ellis, Grant A

2008-06-01

Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line signature verification system using the Singular Value Decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to recognize forgery signatures with a false acceptance rate of less than 1.2%.

Image fusion via nonlocal sparse K-SVD dictionary learning.

PubMed

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

A general approach to regularizing inverse problems with regional data using Slepian wavelets

NASA Astrophysics Data System (ADS)

Michel, Volker; Simons, Frederik J.

2017-12-01

Slepian functions are orthogonal function systems that live on subdomains (for example, geographical regions on the Earth’s surface, or bandlimited portions of the entire spectrum). They have been firmly established as a useful tool for the synthesis and analysis of localized (concentrated or confined) signals, and for the modeling and inversion of noise-contaminated data that are only regionally available or only of regional interest. In this paper, we consider a general abstract setup for inverse problems represented by a linear and compact operator between Hilbert spaces with a known singular-value decomposition (svd). In practice, such an svd is often only given for the case of a global expansion of the data (e.g. on the whole sphere) but not for regional data distributions. We show that, in either case, Slepian functions (associated to an arbitrarily prescribed region and the given compact operator) can be determined and applied to construct a regularization for the ill-posed regional inverse problem. Moreover, we describe an algorithm for constructing the Slepian basis via an algebraic eigenvalue problem. The obtained Slepian functions can be used to derive an svd for the combination of the regionalizing projection and the compact operator. As a result, standard regularization techniques relying on a known svd become applicable also to those inverse problems where the data are regionally given only. In particular, wavelet-based multiscale techniques can be used. An example for the latter case is elaborated theoretically and tested on two synthetic numerical examples.

Qualitatively Assessing Randomness in SVD Results

NASA Astrophysics Data System (ADS)

Lamb, K. W.; Miller, W. P.; Kalra, A.; Anderson, S.; Rodriguez, A.

2012-12-01

Singular Value Decomposition (SVD) is a powerful tool for identifying regions of significant co-variability between two spatially distributed datasets. SVD has been widely used in atmospheric research to define relationships between sea surface temperatures, geopotential height, wind, precipitation and streamflow data for myriad regions across the globe. A typical application for SVD is to identify leading climate drivers (as observed in the wind or pressure data) for a particular hydrologic response variable such as precipitation, streamflow, or soil moisture. One can also investigate the lagged relationship between a climate variable and the hydrologic response variable using SVD. When performing these studies it is important to limit the spatial bounds of the climate variable to reduce the chance of random co-variance relationships being identified. On the other hand, a climate region that is too small may ignore climate signals which have more than a statistical relationship to a hydrologic response variable. The proposed research seeks to identify a qualitative method of identifying random co-variability relationships between two data sets. The research identifies the heterogeneous correlation maps from several past results and compares these results with correlation maps produced using purely random and quasi-random climate data. The comparison identifies a methodology to determine if a particular region on a correlation map may be explained by a physical mechanism or is simply statistical chance.

Spherical Harmonic Analysis of Particle Velocity Distribution Function: Comparison of Moments and Anisotropies using Cluster Data

NASA Technical Reports Server (NTRS)

Gurgiolo, Chris; Vinas, Adolfo F.

2009-01-01

This paper presents a spherical harmonic analysis of the plasma velocity distribution function using high-angular, energy, and time resolution Cluster data obtained from the PEACE spectrometer instrument to demonstrate how this analysis models the particle distribution function and its moments and anisotropies. The results show that spherical harmonic analysis produced a robust physical representation model of the velocity distribution function, resolving the main features of the measured distributions. From the spherical harmonic analysis, a minimum set of nine spectral coefficients was obtained from which the moment (up to the heat flux), anisotropy, and asymmetry calculations of the velocity distribution function were obtained. The spherical harmonic method provides a potentially effective "compression" technique that can be easily carried out onboard a spacecraft to determine the moments and anisotropies of the particle velocity distribution function for any species. These calculations were implemented using three different approaches, namely, the standard traditional integration, the spherical harmonic (SPH) spectral coefficients integration, and the singular value decomposition (SVD) on the spherical harmonic methods. A comparison among the various methods shows that both SPH and SVD approaches provide remarkable agreement with the standard moment integration method.

Robust image watermarking using DWT and SVD for copyright protection

NASA Astrophysics Data System (ADS)

Harjito, Bambang; Suryani, Esti

2017-02-01

The Objective of this paper is proposed a robust combined Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The RGB image is called a cover medium, and watermark image is converted into gray scale. Then, they are transformed using DWT so that they can be split into several subbands, namely sub-band LL2, LH2, HL2. The watermark image embeds into the cover medium on sub-band LL2. This scheme aims to obtain the higher robustness level than the previous method which performs of SVD matrix factorization image for copyright protection. The experiment results show that the proposed method has robustness against several image processing attacks such as Gaussian, Poisson and Salt and Pepper Noise. In these attacks, noise has average Normalized Correlation (NC) values of 0.574863 0.889784, 0.889782 respectively. The watermark image can be detected and extracted.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

Compound matrices

NASA Astrophysics Data System (ADS)

Kravvaritis, Christos; Mitrouli, Marilena

2009-02-01

This paper studies the possibility to calculate efficiently compounds of real matrices which have a special form or structure. The usefulness of such an effort lies in the fact that the computation of compound matrices, which is generally noneffective due to its high complexity, is encountered in several applications. A new approach for computing the Singular Value Decompositions (SVD's) of the compounds of a matrix is proposed by establishing the equality (up to a permutation) between the compounds of the SVD of a matrix and the SVD's of the compounds of the matrix. The superiority of the new idea over the standard method is demonstrated. Similar approaches with some limitations can be adopted for other matrix factorizations, too. Furthermore, formulas for the n - 1 compounds of Hadamard matrices are derived, which dodge the strenuous computations of the respective numerous large determinants. Finally, a combinatorial counting technique for finding the compounds of diagonal matrices is illustrated.

The Research on Denoising of SAR Image Based on Improved K-SVD Algorithm

NASA Astrophysics Data System (ADS)

Tan, Linglong; Li, Changkai; Wang, Yueqin

2018-04-01

SAR images often receive noise interference in the process of acquisition and transmission, which can greatly reduce the quality of images and cause great difficulties for image processing. The existing complete DCT dictionary algorithm is fast in processing speed, but its denoising effect is poor. In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure is introduced in detail. The dictionary has a compact representation and can effectively train the image signal. Then, the sparse dictionary is trained by K-SVD algorithm according to the sparse representation of the dictionary. The algorithm has more advantages in high dimensional data processing. Experimental results show that the proposed algorithm can remove the speckle noise more effectively than the complete DCT dictionary and retain the edge details better.

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

PubMed

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP effects (SNP-BLUP model). When reducing marker density from WGS data to 30 K, SNP-BLUP tended to yield the highest accuracies, at least in the short term. Based on SVD of the genotype matrix, we developed a direct method for the calculation of BayesC estimates of marker effects. Although SVD- and MCMC-based marker effects differed slightly, their prediction accuracies were similar. Assuming that the SVD of the marker genotype matrix is already performed for other reasons (e.g. for SNP-BLUP), computation times for the BayesC predictions were comparable to those of SNP-BLUP.

Precision PEP-II optics measurement with an SVD-enhanced Least-Square fitting

NASA Astrophysics Data System (ADS)

Yan, Y. T.; Cai, Y.

2006-03-01

A singular value decomposition (SVD)-enhanced Least-Square fitting technique is discussed. By automatic identifying, ordering, and selecting dominant SVD modes of the derivative matrix that responds to the variations of the variables, the converging process of the Least-Square fitting is significantly enhanced. Thus the fitting speed can be fast enough for a fairly large system. This technique has been successfully applied to precision PEP-II optics measurement in which we determine all quadrupole strengths (both normal and skew components) and sextupole feed-downs as well as all BPM gains and BPM cross-plane couplings through Least-Square fitting of the phase advances and the Local Green's functions as well as the coupling ellipses among BPMs. The local Green's functions are specified by 4 local transfer matrix components R12, R34, R32, R14. These measurable quantities (the Green's functions, the phase advances and the coupling ellipse tilt angles and axis ratios) are obtained by analyzing turn-by-turn Beam Position Monitor (BPM) data with a high-resolution model-independent analysis (MIA). Once all of the quadrupoles and sextupole feed-downs are determined, we obtain a computer virtual accelerator which matches the real accelerator in linear optics. Thus, beta functions, linear coupling parameters, and interaction point (IP) optics characteristics can be measured and displayed.

Unitary Operators on the Document Space.

ERIC Educational Resources Information Center

Hoenkamp, Eduard

2003-01-01

Discusses latent semantic indexing (LSI) that would allow search engines to reduce the dimension of the document space by mapping it into a space spanned by conceptual indices. Topics include vector space models; singular value decomposition (SVD); unitary operators; the Haar transform; and new algorithms. (Author/LRW)

Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis

NASA Astrophysics Data System (ADS)

Yang, Honggang; Lin, Huibin; Ding, Kang

2018-05-01

The performance of sparse features extraction by commonly used K-Singular Value Decomposition (K-SVD) method depends largely on the signal segment selected in rolling bearing diagnosis, furthermore, the calculating speed is relatively slow and the dictionary becomes so redundant when the fault signal is relatively long. A new sliding window denoising K-SVD (SWD-KSVD) method is proposed, which uses only one small segment of time domain signal containing impacts to perform sliding window dictionary learning and select an optimal pattern with oscillating information of the rolling bearing fault according to a maximum variance principle. An inner product operation between the optimal pattern and the whole fault signal is performed to enhance the characteristic of the impacts' occurrence moments. Lastly, the signal is reconstructed at peak points of the inner product to realize the extraction of the rolling bearing fault features. Both simulation and experiments verify that the method could extract the fault features effectively.

Fast analytical spectral filtering methods for magnetic resonance perfusion quantification.

PubMed

Reddy, Kasireddy V; Mitra, Abhishek; Yalavarthy, Phaneendra K

2016-08-01

The deconvolution in the perfusion weighted imaging (PWI) plays an important role in quantifying the MR perfusion parameters. The PWI application to stroke and brain tumor studies has become a standard clinical practice. The standard approach for this deconvolution is oscillatory-limited singular value decomposition (oSVD) and frequency domain deconvolution (FDD). The FDD is widely recognized as the fastest approach currently available for deconvolution of MR perfusion data. In this work, two fast deconvolution methods (namely analytical fourier filtering and analytical showalter spectral filtering) are proposed. Through systematic evaluation, the proposed methods are shown to be computationally efficient and quantitatively accurate compared to FDD and oSVD.

Effect of background correction on peak detection and quantification in online comprehensive two-dimensional liquid chromatography using diode array detection.

PubMed

Allen, Robert C; John, Mallory G; Rutan, Sarah C; Filgueira, Marcelo R; Carr, Peter W

2012-09-07

A singular value decomposition-based background correction (SVD-BC) technique is proposed for the reduction of background contributions in online comprehensive two-dimensional liquid chromatography (LC×LC) data. The SVD-BC technique was compared to simply subtracting a blank chromatogram from a sample chromatogram and to a previously reported background correction technique for one dimensional chromatography, which uses an asymmetric weighted least squares (AWLS) approach. AWLS was the only background correction technique to completely remove the background artifacts from the samples as evaluated by visual inspection. However, the SVD-BC technique greatly reduced or eliminated the background artifacts as well and preserved the peak intensity better than AWLS. The loss in peak intensity by AWLS resulted in lower peak counts at the detection thresholds established using standards samples. However, the SVD-BC technique was found to introduce noise which led to detection of false peaks at the lower detection thresholds. As a result, the AWLS technique gave more precise peak counts than the SVD-BC technique, particularly at the lower detection thresholds. While the AWLS technique resulted in more consistent percent residual standard deviation values, a statistical improvement in peak quantification after background correction was not found regardless of the background correction technique used. Copyright © 2012 Elsevier B.V. All rights reserved.

Feedback process responsible for intermodel diversity of ENSO variability

NASA Astrophysics Data System (ADS)

An, Soon-Il; Heo, Eun Sook; Kim, Seon Tae

2017-05-01

The origin of the intermodel diversity of the El Niño-Southern Oscillation (ENSO) variability is investigated by applying a singular value decomposition (SVD) analysis between the intermodel tropical Pacific sea surface temperature anomalies (SSTA) variance and the intermodel ENSO stability index (BJ index). The first SVD mode features an ENSO-like pattern for the intermodel SSTA variance (74% of total variance) and the dominant thermocline feedback (TH) for the BJ index (51%). Intermodel TH is mainly modified by the intermodel sensitivity of the zonal thermocline gradient response to zonal winds over the equatorial Pacific (βh), and the intermodel βh is correlated higher with the intermodel off-equatorial wind stress curl anomalies than the equatorial zonal wind stress anomalies. Finally, the intermodel off-equatorial wind stress curl is associated with the meridional shape and intensity of ENSO-related wind patterns, which may cause a model-to-model difference in ENSO variability by influencing the off-equatorial oceanic Rossby wave response.

Background recovery via motion-based robust principal component analysis with matrix factorization

NASA Astrophysics Data System (ADS)

Pan, Peng; Wang, Yongli; Zhou, Mingyuan; Sun, Zhipeng; He, Guoping

2018-03-01

Background recovery is a key technique in video analysis, but it still suffers from many challenges, such as camouflage, lighting changes, and diverse types of image noise. Robust principal component analysis (RPCA), which aims to recover a low-rank matrix and a sparse matrix, is a general framework for background recovery. The nuclear norm is widely used as a convex surrogate for the rank function in RPCA, which requires computing the singular value decomposition (SVD), a task that is increasingly costly as matrix sizes and ranks increase. However, matrix factorization greatly reduces the dimension of the matrix for which the SVD must be computed. Motion information has been shown to improve low-rank matrix recovery in RPCA, but this method still finds it difficult to handle original video data sets because of its batch-mode formulation and implementation. Hence, in this paper, we propose a motion-assisted RPCA model with matrix factorization (FM-RPCA) for background recovery. Moreover, an efficient linear alternating direction method of multipliers with a matrix factorization (FL-ADM) algorithm is designed for solving the proposed FM-RPCA model. Experimental results illustrate that the method provides stable results and is more efficient than the current state-of-the-art algorithms.

Absorption spectrum analysis based on singular value decomposition for photoisomerization and photodegradation in organic dyes

NASA Astrophysics Data System (ADS)

Kawabe, Yutaka; Yoshikawa, Toshio; Chida, Toshifumi; Tada, Kazuhiro; Kawamoto, Masuki; Fujihara, Takashi; Sassa, Takafumi; Tsutsumi, Naoto

2015-10-01

In order to analyze the spectra of inseparable chemical mixtures, many mathematical methods have been developed to decompose them into the components relevant to species from series of spectral data obtained under different conditions. We formulated a method based on singular value decomposition (SVD) of linear algebra, and applied it to two example systems of organic dyes, being successful in reproducing absorption spectra assignable to cis/trans azocarbazole dyes from the spectral data after photoisomerization and to monomer/dimer of cyanine dyes from those during photodegaradation process. For the example of photoisomerization, polymer films containing the azocarbazole dyes were prepared, which have showed updatable holographic stereogram for real images with high performance. We made continuous monitoring of absorption spectrum after optical excitation and found that their spectral shapes varied slightly after the excitation and during recovery process, of which fact suggested the contribution from a generated photoisomer. Application of the method was successful to identify two spectral components due to trans and cis forms of azocarbazoles. Temporal evolution of their weight factors suggested important roles of long lifetimed cis states in azocarbazole derivatives. We also applied the method to the photodegradation of cyanine dyes doped in DNA-lipid complexes which have shown efficient and durable optical amplification and/or lasing under optical pumping. The same SVD method was successful in the extraction of two spectral components presumably due to monomer and H-type dimer. During the photodegradation process, absorption magnitude gradually decreased due to decomposition of molecules and their decaying rates strongly depended on the spectral components, suggesting that the long persistency of the dyes in DNA-complex related to weak tendency of aggregate formation.

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

PubMed

Zhang, Yi; Li, Peiyang; Zhu, Xuyang; Su, Steven W; Guo, Qing; Xu, Peng; Yao, Dezhong

2017-01-01

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.

Continuous analogues of matrix factorizations

PubMed Central

Townsend, Alex; Trefethen, Lloyd N.

2015-01-01

Analogues of singular value decomposition (SVD), QR, LU and Cholesky factorizations are presented for problems in which the usual discrete matrix is replaced by a ‘quasimatrix’, continuous in one dimension, or a ‘cmatrix’, continuous in both dimensions. Two challenges arise: the generalization of the notions of triangular structure and row and column pivoting to continuous variables (required in all cases except the SVD, and far from obvious), and the convergence of the infinite series that define the cmatrix factorizations. Our generalizations of triangularity and pivoting are based on a new notion of a ‘triangular quasimatrix’. Concerning convergence of the series, we prove theorems asserting convergence provided the functions involved are sufficiently smooth. PMID:25568618

Tmax Determined Using a Bayesian Estimation Deconvolution Algorithm Applied to Bolus Tracking Perfusion Imaging: A Digital Phantom Validation Study.

PubMed

Uwano, Ikuko; Sasaki, Makoto; Kudo, Kohsuke; Boutelier, Timothé; Kameda, Hiroyuki; Mori, Futoshi; Yamashita, Fumio

2017-01-10

The Bayesian estimation algorithm improves the precision of bolus tracking perfusion imaging. However, this algorithm cannot directly calculate Tmax, the time scale widely used to identify ischemic penumbra, because Tmax is a non-physiological, artificial index that reflects the tracer arrival delay (TD) and other parameters. We calculated Tmax from the TD and mean transit time (MTT) obtained by the Bayesian algorithm and determined its accuracy in comparison with Tmax obtained by singular value decomposition (SVD) algorithms. The TD and MTT maps were generated by the Bayesian algorithm applied to digital phantoms with time-concentration curves that reflected a range of values for various perfusion metrics using a global arterial input function. Tmax was calculated from the TD and MTT using constants obtained by a linear least-squares fit to Tmax obtained from the two SVD algorithms that showed the best benchmarks in a previous study. Correlations between the Tmax values obtained by the Bayesian and SVD methods were examined. The Bayesian algorithm yielded accurate TD and MTT values relative to the true values of the digital phantom. Tmax calculated from the TD and MTT values with the least-squares fit constants showed excellent correlation (Pearson's correlation coefficient = 0.99) and agreement (intraclass correlation coefficient = 0.99) with Tmax obtained from SVD algorithms. Quantitative analyses of Tmax values calculated from Bayesian-estimation algorithm-derived TD and MTT from a digital phantom correlated and agreed well with Tmax values determined using SVD algorithms.

Constraint elimination in dynamical systems

NASA Technical Reports Server (NTRS)

Singh, R. P.; Likins, P. W.

1989-01-01

Large space structures (LSSs) and other dynamical systems of current interest are often extremely complex assemblies of rigid and flexible bodies subjected to kinematical constraints. A formulation is presented for the governing equations of constrained multibody systems via the application of singular value decomposition (SVD). The resulting equations of motion are shown to be of minimum dimension.

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2004-05-05

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques. Copyright 2004 Wiley Periodicals, Inc.

Application of a sparse representation method using K-SVD to data compression of experimental ambient vibration data for SHM

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Kiremidjian, Anne S.

2011-04-01

This paper introduces a data compression method using the K-SVD algorithm and its application to experimental ambient vibration data for structural health monitoring purposes. Because many damage diagnosis algorithms that use system identification require vibration measurements of multiple locations, it is necessary to transmit long threads of data. In wireless sensor networks for structural health monitoring, however, data transmission is often a major source of battery consumption. Therefore, reducing the amount of data to transmit can significantly lengthen the battery life and reduce maintenance cost. The K-SVD algorithm was originally developed in information theory for sparse signal representation. This algorithm creates an optimal over-complete set of bases, referred to as a dictionary, using singular value decomposition (SVD) and represents the data as sparse linear combinations of these bases using the orthogonal matching pursuit (OMP) algorithm. Since ambient vibration data are stationary, we can segment them and represent each segment sparsely. Then only the dictionary and the sparse vectors of the coefficients need to be transmitted wirelessly for restoration of the original data. We applied this method to ambient vibration data measured from a four-story steel moment resisting frame. The results show that the method can compress the data efficiently and restore the data with very little error.

Using DEDICOM for completely unsupervised part-of-speech tagging.

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

Chew, Peter A.; Bader, Brett William; Rozovskaya, Alla

A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schuetze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to estimate emission- and transition-probability matrices which can thenmore » be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the correlation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging.« less

Remarkable link between projected uncertainties of Arctic sea-ice decline and winter Eurasian climate

NASA Astrophysics Data System (ADS)

Cheung, Hoffman H. N.; Keenlyside, Noel; Omrani, Nour-Eddine; Zhou, Wen

2018-01-01

We identify that the projected uncertainty of the pan-Arctic sea-ice concentration (SIC) is strongly coupled with the Eurasian circulation in the boreal winter (December-March; DJFM), based on a singular value decomposition (SVD) analysis of the forced response of 11 CMIP5 models. In the models showing a stronger sea-ice decline, the Polar cell becomes weaker and there is an anomalous increase in the sea level pressure (SLP) along 60°N, including the Urals-Siberia region and the Iceland low region. There is an accompanying weakening of both the midlatitude westerly winds and the Ferrell cell, where the SVD signals are also related to anomalous sea surface temperature warming in the midlatitude North Atlantic. In the Mediterranean region, the anomalous circulation response shows a decreasing SLP and increasing precipitation. The anomalous SLP responses over the Euro-Atlantic region project on to the negative North Atlantic Oscillation-like pattern. Altogether, pan-Arctic SIC decline could strongly impact the winter Eurasian climate, but we should be cautious about the causality of their linkage.

Detection of the secondary meridional circulation associated with the quasi-biennial oscillation

NASA Astrophysics Data System (ADS)

Ribera, P.; PeñA-Ortiz, C.; Garcia-Herrera, R.; Gallego, D.; Gimeno, L.; HernáNdez, E.

2004-09-01

The quasi-biennial oscillation (QBO) signal in stratospheric zonal and meridional wind, temperature, and geopotential height fields is analyzed based on the use of the National Centers for Environmental Prediction (NCEP) reanalysis (1958-2001). The multitaper method-singular value decomposition (MTM-SVD), a multivariate frequency domain analysis method, is used to detect significant and spatially coherent narrowband oscillations. The QBO is found as the most intense signal in the stratospheric zonal wind. Then, the MTM-SVD method is used to determine the patterns induced by the QBO at every stratospheric level and data field. The secondary meridional circulation associated with the QBO is identified in the obtained patterns. This circulation can be characterized by negative (positive) temperature anomalies associated with adiabatic rising (sinking) motions over zones of easterly (westerly) wind shear and over the subtropics and midlatitudes, while meridional convergence and divergence levels are found separated by a level of maximum zonal wind shear. These vertical and meridional motions form quasi-symmetric circulation cells over both hemispheres, though less intense in the Southern Hemisphere.

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

NASA Astrophysics Data System (ADS)

Gao, Yu-Fei; Gui, Guan; Cong, Xun-Chao; Yang, Yue; Zou, Yan-Bin; Wan, Qun

2017-12-01

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

Modified truncated randomized singular value decomposition (MTRSVD) algorithms for large scale discrete ill-posed problems with general-form regularization

NASA Astrophysics Data System (ADS)

Jia, Zhongxiao; Yang, Yanfei

2018-05-01

In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: subject to , where L is a regularization matrix. Our algorithms are inspired by the modified truncated singular value decomposition (MTSVD) method, which suits only for small to medium scale problems, and randomized SVD (RSVD) algorithms that generate good low rank approximations to A. We use rank-k truncated randomized SVD (TRSVD) approximations to A by truncating the rank- RSVD approximations to A, where q is an oversampling parameter. The resulting algorithms are called modified TRSVD (MTRSVD) methods. At every step, we use the LSQR algorithm to solve the resulting inner least squares problem, which is proved to become better conditioned as k increases so that LSQR converges faster. We present sharp bounds for the approximation accuracy of the RSVDs and TRSVDs for severely, moderately and mildly ill-posed problems, and substantially improve a known basic bound for TRSVD approximations. We prove how to choose the stopping tolerance for LSQR in order to guarantee that the computed and exact best regularized solutions have the same accuracy. Numerical experiments illustrate that the best regularized solutions by MTRSVD are as accurate as the ones by the truncated generalized singular value decomposition (TGSVD) algorithm, and at least as accurate as those by some existing truncated randomized generalized singular value decomposition (TRGSVD) algorithms. This work was supported in part by the National Science Foundation of China (Nos. 11771249 and 11371219).

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

PubMed

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-03-27

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K -nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Dynamic PET and Optical Imaging and Compartment Modeling using a Dual-labeled Cyclic RGD Peptide Probe

PubMed Central

Zhu, Lei; Guo, Ning; Li, Quanzheng; Ma, Ying; Jacboson, Orit; Lee, Seulki; Choi, Hak Soo; Mansfield, James R.; Niu, Gang; Chen, Xiaoyuan

2012-01-01

Purpose: The aim of this study is to determine if dynamic optical imaging could provide comparable kinetic parameters to that of dynamic PET imaging by a near-infrared dye/64Cu dual-labeled cyclic RGD peptide. Methods: The integrin αvβ3 binding RGD peptide was conjugated with a macrocyclic chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for copper labeling and PET imaging and a near-infrared dye ZW-1 for optical imaging. The in vitro biological activity of RGD-C(DOTA)-ZW-1 was characterized by cell staining and receptor binding assay. Sixty-min dynamic PET and optical imaging were acquired on a MDA-MB-435 tumor model. Singular value decomposition (SVD) method was applied to compute the dynamic optical signal from the two-dimensional optical projection images. Compartment models were used to quantitatively analyze and compare the dynamic optical and PET data. Results: The dual-labeled probe 64Cu-RGD-C(DOTA)-ZW-1 showed integrin specific binding in vitro and in vivo. The binding potential (Bp) derived from dynamic optical imaging (1.762 ± 0.020) is comparable to that from dynamic PET (1.752 ± 0.026). Conclusion: The signal un-mixing process using SVD improved the accuracy of kinetic modeling of 2D dynamic optical data. Our results demonstrate that 2D dynamic optical imaging with SVD analysis could achieve comparable quantitative results as dynamic PET imaging in preclinical xenograft models. PMID:22916074

A collaborative filtering-based approach to biomedical knowledge discovery.

PubMed

Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

2018-02-15

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Dynamic PET and Optical Imaging and Compartment Modeling using a Dual-labeled Cyclic RGD Peptide Probe.

PubMed

Zhu, Lei; Guo, Ning; Li, Quanzheng; Ma, Ying; Jacboson, Orit; Lee, Seulki; Choi, Hak Soo; Mansfield, James R; Niu, Gang; Chen, Xiaoyuan

2012-01-01

The aim of this study is to determine if dynamic optical imaging could provide comparable kinetic parameters to that of dynamic PET imaging by a near-infrared dye/(64)Cu dual-labeled cyclic RGD peptide. The integrin α(v)β(3) binding RGD peptide was conjugated with a macrocyclic chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for copper labeling and PET imaging and a near-infrared dye ZW-1 for optical imaging. The in vitro biological activity of RGD-C(DOTA)-ZW-1 was characterized by cell staining and receptor binding assay. Sixty-min dynamic PET and optical imaging were acquired on a MDA-MB-435 tumor model. Singular value decomposition (SVD) method was applied to compute the dynamic optical signal from the two-dimensional optical projection images. Compartment models were used to quantitatively analyze and compare the dynamic optical and PET data. The dual-labeled probe (64)Cu-RGD-C(DOTA)-ZW-1 showed integrin specific binding in vitro and in vivo. The binding potential (Bp) derived from dynamic optical imaging (1.762 ± 0.020) is comparable to that from dynamic PET (1.752 ± 0.026). The signal un-mixing process using SVD improved the accuracy of kinetic modeling of 2D dynamic optical data. Our results demonstrate that 2D dynamic optical imaging with SVD analysis could achieve comparable quantitative results as dynamic PET imaging in preclinical xenograft models.

Singular value decomposition utilizing parallel algorithms on graphical processors

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

Kotas, Charlotte W; Barhen, Jacob

2011-01-01

One of the current challenges in underwater acoustic array signal processing is the detection of quiet targets in the presence of noise. In order to enable robust detection, one of the key processing steps requires data and replica whitening. This, in turn, involves the eigen-decomposition of the sample spectral matrix, Cx = 1/K xKX(k)XH(k) where X(k) denotes a single frequency snapshot with an element for each element of the array. By employing the singular value decomposition (SVD) method, the eigenvectors and eigenvalues can be determined directly from the data without computing the sample covariance matrix, reducing the computational requirements formore » a given level of accuracy (van Trees, Optimum Array Processing). (Recall that the SVD of a complex matrix A involves determining V, , and U such that A = U VH where U and V are orthonormal and is a positive, real, diagonal matrix containing the singular values of A. U and V are the eigenvectors of AAH and AHA, respectively, while the singular values are the square roots of the eigenvalues of AAH.) Because it is desirable to be able to compute these quantities in real time, an efficient technique for computing the SVD is vital. In addition, emerging multicore processors like graphical processing units (GPUs) are bringing parallel processing capabilities to an ever increasing number of users. Since the computational tasks involved in array signal processing are well suited for parallelization, it is expected that these computations will be implemented using GPUs as soon as users have the necessary computational tools available to them. Thus, it is important to have an SVD algorithm that is suitable for these processors. This work explores the effectiveness of two different parallel SVD implementations on an NVIDIA Tesla C2050 GPU (14 multiprocessors, 32 cores per multiprocessor, 1.15 GHz clock - peed). The first algorithm is based on a two-step algorithm which bidiagonalizes the matrix using Householder transformations, and then diagonalizes the intermediate bidiagonal matrix through implicit QR shifts. This is similar to that implemented for real matrices by Lahabar and Narayanan ("Singular Value Decomposition on GPU using CUDA", IEEE International Parallel Distributed Processing Symposium 2009). The implementation is done in a hybrid manner, with the bidiagonalization stage done using the GPU while the diagonalization stage is done using the CPU, with the GPU used to update the U and V matrices. The second algorithm is based on a one-sided Jacobi scheme utilizing a sequence of pair-wise column orthogonalizations such that A is replaced by AV until the resulting matrix is sufficiently orthogonal (that is, equal to U ). V is obtained from the sequence of orthogonalizations, while can be found from the square root of the diagonal elements of AH A and, once is known, U can be found from column scaling the resulting matrix. These implementations utilize CUDA Fortran and NVIDIA's CUB LAS library. The primary goal of this study is to quantify the comparative performance of these two techniques against themselves and other standard implementations (for example, MATLAB). Considering that there is significant overhead associated with transferring data to the GPU and with synchronization between the GPU and the host CPU, it is also important to understand when it is worthwhile to use the GPU in terms of the matrix size and number of concurrent SVDs to be calculated.« less

Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations.

PubMed

Mandke, Kanad; Meier, Jil; Brookes, Matthew J; O'Dea, Reuben D; Van Mieghem, Piet; Stam, Cornelis J; Hillebrand, Arjan; Tewarie, Prejaas

2018-02-01

There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons. Copyright © 2017 Elsevier Inc. All rights reserved.

Video quality assesment using M-SVD

NASA Astrophysics Data System (ADS)

Tao, Peining; Eskicioglu, Ahmet M.

2007-01-01

Objective video quality measurement is a challenging problem in a variety of video processing application ranging from lossy compression to printing. An ideal video quality measure should be able to mimic the human observer. We present a new video quality measure, M-SVD, to evaluate distorted video sequences based on singular value decomposition. A computationally efficient approach is developed for full-reference (FR) video quality assessment. This measure is tested on the Video Quality Experts Group (VQEG) phase I FR-TV test data set. Our experiments show the graphical measure displays the amount of distortion as well as the distribution of error in all frames of the video sequence while the numerical measure has a good correlation with perceived video quality outperforms PSNR and other objective measures by a clear margin.

The comparison between SVD-DCT and SVD-DWT digital image watermarking

NASA Astrophysics Data System (ADS)

Wira Handito, Kurniawan; Fauzi, Zulfikar; Aminy Ma’ruf, Firda; Widyaningrum, Tanti; Muslim Lhaksmana, Kemas

2018-03-01

With internet, anyone can publish their creation into digital data simply, inexpensively, and absolutely easy to be accessed by everyone. However, the problem appears when anyone else claims that the creation is their property or modifies some part of that creation. It causes necessary protection of copyrights; one of the examples is with watermarking method in digital image. The application of watermarking technique on digital data, especially on image, enables total invisibility if inserted in carrier image. Carrier image will not undergo any decrease of quality and also the inserted image will not be affected by attack. In this paper, watermarking will be implemented on digital image using Singular Value Decomposition based on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) by expectation in good performance of watermarking result. In this case, trade-off happen between invisibility and robustness of image watermarking. In embedding process, image watermarking has a good quality for scaling factor < 0.1. The quality of image watermarking in decomposition level 3 is better than level 2 and level 1. Embedding watermark in low-frequency is robust to Gaussian blur attack, rescale, and JPEG compression, but in high-frequency is robust to Gaussian noise.

Data Unfolding with Wiener-SVD Method

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

Tang, W.; Li, X.; Qian, X.

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Data Unfolding with Wiener-SVD Method

DOE PAGES

Tang, W.; Li, X.; Qian, X.; ...

2017-10-04

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt

USGS Publications Warehouse

Hill, Mary C.

2010-01-01

Doherty and Hunt (2009) present important ideas for first-order-second moment sensitivity analysis, but five issues are discussed in this comment. First, considering the composite-scaled sensitivity (CSS) jointly with parameter correlation coefficients (PCC) in a CSS/PCC analysis addresses the difficulties with CSS mentioned in the introduction. Second, their new parameter identifiability statistic actually is likely to do a poor job of parameter identifiability in common situations. The statistic instead performs the very useful role of showing how model parameters are included in the estimated singular value decomposition (SVD) parameters. Its close relation to CSS is shown. Third, the idea from p. 125 that a suitable truncation point for SVD parameters can be identified using the prediction variance is challenged using results from Moore and Doherty (2005). Fourth, the relative error reduction statistic of Doherty and Hunt is shown to belong to an emerging set of statistics here named perturbed calculated variance statistics. Finally, the perturbed calculated variance statistics OPR and PPR mentioned on p. 121 are shown to explicitly include the parameter null-space component of uncertainty. Indeed, OPR and PPR results that account for null-space uncertainty have appeared in the literature since 2000.

Characterization of anticancer agents by their growth inhibitory activity and relationships to mechanism of action and structure.

PubMed

Keskin, O; Bahar, I; Jernigan, R L; Beutler, J A; Shoemaker, R H; Sausville, E A; Covell, D G

2000-04-01

An analysis of the growth inhibitory potency of 122 anticancer agents available from the National Cancer Institute anticancer drug screen is presented. Methods of singular value decomposition (SVD) were applied to determine the matrix of distances between all compounds. These SVD-derived dissimilarity distances were used to cluster compounds that exhibit similar tumor growth inhibitory activity patterns against 60 human cancer cell lines. Cluster analysis divides the 122 standard agents into 25 statistically distinct groups. The first eight groups include structurally diverse compounds with reactive functionalities that act as DNA-damaging agents while the remaining 17 groups include compounds that inhibit nucleic acid biosynthesis and mitosis. Examination of the average activity patterns across the 60 tumor cell lines reveals unique 'fingerprints' associated with each group. A diverse set of structural features are observed for compounds within these groups, with frequent occurrences of strong within-group structural similarities. Clustering of cell types by their response to the 122 anticancer agents divides the 60 cell types into 21 groups. The strongest within-panel groupings were found for the renal, leukemia and ovarian cell panels. These results contribute to the basis for comparisons between log(GI(50)) screening patterns of the 122 anticancer agents and additional tested compounds.

An R-peak detection method that uses an SVD filter and a search back system.

PubMed

Jung, Woo-Hyuk; Lee, Sang-Goog

2012-12-01

In this paper, we present a method for detecting the R-peak of an ECG signal by using an singular value decomposition (SVD) filter and a search back system. The ECG signal was detected in two phases: the pre-processing phase and the decision phase. The pre-processing phase consisted of the stages for the SVD filter, Butterworth High Pass Filter (HPF), moving average (MA), and squaring, whereas the decision phase consisted of a single stage that detected the R-peak. In the pre-processing phase, the SVD filter removed noise while the Butterworth HPF eliminated baseline wander. The MA removed the remaining noise of the signal that had gone through the SVD filter to make the signal smooth, and squaring played a role in strengthening the signal. In the decision phase, the threshold was used to set the interval before detecting the R-peak. When the latest R-R interval (RRI), suggested by Hamilton et al., was greater than 150% of the previous RRI, the method of detecting the R-peak in such an interval was modified to be 150% or greater than the smallest interval of the two most latest RRIs. When the modified search back system was used, the error rate of the peak detection decreased to 0.29%, compared to 1.34% when the modified search back system was not used. Consequently, the sensitivity was 99.47%, the positive predictivity was 99.47%, and the detection error was 1.05%. Furthermore, the quality of the signal in data with a substantial amount of noise was improved, and thus, the R-peak was detected effectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Lifestyle-related disease in Crohn’s disease: Relapse prevention by a semi-vegetarian diet

PubMed Central

Chiba, Mitsuro; Abe, Toru; Tsuda, Hidehiko; Sugawara, Takeshi; Tsuda, Satoko; Tozawa, Haruhiko; Fujiwara, Katsuhiko; Imai, Hideo

2010-01-01

AIM: To investigate whether semi-vegetarian diet (SVD) has a preventive effect against relapse of Crohn’s disease (CD) in patients who have achieved remission, who are a high-risk group for relapse. METHODS: A prospective, single center, 2-year clinical trial was conducted. Twenty-two adult CD patients who achieved clinical remission either medically (n = 17) or surgically (n = 5) and consumed an SVD during hospitalization were advised to continue with an SVD and avoid known high-risk foods for inflammatory bowel disease. The primary endpoint was clinical relapse defined as the appearance of active symptoms of CD. Kaplan-Meier survival analysis was used to calculate the cumulative proportion of patients who had a relapse. A 2-year analysis of relapse rates of patients who followed an SVD and those who did not (an omnivorous diet group) was undertaken. RESULTS: SVD was continued by 16 patients (compliance 73%). Remission was maintained in 15 of 16 patients (94%) in the SVD group vs two of six (33%) in the omnivorous group. Remission rate with SVD was 100% at 1 year and 92% at 2 years. SVD showed significant prevention in the time to relapse compared to that in the omnivorous group (P = 0.0003, log rank test). The concentration of C-reactive protein was normal at the final visit in more than half of the patients in remission who were taking an SVD, who maintained remission during the study (9/15; 60%), who terminated follow-up (8/12; 67%), and who completed 2 years follow-up (7/10; 70%). There was no untoward effect of SVD. CONCLUSION: SVD was highly effective in preventing relapse in CD. PMID:20503448

A Framework for Propagation of Uncertainties in the Kepler Data Analysis Pipeline

NASA Technical Reports Server (NTRS)

Clarke, Bruce D.; Allen, Christopher; Bryson, Stephen T.; Caldwell, Douglas A.; Chandrasekaran, Hema; Cote, Miles T.; Girouard, Forrest; Jenkins, Jon M.; Klaus, Todd C.; Li, Jie;

Development of an Efficient Binaural Simulation for the Analysis of Structural Acoustic Data

NASA Technical Reports Server (NTRS)

Johnson, Marty E.; Lalime, Aimee L.; Grosveld, Ferdinand W.; Rizzi, Stephen A.; Sullivan, Brenda M.

2003-01-01

Applying binaural simulation techniques to structural acoustic data can be very computationally intensive as the number of discrete noise sources can be very large. Typically, Head Related Transfer Functions (HRTFs) are used to individually filter the signals from each of the sources in the acoustic field. Therefore, creating a binaural simulation implies the use of potentially hundreds of real time filters. This paper details two methods of reducing the number of real-time computations required by: (i) using the singular value decomposition (SVD) to reduce the complexity of the HRTFs by breaking them into dominant singular values and vectors and (ii) by using equivalent source reduction (ESR) to reduce the number of sources to be analyzed in real-time by replacing sources on the scale of a structural wavelength with sources on the scale of an acoustic wavelength. The ESR and SVD reduction methods can be combined to provide an estimated computation time reduction of 99.4% for the structural acoustic data tested. In addition, preliminary tests have shown that there is a 97% correlation between the results of the combined reduction methods and the results found with the current binaural simulation techniques

Rapid determination of particle velocity from space-time images using the Radon transform

PubMed Central

Drew, Patrick J.; Blinder, Pablo; Cauwenberghs, Gert; Shih, Andy Y.; Kleinfeld, David

2016-01-01

Laser-scanning methods are a means to observe streaming particles, such as the flow of red blood cells in a blood vessel. Typically, particle velocity is extracted from images formed from cyclically repeated line-scan data that is obtained along the center-line of the vessel; motion leads to streaks whose angle is a function of the velocity. Past methods made use of shearing or rotation of the images and a Singular Value Decomposition (SVD) to automatically estimate the average velocity in a temporal window of data. Here we present an alternative method that makes use of the Radon transform to calculate the velocity of streaming particles. We show that this method is over an order of magnitude faster than the SVD-based algorithm and is more robust to noise. PMID:19459038

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

PubMed Central

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-01-01

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. PMID:28346385

East Asian Summer Monsoon Rainfall: A Historical Perspective of the 1998 Flood over Yangtze River

NASA Technical Reports Server (NTRS)

Weng, H.-Y.; Lau, K.-M.

1999-01-01

One of the main factors that might have caused the disastrous flood in China during 1998 summer is long-term variations that include a trend indicating increasing monsoon rainfall over the Yangtze River Valley. China's 160-station monthly rainfall anomaly for the summers of 1955-98 is analyzed for exploring such long-term variations. Singular value decomposition (SVD) between the summer rainfall and the global sea surface temperature (SST) anomalies reveals that the rainfall over Yangtze River Valley is closely related to global and regional SST variabilities at both interannual and interdecadal timescales. SVD1 mode links the above normal rainfall condition in central China to an El Nino-like SSTA distribution, varying on interannual timescale modified by a trend during the period. SVD3 mode links positive rainfall anomaly in Yangtze River Valley to the warm SST anomaly in the subtropical western Pacific, varying on interannual timescales modified by interdecadal timescales. This link tends to be stronger when the Nino3 area becomes colder and the western subtropical Pacific becomes warmer. The 1998 summer is a transition season when the 1997/98 El Nino event was in its decaying phase, and the SST in the Nino3 area emerged below normal anomaly while the subtropical western Pacific SST above normal. Thus, the first and third SVD modes become dominant in 1998 summer, favoring more Asian summer monsoon rainfall over the Yangtze River Valley.

Frequency, Risk Factors, and Outcome of Coexistent Small Vessel Disease and Intracranial Arterial Stenosis: Results From the Stenting and Aggressive Medical Management for Preventing Recurrent Stroke in Intracranial Stenosis (SAMMPRIS) Trial.

PubMed

Kwon, Hyung-Min; Lynn, Michael J; Turan, Tanya N; Derdeyn, Colin P; Fiorella, David; Lane, Bethany F; Montgomery, Jean; Janis, L Scott; Rumboldt, Zoran; Chimowitz, Marc I

2016-01-01

Intracranial arterial stenosis (ICAS) and small vessel disease (SVD) may coexist. There are limited data on the frequency and risk factors for coexistent SVD and the effect of SVD on stroke recurrence in patients receiving medical treatment for ICAS. To investigate the frequency and risk factors for SVD and the effect of SVD on stroke recurrence in patients with ICAS. A post hoc analysis of the Stenting and Aggressive Medical Management for Preventing Recurrent Stroke in Intracranial Stenosis (SAMMPRIS) study, a prospective, multicenter clinical trial. Among 451 participants, 313 (69.4%) had baseline brain magnetic resonance imaging scans read centrally for SVD that was defined by any of the following: old lacunar infarction, grade 2 to 3 on the Fazekas scale (for high-grade white matter hyperintensities), or microbleeds. Patient enrollment in SAMMPRIS began November 25, 2008, and follow-up ended on April 30, 2013. Data analysis for the present study was performed from May 13, 2014, to July 29, 2015. Risk factors in patients with vs without SVD and the association between SVD and other baseline risk factors with any ischemic stroke and ischemic stroke in the territory of the stenotic artery determined using proportional hazards regression. Of 313 patients, 155 individuals (49.5%) had SVD noted on baseline magnetic resonance imaging. Variables that were significantly higher in patients with SVD, reported as mean (SD), included age, 63.5 (10.5) years (P < .001), systolic blood pressure, 149 (22) mm Hg (P < .001), glucose level, 130 (50) mg/dL (P = .03), and lower Montreal Cognitive Assessment scores (median, ≥24 [interquartile range, 20-26]; P = .02).Other significant variables were the number of patients with diabetes mellitus (88 of 155 [56.8%]; P = .003), coronary artery disease (46 [29.7%]; P = .004), stroke before the qualifying event (59 [38.1%]; P < .001), old infarct in the territory of the stenotic intracranial artery (88 [56.8%]; P < .001), and receiving antithrombotic therapy at the time of the qualifying event (109 [70.3%]; P = .005). The association between SVD and any ischemic stroke was nearly significant in the direction of a higher risk (18 [23.7%]); P = .07) for patients with SVD. On bivariate analysis, SVD was not associated with an increased risk on multivariable analyses (hazard ratio, 1.7 [95% CI, 0.8-3.8]; P = .20). In addition, SVD was not associated with an increased risk of stroke in the territory on either bivariate or multivariable analyses. Although SVD is common in patients with ICAS, the presence of SVD on baseline magnetic resonance imaging is not independently associated with an increased risk of stroke in patients with ICAS. clinicaltrials.gov Identifier: NCT00576693.

Comparison of quantitative myocardial perfusion imaging CT to fluorescent microsphere-based flow from high-resolution cryo-images

NASA Astrophysics Data System (ADS)

Eck, Brendan L.; Fahmi, Rachid; Levi, Jacob; Fares, Anas; Wu, Hao; Li, Yuemeng; Vembar, Mani; Dhanantwari, Amar; Bezerra, Hiram G.; Wilson, David L.

2016-03-01

Myocardial perfusion imaging using CT (MPI-CT) has the potential to provide quantitative measures of myocardial blood flow (MBF) which can aid the diagnosis of coronary artery disease. We evaluated the quantitative accuracy of MPI-CT in a porcine model of balloon-induced LAD coronary artery ischemia guided by fractional flow reserve (FFR). We quantified MBF at baseline (FFR=1.0) and under moderate ischemia (FFR=0.7) using MPI-CT and compared to fluorescent microsphere-based MBF from high-resolution cryo-images. Dynamic, contrast-enhanced CT images were obtained using a spectral detector CT (Philips Healthcare). Projection-based mono-energetic images were reconstructed and processed to obtain MBF. Three MBF quantification approaches were evaluated: singular value decomposition (SVD) with fixed Tikhonov regularization (ThSVD), SVD with regularization determined by the L-Curve criterion (LSVD), and Johnson-Wilson parameter estimation (JW). The three approaches over-estimated MBF compared to cryo-images. JW produced the most accurate MBF, with average error 33.3+/-19.2mL/min/100g, whereas LSVD and ThSVD had greater over-estimation, 59.5+/-28.3mL/min/100g and 78.3+/-25.6 mL/min/100g, respectively. Relative blood flow as assessed by a flow ratio of LAD-to-remote myocardium was strongly correlated between JW and cryo-imaging, with R2=0.97, compared to R2=0.88 and 0.78 for LSVD and ThSVD, respectively. We assessed tissue impulse response functions (IRFs) from each approach for sources of error. While JW was constrained to physiologic solutions, both LSVD and ThSVD produced IRFs with non-physiologic properties due to noise. The L-curve provided noise-adaptive regularization but did not eliminate non-physiologic IRF properties or optimize for MBF accuracy. These findings suggest that model-based MPI-CT approaches may be more appropriate for quantitative MBF estimation and that cryo-imaging can support the development of MPI-CT by providing spatial distributions of MBF.

RF tomography of metallic objects in free space: preliminary results

NASA Astrophysics Data System (ADS)

Li, Jia; Ewing, Robert L.; Berdanier, Charles; Baker, Christopher

2015-05-01

RF tomography has great potential in defense and homeland security applications. A distributed sensing research facility is under development at Air Force Research Lab. To develop a RF tomographic imaging system for the facility, preliminary experiments have been performed in an indoor range with 12 radar sensors distributed on a circle of 3m radius. Ultra-wideband pulses are used to illuminate single and multiple metallic targets. The echoes received by distributed sensors were processed and combined for tomography reconstruction. Traditional matched filter algorithm and truncated singular value decomposition (SVD) algorithm are compared in terms of their complexity, accuracy, and suitability for distributed processing. A new algorithm is proposed for shape reconstruction, which jointly estimates the object boundary and scatter points on the waveform's propagation path. The results show that the new algorithm allows accurate reconstruction of object shape, which is not available through the matched filter and truncated SVD algorithms.

a Unified Matrix Polynomial Approach to Modal Identification

NASA Astrophysics Data System (ADS)

Allemang, R. J.; Brown, D. L.

1998-04-01

One important current focus of modal identification is a reformulation of modal parameter estimation algorithms into a single, consistent mathematical formulation with a corresponding set of definitions and unifying concepts. Particularly, a matrix polynomial approach is used to unify the presentation with respect to current algorithms such as the least-squares complex exponential (LSCE), the polyreference time domain (PTD), Ibrahim time domain (ITD), eigensystem realization algorithm (ERA), rational fraction polynomial (RFP), polyreference frequency domain (PFD) and the complex mode indication function (CMIF) methods. Using this unified matrix polynomial approach (UMPA) allows a discussion of the similarities and differences of the commonly used methods. the use of least squares (LS), total least squares (TLS), double least squares (DLS) and singular value decomposition (SVD) methods is discussed in order to take advantage of redundant measurement data. Eigenvalue and SVD transformation methods are utilized to reduce the effective size of the resulting eigenvalue-eigenvector problem as well.

Time evolution of two holes in t - J chains with anisotropic couplings

NASA Astrophysics Data System (ADS)

Manmana, Salvatore R.; Thyen, Holger; Köhler, Thomas; Kramer, Stephan C.

Using time-dependent Matrix Product State (MPS) methods we study the real-time evolution of hole-excitations in t-J chains close to filling n = 1 . The dynamics in 'standard' t - J chains with SU(2) invariant spin couplings is compared to the one when introducing anisotropic, XXZ-type spin interactions as realizable, e.g., by ultracold polar molecules on optical lattices. The simulations are performed with MPS implementations based on the usual singular value decompositions (SVD) as well as ones using the adaptive cross approximation (ACA) instead. The ACA can be seen as an iterative approach to SVD which is often used, e.g., in the context of finite-element-methods, leading to a substantial speedup. A comparison of the performance of both algorithms in the MPS context is discussed. Financial support via DFG through CRC 1073 (''Atomic scale control of energy conversion''), project B03 is gratefully acknowledged.

Spreading Sequence System for Full Connectivity Relay Network

NASA Technical Reports Server (NTRS)

Kwon, Hyuck M. (Inventor); Pham, Khanh D. (Inventor); Yang, Jie (Inventor)

2018-01-01

Fully connected uplink and downlink fully connected relay network systems using pseudo-noise spreading and despreading sequences subjected to maximizing the signal-to-interference-plus-noise ratio. The relay network systems comprise one or more transmitting units, relays, and receiving units connected via a communication network. The transmitting units, relays, and receiving units each may include a computer for performing the methods and steps described herein and transceivers for transmitting and/or receiving signals. The computer encodes and/or decodes communication signals via optimum adaptive PN sequences found by employing Cholesky decompositions and singular value decompositions (SVD). The PN sequences employ channel state information (CSI) to more effectively and more securely computing the optimal sequences.

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD).

PubMed

Bermúdez Ordoñez, Juan Carlos; Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-05-16

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)

PubMed Central

Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-01-01

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain. PMID:29772731

Excluding Noise from Short Krylov Subspace Approximations to the Truncated Singular Value Decomposition (SVD)

DTIC Science & Technology

2017-09-27

ARL-TR-8161•SEP 2017 US Army Research Laboratory Excluding Noise from Short Krylov Subspace Approximations to the Truncated Singular Value...originator. ARL-TR-8161•SEP 2017 US Army Research Laboratory Excluding Noise from Short Krylov Subspace Approximations to the Truncated Singular Value...unlimited. October 2015–January 2016 US Army Research Laboratory ATTN: RDRL-CIH-C Aberdeen Proving Ground, MD 21005-5066 primary author’s email

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

PubMed Central

Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

2014-01-01

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

Effect of Watermarking on Diagnostic Preservation of Atherosclerotic Ultrasound Video in Stroke Telemedicine.

PubMed

Dey, Nilanjan; Bose, Soumyo; Das, Achintya; Chaudhuri, Sheli Sinha; Saba, Luca; Shafique, Shoaib; Nicolaides, Andrew; Suri, Jasjit S

2016-04-01

Embedding of diagnostic and health care information requires secure encryption and watermarking. This research paper presents a comprehensive study for the behavior of some well established watermarking algorithms in frequency domain for the preservation of stroke-based diagnostic parameters. Two different sets of watermarking algorithms namely: two correlation-based (binary logo hiding) and two singular value decomposition (SVD)-based (gray logo hiding) watermarking algorithms are used for embedding ownership logo. The diagnostic parameters in atherosclerotic plaque ultrasound video are namely: (a) bulb identification and recognition which consists of identifying the bulb edge points in far and near carotid walls; (b) carotid bulb diameter; and (c) carotid lumen thickness all along the carotid artery. The tested data set consists of carotid atherosclerotic movies taken under IRB protocol from University of Indiana Hospital, USA-AtheroPoint™ (Roseville, CA, USA) joint pilot study. ROC (receiver operating characteristic) analysis was performed on the bulb detection process that showed an accuracy and sensitivity of 100 % each, respectively. The diagnostic preservation (DPsystem) for SVD-based approach was above 99 % with PSNR (Peak signal-to-noise ratio) above 41, ensuring the retention of diagnostic parameter devalorization as an effect of watermarking. Thus, the fully automated proposed system proved to be an efficient method for watermarking the atherosclerotic ultrasound video for stroke application.

Deciphering life history transcriptomes in different environments

PubMed Central

Etges, William J.; Trotter, Meredith V.; de Oliveira, Cássia C.; Rajpurohit, Subhash; Gibbs, Allen G.; Tuljapurkar, Shripad

2014-01-01

We compared whole transcriptome variation in six preadult stages and seven adult female ages in two populations of cactophilic Drosophila mojavensis reared on two host plants in order to understand how differences in gene expression influence standing life history variation. We used Singular Value Decomposition (SVD) to identify dominant trajectories of life cycle gene expression variation, performed pair-wise comparisons of stage and age differences in gene expression across the life cycle, identified when genes exhibited maximum levels of life cycle gene expression, and assessed population and host cactus effects on gene expression. Life cycle SVD analysis returned four significant components of transcriptional variation, revealing functional enrichment of genes responsible for growth, metabolic function, sensory perception, neural function, translation and aging. Host cactus effects on female gene expression revealed population and stage specific differences, including significant host plant effects on larval metabolism and development, as well as adult neurotransmitter binding and courtship behavior gene expression levels. In 3 - 6 day old virgin females, significant up-regulation of genes associated with meiosis and oogenesis was accompanied by down-regulation of genes associated with somatic maintenance, evidence for a life history tradeoff. The transcriptome of D. mojavensis reared in natural environments throughout its life cycle revealed core developmental transitions and genome wide influences on life history variation in natural populations. PMID:25442828

Genetic overlap between diagnostic subtypes of ischemic stroke.

PubMed

Holliday, Elizabeth G; Traylor, Matthew; Malik, Rainer; Bevan, Steve; Falcone, Guido; Hopewell, Jemma C; Cheng, Yu-Ching; Cotlarciuc, Ioana; Bis, Joshua C; Boerwinkle, Eric; Boncoraglio, Giorgio B; Clarke, Robert; Cole, John W; Fornage, Myriam; Furie, Karen L; Ikram, M Arfan; Jannes, Jim; Kittner, Steven J; Lincz, Lisa F; Maguire, Jane M; Meschia, James F; Mosley, Thomas H; Nalls, Mike A; Oldmeadow, Christopher; Parati, Eugenio A; Psaty, Bruce M; Rothwell, Peter M; Seshadri, Sudha; Scott, Rodney J; Sharma, Pankaj; Sudlow, Cathie; Wiggins, Kerri L; Worrall, Bradford B; Rosand, Jonathan; Mitchell, Braxton D; Dichgans, Martin; Markus, Hugh S; Levi, Christopher; Attia, John; Wray, Naomi R

2015-03-01

Despite moderate heritability, the phenotypic heterogeneity of ischemic stroke has hampered gene discovery, motivating analyses of diagnostic subtypes with reduced sample sizes. We assessed evidence for a shared genetic basis among the 3 major subtypes: large artery atherosclerosis (LAA), cardioembolism, and small vessel disease (SVD), to inform potential cross-subtype analyses. Analyses used genome-wide summary data for 12 389 ischemic stroke cases (including 2167 LAA, 2405 cardioembolism, and 1854 SVD) and 62 004 controls from the Metastroke consortium. For 4561 cases and 7094 controls, individual-level genotype data were also available. Genetic correlations between subtypes were estimated using linear mixed models and polygenic profile scores. Meta-analysis of a combined LAA-SVD phenotype (4021 cases and 51 976 controls) was performed to identify shared risk alleles. High genetic correlation was identified between LAA and SVD using linear mixed models (rg=0.96, SE=0.47, P=9×10(-4)) and profile scores (rg=0.72; 95% confidence interval, 0.52-0.93). Between LAA and cardioembolism and SVD and cardioembolism, correlation was moderate using linear mixed models but not significantly different from zero for profile scoring. Joint meta-analysis of LAA and SVD identified strong association (P=1×10(-7)) for single nucleotide polymorphisms near the opioid receptor μ1 (OPRM1) gene. Our results suggest that LAA and SVD, which have been hitherto treated as genetically distinct, may share a substantial genetic component. Combined analyses of LAA and SVD may increase power to identify small-effect alleles influencing shared pathophysiological processes. © 2015 American Heart Association, Inc.

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

PubMed Central

Wardlaw, Joanna M; Smith, Eric E; Biessels, Geert J; Cordonnier, Charlotte; Fazekas, Franz; Frayne, Richard; Lindley, Richard I; O'Brien, John T; Barkhof, Frederik; Benavente, Oscar R; Black, Sandra E; Brayne, Carol; Breteler, Monique; Chabriat, Hugues; DeCarli, Charles; de Leeuw, Frank-Erik; Doubal, Fergus; Duering, Marco; Fox, Nick C; Greenberg, Steven; Hachinski, Vladimir; Kilimann, Ingo; Mok, Vincent; Oostenbrugge, Robert van; Pantoni, Leonardo; Speck, Oliver; Stephan, Blossom C M; Teipel, Stefan; Viswanathan, Anand; Werring, David; Chen, Christopher; Smith, Colin; van Buchem, Mark; Norrving, Bo; Gorelick, Philip B; Dichgans, Martin

2013-01-01

Summary Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE). PMID:23867200

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy.

PubMed

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-05

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy

NASA Astrophysics Data System (ADS)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-01

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

SandiaMRCR

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

2012-01-05

SandiaMCR was developed to identify pure components and their concentrations from spectral data. This software efficiently implements the multivariate calibration regression alternating least squares (MCR-ALS), principal component analysis (PCA), and singular value decomposition (SVD). Version 3.37 also includes the PARAFAC-ALS Tucker-1 (for trilinear analysis) algorithms. The alternating least squares methods can be used to determine the composition without or with incomplete prior information on the constituents and their concentrations. It allows the specification of numerous preprocessing, initialization and data selection and compression options for the efficient processing of large data sets. The software includes numerous options including the definition ofmore » equality and non-negativety constraints to realistically restrict the solution set, various normalization or weighting options based on the statistics of the data, several initialization choices and data compression. The software has been designed to provide a practicing spectroscopist the tools required to routinely analysis data in a reasonable time and without requiring expert intervention.« less

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Baroclinic stabilization effect of the Atlantic-Arctic water exchange simulated by the eddy-permitting ocean model and global atmosphere-ocean model

NASA Astrophysics Data System (ADS)

Moshonkin, Sergey; Bagno, Alexey; Gritsun, Andrey; Gusev, Anatoly

2017-04-01

Numerical experiments were performed with the global atmosphere-ocean model INMCM5 (for version of the international project CMIP6, resolution for atmosphere is 2°x1.5°, 21 level) and with the three-dimensional, free surface, sigma coordinate eddy-permitting ocean circulation model for Atlantic (from 30°S) - Arctic and Bering sea domain (0.25 degrees resolution, Institute of Numerical Mathematics Ocean Model or INMOM). Spatial resolution of the INMCM5 oceanic component is 0.5°x0.25°. Both models have 40 s-levels in ocean. Previously, the simulations were carried out for INMCM5 to generate climatic system stable state. Then model was run for 180 years. In the experiment with INMOM, CORE-II data for 1948-2009 were used. As the goal for comparing results of two these numerical models, we selected evolution of the density and velocity anomalies in the 0-300m active ocean layer near Fram Strait in the Greenland Sea, where oceanic cyclonic circulation influences Atlantic-Arctic water exchange. Anomalies were count without climatic seasonal cycle for time scales smaller than 30 years. We use Singular Value Decomposition analysis (SVD) for density-velocity anomalies with time lag from minus one to six months. Both models perform identical stable physical result. They reveal that changes of heat and salt transports by West Spitsbergen and East Greenland currents, caused by atmospheric forcing, produce the baroclinic modes of velocity anomalies in 0-300m layer, thereby stabilizing ocean response on the atmospheric forcing, which stimulates keeping water exchange between the North Atlantic and Arctic Ocean at the certain climatological level. The first SVD-mode of density-velocity anomalies is responsible for the cyclonic circulation variability. The second and third SVD-modes stabilize existing ocean circulation by the anticyclonic vorticity generation. The second and third SVD-modes give 35% of the input to the total dispersion of density anomalies and 16-18% of the input to the total dispersion of velocity anomalies for numerical results as in INMCM5 so in INMOM models. Input to the total dispersion of velocity anomalies for the first SVD-mode is equal to 50% for INMCM5 and only 19% for INMOM. The research was done in the INM RAS. The model INMOM was supported by Russian Foundation for Basic Research (grant №16-05-00534), and the model INMCM was supported by the Russian Scientific Foundation (grant №14-27-00126).

Efficient 3D Watermarked Video Communication with Chaotic Interleaving, Convolution Coding, and LMMSE Equalization

NASA Astrophysics Data System (ADS)

El-Shafai, W.; El-Bakary, E. M.; El-Rabaie, S.; Zahran, O.; El-Halawany, M.; Abd El-Samie, F. E.

2017-06-01

Three-Dimensional Multi-View Video (3D-MVV) transmission over wireless networks suffers from Macro-Blocks losses due to either packet dropping or fading-motivated bit errors. Thus, the robust performance of 3D-MVV transmission schemes over wireless channels becomes a recent considerable hot research issue due to the restricted resources and the presence of severe channel errors. The 3D-MVV is composed of multiple video streams shot by several cameras around a single object, simultaneously. Therefore, it is an urgent task to achieve high compression ratios to meet future bandwidth constraints. Unfortunately, the highly-compressed 3D-MVV data becomes more sensitive and vulnerable to packet losses, especially in the case of heavy channel faults. Thus, in this paper, we suggest the application of a chaotic Baker interleaving approach with equalization and convolution coding for efficient Singular Value Decomposition (SVD) watermarked 3D-MVV transmission over an Orthogonal Frequency Division Multiplexing wireless system. Rayleigh fading and Additive White Gaussian Noise are considered in the real scenario of 3D-MVV transmission. The SVD watermarked 3D-MVV frames are primarily converted to their luminance and chrominance components, which are then converted to binary data format. After that, chaotic interleaving is applied prior to the modulation process. It is used to reduce the channel effects on the transmitted bit streams and it also adds a degree of encryption to the transmitted 3D-MVV frames. To test the performance of the proposed framework; several simulation experiments on different SVD watermarked 3D-MVV frames have been executed. The experimental results show that the received SVD watermarked 3D-MVV frames still have high Peak Signal-to-Noise Ratios and watermark extraction is possible in the proposed framework.

Multidecadal climate variability of global lands and oceans

USGS Publications Warehouse

McCabe, G.J.; Palecki, M.A.

2006-01-01

Principal components analysis (PCA) and singular value decomposition (SVD) are used to identify the primary modes of decadal and multidecadal variability in annual global Palmer Drought Severity Index (PDSI) values and sea-surface temperature (SSTs). The PDSI and SST data for 1925-2003 were detrended and smoothed (with a 10-year moving average) to isolate the decadal and multidecadal variability. The first two principal components (PCs) of the PDSI PCA explained almost 38% of the decadal and multidecadal variance in the detrended and smoothed global annual PDSI data. The first two PCs of detrended and smoothed global annual SSTs explained nearly 56% of the decadal variability in global SSTs. The PDSI PCs and the SST PCs are directly correlated in a pairwise fashion. The first PDSI and SST PCs reflect variability of the detrended and smoothed annual Pacific Decadal Oscillation (PDO), as well as detrended and smoothed annual Indian Ocean SSTs. The second set of PCs is strongly associated with the Atlantic Multidecadal Oscillation (AMO). The SVD analysis of the cross-covariance of the PDSI and SST data confirmed the close link between the PDSI and SST modes of decadal and multidecadal variation and provided a verification of the PCA results. These findings indicate that the major modes of multidecadal variations in SSTs and land-surface climate conditions are highly interrelated through a small number of spatially complex but slowly varying teleconnections. Therefore, these relations may be adaptable to providing improved baseline conditions for seasonal climate forecasting. Published in 2006 by John Wiley & Sons, Ltd.

Application of global sensitivity analysis methods to Takagi-Sugeno-Kang rainfall-runoff fuzzy models

NASA Astrophysics Data System (ADS)

Jacquin, A. P.; Shamseldin, A. Y.

2009-04-01

This study analyses the sensitivity of the parameters of Takagi-Sugeno-Kang rainfall-runoff fuzzy models previously developed by the authors. These models can be classified in two types, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity in the rainfall-runoff relationship. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis (RSA) and Sobol's Variance Decomposition (SVD). In general, the RSA method has the disadvantage of not being able to detect sensitivities arising from parameter interactions. By contrast, the SVD method is suitable for analysing models where the model response surface is expected to be affected by interactions at a local scale and/or local optima, such as the case of the rainfall-runoff fuzzy models analysed in this study. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of two measures of goodness of fit, assessing the model performance from different points of view. These measures are the Nash-Sutcliffe criterion and the index of volumetric fit. The results of the study show that the sensitivity of the model parameters depends on both the type of non-linear effects (i.e. changes in catchment wetness or seasonality) that dominates the catchment's rainfall-runoff relationship and the measure used to assess the model performance. Acknowledgements: This research was supported by FONDECYT, Research Grant 11070130. We would also like to express our gratitude to Prof. Kieran M. O'Connor from the National University of Ireland, Galway, for providing the data used in this study.

Adaptive fault feature extraction from wayside acoustic signals from train bearings

NASA Astrophysics Data System (ADS)

Zhang, Dingcheng; Entezami, Mani; Stewart, Edward; Roberts, Clive; Yu, Dejie

2018-07-01

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods.

[Surface electromyography signal classification using gray system theory].

PubMed

Xie, Hongbo; Ma, Congbin; Wang, Zhizhong; Huang, Hai

2004-12-01

A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.

Tensor Factorization for Low-Rank Tensor Completion.

PubMed

Zhou, Pan; Lu, Canyi; Lin, Zhouchen; Zhang, Chao

2018-03-01

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.

A tripolar pattern as an internal mode of the East Asian summer monsoon

NASA Astrophysics Data System (ADS)

Hirota, Nagio; Takahashi, Masaaki

2012-11-01

A tripolar anomaly pattern with centers located around the Philippines, China/Japan, and East Siberia dominantly appears in climate variations of the East Asian summer monsoon. In this study, we extracted this pattern as the first mode of a singular value decomposition (SVD1) over East Asia. The squared covariance fraction of SVD1 was 59 %, indicating that this pattern can be considered a dominant pattern of climate variations. Moreover, the results of numerical experiments suggested that the structure is also a dominant pattern of linear responses, even if external forcing is distributed homogeneously over the Northern Hemisphere. Thus, the tripolar pattern can be considered an internal mode that is characterized by the internal atmospheric processes. In this pattern, the moist processes strengthen the circulation anomalies, the dynamical energy conversion supplies energy to the anomalies, and the Rossby waves propagate northward in the lower troposphere and southeastward in the upper troposphere. These processes are favorable for the pattern to have large amplitude and to influence a large area.

Face recognition using tridiagonal matrix enhanced multivariance products representation

NASA Astrophysics Data System (ADS)

Ã-zay, Evrim Korkmaz

2017-01-01

This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result.

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

NASA Astrophysics Data System (ADS)

Golafshan, Reza; Yuce Sanliturk, Kenan

2016-03-01

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

PubMed Central

McGivney, Debra F.; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A.

2016-01-01

Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously. PMID:25029380

Stabilizing bidirectional associative memory with Principles in Independent Component Analysis and Null Space (PICANS)

NASA Astrophysics Data System (ADS)

LaRue, James P.; Luzanov, Yuriy

2013-05-01

A new extension to the way in which the Bidirectional Associative Memory (BAM) algorithms are implemented is presented here. We will show that by utilizing the singular value decomposition (SVD) and integrating principles of independent component analysis (ICA) into the nullspace (NS) we have created a novel approach to mitigating spurious attractors. We demonstrate this with two applications. The first application utilizes a one-layer association while the second application is modeled after the several hierarchal associations of ventral pathways. The first application will detail the way in which we manage the associations in terms of matrices. The second application will take what we have learned from the first example and apply it to a cascade of a convolutional neural network (CNN) and perceptron this being our signal processing model of the ventral pathways, i.e., visual systems.

Eigenspace-based fuzzy c-means for sensing trending topics in Twitter

NASA Astrophysics Data System (ADS)

Muliawati, T.; Murfi, H.

2017-07-01

As the information and communication technology are developed, the fulfillment of information can be obtained through social media, like Twitter. The enormous number of internet users has triggered fast and large data flow, thus making the manual analysis is difficult or even impossible. An automated methods for data analysis is needed, one of which is the topic detection and tracking. An alternative method other than latent Dirichlet allocation (LDA) is a soft clustering approach using Fuzzy C-Means (FCM). FCM meets the assumption that a document may consist of several topics. However, FCM works well in low-dimensional data but fails in high-dimensional data. Therefore, we propose an approach where FCM works on low-dimensional data by reducing the data using singular value decomposition (SVD). Our simulations show that this approach gives better accuracies in term of topic recall than LDA for sensing trending topic in Twitter about an event.

Statistical analysis of effective singular values in matrix rank determination

NASA Technical Reports Server (NTRS)

Konstantinides, Konstantinos; Yao, Kung

1988-01-01

A major problem in using SVD (singular-value decomposition) as a tool in determining the effective rank of a perturbed matrix is that of distinguishing between significantly small and significantly large singular values to the end, conference regions are derived for the perturbed singular values of matrices with noisy observation data. The analysis is based on the theories of perturbations of singular values and statistical significance test. Threshold bounds for perturbation due to finite-precision and i.i.d. random models are evaluated. In random models, the threshold bounds depend on the dimension of the matrix, the noisy variance, and predefined statistical level of significance. Results applied to the problem of determining the effective order of a linear autoregressive system from the approximate rank of a sample autocorrelation matrix are considered. Various numerical examples illustrating the usefulness of these bounds and comparisons to other previously known approaches are given.

SVD-aided pseudo principal-component analysis: A new method to speed up and improve determination of the optimum kinetic model from time-resolved data

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

Oang, Key Young; Yang, Cheolhee; Muniyappan, Srinivasan

Determination of the optimum kinetic model is an essential prerequisite for characterizing dynamics and mechanism of a reaction. Here, we propose a simple method, termed as singular value decomposition-aided pseudo principal-component analysis (SAPPA), to facilitate determination of the optimum kinetic model from time-resolved data by bypassing any need to examine candidate kinetic models. We demonstrate the wide applicability of SAPPA by examining three different sets of experimental time-resolved data and show that SAPPA can efficiently determine the optimum kinetic model. In addition, the results of SAPPA for both time-resolved X-ray solution scattering (TRXSS) and transient absorption (TA) data of themore » same protein reveal that global structural changes of protein, which is probed by TRXSS, may occur more slowly than local structural changes around the chromophore, which is probed by TA spectroscopy.« less

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

PubMed

Barba, Lida; Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

PubMed Central

Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT. PMID:28261267

Application of Texture Analysis to Study Small Vessel Disease and Blood-Brain Barrier Integrity.

PubMed

Valdés Hernández, Maria Del C; González-Castro, Victor; Chappell, Francesca M; Sakka, Eleni; Makin, Stephen; Armitage, Paul A; Nailon, William H; Wardlaw, Joanna M

2017-01-01

We evaluate the alternative use of texture analysis for evaluating the role of blood-brain barrier (BBB) in small vessel disease (SVD). We used brain magnetic resonance imaging from 204 stroke patients, acquired before and 20 min after intravenous gadolinium administration. We segmented tissues, white matter hyperintensities (WMH) and applied validated visual scores. We measured textural features in all tissues pre- and post-contrast and used ANCOVA to evaluate the effect of SVD indicators on the pre-/post-contrast change, Kruskal-Wallis for significance between patient groups and linear mixed models for pre-/post-contrast variations in cerebrospinal fluid (CSF) with Fazekas scores. Textural "homogeneity" increase in normal tissues with higher presence of SVD indicators was consistently more overt than in abnormal tissues. Textural "homogeneity" increased with age, basal ganglia perivascular spaces scores ( p  < 0.01) and SVD scores ( p  < 0.05) and was significantly higher in hypertensive patients ( p  < 0.002) and lacunar stroke ( p  = 0.04). Hypertension (74% patients), WMH load (median = 1.5 ± 1.6% of intracranial volume), and age (mean = 65.6 years, SD = 11.3) predicted the pre/post-contrast change in normal white matter, WMH, and index stroke lesion. CSF signal increased with increasing SVD post-contrast. A consistent general pattern of increasing textural "homogeneity" with increasing SVD and post-contrast change in CSF with increasing WMH suggest that texture analysis may be useful for the study of BBB integrity.

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health

PubMed Central

Fogel, Paul; Gaston-Mathé, Yann; Hawkins, Douglas; Fogel, Fajwel; Luta, George; Young, S. Stanley

2016-01-01

Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then Non-negative Matrix Factorization (NMF) can be used to perform the clustering. By its nature, NMF-based clustering is focused on the large values. If the data is normalized by subtracting the row/column means, it becomes of mixed signs and the original NMF cannot be used. Our idea is to split and then concatenate the positive and negative parts of the matrix, after taking the absolute value of the negative elements. NMF applied to the concatenated data, which we call PosNegNMF, offers the advantages of the original NMF approach, while giving equal weight to large and small values. We use two public health datasets to illustrate the new method and compare it with alternative clustering methods, such as K-means and clustering methods based on the Singular Value Decomposition (SVD) or Principal Component Analysis (PCA). With the exception of situations where a reasonably accurate factorization can be achieved using the first SVD component, we recommend that the epidemiologists and environmental scientists use the new method to obtain clusters with improved quality and interpretability. PMID:27213413

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health.

PubMed

Fogel, Paul; Gaston-Mathé, Yann; Hawkins, Douglas; Fogel, Fajwel; Luta, George; Young, S Stanley

2016-05-18

Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then Non-negative Matrix Factorization (NMF) can be used to perform the clustering. By its nature, NMF-based clustering is focused on the large values. If the data is normalized by subtracting the row/column means, it becomes of mixed signs and the original NMF cannot be used. Our idea is to split and then concatenate the positive and negative parts of the matrix, after taking the absolute value of the negative elements. NMF applied to the concatenated data, which we call PosNegNMF, offers the advantages of the original NMF approach, while giving equal weight to large and small values. We use two public health datasets to illustrate the new method and compare it with alternative clustering methods, such as K-means and clustering methods based on the Singular Value Decomposition (SVD) or Principal Component Analysis (PCA). With the exception of situations where a reasonably accurate factorization can be achieved using the first SVD component, we recommend that the epidemiologists and environmental scientists use the new method to obtain clusters with improved quality and interpretability.

Relating ocean-atmospheric climate indices with Australian river streamflow

NASA Astrophysics Data System (ADS)

Shams, Md Shamim; Faisal Anwar, A. H. M.; Lamb, Kenneth W.; Bari, Mohammed

2018-01-01

The relationship between climate indices with Australian river streamflow (ASF) may provide valuable information for long-lead streamflow forecasting for Australian rivers. The current study examines the correlations between three climate indices (SST, 500 mb meridional wind -U500 and 500 mb geopotential height-Z500) and 135 unimpaired ASF gauges for 1971-2011 using the singular value decomposition (SVD) method. First, SVD method was applied to check the SST-ASF correlated regions of influence and then extended SST-ASF variabilities were used to determine the correlated regions within Z500 and U500 fields. Based on the teleconnection, the most correlated region (150°E to 105°W and 35°S to 5°N) was identified and its persistency was checked by lag analysis up to 2 years from seasonal to yearly time-scale. The results displayed positive correlation for the south and south-eastern part of Australia while negative correlation prevails in the north-eastern region (at 95% significance level). The most correlated region was found situated along the South Pacific Convergence Zone (SPCZ) axis which may be considered as a probable climate driver for ASF. The persistency of this region was checked by a separate climate indicator (mean vertical velocity-500 mb) and found prominent in dry period than the wet period. This persistent teleconnected region may be potentially useful for long-lead forecasting of ASF.

Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles

PubMed Central

Maadooliat, Mehdi; Huang, Jianhua Z.

2013-01-01

Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence–structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations. PMID:22926831

Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-03-22

Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithm account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms are illustrated through simulations and applications on real fMRI data.

Texture Analysis of T1-Weighted and Fluid-Attenuated Inversion Recovery Images Detects Abnormalities That Correlate With Cognitive Decline in Small Vessel Disease.

PubMed

Tozer, Daniel J; Zeestraten, Eva; Lawrence, Andrew J; Barrick, Thomas R; Markus, Hugh S

2018-06-04

Magnetic resonance imaging may be useful to assess disease severity in cerebral small vessel disease (SVD), identify those individuals who are most likely to progress to dementia, monitor disease progression, and act as surrogate markers to test new therapies. Texture analysis extracts information on the relationship between signal intensities of neighboring voxels. A potential advantage over techniques, such as diffusion tensor imaging, is that it can be used on clinically obtained magnetic resonance sequences. We determined whether texture parameters (TP) were abnormal in SVD, correlated with cognitive impairment, predicted cognitive decline, or conversion to dementia. In the prospective SCANS study (St George's Cognition and Neuroimaging in Stroke), we assessed TP in 121 individuals with symptomatic SVD at baseline, 99 of whom attended annual cognitive testing for 5 years. Conversion to dementia was recorded for all subjects during the 5-year period. Texture analysis was performed on fluid-attenuated inversion recovery and T1-weighted images. The TP obtained from the SVD cohort were cross-sectionally compared with 54 age-matched controls scanned on the same magnetic resonance imaging system. There were highly significant differences in several TP between SVD cases and controls. Within the SVD population, TP were highly correlated to other magnetic resonance imaging parameters (brain volume, white matter lesion volume, lacune count). TP correlated with executive function and global function at baseline and predicted conversion to dementia, after controlling for age, sex, premorbid intelligence quotient, and magnetic resonance parameters. TP, which can be obtained from routine clinical images, are abnormal in SVD, and the degree of abnormality correlates with executive dysfunction and global cognition at baseline and decline during 5 years. TP may be useful to assess disease severity in clinically collected data. This needs testing in data clinically acquired across multiple sites. © 2018 The Authors.

Watermarking scheme based on singular value decomposition and homomorphic transform

NASA Astrophysics Data System (ADS)

Verma, Deval; Aggarwal, A. K.; Agarwal, Himanshu

2017-10-01

A semi-blind watermarking scheme based on singular-value-decomposition (SVD) and homomorphic transform is pro-posed. This scheme ensures the digital security of an eight bit gray scale image by inserting an invisible eight bit gray scale wa-termark into it. The key approach of the scheme is to apply the homomorphic transform on the host image to obtain its reflectance component. The watermark is embedded into the singular values that are obtained by applying the singular value decomposition on the reflectance component. Peak-signal-to-noise-ratio (PSNR), normalized-correlation-coefficient (NCC) and mean-structural-similarity-index-measure (MSSIM) are used to evaluate the performance of the scheme. Invisibility of watermark is ensured by visual inspection and high value of PSNR of watermarked images. Presence of watermark is ensured by visual inspection and high values of NCC and MSSIM of extracted watermarks. Robustness of the scheme is verified by high values of NCC and MSSIM for attacked watermarked images.

Data analysis of photon beam position at PLS-II

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

Ko, J.; Shin, S., E-mail: tlssh@postech.ac.kr; Huang, Jung-Yun

In the third generation light source, photon beam position stability is critical issue on user experiment. Generally photon beam position monitors have been developed for the detection of the real photon beam position and the position is controlled by feedback system in order to keep the reference photon beam position. In the PLS-II, photon beam position stability for front end of particular beam line, in which photon beam position monitor is installed, has been obtained less than rms 1μm for user service period. Nevertheless, detail analysis for photon beam position data in order to demonstrate the performance of photon beammore » position monitor is necessary, since it can be suffers from various unknown noises. (for instance, a back ground contamination due to upstream or downstream dipole radiation, undulator gap dependence, etc.) In this paper, we will describe the start to end study for photon beam position stability and the Singular Value Decomposition (SVD) analysis to demonstrate the reliability on photon beam position data.« less

Screening by imaging: scaling up single-DNA-molecule analysis with a novel parabolic VA-TIRF reflector and noise-reduction techniques.

PubMed

van 't Hoff, Marcel; Reuter, Marcel; Dryden, David T F; Oheim, Martin

2009-09-21

Bacteriophage lambda-DNA molecules are frequently used as a scaffold to characterize the action of single proteins unwinding, translocating, digesting or repairing DNA. However, scaling up such single-DNA-molecule experiments under identical conditions to attain statistically relevant sample sizes remains challenging. Additionally the movies obtained are frequently noisy and difficult to analyse with any precision. We address these two problems here using, firstly, a novel variable-angle total internal reflection fluorescence (VA-TIRF) reflector composed of a minimal set of optical reflective elements, and secondly, using single value decomposition (SVD) to improve the signal-to-noise ratio prior to analysing time-lapse image stacks. As an example, we visualize under identical optical conditions hundreds of surface-tethered single lambda-DNA molecules, stained with the intercalating dye YOYO-1 iodide, and stretched out in a microcapillary flow. Another novelty of our approach is that we arrange on a mechanically driven stage several capillaries containing saline, calibration buffer and lambda-DNA, respectively, thus extending the approach to high-content, high-throughput screening of single molecules. Our length measurements of individual DNA molecules from noise-reduced kymograph images using SVD display a 6-fold enhanced precision compared to raw-data analysis, reaching approximately 1 kbp resolution. Combining these two methods, our approach provides a straightforward yet powerful way of collecting statistically relevant amounts of data in a semi-automated manner. We believe that our conceptually simple technique should be of interest for a broader range of single-molecule studies, well beyond the specific example of lambda-DNA shown here.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Low Dimensional Analysis of Wing Surface Morphology in Hummingbird Free Flight

NASA Astrophysics Data System (ADS)

Shallcross, Gregory; Ren, Yan; Liu, Geng; Dong, Haibo; Tobalske, Bret

2015-11-01

Surface morphing in flapping wings is a hallmark of bird flight. In current work, the role of dynamic wing morphing of a free flying hummingbird is studied in detail. A 3D image-based surface reconstruction method is used to obtain the kinematics and deformation of hummingbird wings from high-quality high-speed videos. The observed wing surface morphing is highly complex and a number of modeling methods including singular value decomposition (SVD) are used to obtain the fundamental kinematical modes with distinct motion features. Their aerodynamic roles are investigated by conducting immersed-boundary-method based flow simulations. The results show that the chord-wise deformation modes play key roles in the attachment of leading-edge vortex, thus improve the performance of the flapping wings. This work is supported by NSF CBET-1313217 and AFOSR FA9550-12-1-0071.

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.

PubMed

Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD's robustness and wide applicability.

Matched field localization based on CS-MUSIC algorithm

NASA Astrophysics Data System (ADS)

Guo, Shuangle; Tang, Ruichun; Peng, Linhui; Ji, Xiaopeng

2016-04-01

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered. A matched field localization algorithm based on CS-MUSIC (Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning. The signal matrix is calculated through the SVD (Singular Value Decomposition) of the observation matrix. The observation matrix in the sparse mathematical model is replaced by the signal matrix, and a new concise sparse mathematical model is obtained, which means not only the scale of the localization problem but also the noise level is reduced; then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS (Compressive Sensing) method and MUSIC (Multiple Signal Classification) method. The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots, and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large, which will be proved in this paper.

Distinguishing autofluorescence of normal, benign, and cancerous breast tissues through wavelet domain correlation studies.

PubMed

Gharekhan, Anita H; Arora, Siddharth; Oza, Ashok N; Sureshkumar, Mundan B; Pradhan, Asima; Panigrahi, Prasanta K

2011-08-01

Using the multiresolution ability of wavelets and effectiveness of singular value decomposition (SVD) to identify statistically robust parameters, we find a number of local and global features, capturing spectral correlations in the co- and cross-polarized channels, at different scales (of human breast tissues). The copolarized component, being sensitive to intrinsic fluorescence, shows different behavior for normal, benign, and cancerous tissues, in the emission domain of known fluorophores, whereas the perpendicular component, being more prone to the diffusive effect of scattering, points out differences in the Kernel-Smoother density estimate employed to the principal components, between malignant, normal, and benign tissues. The eigenvectors, corresponding to the dominant eigenvalues of the correlation matrix in SVD, also exhibit significant differences between the three tissue types, which clearly reflects the differences in the spectral correlation behavior. Interestingly, the most significant distinguishing feature manifests in the perpendicular component, corresponding to porphyrin emission range in the cancerous tissue. The fact that perpendicular component is strongly influenced by depolarization, and porphyrin emissions in cancerous tissue has been found to be strongly depolarized, may be the possible cause of the above observation.

Hemorrhage recurrence risk factors in cerebral amyloid angiopathy: Comparative analysis of the overall small vessel disease severity score versus individual neuroimaging markers.

PubMed

Boulouis, Gregoire; Charidimou, Andreas; Pasi, Marco; Roongpiboonsopit, Duangnapa; Xiong, Li; Auriel, Eitan; van Etten, Ellis S; Martinez-Ramirez, Sergi; Ayres, Alison; Vashkevich, Anastasia; Schwab, Kristin M; Rosand, Jonathan; Goldstein, Joshua N; Gurol, M Edip; Greenberg, Steven M; Viswanathan, Anand

2017-09-15

An MRI-based score of total small vessel disease burden (CAA-SVD-Score) in cerebral amyloid angiopathy (CAA) has been demonstrated to correlate with severity of pathologic changes. Evidence suggests that CAA-related intracerebral hemorrhage (ICH) recurrence risk is associated with specific disease imaging manifestations rather than overall severity. We compared the correlation between the CAA-SVD-Score with the risk of recurrent CAA-related lobar ICH versus the predictive role of each of its components. Consecutive patients with CAA-related ICH from a single-center prospective cohort were analyzed. Radiological markers of CAA related SVD damage were quantified and categorized according to the CAA-SVD-Score (0-6 points). Subjects were followed prospectively for recurrent symptomatic ICH. Adjusted Cox proportional hazards models were used to investigate associations between the CAA-SVD-Score as well as each of the individual MRI signatures of CAA and the risk of recurrent ICH. In 229 CAA patients with ICH, a total of 56 recurrent ICH events occurred during a median follow-up of 2.8years [IQR 0.9-5.4years, 781 person-years). Higher CAA-SVD-Score (HR=1.26 per additional point, 95%CI [1.04-1.52], p=0.015) and older age were independently associated with higher ICH recurrence risk. Analysis of individual markers of CAA showed that CAA-SVD-Score findings were due to the independent effect of disseminated superficial siderosis (HR for disseminated cSS vs none: 2.89, 95%CI [1.47-5.5], p=0.002) and high degree of perivascular spaces enlargement (RR=3.50-95%CI [1.04-21], p=0.042). In lobar CAA-ICH patients, higher CAA-SVD-Score does predict recurrent ICH. Amongst individual elements of the score, superficial siderosis and dilated perivascular spaces are the only markers independently associated with ICH recurrence, contributing to the evidence for distinct CAA phenotypes singled out by neuro-imaging manifestations. Copyright © 2017 Elsevier B.V. All rights reserved.

Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

NASA Astrophysics Data System (ADS)

Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

2014-03-01

We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.

Apathy, but not depression, is associated with executive dysfunction in cerebral small vessel disease

PubMed Central

Hollocks, Matthew J.; Morris, Robin G.; Markus, Hugh S.

2017-01-01

Objective To determine the prevalence of apathy and depression in cerebral small vessel disease (SVD), and the relationships between both apathy and depression with cognition. To examine whether apathy is specifically related to impairment in executive functioning and processing speed. Methods 196 patients with a clinical lacunar stroke and an anatomically corresponding lacunar infarct on MRI were compared to 300 stroke-free controls. Apathy and depression were measured using the Geriatric Depression Scale, and cognitive functioning was assessed using an SVD cognitive screening tool, the Brief Memory and Executive Test, which measures executive functioning/processing speed and memory/orientation. Path analysis and binary logistic regression were used to assess the relation between apathy, depression and cognitive impairment. Results 31 participants with SVD (15.8%) met criteria for apathy only, 23 (11.8%) for both apathy and depression, and 2 (1.0%) for depression only. In the SVD group the presence of apathy was related to global cognition, and specifically to impaired executive functioning/processing speed, but not memory/orientation. The presence of depression was not related to global cognition, impaired executive functioning/processing speed or memory/orientation. Conclusions Apathy is a common feature of SVD and is associated with impaired executive functioning/processing speed suggesting the two may share biological mechanisms. Screening for apathy should be considered in SVD, and further work is required to develop and evaluate effective apathy treatment or management in SVD. PMID:28493898

Gene expression suggests spontaneously hypertensive rats may have altered metabolism and reduced hypoxic tolerance.

PubMed

Ritz, Marie-Françoise; Grond-Ginsbach, Caspar; Engelter, Stefan; Lyrer, Philippe

2012-02-01

Cerebral small vessel disease (SVD) is an important cause of stroke, cognitive decline and vascular dementia (VaD). It is associated with diffuse white matter abnormalities and small deep cerebral ischemic infarcts. The molecular mechanisms involved in the development and progression of SVD are unclear. As hypertension is a major risk factor for developing SVD, Spontaneously Hypertensive Rats (SHR) are considered an appropriate experimental model for SVD. Prior work suggested an imbalance between the number of blood microvessels and astrocytes at the level of the neurovascular unit in 2-month-old SHR, leading to neuronal hypoxia in the brain of 9-month-old animals. To identify genes and pathways involved in the development of SVD, we compared the gene expression profile in the cortex of 2 and 9-month-old of SHR with age-matched normotensive Wistar Kyoto (WKY) rats using microarray-based technology. The results revealed significant differences in expression of genes involved in energy and lipid metabolisms, mitochondrial functions, oxidative stress and ischemic responses between both groups. These results strongly suggest that SHR suffer from chronic hypoxia, and therefore are unable to tolerate ischemia-like conditions, and are more vulnerable to high-energy needs than WKY. This molecular analysis gives new insights about pathways accounting for the development of SVD.

Pistachio (Pistacia vera L.) is a new natural host of Hop stunt viroid.

PubMed

Elleuch, Amine; Hamdi, Imen; Ellouze, Olfa; Ghrab, Mohamed; Fkahfakh, Hatem; Drira, Noureddine

2013-10-01

Besides hop, Hop stunt viroid (HpSVd) infects many woody species including grapevine, citrus, peach, plum, apricot, almond, pomegranate, mulberry and jujube. Here, we report the first detection of HpSVd in pistachio (Pistacia vera L.). Samples corresponding to 16 pistachio cultivars were obtained from a nearby almond collection. From these samples, low molecular weight RNAs were extracted for double polyacrylamide gel electrophoresis, northern-blot analysis and reverse transcription polymerase chain reaction assays. HpSVd was detected in 4 of the 16 pistachio cultivars in the first year and in 6 in the second, being also detected in the almond collection. Examination of the nucleotide sequences of pistachio and almond isolates revealed 13 new sequence variants. Sequences from pistachio shared 92-96 % similarity with the first reported HpSVd sequence (GenBank X00009), and multiple alignment and phylogenetic analyses showed that one pistachio isolate (HpSVdPis67Jabari) clustered with the plum group, whereas all the others clustered with the hop, and the recombinants plum-citrus and plum-Hop/cit3 groups. By identifying pistachio as a new natural host, we confirm that HpSVd is an ubiquitous and genetically variable viroid that infects many different fruit trees cultivated worldwide.

Erasing the Milky Way: new cleaning technique applied to GBT intensity mapping data

NASA Astrophysics Data System (ADS)

Wolz, L.; Blake, C.; Abdalla, F. B.; Anderson, C. J.; Chang, T.-C.; Li, Y.-C.; Masui, K. W.; Switzer, E.; Pen, U.-L.; Voytek, T. C.; Yadav, J.

2017-02-01

We present the first application of a new foreground removal pipeline to the current leading H I intensity mapping data set, obtained by the Green Bank Telescope (GBT). We study the 15- and 1-h-field data of the GBT observations previously presented in Mausui et al. and Switzer et al., covering about 41 deg2 at 0.6 < z < 1.0, for which cross-correlations may be measured with the galaxy distribution of the WiggleZ Dark Energy Survey. In the presented pipeline, we subtract the Galactic foreground continuum and the point-source contamination using an independent component analysis technique (FASTICA), and develop a Fourier-based optimal estimator to compute the temperature power spectrum of the intensity maps and cross-correlation with the galaxy survey data. We show that FASTICA is a reliable tool to subtract diffuse and point-source emission through the non-Gaussian nature of their probability distributions. The temperature power spectra of the intensity maps are dominated by instrumental noise on small scales which FASTICA, as a conservative subtraction technique of non-Gaussian signals, cannot mitigate. However, we determine similar GBT-WiggleZ cross-correlation measurements to those obtained by the singular value decomposition (SVD) method, and confirm that foreground subtraction with FASTICA is robust against 21 cm signal loss, as seen by the converged amplitude of these cross-correlation measurements. We conclude that SVD and FASTICA are complementary methods to investigate the foregrounds and noise systematics present in intensity mapping data sets.

Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change.

PubMed

Williams, Owen A; Zeestraten, Eva A; Benjamin, Philip; Lambert, Christian; Lawrence, Andrew J; Mackinnon, Andrew D; Morris, Robin G; Markus, Hugh S; Charlton, Rebecca A; Barrick, Thomas R

2017-01-01

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS ( p  < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models ( p  = 0.002). Change in DSEG θ was also related to change in all other MRI markers ( p  < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity. DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs).

A Molecular Dynamic Modeling of Hemoglobin-Hemoglobin Interactions

NASA Astrophysics Data System (ADS)

Wu, Tao; Yang, Ye; Sheldon Wang, X.; Cohen, Barry; Ge, Hongya

2010-05-01

In this paper, we present a study of hemoglobin-hemoglobin interaction with model reduction methods. We begin with a simple spring-mass system with given parameters (mass and stiffness). With this known system, we compare the mode superposition method with Singular Value Decomposition (SVD) based Principal Component Analysis (PCA). Through PCA we are able to recover the principal direction of this system, namely the model direction. This model direction will be matched with the eigenvector derived from mode superposition analysis. The same technique will be implemented in a much more complicated hemoglobin-hemoglobin molecule interaction model, in which thousands of atoms in hemoglobin molecules are coupled with tens of thousands of T3 water molecule models. In this model, complex inter-atomic and inter-molecular potentials are replaced by nonlinear springs. We employ the same method to get the most significant modes and their frequencies of this complex dynamical system. More complex physical phenomena can then be further studied by these coarse grained models.

C-2W Magnetic Measurement Suite

NASA Astrophysics Data System (ADS)

Roche, T.; Thompson, M. C.; Griswold, M.; Knapp, K.; Koop, B.; Ottaviano, A.; Tobin, M.; TAE, Tri Alpha Energy, Inc. Team

2017-10-01

Commissioning and early operations are underway on C-2W, Tri Alpha Energy's new FRC experiment. The increased complexity level of this machine requires an equally enhanced diagnostic capability. A fundamental component of any magnetically confined fusion experiment is a firm understanding of the magnetic field itself. C-2W is outfitted with over 700 magnetic field probes, 550 internal and 150 external. Innovative in-vacuum annular flux loop / B-dot combination probes will provide information about plasma shape, size, pressure, energy, total temperature, and trapped flux when coupled with establish theoretical interpretations. The massive Mirnov array, consisting of eight rings of eight 3D probes, will provide detailed information about plasma motion, stability, and MHD modal content with the aid of singular value decomposition (SVD) analysis. Internal Rogowski probes will detect the presence of axial currents flowing in the plasma jet in multiple axial locations. Initial data from this array of diagnostics will be presented along with some interpretation and discussion of the analysis techniques used.

The stratospheric QBO signal in the NCEP reanalysis, 1958-2001

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Gallego, David; Peña-Ortiz, Cristina; Gimeno, Luis; Garcia-Herrera, Ricardo; Hernandez, Emiliano; Calvo, Natalia

2003-07-01

The spatiotemporal evolution of the zonal wind in the stratosphere is analyzed based on the use of the NCEP reanalysis (1958-2001). MultiTaper Method-Singular Value Decomposition (MTM-SVD), a frequency-domain analysis method, is applied to isolate significant spatially-coherent variability with narrowband oscillatory character. A quasibiennial oscillation is detected as the most intense coherent signal in the stratosphere, the signal being less intense in the lower levels. There is a clear downward propagation of the signal with time at low latitudes, not evident at mid and high latitudes. There are differences in the behavior of the signal over both hemispheres, being much weaker over the SH. In the NH an anomaly in the zonal wind field, in phase with the equatorial signal, is detected at approximately 60°N. Two different areas at subtropical latitudes are detected to be characterized by wind anomalies opposed to that of the equator.

Reducing Memory Cost of Exact Diagonalization using Singular Value Decomposition

NASA Astrophysics Data System (ADS)

Weinstein, Marvin; Chandra, Ravi; Auerbach, Assa

2012-02-01

We present a modified Lanczos algorithm to diagonalize lattice Hamiltonians with dramatically reduced memory requirements. In contrast to variational approaches and most implementations of DMRG, Lanczos rotations towards the ground state do not involve incremental minimizations, (e.g. sweeping procedures) which may get stuck in false local minima. The lattice of size N is partitioned into two subclusters. At each iteration the rotating Lanczos vector is compressed into two sets of nsvd small subcluster vectors using singular value decomposition. For low entanglement entropy See, (satisfied by short range Hamiltonians), the truncation error is bounded by (-nsvd^1/See). Convergence is tested for the Heisenberg model on Kagom'e clusters of 24, 30 and 36 sites, with no lattice symmetries exploited, using less than 15GB of dynamical memory. Generalization of the Lanczos-SVD algorithm to multiple partitioning is discussed, and comparisons to other techniques are given. Reference: arXiv:1105.0007

Evaluation of the 2-(1-Hexyloxyethyl)-2-devinyl pyropheophorbide (HPPH) mediated photodynamic therapy by macroscopic singlet oxygen modeling [J. Biophotonics 9, No. 11-12, 1344-1354 (2016)].

PubMed

Penjweini, Rozhin; Kim, Michele M; Liu, Baochang; Zhu, Timothy C

2017-03-01

In the article by R. Penjweini, M. M. Kim et al. (doi: 10.1002/jbio.201600121), published in J. Biophotonics 9, 1344-1354 (2016), the constants C 01 , C 02 , b 1 , and b 2 determined from fitting the fluorescence single value decomposition (SVD) for phantoms with different optical properties and the corresponding Figure 2(a) are not correct. This erratum is published to correct the Section 2.3 and Figure 2(a). © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Investigating the Differences in the Total and Active Microbial Community of Mid-Atlantic Ridge Sediments

NASA Astrophysics Data System (ADS)

Sobol, M. S.; Zinke, L. A.; Orcutt, B.; Mills, H. J.; Edwards, K. J.; Girguis, P. R.; Reese, B. K.

2016-02-01

Microbes in the marine deep subsurface are key mediators of many geochemical cycles. It is important to understand how microbial communities and the diversity of those communities impacts geochemical cycling. Sediment cores were collected from IODP (Integrated Ocean Drilling Program) Expedition 336 to the western flank of the mid-Atlantic ridge also referred to as North Pond. The dissolved oxygen concentration decreased with depth for 60-70 mbsf, followed by a sharp increase in oxygen until it terminated at the basement. The 16S rRNA genes (DNA) and transcripts (RNA) were extracted simultaneously using a method designed by Reese et al. (2013) to differentiate between the total and active microbial community structures, respectively, as well as correlate the putative metabolism with the geochemistry. We observed many differences between the active and total communities. Sequences most closely related to Cyanobacteria were found to dominate the total community at both sites, but were found in small numbers in the active community. The most abundant phyla in the active community were Alphaproteobacteria, which suggests that they may have high activity even though the abundance was not as great in the total community. This suggests that, even in small numbers, bacteria are capable of contributing greatly to their environment. Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) showed that iron-reducing bacteria in the active (RNA) community correlated strongly with solid phase iron oxides. SVD also showed that the putative nitrate reducers in the active community were found in greater abundance where porewater NO3- and NO2- total concentrations were elevated. Overall, the active (RNA) community correlated significantly with the geochemistry whereas the total (DNA) community did not. Therefore, RNA analysis yields a more accurate representation of how microbial communities impact geochemical cycling.

Sampling considerations for modal analysis with damping

NASA Astrophysics Data System (ADS)

Park, Jae Young; Wakin, Michael B.; Gilbert, Anna C.

2015-03-01

Structural health monitoring (SHM) systems are critical for monitoring aging infrastructure (such as buildings or bridges) in a cost-effective manner. Wireless sensor networks that sample vibration data over time are particularly appealing for SHM applications due to their flexibility and low cost. However, in order to extend the battery life of wireless sensor nodes, it is essential to minimize the amount of vibration data these sensors must collect and transmit. In recent work, we have studied the performance of the Singular Value Decomposition (SVD) applied to the collection of data and provided new finite sample analysis characterizing conditions under which this simple technique{also known as the Proper Orthogonal Decomposition (POD){can correctly estimate the mode shapes of the structure. Specifically, we provided theoretical guarantees on the number and duration of samples required in order to estimate a structure's mode shapes to a desired level of accuracy. In that previous work, however, we considered simplified Multiple-Degree-Of-Freedom (MDOF) systems with no damping. In this paper we consider MDOF systems with proportional damping and show that, with sufficiently light damping, the POD can continue to provide accurate estimates of a structure's mode shapes. We support our discussion with new analytical insight and experimental demonstrations. In particular, we study the tradeoffs between the level of damping, the sampling rate and duration, and the accuracy to which the structure's mode shapes can be estimated.

Numerical Analysis and Improved Algorithms for Lyapunov-Exponent Calculation of Discrete-Time Chaotic Systems

NASA Astrophysics Data System (ADS)

He, Jianbin; Yu, Simin; Cai, Jianping

2016-12-01

Lyapunov exponent is an important index for describing chaotic systems behavior, and the largest Lyapunov exponent can be used to determine whether a system is chaotic or not. For discrete-time dynamical systems, the Lyapunov exponents are calculated by an eigenvalue method. In theory, according to eigenvalue method, the more accurate calculations of Lyapunov exponent can be obtained with the increment of iterations, and the limits also exist. However, due to the finite precision of computer and other reasons, the results will be numeric overflow, unrecognized, or inaccurate, which can be stated as follows: (1) The iterations cannot be too large, otherwise, the simulation result will appear as an error message of NaN or Inf; (2) If the error message of NaN or Inf does not appear, then with the increment of iterations, all Lyapunov exponents will get close to the largest Lyapunov exponent, which leads to inaccurate calculation results; (3) From the viewpoint of numerical calculation, obviously, if the iterations are too small, then the results are also inaccurate. Based on the analysis of Lyapunov-exponent calculation in discrete-time systems, this paper investigates two improved algorithms via QR orthogonal decomposition and SVD orthogonal decomposition approaches so as to solve the above-mentioned problems. Finally, some examples are given to illustrate the feasibility and effectiveness of the improved algorithms.

Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease.

PubMed

Lambert, Christian; Sam Narean, Janakan; Benjamin, Philip; Zeestraten, Eva; Barrick, Thomas R; Markus, Hugh S

2015-01-01

Cerebral small vessel disease (SVD) is a heterogeneous group of pathological disorders that affect the small vessels of the brain and are an important cause of cognitive impairment. The ischaemic consequences of this disease can be detected using MRI, and include white matter hyperintensities (WMH), lacunar infarcts and microhaemorrhages. The relationship between SVD disease severity, as defined by WMH volume, in sporadic age-related SVD and cortical thickness has not been well defined. However, regional cortical thickness change would be expected due to associated phenomena such as underlying ischaemic white matter damage, and the observation that widespread cortical thinning is observed in the related genetic condition CADASIL (Righart et al., 2013). Using MRI data, we have developed a semi-automated processing pipeline for the anatomical analysis of individuals with cerebral small vessel disease and applied it cross-sectionally to 121 subjects diagnosed with this condition. Using a novel combined automated white matter lesion segmentation algorithm and lesion repair step, highly accurate warping to a group average template was achieved. The volume of white matter affected by WMH was calculated, and used as a covariate of interest in a voxel-based morphometry and voxel-based cortical thickness analysis. Additionally, Gaussian Process Regression (GPR) was used to assess if the severity of SVD, measured by WMH volume, could be predicted from the morphometry and cortical thickness measures. We found significant (Family Wise Error corrected p < 0.05) volumetric decline with increasing lesion load predominately in the parietal lobes, anterior insula and caudate nuclei bilaterally. Widespread significant cortical thinning was found bilaterally in the dorsolateral prefrontal, parietal and posterio-superior temporal cortices. These represent distinctive patterns of cortical thinning and volumetric reduction compared to ageing effects in the same cohort, which exhibited greater changes in the occipital and sensorimotor cortices. Using GPR, the absolute WMH volume could be significantly estimated from the grey matter density and cortical thickness maps (Pearson's coefficients 0.80 and 0.75 respectively). We demonstrate that SVD severity is associated with regional cortical thinning. Furthermore a quantitative measure of SVD severity (WMH volume) can be predicted from grey matter measures, supporting an association between white and grey matter damage. The pattern of cortical thinning and volumetric decline is distinctive for SVD severity compared to ageing. These results, taken together, suggest that there is a phenotypic pattern of atrophy associated with SVD severity.

Intraseasonal variability of winter precipitation over central asia and the western tibetan plateau from 1979 to 2013 and its relationship with the North Atlantic Oscillation

NASA Astrophysics Data System (ADS)

Liu, Heng; Liu, Xiaodong; Dong, Buwen

2017-09-01

Winter precipitation over Central Asia and the western Tibetan Plateau (CAWTP) is mainly a result of the interaction between the westerly circulation and the high mountains around the plateau. Empirical Orthogonal Functions (EOFs), Singular Value Decomposition (SVD), linear regression and composite analysis were used to analyze winter daily precipitation and other meteorological elements in this region from 1979 to 2013, in order to understand how interactions between the regional circulation and topography affect the intraseasonal variability in precipitation. The SVD analysis shows that the winter daily precipitation variability distribution is characterized by a dipole pattern with opposite signs over the northern Pamir Plateau and over the Karakoram Himalaya, similar to the second mode of EOF analysis. This dipole pattern of precipitation anomaly is associated with local anomalies in both the 700 hPa moisture transport and the 500 hPa geopotential height and is probably caused by oscillations in the regional and large-scale circulations, which can influence the westerly disturbance tracks and water vapor transport. The linear regression shows that the anomalous mid-tropospheric circulation over CAWTP corresponds to an anti-phase variation of the 500 hPa geopotential height anomalies over the southern and northern North Atlantic 10 days earlier (at 95% significance level), that bears a similarity to the North Atlantic Oscillation (NAO). The composite analysis reveals that the NAO impacts the downstream regions including CAWTP by controlling south-north two branches of the middle latitude westerly circulation around the Eurasian border. During the positive phases of the NAO, the northern branch of the westerly circulation goes around the northwest Tibetan Plateau, whereas the southern branch encounters the southwest Tibetan Plateau, which leads to reduced precipitation over the northern Pamir Plateau and increased precipitation over the Karakoram Himalaya, and vice versa.

Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions.

PubMed

Jha, Abhinav K; Barrett, Harrison H; Frey, Eric C; Clarkson, Eric; Caucci, Luca; Kupinski, Matthew A

2015-09-21

Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and implemented for graphics processing units (GPUs). Further, this approach leverages another important advantage of PP systems, namely the possibility to perform photon-by-photon real-time reconstruction. We demonstrate the application of the approach to perform reconstruction in a simulated 2D SPECT system. The results help to validate and demonstrate the utility of the proposed method and show that PP systems can help overcome the aliasing artifacts that are otherwise intrinsically present in PC systems.

Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions

NASA Astrophysics Data System (ADS)

Jha, Abhinav K.; Barrett, Harrison H.; Frey, Eric C.; Clarkson, Eric; Caucci, Luca; Kupinski, Matthew A.

2015-09-01

Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and implemented for graphics processing units (GPUs). Further, this approach leverages another important advantage of PP systems, namely the possibility to perform photon-by-photon real-time reconstruction. We demonstrate the application of the approach to perform reconstruction in a simulated 2D SPECT system. The results help to validate and demonstrate the utility of the proposed method and show that PP systems can help overcome the aliasing artifacts that are otherwise intrinsically present in PC systems.

Lesion location and cognitive impact of cerebral small vessel disease.

PubMed

Biesbroek, J Matthijs; Weaver, Nick A; Biessels, Geert Jan

2017-04-25

Cerebral small vessel disease (SVD) is an important cause of cognitive impairment. Important MRI manifestations of SVD include white matter hyperintensities (WMH) and lacunes. This narrative review addresses the role of anatomical lesion location in the impact of SVD on cognition, integrating findings from early autopsy studies with emerging findings from recent studies with advanced image analysis techniques. Early autopsy and imaging studies of small case series indicate that single lacunar infarcts in, for example the thalamus, caudate nucleus or internal capsule can cause marked cognitive impairment. However, the findings of such case studies may not be generalizable. Emerging location-based image analysis approaches are now being applied to large cohorts. Recent studies show that WMH burden in strategic white matter tracts, such as the forceps minor or anterior thalamic radiation (ATR), is more relevant in explaining variance in cognitive functioning than global WMH volume. These findings suggest that the future diagnostic work-up of memory clinic patients could potentially be improved by shifting from a global assessment of WMH and lacune burden towards a quantitative assessment of lesion volumes within strategic brain regions. In this review, a summary of currently known strategic regions for SVD-related cognitive impairment is provided, highlighting recent technical developments in SVD research. The potential and challenges of location-based approaches for diagnostic purposes in clinical practice are discussed, along with their potential prognostic and therapeutic applications. © 2017 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Long-Term Durability of Bioprosthetic Aortic Valves: Implications From 12,569 Implants

PubMed Central

Johnston, Douglas R.; Soltesz, Edward G.; Vakil, Nakul; Rajeswaran, Jeevanantham; Roselli, Eric E.; Sabik, Joseph F.; Smedira, Nicholas G.; Svensson, Lars G.; Lytle, Bruce W.; Blackstone, Eugene H.

2016-01-01

Background Increased life expectancy and younger patients’ desire to avoid lifelong anticoagulation requires a better understanding of bioprosthetic valve failure. This study evaluates risk factors associated with explantation for structural valve deterioration (SVD) in a long-term series of Carpentier-Edwards PERIMOUNT aortic valves (AV). Methods From June 1982 to January 2011, 12,569 patients underwent AV replacement with Edwards Lifesciences Carpentier-Edwards PERIMOUNT stented bovine pericardial prostheses, models 2700PM (n = 310) or 2700 (n = 12,259). Mean age was 71 ± 11 years (range, 18 to 98 years). 93% had native AV disease, 48% underwent concomitant coronary artery bypass grafting, and 26% had additional valve surgery. There were 81,706 patient-years of systematic follow-up data available for analysis. Demographics, intraoperative variables, and 27,386 echocardiographic records were used to identify risks for explant for SVD and assess longitudinal changes in transprosthesis gradients using time-varying covariable analyses. Results Three hundred fifty-four explants were performed, with 41% related to endocarditis and 44% to SVD. Actuarial estimates of explant for SVD at 10 and 20 years were 1.9% and 15% overall, respectively, and in patients younger than 60 years, 5.6% and 46%, respectively. Younger age (p < 0.0001), lipid-lowering drugs (p = 0.002), prosthesis–patient mismatch (p = 0.001), and higher postoperative peak and mean AV gradients were associated with explant for SVD (p < 0.0001). The effect of gradient on SVD was greatest in patients younger than 60 years. Conclusions Durability of the Carpentier-Edwards PERIMOUNT aortic valve is excellent even in younger patients. Explant for SVD is related to gradient at implantation, especially in younger patients. Strategies to reduce early postoperative AV gradients, such as root enlargement or more efficient prostheses, should be considered. PMID:25662439

Top-down constraints on global N2O emissions at optimal resolution: application of a new dimension reduction technique

NASA Astrophysics Data System (ADS)

Wells, Kelley C.; Millet, Dylan B.; Bousserez, Nicolas; Henze, Daven K.; Griffis, Timothy J.; Chaliyakunnel, Sreelekha; Dlugokencky, Edward J.; Saikawa, Eri; Xiang, Gao; Prinn, Ronald G.; O'Doherty, Simon; Young, Dickon; Weiss, Ray F.; Dutton, Geoff S.; Elkins, James W.; Krummel, Paul B.; Langenfelds, Ray; Steele, L. Paul

2018-01-01

We present top-down constraints on global monthly N2O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N2O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° × 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 Tg N yr-1 (SVD-based inversion) to 17.5-17.7 Tg N yr-1 (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N2O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N2O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N2O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion.

Cross-language information retrieval using PARAFAC2.

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

Bader, Brett William; Chew, Peter; Abdelali, Ahmed

A standard approach to cross-language information retrieval (CLIR) uses Latent Semantic Analysis (LSA) in conjunction with a multilingual parallel aligned corpus. This approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. However, the approach has severe drawbacks when applied to a related task, that of clustering documents 'language-independently', so that documents about similar topics end up closest to one another in the semantic space regardless of their language. The problem is that documents are generally more similar tomore » other documents in the same language than they are to documents in a different language, but on the same topic. As a result, when using multilingual LSA, documents will in practice cluster by language, not by topic. We propose a novel application of PARAFAC2 (which is a variant of PARAFAC, a multi-way generalization of the singular value decomposition [SVD]) to overcome this problem. Instead of forming a single multilingual term-by-document matrix which, under LSA, is subjected to SVD, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. The goal is to compute an SVD for each language such that V (the matrix of right singular vectors) is the same across all languages. Effectively, PARAFAC2 imposes the constraint, not present in standard LSA, that the 'concepts' in all documents in the parallel corpus are the same regardless of language. Intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem. From our results, we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering, but also for solving other problems in cross-language information retrieval.« less

Crystal Identification in Dual-Layer-Offset DOI-PET Detectors Using Stratified Peak Tracking Based on SVD and Mean-Shift Algorithm

NASA Astrophysics Data System (ADS)

Wei, Qingyang; Dai, Tiantian; Ma, Tianyu; Liu, Yaqiang; Gu, Yu

2016-10-01

An Anger-logic based pixelated PET detector block requires a crystal position map (CPM) to assign the position of each detected event to a most probable crystal index. Accurate assignments are crucial to PET imaging performance. In this paper, we present a novel automatic approach to generate the CPMs for dual-layer offset (DLO) PET detectors using a stratified peak tracking method. In which, the top and bottom layers are distinguished by their intensity difference and the peaks of the top and bottom layers are tracked based on a singular value decomposition (SVD) and mean-shift algorithm in succession. The CPM is created by classifying each pixel to its nearest peak and assigning the pixel with the crystal index of that peak. A Matlab-based graphical user interface program was developed including the automatic algorithm and a manual interaction procedure. The algorithm was tested for three DLO PET detector blocks. Results show that the proposed method exhibits good performance as well as robustness for all the three blocks. Compared to the existing methods, our approach can directly distinguish the layer and crystal indices using the information of intensity and offset grid pattern.

Rapid perfusion quantification using Welch-Satterthwaite approximation and analytical spectral filtering

NASA Astrophysics Data System (ADS)

Krishnan, Karthik; Reddy, Kasireddy V.; Ajani, Bhavya; Yalavarthy, Phaneendra K.

2017-02-01

CT and MR perfusion weighted imaging (PWI) enable quantification of perfusion parameters in stroke studies. These parameters are calculated from the residual impulse response function (IRF) based on a physiological model for tissue perfusion. The standard approach for estimating the IRF is deconvolution using oscillatory-limited singular value decomposition (oSVD) or Frequency Domain Deconvolution (FDD). FDD is widely recognized as the fastest approach currently available for deconvolution of CT Perfusion/MR PWI. In this work, three faster methods are proposed. The first is a direct (model based) crude approximation to the final perfusion quantities (Blood flow, Blood volume, Mean Transit Time and Delay) using the Welch-Satterthwaite approximation for gamma fitted concentration time curves (CTC). The second method is a fast accurate deconvolution method, we call Analytical Fourier Filtering (AFF). The third is another fast accurate deconvolution technique using Showalter's method, we call Analytical Showalter's Spectral Filtering (ASSF). Through systematic evaluation on phantom and clinical data, the proposed methods are shown to be computationally more than twice as fast as FDD. The two deconvolution based methods, AFF and ASSF, are also shown to be quantitatively accurate compared to FDD and oSVD.

Joint Smoothed l₀-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar.

PubMed

Liu, Jing; Zhou, Weidong; Juwono, Filbert H

2017-05-08

Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l 0 -norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations

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

Peng, Bo; Kowalski, Karol

The representation and storage of two-electron integral tensors are vital in large- scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this paper, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition ofmore » the two-electron integral tensor in our implementation. For the size of atomic basis set N_b ranging from ~ 100 up to ~ 2, 000, the observed numerical scaling of our implementation shows O(N_b^{2.5~3}) versus O(N_b^{3~4}) of single CD in most of other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic-orbital (AO) two-electron integral tensor from O(N_b^4) to O(N_b^2 log_{10}(N_b)) with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled- cluster formalism employing single and double excitations (CCSD) on several bench- mark systems including the C_{60} molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10^{-4} to 10^{-3} to give acceptable compromise between efficiency and accuracy.« less

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations.

PubMed

Peng, Bo; Kowalski, Karol

2017-09-12

The representation and storage of two-electron integral tensors are vital in large-scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this work, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of the atomic basis set, N b , ranging from ∼100 up to ∼2,000, the observed numerical scaling of our implementation shows [Formula: see text] versus [Formula: see text] cost of performing single CD on the two-electron integral tensor in most of the other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic orbital (AO) two-electron integral tensor from [Formula: see text] to [Formula: see text] with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled cluster formalism employing single and double excitations (CCSD) on several benchmark systems including the C 60 molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10 -4 to 10 -3 to give acceptable compromise between efficiency and accuracy.

Structural network efficiency is associated with cognitive impairment in small-vessel disease.

PubMed

Lawrence, Andrew J; Chung, Ai Wern; Morris, Robin G; Markus, Hugh S; Barrick, Thomas R

2014-07-22

To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. © 2014 American Academy of Neurology.

Structural network efficiency is associated with cognitive impairment in small-vessel disease

PubMed Central

Chung, Ai Wern; Morris, Robin G.; Markus, Hugh S.; Barrick, Thomas R.

2014-01-01

Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. PMID:24951477

An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

PubMed

Zhang, Ye; Yu, Tenglong; Wang, Wenwu

2014-01-01

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

Depression in small-vessel disease relates to white matter ultrastructural damage, not disability.

PubMed

Brookes, Rebecca L; Herbert, Vanessa; Lawrence, Andrew J; Morris, Robin G; Markus, Hugh S

2014-10-14

To determine whether cerebral small-vessel disease (SVD) is a specific risk factor for depression, whether any association is mediated via white matter damage, and to study the role of depressive symptoms and disability on quality of life (QoL) in this patient group. Using path analyses in cross-sectional data, we modeled the relationships among depression, disability, and QoL in patients with SVD presenting with radiologically confirmed lacunar stroke (n = 100), and replicated results in a second SVD cohort (n = 100). We then compared the same model in a non-SVD stroke cohort (n = 50) and healthy older adults (n = 203). In a further study, to determine the role of white matter damage in mediating the association with depression, a subgroup of patients with SVD (n = 101) underwent diffusion tensor imaging (DTI). Reduced QoL was associated with depression in patients with SVD, but this association was not mediated by disability or cognition; very similar results were found in the replication SVD cohort. In contrast, the non-SVD stroke group and the healthy older adult group showed a direct relationship between disability and depression. The DTI study showed that fractional anisotropy, a marker of white matter damage, was related to depressive symptoms in patients with SVD. These results suggest that in stroke patients without SVD, disability is an important causal factor for depression, whereas in SVD stroke, other factors specific to this stroke subtype have a causal role. White matter damage detected on DTI is one factor that mediates the association between SVD and depression. © 2014 American Academy of Neurology.

Analysis and modelling of septic shock microarray data using Singular Value Decomposition.

PubMed

Allanki, Srinivas; Dixit, Madhulika; Thangaraj, Paul; Sinha, Nandan Kumar

2017-06-01

Being a high throughput technique, enormous amounts of microarray data has been generated and there arises a need for more efficient techniques of analysis, in terms of speed and accuracy. Finding the differentially expressed genes based on just fold change and p-value might not extract all the vital biological signals that occur at a lower gene expression level. Besides this, numerous mathematical models have been generated to predict the clinical outcome from microarray data, while very few, if not none, aim at predicting the vital genes that are important in a disease progression. Such models help a basic researcher narrow down and concentrate on a promising set of genes which leads to the discovery of gene-based therapies. In this article, as a first objective, we have used the lesser known and used Singular Value Decomposition (SVD) technique to build a microarray data analysis tool that works with gene expression patterns and intrinsic structure of the data in an unsupervised manner. We have re-analysed a microarray data over the clinical course of Septic shock from Cazalis et al. (2014) and have shown that our proposed analysis provides additional information compared to the conventional method. As a second objective, we developed a novel mathematical model that predicts a set of vital genes in the disease progression that works by generating samples in the continuum between health and disease, using a simple normal-distribution-based random number generator. We also verify that most of the predicted genes are indeed related to septic shock. Copyright © 2017 Elsevier Inc. All rights reserved.

Erasing the Milky Way: New Cleaning Technique Applied to GBT Intensity Mapping Data

NASA Technical Reports Server (NTRS)

Wolz, L.; Blake, C.; Abdalla, F. B.; Anderson, C. J.; Chang, T.-C.; Li, Y.-C.; Masi, K.W.; Switzer, E.; Pen, U.-L.; Voytek, T. C.;

Covariability of Central America/Mexico winter precipitation and tropical sea surface temperatures

NASA Astrophysics Data System (ADS)

Pan, Yutong; Zeng, Ning; Mariotti, Annarita; Wang, Hui; Kumar, Arun; Sánchez, René Lobato; Jha, Bhaskar

2018-06-01

In this study, the relationships between Central America/Mexico (CAM) winter precipitation and tropical Pacific/Atlantic sea surface temperatures (SSTs) are examined based on 68-year (1948-2015) observations and 59-year (1957-2015) atmospheric model simulations forced by observed SSTs. The covariability of the winter precipitation and SSTs is quantified using the singular value decomposition (SVD) method with observational data. The first SVD mode relates out-of-phase precipitation anomalies in northern Mexico and Central America to the tropical Pacific El Niño/La Niña SST variation. The second mode links a decreasing trend in the precipitation over Central America to the warming of SSTs in the tropical Atlantic, as well as in the tropical western Pacific and the tropical Indian Ocean. The first mode represents 67% of the covariance between the two fields, indicating a strong association between CAM winter precipitation and El Niño/La Niña, whereas the second mode represents 20% of the covariance. The two modes account for 32% of CAM winter precipitation variance, of which, 17% is related to the El Niño/La Niña SST and 15% is related to the SST warming trend. The atmospheric circulation patterns, including 500-hPa height and low-level winds obtained by linear regressions against the SVD SST time series, are dynamically consistent with the precipitation anomaly patterns. The model simulations driven by the observed SSTs suggest that these precipitation anomalies are likely a response to tropical SST forcing. It is also shown that there is significant potential predictability of CAM winter precipitation given tropical SST information.

Inverse electrocardiographic transformations: dependence on the number of epicardial regions and body surface data points.

PubMed

Johnston, P R; Walker, S J; Hyttinen, J A; Kilpatrick, D

1994-04-01

The inverse problem of electrocardiography, the computation of epicardial potentials from body surface potentials, is influenced by the desired resolution on the epicardium, the number of recording points on the body surface, and the method of limiting the inversion process. To examine the role of these variables in the computation of the inverse transform, Tikhonov's zero-order regularization and singular value decomposition (SVD) have been used to invert the forward transfer matrix. The inverses have been compared in a data-independent manner using the resolution and the noise amplification as endpoints. Sets of 32, 50, 192, and 384 leads were chosen as sets of body surface data, and 26, 50, 74, and 98 regions were chosen to represent the epicardium. The resolution and noise were both improved by using a greater number of electrodes on the body surface. When 60% of the singular values are retained, the results show a trade-off between noise and resolution, with typical maximal epicardial noise levels of less than 0.5% of maximum epicardial potentials for 26 epicardial regions, 2.5% for 50 epicardial regions, 7.5% for 74 epicardial regions, and 50% for 98 epicardial regions. As the number of epicardial regions is increased, the regularization technique effectively fixes the noise amplification but markedly decreases the resolution, whereas SVD results in an increase in noise and a moderate decrease in resolution. Overall the regularization technique performs slightly better than SVD in the noise-resolution relationship. There is a region at the posterior of the heart that was poorly resolved regardless of the number of regions chosen. The variance of the resolution was such as to suggest the use of variable-size epicardial regions based on the resolution.

Long-term survey of lion-roar emissions inside the terrestrial magnetosheath obtained from the STAFF-SA measurements onboard the Cluster spacecraft

NASA Astrophysics Data System (ADS)

Pisa, D.; Krupar, V.; Kruparova, O.; Santolik, O.

2017-12-01

Intense whistler-mode emissions known as 'lion-roars' are often observed inside the terrestrial magnetosheath, where the solar wind plasma flow slows down, and the local magnetic field increases ahead of a planetary magnetosphere. Plasma conditions in this transient region lead to the electron temperature anisotropy, which can result in the whistler-mode waves. The lion-roars are narrow-band emissions with typical frequencies between 0.1-0.5 Fce, where Fce is the electron cyclotron frequency. We present results of a long-term survey obtained by the Spatio Temporal Analysis Field Fluctuations - Spectral Analyzer (STAFF-SA) instruments on board the four Cluster spacecraft between 2001 and 2010. We have visually identified the time-frequency intervals with the intense lion-roar signature. Using the Singular Value Decomposition (SVD) method, we analyzed the wave propagation properties. We show the spatial, frequency and wave power distributions. Finally, the wave properties as a function of upstream solar wind conditions are discussed.

Prioritization of Disease Susceptibility Genes Using LSM/SVD.

PubMed

Gong, Lejun; Yang, Ronggen; Yan, Qin; Sun, Xiao

2013-12-01

Understanding the role of genetics in diseases is one of the most important tasks in the postgenome era. It is generally too expensive and time consuming to perform experimental validation for all candidate genes related to disease. Computational methods play important roles for prioritizing these candidates. Herein, we propose an approach to prioritize disease genes using latent semantic mapping based on singular value decomposition. Our hypothesis is that similar functional genes are likely to cause similar diseases. Measuring the functional similarity between known disease susceptibility genes and unknown genes is to predict new disease susceptibility genes. Taking autism as an instance, the analysis results of the top ten genes prioritized demonstrate they might be autism susceptibility genes, which also indicates our approach could discover new disease susceptibility genes. The novel approach of disease gene prioritization could discover new disease susceptibility genes, and latent disease-gene relations. The prioritized results could also support the interpretive diversity and experimental views as computational evidence for disease researchers.

Precoded spatial multiplexing MIMO system with spatial component interleaver.

PubMed

Gao, Xiang; Wu, Zhanji

In this paper, the performance of precoded bit-interleaved coded modulation (BICM) spatial multiplexing multiple-input multiple-output (MIMO) system with spatial component interleaver is investigated. For the ideal precoded spatial multiplexing MIMO system with spatial component interleaver based on singular value decomposition (SVD) of the MIMO channel, the average pairwise error probability (PEP) of coded bits is derived. Based on the PEP analysis, the optimum spatial Q-component interleaver design criterion is provided to achieve the minimum error probability. For the limited feedback precoded proposed scheme with linear zero forcing (ZF) receiver, in order to minimize a bound on the average probability of a symbol vector error, a novel effective signal-to-noise ratio (SNR)-based precoding matrix selection criterion and a simplified criterion are proposed. Based on the average mutual information (AMI)-maximization criterion, the optimal constellation rotation angles are investigated. Simulation results indicate that the optimized spatial multiplexing MIMO system with spatial component interleaver can achieve significant performance advantages compared to the conventional spatial multiplexing MIMO system.

Application of Diffusion Tensor Imaging Parameters to Detect Change in Longitudinal Studies in Cerebral Small Vessel Disease.

PubMed

Zeestraten, Eva Anna; Benjamin, Philip; Lambert, Christian; Lawrence, Andrew John; Williams, Owen Alan; Morris, Robin Guy; Barrick, Thomas Richard; Markus, Hugh Stephen

2016-01-01

Cerebral small vessel disease (SVD) is the major cause of vascular cognitive impairment, resulting in significant disability and reduced quality of life. Cognitive tests have been shown to be insensitive to change in longitudinal studies and, therefore, sensitive surrogate markers are needed to monitor disease progression and assess treatment effects in clinical trials. Diffusion tensor imaging (DTI) is thought to offer great potential in this regard. Sensitivity of the various parameters that can be derived from DTI is however unknown. We aimed to evaluate the differential sensitivity of DTI markers to detect SVD progression, and to estimate sample sizes required to assess therapeutic interventions aimed at halting decline based on DTI data. We investigated 99 patients with symptomatic SVD, defined as clinical lacunar syndrome with MRI confirmation of a corresponding infarct as well as confluent white matter hyperintensities over a 3 year follow-up period. We evaluated change in DTI histogram parameters using linear mixed effect models and calculated sample size estimates. Over a three-year follow-up period we observed a decline in fractional anisotropy and increase in diffusivity in white matter tissue and most parameters changed significantly. Mean diffusivity peak height was the most sensitive marker for SVD progression as it had the smallest sample size estimate. This suggests disease progression can be monitored sensitively using DTI histogram analysis and confirms DTI's potential as surrogate marker for SVD.

Statistical Feature Extraction for Artifact Removal from Concurrent fMRI-EEG Recordings

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H.

2011-01-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphases are directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use a channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable by the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. PMID:22036675

Spectral and entropic characterizations of Wigner functions: applications to model vibrational systems.

PubMed

Luzanov, A V

2008-09-07

The Wigner function for the pure quantum states is used as an integral kernel of the non-Hermitian operator K, to which the standard singular value decomposition (SVD) is applied. It provides a set of the squared singular values treated as probabilities of the individual phase-space processes, the latter being described by eigenfunctions of KK(+) (for coordinate variables) and K(+)K (for momentum variables). Such a SVD representation is employed to obviate the well-known difficulties in the definition of the phase-space entropy measures in terms of the Wigner function that usually allows negative values. In particular, the new measures of nonclassicality are constructed in the form that automatically satisfies additivity for systems composed of noninteracting parts. Furthermore, the emphasis is given on the geometrical interpretation of the full entropy measure as the effective phase-space volume in the Wigner picture of quantum mechanics. The approach is exemplified by considering some generic vibrational systems. Specifically, for eigenstates of the harmonic oscillator and a superposition of coherent states, the singular value spectrum is evaluated analytically. Numerical computations are given for the nonlinear problems (the Morse and double well oscillators, and the Henon-Heiles system). We also discuss the difficulties in implementation of a similar technique for electronic problems.

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.

PubMed

Liu, Zhongming; de Zwart, Jacco A; van Gelderen, Peter; Kuo, Li-Wei; Duyn, Jeff H

2012-02-01

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use. Published by Elsevier Inc.

Spectral biclustering of microarray data: coclustering genes and conditions.

PubMed

Kluger, Yuval; Basri, Ronen; Chang, Joseph T; Gerstein, Mark

2003-04-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Is Dynamic Cerebral Autoregulation Bilaterally Impaired after Unilateral Acute Ischemic Stroke?

PubMed

Xiong, Li; Tian, Ge; Lin, Wenhua; Wang, Wei; Wang, Lijuan; Leung, Thomas; Mok, Vincent; Liu, Jia; Chen, Xiangyan; Wong, Ka Sing

2017-05-01

Whether dynamic cerebral autoregulation (dCA) is impaired focally in the affected hemisphere or bilaterally in both the affected and nonaffected hemispheres after ischemic stroke remains controversial. We therefore investigated the pattern of dCA in acute ischemic stroke patients with different subtypes. Sixty acute ischemic stroke patients with unilateral anterior circulation infarct [30 with large artery atherosclerosis (LAA), 13 with small vessel disease (SVD), and 17 with coexisting LAA and SVD] and 16 healthy controls were enrolled. Spontaneous arterial blood pressure and cerebral blood flow velocity fluctuations in both bilateral middle cerebral arteries using transcranial Doppler were recorded over 10 minutes. Transfer function analysis was applied to obtain autoregulatory parameters, autoregulation index (ARI), phase difference (PD), and gain. PD was significantly lower on both the ipsilateral and contralateral sides in the LAA group (ipsilateral, 30.74 degrees; contralateral, 29.17 degrees) and the coexisting LAA and SVD group (20.23 degrees; 13.10 degrees) than that in healthy controls (left side, 51.66 degrees; right side, 58.48 degrees) (all P < .05), but there were no significant differences between the 2 sides when compared with each other in all groups. However, in the coexisting LAA and SVD group, phase on both sides was significantly lower when compared with that in the LAA and SVD groups, respectively. The results of ARI were consistent with the findings in PD. The results indicate that dCA is bilaterally impaired in acute ischemic patients with LAA, and the coexisting SVD may aggravate the bilateral impairment of dCA. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Forcing mechanism of the seasonally asymmetric quasi-biennial oscillation secondary circulation in ERA-40 and MAECHAM5

NASA Astrophysics Data System (ADS)

Peña-Ortiz, C.; Ribera, P.; García-Herrera, R.; Giorgetta, M. A.; García, R. R.

2008-08-01

The seasonality of the quasi-biennial oscillation (QBO) and its secondary circulation is analyzed in the European Reanalysis (ERA-40) and Middle Atmosphere European Centre Hamburg Model (MAECHAM5) general circulation model data sets through the multitaper method-singular value decomposition (MTM-SVD). In agreement with previous studies, the results reveal a strong seasonal dependence of the QBO secondary circulation. This is characterized by a two-cell structure symmetric about the equator during autumn and spring. However, anomalies strongly weaken in the summer hemisphere and strengthen in the winter hemisphere, leading to an asymmetric QBO secondary circulation characterized by a single-cell structure displaced into the winter hemisphere during the solstices. In ERA-40, this asymmetry is more pronounced during the northern than during the southern winter. These results provide the first observation of the QBO secondary circulation asymmetries in the ERA-40 reanalysis data set across the full stratosphere and the lower mesosphere, up to 0.1 hPa. The MTM-SVD reconstruction of the seasonal QBO signals in the residual circulation and the QBO signals in Eliassen Palm (EP) flux divergences suggest a particular mechanism for the seasonal asymmetries of the QBO secondary circulation and its extension across the midlatitudes. The analysis shows that the QBO modulates the EP flux in the winter hemispheric surf zone poleward of the QBO jets. The zonal wind forcing by EP flux divergence is transformed by the Coriolis effect into a meridional wind signal. The seasonality in the stratospheric EP flux and the hemispheric differences in planetary wave forcing cause the observed seasonality in the QBO secondary circulation and its hemispheric differences.

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.

PubMed

Wei, Runmin; Wang, Jingye; Su, Mingming; Jia, Erik; Chen, Shaoqiu; Chen, Tianlu; Ni, Yan

2018-01-12

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).

Predicting responses from Rasch measures.

PubMed

Linacre, John M

2010-01-01

There is a growing family of Rasch models for polytomous observations. Selecting a suitable model for an existing dataset, estimating its parameters and evaluating its fit is now routine. Problems arise when the model parameters are to be estimated from the current data, but used to predict future data. In particular, ambiguities in the nature of the current data, or overfit of the model to the current dataset, may mean that better fit to the current data may lead to worse fit to future data. The predictive power of several Rasch and Rasch-related models are discussed in the context of the Netflix Prize. Rasch-related models are proposed based on Singular Value Decomposition (SVD) and Boltzmann Machines.

Letters: Noise Equalization for Ultrafast Plane Wave Microvessel Imaging

PubMed Central

Song, Pengfei; Manduca, Armando; Trzasko, Joshua D.

2017-01-01

Ultrafast plane wave microvessel imaging significantly improves ultrasound Doppler sensitivity by increasing the number of Doppler ensembles that can be collected within a short period of time. The rich spatiotemporal plane wave data also enables more robust clutter filtering based on singular value decomposition (SVD). However, due to the lack of transmit focusing, plane wave microvessel imaging is very susceptible to noise. This study was designed to: 1) study the relationship between ultrasound system noise (primarily time gain compensation-induced) and microvessel blood flow signal; 2) propose an adaptive and computationally cost-effective noise equalization method that is independent of hardware or software imaging settings to improve microvessel image quality. PMID:28880169

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

A novel key-frame extraction approach for both video summary and video index.

PubMed

Lei, Shaoshuai; Xie, Gang; Yan, Gaowei

2014-01-01

Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception.

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

PubMed Central

Grossi, Giuliano; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability. PMID:28103283

Clinical characteristics and outcomes with rivaroxaban vs. warfarin in patients with non-valvular atrial fibrillation but underlying native mitral and aortic valve disease participating in the ROCKET AF trial.

PubMed

Breithardt, Günter; Baumgartner, Helmut; Berkowitz, Scott D; Hellkamp, Anne S; Piccini, Jonathan P; Stevens, Susanna R; Lokhnygina, Yuliya; Patel, Manesh R; Halperin, Jonathan L; Singer, Daniel E; Hankey, Graeme J; Hacke, Werner; Becker, Richard C; Nessel, Christopher C; Mahaffey, Kenneth W; Fox, Keith A A; Califf, Robert M

2014-12-14

We investigated clinical characteristics and outcomes of patients with significant valvular disease (SVD) in the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) trial. ROCKET AF excluded patients with mitral stenosis or artificial valve prostheses. We used Cox regression to adjust comparisons for potential confounders. Among 14 171 patients, 2003 (14.1%) had SVD; they were older and had more comorbidities than patients without SVD. The rate of stroke or systemic embolism with rivaroxaban vs. warfarin was consistent among patients with SVD [2.01 vs. 2.43%; hazard ratio (HR) 0.83, 95% confidence interval (CI) 0.55-1.27] and without SVD (1.96 vs. 2.22%; HR 0.89, 95% CI 0.75-1.07; interaction P = 0.76). However, rates of major and non-major clinically relevant bleeding with rivaroxaban vs. warfarin were higher in patients with SVD (19.8% rivaroxaban vs. 16.8% warfarin; HR 1.25, 95% CI 1.05-1.49) vs. those without (14.2% rivaroxaban vs. 14.1% warfarin; HR 1.01, 95% CI 0.94-1.10; interaction P = 0.034), even when controlling for risk factors and potential confounders. In intracranial haemorrhage, there was no interaction between patients with and without SVD where the overall rate was lower among those randomized to rivaroxaban. Many patients with 'non-valvular atrial fibrillation' have significant valve lesions. Their risk of stroke is similar to that of patients without SVD after controlling for stroke risk factors. Efficacy of rivaroxaban vs. warfarin was similar in patients with and without SVD; however, the observed risk of bleeding was higher with rivaroxaban in patients with SVD but was the same among those without SVD. Atrial fibrillation patients with and without SVD experience the same stroke-preventive benefit of oral anticoagulants. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Cardiology.

Clinical characteristics and outcomes with rivaroxaban vs. warfarin in patients with non-valvular atrial fibrillation but underlying native mitral and aortic valve disease participating in the ROCKET AF trial

PubMed Central

Breithardt, Günter; Baumgartner, Helmut; Berkowitz, Scott D.; Hellkamp, Anne S.; Piccini, Jonathan P.; Stevens, Susanna R.; Lokhnygina, Yuliya; Patel, Manesh R.; Halperin, Jonathan L.; Singer, Daniel E.; Hankey, Graeme J.; Hacke, Werner; Becker, Richard C.; Nessel, Christopher C.; Mahaffey, Kenneth W.; Fox, Keith A. A.; Califf, Robert M.

2014-01-01

Aims We investigated clinical characteristics and outcomes of patients with significant valvular disease (SVD) in the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) trial. Methods and results ROCKET AF excluded patients with mitral stenosis or artificial valve prostheses. We used Cox regression to adjust comparisons for potential confounders. Among 14 171 patients, 2003 (14.1%) had SVD; they were older and had more comorbidities than patients without SVD. The rate of stroke or systemic embolism with rivaroxaban vs. warfarin was consistent among patients with SVD [2.01 vs. 2.43%; hazard ratio (HR) 0.83, 95% confidence interval (CI) 0.55–1.27] and without SVD (1.96 vs. 2.22%; HR 0.89, 95% CI 0.75–1.07; interaction P = 0.76). However, rates of major and non-major clinically relevant bleeding with rivaroxaban vs. warfarin were higher in patients with SVD (19.8% rivaroxaban vs. 16.8% warfarin; HR 1.25, 95% CI 1.05–1.49) vs. those without (14.2% rivaroxaban vs. 14.1% warfarin; HR 1.01, 95% CI 0.94–1.10; interaction P = 0.034), even when controlling for risk factors and potential confounders. In intracranial haemorrhage, there was no interaction between patients with and without SVD where the overall rate was lower among those randomized to rivaroxaban. Conclusions Many patients with ‘non-valvular atrial fibrillation’ have significant valve lesions. Their risk of stroke is similar to that of patients without SVD after controlling for stroke risk factors. Efficacy of rivaroxaban vs. warfarin was similar in patients with and without SVD; however, the observed risk of bleeding was higher with rivaroxaban in patients with SVD but was the same among those without SVD. Atrial fibrillation patients with and without SVD experience the same stroke-preventive benefit of oral anticoagulants. PMID:25148838

SVD/MCMC Data Analysis Pipeline for Global Redshifted 21-cm Spectrum Observations of the Cosmic Dawn and Dark Ages

NASA Astrophysics Data System (ADS)

Burns, Jack O.; Tauscher, Keith; Rapetti, David; Mirocha, Jordan; Switzer, Eric

2018-01-01

We have designed a complete data analysis pipeline for constraining Cosmic Dawn physics using sky-averaged spectra in the VHF range (40-200 MHz) obtained either from the ground (e.g., the Experiment to Detect Global Epoch of Reionization Signal, EDGES; and the Cosmic Twilight Polarimeter, CTP) or from orbit above the lunar farside (e.g., the Dark Ages Radio Explorer, DARE). In the case of DARE, we avoid Earth-based RFI, ionospheric effects, and radio solar emissions (when observing at night). To extract the 21-cm spectrum, we parametrize the cosmological signal and systematics with two separate sets of modes defined through Singular Value Decomposition (SVD) of training set curves. The training set for the 21-cm spin-flip brightness temperatures is composed of theoretical models of the first stars, galaxies and black holes created by varying physical parameters within the ares code. The systematics training set is created using sky and beam data to model the beam-weighted foregrounds (which are about four orders of magnitude larger than the signal) as well as expected lab data to model receiver systematics. To constrain physical parameters determining the 21-cm spectrum, we apply to the extracted signal a series of consecutive fitting techniques including two usages of a Markov Chain Monte Carlo (MCMC) algorithm. Importantly, our pipeline efficiently utilizes the significant differences between the foreground and the 21-cm signal in spatial and spectral variations. In addition, it incorporates for the first time polarization data, dramatically improving the constraining power. We are currently validating this end-to-end pipeline using detailed simulations of the signal, foregrounds and instruments. This work was directly supported by the NASA Solar System Exploration Research Virtual Institute cooperative agreement number 80ARC017M0006 and funding from the NASA Ames Research Center cooperative agreement NNX16AF59G.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

Free water determines diffusion alterations and clinical status in cerebral small vessel disease.

PubMed

Duering, Marco; Finsterwalder, Sofia; Baykara, Ebru; Tuladhar, Anil Man; Gesierich, Benno; Konieczny, Marek J; Malik, Rainer; Franzmeier, Nicolai; Ewers, Michael; Jouvent, Eric; Biessels, Geert Jan; Schmidt, Reinhold; de Leeuw, Frank-Erik; Pasternak, Ofer; Dichgans, Martin

2018-06-01

Diffusion tensor imaging detects early tissue alterations in Alzheimer's disease and cerebral small vessel disease (SVD). However, the origin of diffusion alterations in SVD is largely unknown. To gain further insight, we applied free water (FW) imaging to patients with genetically defined SVD (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy [CADASIL], n = 57), sporadic SVD (n = 444), and healthy controls (n = 28). We modeled freely diffusing water in the extracellular space (FW) and measures reflecting fiber structure (tissue compartment). We tested associations between these measures and clinical status (processing speed and disability). Diffusion alterations in SVD were mostly driven by increased FW and less by tissue compartment alterations. Among imaging markers, FW showed the strongest association with clinical status (R 2 up to 34%, P < .0001). Findings were consistent across patients with CADASIL and sporadic SVD. Diffusion alterations and clinical status in SVD are largely determined by extracellular fluid increase rather than alterations of white matter fiber organization. Copyright © 2018 the Alzheimer's Association. All rights reserved.

Cerebral Small Vessel Disease: Targeting Oxidative Stress as a Novel Therapeutic Strategy?

PubMed Central

De Silva, T. Michael; Miller, Alyson A.

2016-01-01

Cerebral small vessel disease (SVD) is a major contributor to stroke, and a leading cause of cognitive impairment and dementia. Despite the devastating effects of cerebral SVD, the pathogenesis of cerebral SVD is still not completely understood. Moreover, there are no specific pharmacological strategies for its prevention or treatment. Cerebral SVD is characterized by marked functional and structural abnormalities of the cerebral microcirculation. The clinical manifestations of these pathological changes include lacunar infarcts, white matter hyperintensities, and cerebral microbleeds. The main purpose of this review is to discuss evidence implicating oxidative stress in the arteriopathy of both non-amyloid and amyloid (cerebral amyloid angiopathy) forms of cerebral SVD and its most important risk factors (hypertension and aging), as well as its contribution to cerebral SVD-related brain injury and cognitive impairment. We also highlight current evidence of the involvement of the NADPH oxidases in the development of oxidative stress, enzymes that are a major source of reactive oxygen species in the cerebral vasculature. Lastly, we discuss potential pharmacological strategies for oxidative stress in cerebral SVD, including some of the historical and emerging NADPH oxidase inhibitors. PMID:27014073

Application of Diffusion Tensor Imaging Parameters to Detect Change in Longitudinal Studies in Cerebral Small Vessel Disease

PubMed Central

Zeestraten, Eva Anna; Benjamin, Philip; Lambert, Christian; Lawrence, Andrew John; Williams, Owen Alan; Morris, Robin Guy; Barrick, Thomas Richard; Markus, Hugh Stephen

2016-01-01

Cerebral small vessel disease (SVD) is the major cause of vascular cognitive impairment, resulting in significant disability and reduced quality of life. Cognitive tests have been shown to be insensitive to change in longitudinal studies and, therefore, sensitive surrogate markers are needed to monitor disease progression and assess treatment effects in clinical trials. Diffusion tensor imaging (DTI) is thought to offer great potential in this regard. Sensitivity of the various parameters that can be derived from DTI is however unknown. We aimed to evaluate the differential sensitivity of DTI markers to detect SVD progression, and to estimate sample sizes required to assess therapeutic interventions aimed at halting decline based on DTI data. We investigated 99 patients with symptomatic SVD, defined as clinical lacunar syndrome with MRI confirmation of a corresponding infarct as well as confluent white matter hyperintensities over a 3 year follow-up period. We evaluated change in DTI histogram parameters using linear mixed effect models and calculated sample size estimates. Over a three-year follow-up period we observed a decline in fractional anisotropy and increase in diffusivity in white matter tissue and most parameters changed significantly. Mean diffusivity peak height was the most sensitive marker for SVD progression as it had the smallest sample size estimate. This suggests disease progression can be monitored sensitively using DTI histogram analysis and confirms DTI’s potential as surrogate marker for SVD. PMID:26808982

Effects of amyloid and small vessel disease on white matter network disruption.

PubMed

Kim, Hee Jin; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Ye, Byoung Seok; Kim, Yeo Jin; Cho, Hanna; Choe, Yearn Seong; Lee, Kyung Han; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Na, Duk L; Seo, Sang Won

2015-01-01

There is growing evidence that the human brain is a large scale complex network. The structural network is reported to be disrupted in cognitively impaired patients. However, there have been few studies evaluating the effects of amyloid and small vessel disease (SVD) markers, the common causes of cognitive impairment, on structural networks. Thus, we evaluated the association between amyloid and SVD burdens and structural networks using diffusion tensor imaging (DTI). Furthermore, we determined if network parameters predict cognitive impairments. Graph theoretical analysis was applied to DTI data from 232 cognitively impaired patients with varying degrees of amyloid and SVD burdens. All patients underwent Pittsburgh compound-B (PiB) PET to detect amyloid burden, MRI to detect markers of SVD, including the volume of white matter hyperintensities and the number of lacunes, and detailed neuropsychological testing. The whole-brain network was assessed by network parameters of integration (shortest path length, global efficiency) and segregation (clustering coefficient, transitivity, modularity). PiB retention ratio was not associated with any white matter network parameters. Greater white matter hyperintensity volumes or lacunae numbers were significantly associated with decreased network integration (increased shortest path length, decreased global efficiency) and increased network segregation (increased clustering coefficient, increased transitivity, increased modularity). Decreased network integration or increased network segregation were associated with poor performances in attention, language, visuospatial, memory, and frontal-executive functions. Our results suggest that SVD alters white matter network integration and segregation, which further predicts cognitive dysfunction.

Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression.

PubMed

Kumar, Ranjeet; Kumar, A; Singh, G K

2016-06-01

In the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. This paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. The evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162bps data instead of 3960bps uncompressed data. The proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Total MRI Small Vessel Disease Burden Correlates with Cognitive Performance, Cortical Atrophy, and Network Measures in a Memory Clinic Population.

PubMed

Banerjee, Gargi; Jang, Hyemin; Kim, Hee Jin; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Na, Duk L; Seo, Sang Won; Werring, David John

2018-01-01

Recent evidence suggests that combining individual imaging markers of cerebral small vessel disease (SVD) may more accurately reflect its overall burden and better correlate with clinical measures. We wished to establish the clinical relevance of the total SVD score in a memory clinic population by investigating the association with SVD score and cognitive performance, cortical atrophy, and structural network measures, after adjusting for amyloid-β burden. We included 243 patients with amnestic mild cognitive impairment (MCI), Alzheimer's disease dementia, subcortical vascular MCI, or subcortical vascular dementia. All underwent MR and [11C] PiB-PET scanning and had standardized cognitive testing. Multiple linear regression was used to evaluate the relationships between SVD score and cognition, cortical thickness, and structural network measures. Path analyses were performed to evaluate whether network disruption mediates the effects of SVD score on cortical thickness and cognition. Total SVD score was associated with the performance of frontal (β - 4.31, SE 2.09, p = 0.040) and visuospatial (β - 0.95, SE 0.44, p = 0.032) tasks, and with reduced cortical thickness in widespread brain regions. Total SVD score was negatively correlated with nodal efficiency, as well as changes in brain network organization, with evidence of reduced integration and increasing segregation. Path analyses showed that the associations between SVD score and frontal and visuospatial scores were partially mediated by decreases in their corresponding nodal efficiency and cortical thickness. Total SVD burden has clinical relevance in a memory clinic population and correlates with cognition, and cortical atrophy, as well as structural network disruption.

SAR measurements of surface displacements at Augustine Volcano, Alaska from 1992 to 2005

USGS Publications Warehouse

Lee, C.-W.; Lu, Z.; Kwoun, Oh-Ig

2007-01-01

Augustine volcano is an active stratovolcano located at the southwest of Anchorage, Alaska. Augustine volcano had experienced seven significantly explosive eruptions in 1812, 1883, 1908, 1935, 1963, 1976, and 1986, and a minor eruption in January 2006. We measured the surface displacements of the volcano by radar interferometry and GPS before and after the eruption in 2006. ERS-1/2, RADARSAT-1 and ENVISAT SAR data were used for the study. Multiple interferograms were stacked to reduce artifacts caused by different atmospheric conditions. Least square (LS) method was used to reduce atmospheric artifacts. Singular value decomposition (SVD) method was applied for retrieval of time sequential deformations. Satellite radar interferometry helps to understand the surface displacements system of Augustine volcano. ?? 2007 IEEE.

SAR measurements of surface displacements at Augustine Volcano, Alaska from 1992 to 2005

USGS Publications Warehouse

Lee, C.-W.; Lu, Z.; Kwoun, Oh-Ig

2008-01-01

Augustine volcano is an active stratovolcano located at the southwest of Anchorage, Alaska. Augustine volcano had experienced seven significantly explosive eruptions in 1812, 1883, 1908, 1935, 1963, 1976, and 1986, and a minor eruption in January 2006. We measured the surface displacements of the volcano by radar interferometry and GPS before and after the eruption in 2006. ERS-1/2, RADARSAT-1 and ENVISAT SAR data were used for the study. Multiple interferograms were stacked to reduce artifacts caused by different atmospheric conditions. Least square (LS) method was used to reduce atmospheric artifacts. Singular value decomposition (SVD) method was applied for retrieval of time sequential deformations. Satellite radar interferometry helps to understand the surface displacements system of Augustine volcano. ?? 2007 IEEE.

A non-linear regression method for CT brain perfusion analysis

NASA Astrophysics Data System (ADS)

Bennink, E.; Oosterbroek, J.; Viergever, M. A.; Velthuis, B. K.; de Jong, H. W. A. M.

2015-03-01

CT perfusion (CTP) imaging allows for rapid diagnosis of ischemic stroke. Generation of perfusion maps from CTP data usually involves deconvolution algorithms providing estimates for the impulse response function in the tissue. We propose the use of a fast non-linear regression (NLR) method that we postulate has similar performance to the current academic state-of-art method (bSVD), but that has some important advantages, including the estimation of vascular permeability, improved robustness to tracer-delay, and very few tuning parameters, that are all important in stroke assessment. The aim of this study is to evaluate the fast NLR method against bSVD and a commercial clinical state-of-art method. The three methods were tested against a published digital perfusion phantom earlier used to illustrate the superiority of bSVD. In addition, the NLR and clinical methods were also tested against bSVD on 20 clinical scans. Pearson correlation coefficients were calculated for each of the tested methods. All three methods showed high correlation coefficients (>0.9) with the ground truth in the phantom. With respect to the clinical scans, the NLR perfusion maps showed higher correlation with bSVD than the perfusion maps from the clinical method. Furthermore, the perfusion maps showed that the fast NLR estimates are robust to tracer-delay. In conclusion, the proposed fast NLR method provides a simple and flexible way of estimating perfusion parameters from CT perfusion scans, with high correlation coefficients. This suggests that it could be a better alternative to the current clinical and academic state-of-art methods.

Characterizing the 21-cm absorption trough with pattern recognition and a numerical sampler

NASA Astrophysics Data System (ADS)

Tauscher, Keith A.; Rapetti, David; Burns, Jack O.; Monsalve, Raul A.; Bowman, Judd D.

2018-06-01

The highly redshifted sky-averaged 21-cm spectrum from neutral hydrogen is a key probe to a period of the Universe never before studied. Recent experimental advances have led to increasingly tightened constraints and the Experiment to Detect the Global Eor Signal (EDGES) has presented evidence for a detection of this global signal. In order to glean scientifically valuable information from these new measurements in a consistent manner, sophisticated fitting procedures must be applied. Here, I present a pipeline known as pylinex which takes advantage of Singular Value Decomposition (SVD), a pattern recognition tool, to leverage structure in the data induced by the design of an experiment to fit for signals in the experiment's data in the presence of large systematics (such as the beam-weighted foregrounds), especially those without parametric forms. This method requires training sets for each component of the data. Once the desired signal is extracted in SVD eigenmode coefficient space, the posterior distribution must be consistently transformed into a physical parameter space. This is done with the combination of a numerical least squares fitter and a Markov Chain Monte Carlo (MCMC) distribution sampler. After describing the pipeline's procedures and techniques, I present preliminary results of applying it to the EDGES low-band data used for their detection. The results include estimates of the signal in frequency space with errors and relevant parameter distributions.

PICKY: a novel SVD-based NMR spectra peak picking method.

PubMed

Alipanahi, Babak; Gao, Xin; Karakoc, Emre; Donaldson, Logan; Li, Ming

2009-06-15

Picking peaks from experimental NMR spectra is a key unsolved problem for automated NMR protein structure determination. Such a process is a prerequisite for resonance assignment, nuclear overhauser enhancement (NOE) distance restraint assignment, and structure calculation tasks. Manual or semi-automatic peak picking, which is currently the prominent way used in NMR labs, is tedious, time consuming and costly. We introduce new ideas, including noise-level estimation, component forming and sub-division, singular value decomposition (SVD)-based peak picking and peak pruning and refinement. PICKY is developed as an automated peak picking method. Different from the previous research on peak picking, we provide a systematic study of the proposed method. PICKY is tested on 32 real 2D and 3D spectra of eight target proteins, and achieves an average of 88% recall and 74% precision. PICKY is efficient. It takes PICKY on average 15.7 s to process an NMR spectrum. More important than these numbers, PICKY actually works in practice. We feed peak lists generated by PICKY to IPASS for resonance assignment, feed IPASS assignment to SPARTA for fragments generation, and feed SPARTA fragments to FALCON for structure calculation. This results in high-resolution structures of several proteins, for example, TM1112, at 1.25 A. PICKY is available upon request. The peak lists of PICKY can be easily loaded by SPARKY to enable a better interactive strategy for rapid peak picking.

Geometry of the 1954 Fairview Peak-Dixie Valley earthquake sequence from a joint inversion of leveling and triangulation data

USGS Publications Warehouse

Hodgkinson, K.M.; Stein, R.S.; Marshall, G.

1996-01-01

In 1954, four earthquakes greater than Ms=6.0 occurred within a 30-km radius and in a period of 6 months. Elevation and angle changes calculated from repeated leveling and triangulation surveys which span the coseismic period provide constraints on the fault geometries and coseismic slip of the faults which were activated. The quality of the coseismic geodetic data is assessed. Corrections are applied to the leveling data for subsidence due to groundwater withdrawal in the Fallon area, and a rod miscalibration error of 150??30 ppm is isolated in leveling surveys made in 1967. The leveling and triangulation observations are then simultaneously inverted using the single value decomposition (SVD) inversion method to determine fault geometries and coseismic slip. Using SVD, it is possible to determine on which faults slip is resolvable given the data distribution. The faults are found to dip between 50?? and 80?? and extend to depths of 5 to 14 km. The geodetically derived slip values are generally equal to, or greater than, the maximum observed displacement along the surface scarps. Where slip is resolvable the geodetic data indicates the 1954 sequence contained a significant component of right-lateral slip. This is consistent with the N15??W trending shear zone which geodetic surveys have detected in western Nevada. Copyright 1996 by the American Geophysical Union.

Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions

PubMed Central

Kluger, Yuval; Basri, Ronen; Chang, Joseph T.; Gerstein, Mark

2003-01-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find “marker genes” that are differentially expressed in particular sets of “conditions.” We have developed a method that simultaneously clusters genes and conditions, finding distinctive “checkerboard” patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data). PMID:12671006

Linear signatures in nonlinear gyrokinetics: interpreting turbulence with pseudospectra

DOE PAGES

Hatch, D. R.; Jenko, F.; Navarro, A. Banon; ...

2016-07-26

A notable feature of plasma turbulence is its propensity to retain features of the underlying linear eigenmodes in a strongly turbulent state—a property that can be exploited to predict various aspects of the turbulence using only linear information. In this context, this work examines gradient-driven gyrokinetic plasma turbulence through three lenses—linear eigenvalue spectra, pseudospectra, and singular value decomposition (SVD). We study a reduced gyrokinetic model whose linear eigenvalue spectra include ion temperature gradient driven modes, stable drift waves, and kinetic modes representing Landau damping. The goal is to characterize in which ways, if any, these familiar ingredients are manifest inmore » the nonlinear turbulent state. This pursuit is aided by the use of pseudospectra, which provide a more nuanced view of the linear operator by characterizing its response to perturbations. We introduce a new technique whereby the nonlinearly evolved phase space structures extracted with SVD are linked to the linear operator using concepts motivated by pseudospectra. Using this technique, we identify nonlinear structures that have connections to not only the most unstable eigenmode but also subdominant modes that are nonlinearly excited. The general picture that emerges is a system in which signatures of the linear physics persist in the turbulence, albeit in ways that cannot be fully explained by the linear eigenvalue approach; a non-modal treatment is necessary to understand key features of the turbulence.« less

Only White Matter Hyperintensities Predicts Post-Stroke Cognitive Performances Among Cerebral Small Vessel Disease Markers: Results from the TABASCO Study.

PubMed

Molad, Jeremy; Kliper, Efrat; Korczyn, Amos D; Ben Assayag, Einor; Ben Bashat, Dafna; Shenhar-Tsarfaty, Shani; Aizenstein, Orna; Shopin, Ludmila; Bornstein, Natan M; Auriel, Eitan

2017-01-01

White matter hyperintensities (WMH) were shown to predict cognitive decline following stroke or transient ischemic attack (TIA). However, WMH are only one among other radiological markers of cerebral small vessel disease (SVD). The aim of this study was to determine whether adding other SVD markers to WMH improves prediction of post-stroke cognitive performances. Consecutive first-ever stroke or TIA patients (n = 266) from the Tel Aviv Acute Brain Stroke Cohort (TABASCO) study were enrolled. MRI scans were performed within seven days of stroke onset. We evaluated the relationship between cognitive performances one year following stroke, and previously suggested total SVD burden score including WMH, lacunes, cerebral microbleeds (CMB), and perivascular spaces (PVS). Significant negative associations were found between WMH and cognition (p < 0.05). Adding other SVD markers (lacunes, CMB, PVS) to WMH did not improve predication of post-stroke cognitive performances. Negative correlations between SVD burden score and cognitive scores were observed for global cognitive, memory, and visual spatial scores (all p < 0.05). However, following an adjustment for confounders, no associations remained significant. WMH score was associated with poor post-stroke cognitive performance. Adding other SVD markers or SVD burden score, however, did not improve prediction.

Impacts of El Niño and El Niño Modoki on the precipitation in Colombia

NASA Astrophysics Data System (ADS)

Córdoba Machado, Samir; Palomino Lemus, Reiner; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda; Jesús Esteban Parra, María

2015-04-01

The influence of the tropical Pacific SST on precipitation in Colombia is examined using 341 stations covering the period 1979-2009. Through a Singular Value Decomposition (SVD) the two main coupled variability modes show SST patterns clearly associated with El Niño (EN) and El Niño Modoki (ENM), respectively, presenting great coupling strength with the corresponding seasonal precipitation modes in Colombia. The results reveal that, mainly in winter and summer, EN and ENM events are associated with a significant rainfall decrease over northern, central, and western Colombia. The opposite effect occurs in some localities during spring, summer, and autumn. The southwestern region of Colombia exhibits an opposite behaviour connected to EN and ENM events during years when both events do not coexist, showing that the seasonal precipitation response is not linear. The Partial Regression Analysis used to quantify separately the influence of the two types of ENSO on seasonal precipitation shows the importance of both types in the reconstruction process. The results obtained in this study establish the base for modeling and forecasting the seasonal precipitation in Colombia using the tropical Pacific SST associated with El Niño and El Niño Modoki. Keywords: Seasonal precipitation, Tropical Pacific SST, El Niño, El Niño Modoki, Singular Value Decomposition, Colombia. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

An efficient ASIC implementation of 16-channel on-line recursive ICA processor for real-time EEG system.

PubMed

Fang, Wai-Chi; Huang, Kuan-Ju; Chou, Chia-Ching; Chang, Jui-Chung; Cauwenberghs, Gert; Jung, Tzyy-Ping

2014-01-01

This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.

Inversion of gravity gradient tensor data: does it provide better resolution?

NASA Astrophysics Data System (ADS)

Paoletti, V.; Fedi, M.; Italiano, F.; Florio, G.; Ialongo, S.

2016-04-01

The gravity gradient tensor (GGT) has been increasingly used in practical applications, but the advantages and the disadvantages of the analysis of GGT components versus the analysis of the vertical component of the gravity field are still debated. We analyse the performance of joint inversion of GGT components versus separate inversion of the gravity field alone, or of one tensor component. We perform our analysis by inspection of the Picard Plot, a Singular Value Decomposition tool, and analyse both synthetic data and gradiometer measurements carried out at the Vredefort structure, South Africa. We show that the main factors controlling the reliability of the inversion are algebraic ambiguity (the difference between the number of unknowns and the number of available data points) and signal-to-noise ratio. Provided that algebraic ambiguity is kept low and the noise level is small enough so that a sufficient number of SVD components can be included in the regularized solution, we find that: (i) the choice of tensor components involved in the inversion is not crucial to the overall reliability of the reconstructions; (ii) GGT inversion can yield the same resolution as inversion with a denser distribution of gravity data points, but with the advantage of using fewer measurement stations.

Structure-seeking multilinear methods for the analysis of fMRI data.

PubMed

Andersen, Anders H; Rayens, William S

2004-06-01

In comprehensive fMRI studies of brain function, the data structures often contain higher-order ways such as trial, task condition, subject, and group in addition to the intrinsic dimensions of time and space. While multivariate bilinear methods such as principal component analysis (PCA) have been used successfully for extracting information about spatial and temporal features in data from a single fMRI run, the need to unfold higher-order data sets into bilinear arrays has led to decompositions that are nonunique and to the loss of multiway linkages and interactions present in the data. These additional dimensions or ways can be retained in multilinear models to produce structures that are unique and which admit interpretations that are neurophysiologically meaningful. Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model. A trilinear model was fitted to a data cube of dimensions voxels by time by run. Similarly, a quadrilinear model was fitted to a higher-way structure of dimensions voxels by time by trial by run. The spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

Redundancy Analysis of Capacitance Data of a Coplanar Electrode Array for Fast and Stable Imaging Processing

PubMed Central

Wen, Yintang; Zhang, Zhenda; Zhang, Yuyan; Sun, Dongtao

2017-01-01

A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced. PMID:29295537

Depressive symptoms as a predictor of quality of life in cerebral small vessel disease, acting independently of disability; a study in both sporadic SVD and CADASIL

PubMed Central

Brookes, Rebecca L; Willis, Thomas A; Patel, Bhavini; Morris, Robin G; Markus, Hugh S

2013-01-01

Background Cerebral small vessel disease (SVD) causes lacunar stroke, and more recently has been implicated as a cause of depression. Factors causing reduced quality of life (QoL) in SVD, including the relative contributions of disability and depressive symptoms, remain uncertain. Hypothesis Depressive symptoms are a major predictor of reduced QoL in SVD, acting independently of disability. Methods The Stroke-Specific QoL scale was completed by 100 patients with SVD (lacunar stroke with MRI lacunar infarct) and 55 controls. We repeated the protocol in 40 patients with the young onset genetic form of SVD, CADASIL, and 35 controls. Disability (modified Rankin Scale), [instrumental] activities of daily living (IADL, ADL), cognition (Mini Mental State Examination) and depressive symptoms (Geriatric Depression Scale, Montgomery-Åsberg Depression Rating Scale) were measured. Results QoL was significantly lower in SVD than controls: mean (SD), 196.8 (35.2) versus 226.8(15.3), p<.0001. Depressive symptoms were the major predictor of QoL, accounting for 52.9% of variance. The only other independent predictor of QoL was disability, accounting for an additional 18.4%. A similar pattern was found in CADASIL with reduced QoL (202.0(29.7) versus controls (228.6 (13.1); p<.0001), and depressive symptoms accounting for 42.2% of variance. Disability accounted for an additional 17.6%. Relationships between depression and QoL, and disability and QoL, were independent of one another. Conclusions Depressive symptoms, often unrecognized, are a major determinant of reduced QoL in SVD. They account for greater reduction than disability, and the association is independent of disability. This relationship may reflect the proposed causal association between white matter disease and depression. Treatment of depressive symptoms might significantly improve QoL in SVD. PMID:22364606

Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

2016-09-01

For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

A Partial Least-Squares Analysis of Health-Related Quality-of-Life Outcomes After Aneurysmal Subarachnoid Hemorrhage.

PubMed

Young, Julia M; Morgan, Benjamin R; Mišić, Bratislav; Schweizer, Tom A; Ibrahim, George M; Macdonald, R Loch

2015-12-01

Individuals who have aneurysmal subarachnoid hemorrhages (SAHs) experience decreased health-related qualities of life (HRQoLs) that persist after the primary insult. To identify clinical variables that concurrently associate with HRQoL outcomes by using a partial least-squares approach, which has the distinct advantage of explaining multidimensional variance where predictor variables may be highly collinear. Data collected from the CONSCIOUS-1 trial was used to extract 29 clinical variables including SAH presentation, hospital procedures, and demographic information in addition to 5 HRQoL outcome variables for 256 individuals. A partial least-squares analysis was performed by calculating a heterogeneous correlation matrix and applying singular value decomposition to determine components that best represent the correlations between the 2 sets of variables. Bootstrapping was used to estimate statistical significance. The first 2 components accounting for 81.6% and 7.8% of the total variance revealed significant associations between clinical predictors and HRQoL outcomes. The first component identified associations between disability in self-care with longer durations of critical care stay, invasive intracranial monitoring, ventricular drain time, poorer clinical grade on presentation, greater amounts of cerebral spinal fluid drainage, and a history of hypertension. The second component identified associations between disability due to pain and discomfort as well as anxiety and depression with greater body mass index, abnormal heart rate, longer durations of deep sedation and critical care, and higher World Federation of Neurosurgical Societies and Hijdra scores. By applying a data-driven, multivariate approach, we identified robust associations between SAH clinical presentations and HRQoL outcomes. EQ-VAS, EuroQoL visual analog scaleHRQoL, health-related quality of lifeICU, intensive care unitIVH, intraventricular hemorrhagePLS, partial least squaresSAH, subarachnoid hemorrhageSVD, singular value decompositionWFNS, World Federation of Neurosurgical Societies.

Age-Specific Associations of Renal Impairment With Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease in Transient Ischemic Attack and Stroke.

PubMed

Liu, Bian; Lau, Kui Kai; Li, Linxin; Lovelock, Caroline; Liu, Ming; Kuker, Wilhelm; Rothwell, Peter M

2018-04-01

It has been hypothesized that cerebral small vessel disease (SVD) and chronic renal impairment may be part of a multisystem small-vessel disorder, but their association may simply be as a result of shared risk factors (eg, hypertension) rather than to a systemic susceptibility to premature SVD. However, most previous studies were hospital based, most had inadequate adjustment for hypertension, many were confined to patients with lacunar stroke, and none stratified by age. In a population-based study of transient ischemic attack and ischemic stroke (OXVASC [Oxford Vascular Study]), we evaluated the magnetic resonance imaging markers of cerebral SVD, including lacunes, white matter hyperintensities, cerebral microbleeds, and enlarged perivascular space. We studied the age-specific associations of renal impairment (estimated glomerular filtration rate <60 mL/min per 1.73 m 2 ) and total SVD burden (total SVD score) adjusting for age, sex, vascular risk factors, and premorbid blood pressure (mean blood pressure during 15 years preevent). Of 1080 consecutive patients, 1028 (95.2%) had complete magnetic resonance imaging protocol and creatinine measured at baseline. Renal impairment was associated with total SVD score (odds ratio [OR], 2.16; 95% confidence interval [CI], 1.69-2.75; P <0.001), but only at age <60 years (<60 years: OR, 3.97; 95% CI, 1.69-9.32; P =0.002; 60-79 years: OR, 1.01; 95% CI, 0.72-1.41; P =0.963; ≥80 years: OR, 0.95; 95% CI, 0.59-1.54; P =0.832). The overall association of renal impairment and total SVD score was also attenuated after adjustment for age, sex, history of hypertension, diabetes mellitus, and premorbid average systolic blood pressure (adjusted OR, 0.76; 95% CI, 0.56-1.02; P =0.067), but the independent association of renal impairment and total SVD score at age <60 years was maintained (adjusted OR, 3.11; 95% CI, 1.21-7.98; P =0.018). Associations of renal impairment and SVD were consistent for each SVD marker at age <60 years but were strongest for cerebral microbleeds (OR, 5.84; 95% CI, 1.45-23.53; P =0.013) and moderate-severe periventricular white matter hyperintensities (OR, 6.28; 95% CI, 1.54-25.63; P =0.010). The association of renal impairment and cerebral SVD was attenuated with adjustment for shared risk factors at older ages, but remained at younger ages, consistent with a shared susceptibility to premature disease. © 2018 The Authors.

Brain atrophy and cerebral small vessel disease: a prospective follow-up study.

PubMed

Nitkunan, Arani; Lanfranconi, Silvia; Charlton, Rebecca A; Barrick, Thomas R; Markus, Hugh S

2011-01-01

cerebral small vessel disease (SVD) is the most common cause of vascular dementia. Interest in the use of surrogate markers is increasing. The aims of this study were to determine if brain volume was different between patients with SVD and control subjects, whether it correlated with cognition in SVD, and whether changes in brain volume could be detected during prospective follow-up. thirty-five patients (mean age, 68.8 years) who had a lacunar stroke and radiological evidence of confluent leukoaraiosis and 70 age- and gender-matched control subjects were recruited. Whole-brain T1-weighted imaging and neuropsychological testing were performed after 1 year on all patients and after 2 years for the control subjects. Fully automated software was used to determine brain volume and percentage brain volume change. An executive function score was derived. there was a significant difference in brain volume between the patients with SVD and control subjects (mean ± SD [mL] 1529 ± 84 versus 1573 ± 69, P=0.019). In the patients with SVD, there was a significant association between brain volume and executive function (r=0.501, P<0.05). The mean ± SD yearly brain atrophy rate for patients with SVD and control subjects was significantly different (-0.914% ± 0.8% versus -0.498% ± 0.4%, respectively, P=0.017). No change in executive function score was detected over this period. brain volume is reduced in SVD and a decline is detectable prospectively. The correlation with executive function at a cross-sectional level and the change in brain volume with time are both promising for the use of brain atrophy as a surrogate marker of SVD progression.

Cerebral small vessel disease, medial temporal lobe atrophy and cognitive status in patients with ischaemic stroke and transient ischaemic attack.

PubMed

Arba, F; Quinn, T; Hankey, G J; Ali, M; Lees, K R; Inzitari, D

2017-02-01

Small vessel disease (SVD) and Alzheimer's disease (AD) are two common causes of cognitive impairment and dementia, traditionally considered as distinct processes. The relationship between radiological features suggestive of AD and SVD was explored, and the association of each of these features with cognitive status at 1 year was investigated in patients with stroke or transient ischaemic attack. Anonymized data were accessed from the Virtual International Stroke Trials Archive (VISTA). Medial temporal lobe atrophy (MTA; a marker of AD) and markers of SVD were rated using validated ordinal visual scales. Cognitive status was evaluated with the Mini Mental State Examination (MMSE) 1 year after the index stroke. Logistic regression models were used to investigate independent associations between (i) baseline SVD features and MTA and (ii) all baseline neuroimaging features and cognitive status 1 year post-stroke. In all, 234 patients were included, mean (±SD) age 65.7 ± 13.1 years, 145 (62%) male. Moderate to severe MTA was present in 104 (44%) patients. SVD features were independently associated with MTA (P < 0.001). After adjusting for age, sex, disability after stroke, hypertension and diabetes mellitus, MTA was the only radiological feature independently associated with cognitive impairment, defined using thresholds of MMSE ≤ 26 (odds ratio 1.94; 95% confidence interval 1.28-2.94) and MMSE ≤ 23 (odds ratio 2.31; 95% confidence interval 1.48-3.62). In patients with ischaemic cerebrovascular disease, SVD features are associated with MTA, which is a common finding in stroke survivors. SVD and AD type neurodegeneration coexist, but the AD marker MTA, rather than SVD markers, is associated with post-stroke cognitive impairment. © 2016 EAN.

[Serological examinations for swine vesicular disease (SVD) in a closed pig breeding herd using ELISA].

PubMed

Pannwitz, Gunter; Haas, Bernd; Hoffmann, Bernd; Fischer, Sebastian

2009-01-01

In a closed pig establishment housing about 18,000 pigs, 2895 gilts were tested pre-export for SVD (swine vesicular disease) antibodies using Ceditest/PrioCHECK SVDV-AB ELISA. 130 gilts (4.5%) tested positive. In addition, 561 animals of this farm were sampled per random for SVD serology. One in 241 weaners (0.4%), eight in 150 gilts (5.3%) and 18 in 170 (10.6%) pregnant sows tested ELISA SVD-antibody positive. Of the ELISA positive samples, 23 tested positive in VNT (virus neutralization test). Of these, 20 VNT-positive animals were re-sampled two weeks later and re-tested via ELISA and VNT in different laboratories, displaying falling titres with one to two animals remaining VNT-positive. Epidemiological investigations and clinical examinations on site did not yield any evidence for SVD. 745 faecal samples taken from individual pigs and collected from pens tested negative in SVDV-RNA-PCR. 40 of these samples tested negative in virus isolation on cell culture. Pathological examinations on fallen pigs did not reveal any evidence for SVD either. After comparing our ELISA results with data recorded in the ELISA validation by Chenard et al. (1998), we propose that the published test performance is perhaps not currently applicable for the commercial test. Provided that SVD-antibody negative pigs were tested, a specificity of 99.6% in weaners, 95.5% in gilts and 89.4% in pregnant sows would appear to be more appropriate for the Ceditest/PrioCHECK SVDV-AB ELISA. Details are provided for all examined pigs regarding husbandry, breed, age, weeks pregnant and previous vaccinations. The results of other serological tests on the same sera are given. Possible clusterings of false-positive SVD-ELISA results are discussed.

[Management of cerebral small vessel disease for the diagnosis and treatment of dementia].

PubMed

Ihara, Masafumi

2013-07-01

With the demographic shift in life expectancy inexorably increasing in developed countries, dementia is set to become one of the most important health problems worldwide. In recent years, cerebral small vessel disease (SVD) has received much attention as an important cause of dementia. The reason for this is twofold: firstly, arteriosclerosis (type 1 SVD) is the leading cause of vascular cognitive impairment, and secondly, cerebral amyloid angiopathy (CAA; type 2 SVD) is an almost invariable accompaniment of Alzheimer's disease. SVD is known to induce a variety of pathological changes; for example, type 1 SVD results in lacunar infarction, deep microbleeds, and white matter damage, while type 2 SVD leads to cortical microinfarcts, lobar microbleeds, and white matter damage. SVD is considered a spectrum of abnormalities, with the majority of patients experiencing symptoms from both type 1 and type 2 SVD as the disease progresses. The discouraging results of immunotherapy clinical trials for Alzheimer's disease have shifted the scientific attention from the classical neuron-centric approach towards a novel neurovascular approach. As arteries stiffen with age or with other co-morbid factors such as life-related diseases, amyloid β (Aβ) synthesis becomes upregulated, resulting in the deposition of insoluble Aβ not only in the parenchyma as senile plaques but also in the perivascular drainage pathways as CAA. Therefore, therapeutic strategies such as vasoactive drugs that enhance the patency of this Aβ drainage pathway may facilitate Aβ removal and help prevent cognitive decline in the elderly. Based on this emerging paradigm, clinical trials are warranted to investigate whether a neurovascular therapeutic approach can effectively halt cognitive decline and act as a preemptive medicine for patients at risk of dementia.

Belle II SVD ladder assembly procedure and electrical qualification

NASA Astrophysics Data System (ADS)

Adamczyk, K.; Aihara, H.; Angelini, C.; Aziz, T.; Babu, Varghese; Bacher, S.; Bahinipati, S.; Barberio, E.; Baroncelli, T.; Basith, A. K.; Batignani, G.; Bauer, A.; Behera, P. K.; Bergauer, T.; Bettarini, S.; Bhuyan, B.; Bilka, T.; Bosi, F.; Bosisio, L.; Bozek, A.; Buchsteiner, F.; Casarosa, G.; Ceccanti, M.; Červenkov, D.; Chendvankar, S. R.; Dash, N.; Divekar, S. T.; Doležal, Z.; Dutta, D.; Forti, F.; Friedl, M.; Hara, K.; Higuchi, T.; Horiguchi, T.; Irmler, C.; Ishikawa, A.; Jeon, H. B.; Joo, C.; Kandra, J.; Kang, K. H.; Kato, E.; Kawasaki, T.; Kodyš, P.; Kohriki, T.; Koike, S.; Kolwalkar, M. M.; Kvasnička, P.; Lanceri, L.; Lettenbicher, J.; Mammini, P.; Mayekar, S. N.; Mohanty, G. B.; Mohanty, S.; Morii, T.; Nakamura, K. R.; Natkaniec, Z.; Negishi, K.; Nisar, N. K.; Onuki, Y.; Ostrowicz, W.; Paladino, A.; Paoloni, E.; Park, H.; Pilo, F.; Profeti, A.; Rao, K. K.; Rashevskaya, I.; Rizzo, G.; Rozanska, M.; Sandilya, S.; Sasaki, J.; Sato, N.; Schultschik, S.; Schwanda, C.; Seino, Y.; Shimizu, N.; Stypula, J.; Tanaka, S.; Tanida, K.; Taylor, G. N.; Thalmeier, R.; Thomas, R.; Tsuboyama, T.; Uozumi, S.; Urquijo, P.; Vitale, L.; Volpi, M.; Watanuki, S.; Watson, I. J.; Webb, J.; Wiechczynski, J.; Williams, S.; Würkner, B.; Yamamoto, H.; Yin, H.; Yoshinobu, T.; Belle II SVD Collaboration

2016-07-01

The Belle II experiment at the SuperKEKB asymmetric e+e- collider in Japan will operate at a luminosity approximately 50 times larger than its predecessor (Belle). At its heart lies a six-layer vertex detector comprising two layers of pixelated silicon detectors (PXD) and four layers of double-sided silicon microstrip detectors (SVD). One of the key measurements for Belle II is time-dependent CP violation asymmetry, which hinges on a precise charged-track vertex determination. Towards this goal, a proper assembly of the SVD components with precise alignment ought to be performed and the geometrical tolerances should be checked to fall within the design limits. We present an overview of the assembly procedure that is being followed, which includes the precision gluing of the SVD module components, wire-bonding of the various electrical components, and precision three dimensional coordinate measurements of the jigs used in assembly as well as of the final SVD modules.

Optimization methodology for the global 10 Hz orbit feedback in RHIC

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

Liu, Chuyu; Hulsart, R.; Mernick, K.

To combat beam oscillations induced by triplet vibrations at the Relativistic Heavy Ion Collider (RHIC), a global orbit feedback system was developed and applied at injection and top energy in 2011, and during beam acceleration in 2012. Singular Value Decomposition (SVD) was employed to determine the strengths and currents of the applied corrections. The feedback algorithm was optimized for different magnetic configurations (lattices) at fixed beam energies and during beam acceleration. While the orbit feedback performed well since its inception, corrector current transients and feedback-induced beam oscillations were observed during the polarized proton program in 2015. In this paper, wemore » present the feedback algorithm, the optimization of the algorithm for various lattices and the solution adopted to mitigate the observed current transients during beam acceleration.« less

Classification of subsurface objects using singular values derived from signal frames

DOEpatents

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Optimization methodology for the global 10 Hz orbit feedback in RHIC

DOE PAGES

Liu, Chuyu; Hulsart, R.; Mernick, K.; ...

2018-05-08

To combat beam oscillations induced by triplet vibrations at the Relativistic Heavy Ion Collider (RHIC), a global orbit feedback system was developed and applied at injection and top energy in 2011, and during beam acceleration in 2012. Singular Value Decomposition (SVD) was employed to determine the strengths and currents of the applied corrections. The feedback algorithm was optimized for different magnetic configurations (lattices) at fixed beam energies and during beam acceleration. While the orbit feedback performed well since its inception, corrector current transients and feedback-induced beam oscillations were observed during the polarized proton program in 2015. In this paper, wemore » present the feedback algorithm, the optimization of the algorithm for various lattices and the solution adopted to mitigate the observed current transients during beam acceleration.« less

Vacuum Magnetic Field Mapping of the Compact Toroidal Hybrid (CTH)

NASA Astrophysics Data System (ADS)

Peterson, J. T.; Hanson, J.; Hartwell, G. J.; Knowlton, S. F.; Montgomery, C.; Munoz, J.

2007-11-01

Vacuum magnetic field mapping experiments are performed on the CTH torsatron with a movable electron gun and phosphor-coated screen or movable wand at two different toroidal locations. These experiments compare the experimentally measured magnetic configuration produced by the as-built coil set, to the magnetic configuration simulated with the IFT Biot-Savart code using the measured coil set parameters. Efforts to minimize differences between the experimentally measured location of the magnetic axis and its predicted value utilizing a Singular Value Decomposition (SVD) process result in small modifications of the helical coil winding law used to model the vacuum magnetic field geometry of CTH. Because these studies are performed at relatively low fields B = 0.01 - 0.05 T, a uniform ambient magnetic field is included in the minimization procedure.

Detecting chaos, determining the dimensions of tori and predicting slow diffusion in Fermi-Pasta-Ulam lattices by the Generalized Alignment Index method

NASA Astrophysics Data System (ADS)

Skokos, C.; Bountis, T.; Antonopoulos, C.

2008-12-01

The recently introduced GALI method is used for rapidly detecting chaos, determining the dimensionality of regular motion and predicting slow diffusion in multi-dimensional Hamiltonian systems. We propose an efficient computation of the GALIk indices, which represent volume elements of k randomly chosen deviation vectors from a given orbit, based on the Singular Value Decomposition (SVD) algorithm. We obtain theoretically and verify numerically asymptotic estimates of GALIs long-time behavior in the case of regular orbits lying on low-dimensional tori. The GALIk indices are applied to rapidly detect chaotic oscillations, identify low-dimensional tori of Fermi-Pasta-Ulam (FPU) lattices at low energies and predict weak diffusion away from quasiperiodic motion, long before it is actually observed in the oscillations.

Evolution of gut microbiota composition from birth to 24 weeks in the INFANTMET Cohort.

PubMed

Hill, Cian J; Lynch, Denise B; Murphy, Kiera; Ulaszewska, Marynka; Jeffery, Ian B; O'Shea, Carol Anne; Watkins, Claire; Dempsey, Eugene; Mattivi, Fulvio; Tuohy, Kieran; Ross, R Paul; Ryan, C Anthony; O' Toole, Paul W; Stanton, Catherine

2017-01-17

The gut is the most extensively studied niche of the human microbiome. The aim of this study was to characterise the initial gut microbiota development of a cohort of breastfed infants (n = 192) from 1 to 24 weeks of age. V4-V5 region 16S rRNA amplicon Illumina sequencing and, in parallel, bacteriological culture. The metabolomic profile of infant urine at 4 weeks of age was also examined by LC-MS. Full-term (FT), spontaneous vaginally delivered (SVD) infants' microbiota remained stable at both phylum and genus levels during the 24-week period examined. FT Caesarean section (CS) infants displayed an increased faecal abundance of Firmicutes (p < 0.01) and lower abundance of Actinobacteria (p < 0.001) after the first week of life compared to FT-SVD infants. FT-CS infants gradually progressed to harbouring a microbiota closely resembling FT-SVD (which remained stable) by week 8 of life, which was maintained at week 24. The gut microbiota of preterm (PT) infants displayed a significantly greater abundance of Proteobacteria compared to FT infants (p < 0.001) at week 1. Metabolomic analysis of urine at week 4 indicated PT-CS infants have a functionally different metabolite profile than FT (both CS and SVD) infants. Co-inertia analysis showed co-variation between the urine metabolome and the faecal microbiota of the infants. Tryptophan and tyrosine metabolic pathways, as well as fatty acid and bile acid metabolism, were found to be affected by delivery mode and gestational age. These findings confirm that mode of delivery and gestational age both have significant effects on early neonatal microbiota composition. There is also a significant difference between the metabolite profile of FT and PT infants. Prolonged breastfeeding was shown to have a significant effect on the microbiota composition of FT-CS infants at 24 weeks of age, but interestingly not on that of FT-SVD infants. Twins had more similar microbiota to one another than between two random infants, reflecting the influence of similarities in both host genetics and the environment on the microbiota..

High amount of dietary fiber not harmful but favorable for Crohn disease.

PubMed

Chiba, Mitsuro; Tsuji, Tsuyotoshi; Nakane, Kunio; Komatsu, Masafumi

2015-01-01

Current chronic diseases are a reflection of the westernized diet that features a decreased consumption of dietary fiber. Indigestible dietary fiber is metabolized by gut bacteria, including Faecalibacterium prausnitzii, to butyrate, which has a critical role in colonic homeostasis owing to a variety of functions. Dietary fiber intake has been significantly inversely associated with the risk of chronic diseases. Crohn disease (CD) is not an exception. However, even authors who reported the inverse association between dietary fiber and a risk of CD made no recommendation of dietary fiber intake to CD patients. Some correspondence was against advocating high fiber intake in CD. We initiated a semivegetarian diet (SVD), namely a lacto-ovo-vegetarian diet, for patients with inflammatory bowel disease. Our SVD contains 32.4 g of dietary fiber in 2000 kcal. There was no untoward effect of the SVD. The remission rate with combined infliximab and SVD for newly diagnosed CD patients was 100%. Maintenance of remission on SVD without scheduled maintenance therapy with biologic drugs was 92% at 2 years. These excellent short- and long-term results can be explained partly by SVD. The fecal bacterial count of F prausnitzii in patients with CD is significantly lower than in healthy controls. Diet reviews recommend plant-based diets to treat and to prevent a variety of chronic diseases. SVD belongs to plant-based diets that inevitably contain considerable amounts of dietary fiber. Our clinical experience and available data provide a rationale to recommend a high fiber intake to treat CD.

Blood-brain barrier dysfunction and cerebral small vessel disease (arteriolosclerosis) in brains of older people.

PubMed

Bridges, Leslie R; Andoh, Joycelyn; Lawrence, Andrew J; Khoong, Cheryl H L; Poon, Wayne; Esiri, Margaret M; Markus, Hugh S; Hainsworth, Atticus H

2014-11-01

The blood-brain barrier protects brain tissue from potentially harmful plasma components. Small vessel disease (SVD; also termed arteriolosclerosis) is common in the brains of older people and is associated with lacunar infarcts, leukoaraiosis, and vascular dementia. To determine whether plasma extravasation is associated with SVD, we immunolabeled the plasma proteins fibrinogen and immunoglobulin G, which are assumed to reflect blood-brain barrier dysfunction, in deep gray matter (DGM; anterior caudate-putamen) and deep subcortical white matter (DWM) in the brains of a well-characterized cohort of donated brains with minimal Alzheimer disease pathology (Braak Stages 0-II) (n = 84; aged 65 years or older). Morphometric measures of fibrinogen labeling were compared between people with neuropathologically defined SVD and aged control subjects. Parenchymal cellular labeling with fibrinogen and immunoglobulin G was detectable in DGM and DWM in many subjects (>70%). Quantitative measures of fibrinogen were not associated with SVD in DGM or DWM; SVD severity was correlated between DGM and DWM (p < 0.0001). Fibrinogen in DGM showed a modest association with a history of hypertension; DWM fibrinogen was associated with dementia and cerebral amyloid angiopathy (all p < 0.05). In DWM, SVD was associated with leukoaraiosis identified in life (p < 0.05), but fibrinogen was not. Our data suggest that, in aged brains, plasma extravasation and hence local blood-brain barrier dysfunction are common but do not support an association with SVD.

Lorentz force electrical impedance tomography using magnetic field measurements.

PubMed

Zengin, Reyhan; Gençer, Nevzat Güneri

2016-08-21

In this study, magnetic field measurement technique is investigated to image the electrical conductivity properties of biological tissues using Lorentz forces. This technique is based on electrical current induction using ultrasound together with an applied static magnetic field. The magnetic field intensity generated due to induced currents is measured using two coil configurations, namely, a rectangular loop coil and a novel xy coil pair. A time-varying voltage is picked-up and recorded while the acoustic wave propagates along its path. The forward problem of this imaging modality is defined as calculation of the pick-up voltages due to a given acoustic excitation and known body properties. Firstly, the feasibility of the proposed technique is investigated analytically. The basic field equations governing the behaviour of time-varying electromagnetic fields are presented. Secondly, the general formulation of the partial differential equations for the scalar and magnetic vector potentials are derived. To investigate the feasibility of this technique, numerical studies are conducted using a finite element method based software. To sense the pick-up voltages a novel coil configuration (xy coil pairs) is proposed. Two-dimensional numerical geometry with a 16-element linear phased array (LPA) ultrasonic transducer (1 MHz) and a conductive body (breast fat) with five tumorous tissues is modeled. The static magnetic field is assumed to be 4 Tesla. To understand the performance of the imaging system, the sensitivity matrix is analyzed. The sensitivity matrix is obtained for two different locations of LPA transducer with eleven steering angles from [Formula: see text] to [Formula: see text] at intervals of [Formula: see text]. The characteristics of the imaging system are shown with the singular value decomposition (SVD) of the sensitivity matrix. The images are reconstructed with the truncated SVD algorithm. The signal-to-noise ratio in measurements is assumed 80 dB. Simulation studies based on the sensitivity matrix analysis reveal that perturbations with [Formula: see text] mm size can be detected up to a 3.5 cm depth.

Lorentz force electrical impedance tomography using magnetic field measurements

NASA Astrophysics Data System (ADS)

Zengin, Reyhan; Güneri Gençer, Nevzat

2016-08-01

In this study, magnetic field measurement technique is investigated to image the electrical conductivity properties of biological tissues using Lorentz forces. This technique is based on electrical current induction using ultrasound together with an applied static magnetic field. The magnetic field intensity generated due to induced currents is measured using two coil configurations, namely, a rectangular loop coil and a novel xy coil pair. A time-varying voltage is picked-up and recorded while the acoustic wave propagates along its path. The forward problem of this imaging modality is defined as calculation of the pick-up voltages due to a given acoustic excitation and known body properties. Firstly, the feasibility of the proposed technique is investigated analytically. The basic field equations governing the behaviour of time-varying electromagnetic fields are presented. Secondly, the general formulation of the partial differential equations for the scalar and magnetic vector potentials are derived. To investigate the feasibility of this technique, numerical studies are conducted using a finite element method based software. To sense the pick-up voltages a novel coil configuration (xy coil pairs) is proposed. Two-dimensional numerical geometry with a 16-element linear phased array (LPA) ultrasonic transducer (1 MHz) and a conductive body (breast fat) with five tumorous tissues is modeled. The static magnetic field is assumed to be 4 Tesla. To understand the performance of the imaging system, the sensitivity matrix is analyzed. The sensitivity matrix is obtained for two different locations of LPA transducer with eleven steering angles from -{{25}\\circ} to {{25}\\circ} at intervals of {{5}\\circ} . The characteristics of the imaging system are shown with the singular value decomposition (SVD) of the sensitivity matrix. The images are reconstructed with the truncated SVD algorithm. The signal-to-noise ratio in measurements is assumed 80 dB. Simulation studies based on the sensitivity matrix analysis reveal that perturbations with 5~\\text{mm}× 5 mm size can be detected up to a 3.5 cm depth.

Comparative study of inversion methods of three-dimensional NMR and sensitivity to fluids

NASA Astrophysics Data System (ADS)

Tan, Maojin; Wang, Peng; Mao, Keyu

2014-04-01

Three-dimensional nuclear magnetic resonance (3D NMR) logging can simultaneously measure transverse relaxation time (T2), longitudinal relaxation time (T1), and diffusion coefficient (D). These parameters can be used to distinguish fluids in the porous reservoirs. For 3D NMR logging, the relaxation mechanism and mathematical model, Fredholm equation, are introduced, and the inversion methods including Singular Value Decomposition (SVD), Butler-Reeds-Dawson (BRD), and Global Inversion (GI) methods are studied in detail, respectively. During one simulation test, multi-echo CPMG sequence activation is designed firstly, echo trains of the ideal fluid models are synthesized, then an inversion algorithm is carried on these synthetic echo trains, and finally T2-T1-D map is built. Futhermore, SVD, BRD, and GI methods are respectively applied into a same fluid model, and the computing speed and inversion accuracy are compared and analyzed. When the optimal inversion method and matrix dimention are applied, the inversion results are in good aggreement with the supposed fluid model, which indicates that the inversion method of 3D NMR is applieable for fluid typing of oil and gas reservoirs. Additionally, the forward modeling and inversion tests are made in oil-water and gas-water models, respectively, the sensitivity to the fluids in different magnetic field gradients is also examined in detail. The effect of magnetic gradient on fluid typing in 3D NMR logging is stuied and the optimal manetic gradient is choosen.

PICKY: a novel SVD-based NMR spectra peak picking method

PubMed Central

Alipanahi, Babak; Gao, Xin; Karakoc, Emre; Donaldson, Logan; Li, Ming

2009-01-01

Motivation: Picking peaks from experimental NMR spectra is a key unsolved problem for automated NMR protein structure determination. Such a process is a prerequisite for resonance assignment, nuclear overhauser enhancement (NOE) distance restraint assignment, and structure calculation tasks. Manual or semi-automatic peak picking, which is currently the prominent way used in NMR labs, is tedious, time consuming and costly. Results: We introduce new ideas, including noise-level estimation, component forming and sub-division, singular value decomposition (SVD)-based peak picking and peak pruning and refinement. PICKY is developed as an automated peak picking method. Different from the previous research on peak picking, we provide a systematic study of the proposed method. PICKY is tested on 32 real 2D and 3D spectra of eight target proteins, and achieves an average of 88% recall and 74% precision. PICKY is efficient. It takes PICKY on average 15.7 s to process an NMR spectrum. More important than these numbers, PICKY actually works in practice. We feed peak lists generated by PICKY to IPASS for resonance assignment, feed IPASS assignment to SPARTA for fragments generation, and feed SPARTA fragments to FALCON for structure calculation. This results in high-resolution structures of several proteins, for example, TM1112, at 1.25 Å. Availability: PICKY is available upon request. The peak lists of PICKY can be easily loaded by SPARKY to enable a better interactive strategy for rapid peak picking. Contact: mli@uwaterloo.ca PMID:19477998

Can sun-induced chlorophyll fluorescence track diurnal variations of GPP in an evergreen needle leaf forest?

NASA Astrophysics Data System (ADS)

Kim, J.; Ryu, Y.; Dechant, B.; Cho, S.; Kim, H. S.; Yang, K.

2017-12-01

The emerging technique of remotely sensed sun-induced fluorescence (SIF) has advanced our ability to estimate plant photosynthetic activity at regional and global scales. Continuous observations of SIF and gross primary productivity (GPP) at the canopy scale in evergreen needleleaf forests, however, have not yet been presented in the literature so far. Here, we report a time series of near-surface measurements of canopy-scale SIF, hyperspectral reflectance and GPP during the senescence period in an evergreen needleleaf forest in South Korea. Mean canopy height was 30 m and a hyperspectrometer connected with a single fiber and rotating prism, which measures bi-hemispheric irradiance, was installed 20 m above the canopy. SIF was retrieved in the spectral range 740-790 nm at a temporal resolution of 1 min. We tested different SIF retrieval methods, such as Fraunhofer line depth (FLD), spectral fitting method (SFM) and singular vector decomposition (SVD) against GPP estimated by eddy covariance and absorbed photosynthetically active radiation (APAR). We found that the SVD-retrieved SIF signal shows linear relationships with GPP (R2 = 0.63) and APAR (R2 = 0.52) while SFM- and FLD-retrieved SIF performed poorly. We suspect the larger influence of atmospheric oxygen absorption between the sensor and canopy might explain why SFM and FLD methods showed poor results. Data collection will continue and the relationships between SIF, GPP and APAR will be studied during the senescence period.

Magnetic design and method of a superconducting magnet for muon g - 2/EDM precise measurements in a cylindrical volume with homogeneous magnetic field

NASA Astrophysics Data System (ADS)

Abe, M.; Murata, Y.; Iinuma, H.; Ogitsu, T.; Saito, N.; Sasaki, K.; Mibe, T.; Nakayama, H.

2018-05-01

A magnetic field design method of magneto-motive force (coil block (CB) and iron yoke) placements for g - 2/EDM measurements has been developed and a candidate placements were designed under superconducting limitations of current density 125 A/mm2 and maximum magnetic field on CBs less than 5.5 T. Placements of CBs and an iron yoke with poles were determined by tuning SVD (singular value decomposition) eigenmode strengths. The SVD was applied on a response matrix from magneto-motive forces to the magnetic fields in the muon storage region and two-dimensional (2D) placements of magneto-motive forces were designed by tuning the magnetic field eigenmode strengths obtained by the magnetic field. The tuning was performed iteratively. Magnetic field ripples in the azimuthal direction were minimized for the design. The candidate magnetic design had five CBs and an iron yoke with center iron poles. The magnet satisfied specifications of homogeneity (0.2 ppm peak-to-peak in 2D placements (the cylindrical coordinate of the radial position R and axial position Z) and less than 1.0 ppm ripples in the ring muon storage volume (0.318 m < R < 0 . 348 m and -0.05 < Z < 0.05 m) with 3.0 T strength and a slightly negative BR (magnetic field radial component) at Z > 0.0 m) for the spiral muon injection from the iron yoke at top.

Age-Specific Associations of Renal Impairment With Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease in Transient Ischemic Attack and Stroke

PubMed Central

Liu, Bian; Lau, Kui Kai; Li, Linxin; Lovelock, Caroline; Liu, Ming; Kuker, Wilhelm

2018-01-01

Background and Purpose— It has been hypothesized that cerebral small vessel disease (SVD) and chronic renal impairment may be part of a multisystem small-vessel disorder, but their association may simply be as a result of shared risk factors (eg, hypertension) rather than to a systemic susceptibility to premature SVD. However, most previous studies were hospital based, most had inadequate adjustment for hypertension, many were confined to patients with lacunar stroke, and none stratified by age. Methods— In a population-based study of transient ischemic attack and ischemic stroke (OXVASC [Oxford Vascular Study]), we evaluated the magnetic resonance imaging markers of cerebral SVD, including lacunes, white matter hyperintensities, cerebral microbleeds, and enlarged perivascular space. We studied the age-specific associations of renal impairment (estimated glomerular filtration rate <60 mL/min per 1.73 m2) and total SVD burden (total SVD score) adjusting for age, sex, vascular risk factors, and premorbid blood pressure (mean blood pressure during 15 years preevent). Results— Of 1080 consecutive patients, 1028 (95.2%) had complete magnetic resonance imaging protocol and creatinine measured at baseline. Renal impairment was associated with total SVD score (odds ratio [OR], 2.16; 95% confidence interval [CI], 1.69–2.75; P<0.001), but only at age <60 years (<60 years: OR, 3.97; 95% CI, 1.69–9.32; P=0.002; 60–79 years: OR, 1.01; 95% CI, 0.72–1.41; P=0.963; ≥80 years: OR, 0.95; 95% CI, 0.59–1.54; P=0.832). The overall association of renal impairment and total SVD score was also attenuated after adjustment for age, sex, history of hypertension, diabetes mellitus, and premorbid average systolic blood pressure (adjusted OR, 0.76; 95% CI, 0.56–1.02; P=0.067), but the independent association of renal impairment and total SVD score at age <60 years was maintained (adjusted OR, 3.11; 95% CI, 1.21–7.98; P=0.018). Associations of renal impairment and SVD were consistent for each SVD marker at age <60 years but were strongest for cerebral microbleeds (OR, 5.84; 95% CI, 1.45–23.53; P=0.013) and moderate–severe periventricular white matter hyperintensities (OR, 6.28; 95% CI, 1.54–25.63; P=0.010). Conclusions— The association of renal impairment and cerebral SVD was attenuated with adjustment for shared risk factors at older ages, but remained at younger ages, consistent with a shared susceptibility to premature disease. PMID:29523652

Retrieval of Enterobacteriaceae drug targets using singular value decomposition.

PubMed

Silvério-Machado, Rita; Couto, Bráulio R G M; Dos Santos, Marcos A

2015-04-15

The identification of potential drug target proteins in bacteria is important in pharmaceutical research for the development of new antibiotics to combat bacterial agents that cause diseases. A new model that combines the singular value decomposition (SVD) technique with biological filters composed of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of Escherichia coli (strain K12) has been created to predict potential antibiotic drug targets in the Enterobacteriaceae family. This model identified 99 potential drug target proteins in the studied family, which exhibit eight different functions and are protein-coding essential genes or similar to protein-coding essential genes of E.coli (strain K12), indicating that the disruption of the activities of these proteins is critical for cells. Proteins from bacteria with described drug resistance were found among the retrieved candidates. These candidates have no similarity to the human proteome, therefore exhibiting the advantage of causing no adverse effects or at least no known adverse effects on humans. rita_silverio@hotmail.com. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

High quality high spatial resolution functional classification in low dose dynamic CT perfusion using singular value decomposition (SVD) and k-means clustering

NASA Astrophysics Data System (ADS)

Pisana, Francesco; Henzler, Thomas; Schönberg, Stefan; Klotz, Ernst; Schmidt, Bernhard; Kachelrieß, Marc

2017-03-01

Dynamic CT perfusion acquisitions are intrinsically high-dose examinations, due to repeated scanning. To keep radiation dose under control, relatively noisy images are acquired. Noise is then further enhanced during the extraction of functional parameters from the post-processing of the time attenuation curves of the voxels (TACs) and normally some smoothing filter needs to be employed to better visualize any perfusion abnormality, but sacrificing spatial resolution. In this study we propose a new method to detect perfusion abnormalities keeping both high spatial resolution and high CNR. To do this we first perform the singular value decomposition (SVD) of the original noisy spatial temporal data matrix to extract basis functions of the TACs. Then we iteratively cluster the voxels based on a smoothed version of the three most significant singular vectors. Finally, we create high spatial resolution 3D volumes where to each voxel is assigned a distance from the centroid of each cluster, showing how functionally similar each voxel is compared to the others. The method was tested on three noisy clinical datasets: one brain perfusion case with an occlusion in the left internal carotid, one healthy brain perfusion case, and one liver case with an enhancing lesion. Our method successfully detected all perfusion abnormalities with higher spatial precision when compared to the functional maps obtained with a commercially available software. We conclude this method might be employed to have a rapid qualitative indication of functional abnormalities in low dose dynamic CT perfusion datasets. The method seems to be very robust with respect to both spatial and temporal noise and does not require any special a priori assumption. While being more robust respect to noise and with higher spatial resolution and CNR when compared to the functional maps, our method is not quantitative and a potential usage in clinical routine could be as a second reader to assist in the maps evaluation, or to guide a dataset smoothing before the modeling part.

Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease.

PubMed

Nestor, Sean M; Mišić, Bratislav; Ramirez, Joel; Zhao, Jiali; Graham, Simon J; Verhoeff, Nicolaas P L G; Stuss, Donald T; Masellis, Mario; Black, Sandra E

2017-07-01

Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD. We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects. SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD. These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

STEADFAST: Psychotherapeutic Intervention Improves Postural Strategy of Somatoform Vertigo and Dizziness

PubMed Central

Best, Christoph; Tschan, Regine; Stieber, Nikola; Beutel, Manfred E.; Eckhardt-Henn, Annegret; Dieterich, Marianne

2015-01-01

Patients with somatoform vertigo and dizziness (SVD) disorders often report instability of stance or gait and fear of falling. Posturographic measurements indeed indicated a pathological postural strategy. Our goal was to evaluate the effectiveness of a psychotherapeutic and psychoeducational short-term intervention (PTI) using static posturography and psychometric examination. Seventeen SVD patients took part in the study. The effects of PTI on SVD were evaluated with quantitative static posturography. As primary endpoint a quotient characterizing the relation between horizontal and vertical sway was calculated (Q H/V), reflecting the individual postural strategy. Results of static posturography were compared to those of age- and gender-matched healthy volunteers (n = 28); baseline measurements were compared to results after PTI. The secondary endpoint was the participation-limiting consequences of SVD as measured by the Vertigo Handicap Questionnaire (VHQ). Compared to the healthy volunteers, the patients with SVD showed a postural strategy characterized by stiffening-up that resulted in a significantly reduced body sway quotient before PTI (patients: Q H/V = 0.31 versus controls: Q H/V = 0.38; p = 0.022). After PTI the postural behavior normalized, and psychological distress was reduced. PTI therefore appears to modify pathological balance behaviour. The postural strategy of patients with SVD possibly results from anxious anticipatory cocontraction of the antigravity muscles. PMID:26843786

STEADFAST: Psychotherapeutic Intervention Improves Postural Strategy of Somatoform Vertigo and Dizziness.

PubMed

Best, Christoph; Tschan, Regine; Stieber, Nikola; Beutel, Manfred E; Eckhardt-Henn, Annegret; Dieterich, Marianne

2015-01-01

Patients with somatoform vertigo and dizziness (SVD) disorders often report instability of stance or gait and fear of falling. Posturographic measurements indeed indicated a pathological postural strategy. Our goal was to evaluate the effectiveness of a psychotherapeutic and psychoeducational short-term intervention (PTI) using static posturography and psychometric examination. Seventeen SVD patients took part in the study. The effects of PTI on SVD were evaluated with quantitative static posturography. As primary endpoint a quotient characterizing the relation between horizontal and vertical sway was calculated (Q H/V ), reflecting the individual postural strategy. Results of static posturography were compared to those of age- and gender-matched healthy volunteers (n = 28); baseline measurements were compared to results after PTI. The secondary endpoint was the participation-limiting consequences of SVD as measured by the Vertigo Handicap Questionnaire (VHQ). Compared to the healthy volunteers, the patients with SVD showed a postural strategy characterized by stiffening-up that resulted in a significantly reduced body sway quotient before PTI (patients: Q H/V = 0.31 versus controls: Q H/V = 0.38; p = 0.022). After PTI the postural behavior normalized, and psychological distress was reduced. PTI therefore appears to modify pathological balance behaviour. The postural strategy of patients with SVD possibly results from anxious anticipatory cocontraction of the antigravity muscles.

High Amount of Dietary Fiber Not Harmful But Favorable for Crohn Disease

PubMed Central

Chiba, Mitsuro; Tsuji, Tsuyotoshi; Nakane, Kunio; Komatsu, Masafumi

2015-01-01

Current chronic diseases are a reflection of the westernized diet that features a decreased consumption of dietary fiber. Indigestible dietary fiber is metabolized by gut bacteria, including Faecalibacterium prausnitzii, to butyrate, which has a critical role in colonic homeostasis owing to a variety of functions. Dietary fiber intake has been significantly inversely associated with the risk of chronic diseases. Crohn disease (CD) is not an exception. However, even authors who reported the inverse association between dietary fiber and a risk of CD made no recommendation of dietary fiber intake to CD patients. Some correspondence was against advocating high fiber intake in CD. We initiated a semivegetarian diet (SVD), namely a lacto-ovo-vegetarian diet, for patients with inflammatory bowel disease. Our SVD contains 32.4 g of dietary fiber in 2000 kcal. There was no untoward effect of the SVD. The remission rate with combined infliximab and SVD for newly diagnosed CD patients was 100%. Maintenance of remission on SVD without scheduled maintenance therapy with biologic drugs was 92% at 2 years. These excellent short- and long-term results can be explained partly by SVD. The fecal bacterial count of F prausnitzii in patients with CD is significantly lower than in healthy controls. Diet reviews recommend plant-based diets to treat and to prevent a variety of chronic diseases. SVD belongs to plant-based diets that inevitably contain considerable amounts of dietary fiber. Our clinical experience and available data provide a rationale to recommend a high fiber intake to treat CD. PMID:25663207

Learning overcomplete representations from distributed data: a brief review

NASA Astrophysics Data System (ADS)

Raja, Haroon; Bajwa, Waheed U.

2016-05-01

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates the design of dictionary learning algorithms that consider distributed nature of data as one of the problem variables. Just like centralized settings, distributed dictionary learning problem can be posed in more than one way depending on the problem setup. Most notable distinguishing features are the online versus batch nature of data and the representative versus discriminative nature of the dictionaries. In this paper, several distributed dictionary learning algorithms that are designed to tackle different problem setups are reviewed. One of these algorithms is cloud K-SVD, which solves the dictionary learning problem for batch data in distributed settings. One distinguishing feature of cloud K-SVD is that it has been shown to converge to its centralized counterpart, namely, the K-SVD solution. On the other hand, no such guarantees are provided for other distributed dictionary learning algorithms. Convergence of cloud K-SVD to the centralized K-SVD solution means problems that are solvable by K-SVD in centralized settings can now be solved in distributed settings with similar performance. Finally, cloud K-SVD is used as an example to show the advantages that are attainable by deploying distributed dictionary algorithms for real world distributed datasets.

Fiber optic sensor for continuous health monitoring in CFRP composite materials

NASA Astrophysics Data System (ADS)

Rippert, Laurent; Papy, Jean-Michel; Wevers, Martine; Van Huffel, Sabine

2002-07-01

An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterization. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyze this information.

Spatial Analysis of Gravity Data in the California, Nevada, and Utah (US)

NASA Astrophysics Data System (ADS)

Ferani, NA; Hartantyo, E.; Niasari, SW

2018-04-01

The geological condition of western North America is very complex because of the encounter of three major plates namely North America, Juan de Fuca, and Pacific Plate. The process of Juan de Fuca subduction and Pacific transform against North America plate created many mountains and produced Great Basin that we can see extending across California, Nevada, and Utah. The varied natural condition causes the varied value of gravity anomaly distribution. Using Topex free-air anomaly analyzed with second vertical derivative (SVD), we can analyze the fracture structures that occur in the Great Basin. The results show that the maximal SVD anomaly value is higher than the minimal SVD anomaly value at the western and eastern border of Great Basin. This explains that the two of Great Basin border are normal faults with trend direction NW-SE in the western boundary and NE-SW trending in the eastern boundary. This research result corresponds with the high seismicity data along the fault. Through this research, we can know that topex free-air anomaly data can be used to determine the type and trend of fault on a regional scale.

Using linear algebra for protein structural comparison and classification

PubMed Central

2009-01-01

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in. PMID:21637532

Using linear algebra for protein structural comparison and classification.

PubMed

Gomide, Janaína; Melo-Minardi, Raquel; Dos Santos, Marcos Augusto; Neshich, Goran; Meira, Wagner; Lopes, Júlio César; Santoro, Marcelo

2009-07-01

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Joint inversion of fundamental and higher mode Rayleigh waves

USGS Publications Warehouse

Luo, Y.-H.; Xia, J.-H.; Liu, J.-P.; Liu, Q.-S.

2008-01-01

In this paper, we analyze the characteristics of the phase velocity of fundamental and higher mode Rayleigh waves in a six-layer earth model. The results show that fundamental mode is more sensitive to the shear velocities of shallow layers (< 7 m) and concentrated in a very narrow band (around 18 Hz) while higher modes are more sensitive to the parameters of relatively deeper layers and distributed over a wider frequency band. These properties provide a foundation of using a multi-mode joint inversion to define S-wave velocity. Inversion results of both synthetic data and a real-world example demonstrate that joint inversion with the damped least squares method and the SVD (Singular Value Decomposition) technique to invert Rayleigh waves of fundamental and higher modes can effectively reduce the ambiguity and improve the accuracy of inverted S-wave velocities.

Simplex volume analysis for finding endmembers in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Li, Hsiao-Chi; Song, Meiping; Chang, Chein-I.

2015-05-01

Using maximal simplex volume as an optimal criterion for finding endmembers is a common approach and has been widely studied in the literature. Interestingly, very little work has been reported on how simplex volume is calculated. It turns out that the issue of calculating simplex volume is much more complicated and involved than what we may think. This paper investigates this issue from two different aspects, geometric structure and eigen-analysis. The geometric structure is derived from its simplex structure whose volume can be calculated by multiplying its base with its height. On the other hand, eigen-analysis takes advantage of the Cayley-Menger determinant to calculate the simplex volume. The major issue of this approach is that when the matrix is ill-rank where determinant is desired. To deal with this problem two methods are generally considered. One is to perform data dimensionality reduction to make the matrix to be of full rank. The drawback of this method is that the original volume has been shrunk and the found volume of a dimensionality-reduced simplex is not the real original simplex volume. Another is to use singular value decomposition (SVD) to find singular values for calculating simplex volume. The dilemma of this method is its instability in numerical calculations. This paper explores all of these three methods in simplex volume calculation. Experimental results show that geometric structure-based method yields the most reliable simplex volume.

Bioprosthetic mitral valve replacement in patients aged 65 years or younger: long-term outcomes with the Carpentier-Edwards PERIMOUNT pericardial valve.

PubMed

Bourguignon, Thierry; Espitalier, Fabien; Pantaleon, Clémence; Vermes, Emmanuelle; El-Arid, Jean Marc; Loardi, Claudia; Karam, Elias; Candolfi, Pascal; Ivanes, Fabrice; Aupart, Michel

2018-02-12

Mitral valve replacement using a bioprosthesis remains controversial in young patients because data on long-term outcomes are missing. This study evaluated the long-term results of the PERIMOUNT pericardial mitral bioprosthesis in patients aged 65 years or younger. From 1984 to 2010, 148 Carpentier-Edwards PERIMOUNT mitral bioprostheses were implanted in 148 patients aged 65 years or younger. Baseline clinical, perioperative and follow-up data were recorded prospectively. Structural valve deterioration (SVD) was defined by strict echocardiographic assessment. The mean follow-up period was 8.6 ± 5.5 years, for a total of 1269 valve-years. Operative mortality rate was 2.0%. Fifty-one late deaths occurred (linearized rate 4.0% per valve-year). Actuarial survival rates averaged 70 ± 4%, 53 ± 6% and 31 ± 7% after 10, 15 and 20 years of follow-up, respectively. Actuarial freedom from SVD at 10, 15 and 20 years was 78 ± 5%, 47 ± 7% and 19 ± 7%, respectively. Reoperation was associated with no operative mortality. Actuarial freedom from reoperation due to SVD at 10, 15 and 20 years was 82 ± 4%, 50 ± 6% and 25 ± 8%, respectively. Competing risk analysis demonstrated an actual risk of explantation secondary to SVD at 20 years of 44 ± 5%. Expected valve durability was 14.2 years for this age group. In the selected patients aged 65 years or younger undergoing mitral valve replacement with a pericardial bioprosthesis, the expected valve durability was 14.2 years. Reoperation for SVD was associated with a low risk of mortality. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Small vessel disease burden in cerebral amyloid angiopathy without symptomatic hemorrhage

PubMed Central

Charidimou, Andreas; Jessel, Michael J.; Xiong, Li; Roongpiboonsopit, Duangnapa; Fotiadis, Panagiotis; Pasi, Marco; Ayres, Alison; Merrill, M. Emily; Schwab, Kristin M.; Rosand, Jonathan; Gurol, M. Edip; Greenberg, Steven M.; Viswanathan, Anand

2017-01-01

Objective: Cerebral amyloid angiopathy (CAA) is a common age-related small vessel disease (SVD). Patients without intracerebral hemorrhage (ICH) typically present with transient focal neurologic episodes (TFNEs) or cognitive symptoms. We sought to determine if SVD lesion burden differed between patients with CAA first presenting with TFNEs vs cognitive symptoms. Methods: A total of 647 patients presenting either to a stroke department (n = 205) or an outpatient memory clinic (n = 442) were screened for eligibility. Patients meeting modified Boston criteria for probable CAA were included and markers of SVD were quantified, including cerebral microbleeds (CMBs), perivascular spaces, cortical superficial siderosis (cSS), and white matter hyperintensities (WMHs). Patients were classified according to presentation symptoms (TFNEs vs cognitive). Total CAA-SVD burden was assessed using a validated summary score. Individual neuroimaging markers and total SVD burden were compared between groups using univariable and multivariable models. Results: There were 261 patients with probable CAA included. After adjustment for confounders, patients first seen for TFNEs (n = 97) demonstrated a higher prevalence of cSS (p < 0.0001), higher WMH volumes (p = 0.03), and a trend toward higher CMB counts (p = 0.09). The total SVD summary score was higher in patients seen for TFNEs (adjusted odds ratio per additional score point 1.46, 95% confidence interval 1.16–1.84, p = 0.013). Conclusions: Patients with probable CAA without ICH first evaluated for TFNEs bear a higher burden of structural MRI SVD-related damage compared to those first seen for cognitive symptoms. This study sheds light on neuroimaging profile differences across clinical phenotypes of patients with CAA without ICH. PMID:28130469

Association of postural instability with asymptomatic cerebrovascular damage and cognitive decline: the Japan Shimanami health promoting program study.

PubMed

Tabara, Yasuharu; Okada, Yoko; Ohara, Maya; Uetani, Eri; Kido, Tomoko; Ochi, Namiko; Nagai, Tokihisa; Igase, Michiya; Miki, Tetsuro; Matsuda, Fumihiko; Kohara, Katsuhiko

2015-01-01

Asymptomatic cerebral small-vessel disease (cSVD) in elderly individuals are potent risk factors for stroke. In addition to common clinical risk factors, postural instability has been postulated to be associated with cSVD in older frail patients. Here, we conducted a cross-sectional study to understand the possible link between postural instability and asymptomatic cSVD further, namely periventricular hyperintensity, lacunar infarction, and microbleeds, as well as cognitive function, in a middle-aged to elderly general population (n=1387). Postural instability was assessed based on one-leg standing time (OLST) and posturography findings. cSVD was evaluated by brain MRI. Mild cognitive impairment was assessed using a computer-based questionnaire, and carotid intima-media thickness as an index of atherosclerosis was measured via ultrasonography. Frequency of short OLST, in particular <20 s, increased linearly with severity of cSVD (lacunar infarction lesion: none, 9.7%; 1, 16.0%; >2, 34.5%; microbleeds lesion: none, 10.1%; 1, 15.3%; >2, 30.0%; periventricular hyperintensity grade: 0, 5.7%; 1, 11.5%; >2, 23.7%). The association of short OLST with lacunar infarction and microbleeds but not periventricular hyperintensity remained significant even after adjustment for possible covariates (lacunar infarction, P=0.009; microbleeds, P=0.003; periventricular hyperintensity, P=0.601). In contrast, no significant association was found between posturographic parameters and cSVD, whereas these parameters were linearly associated with OLST. Short OLST was also significantly associated with reduced cognitive function independent of covariates, including cSVD (P=0.002). Postural instability was found to be associated with early pathological changes in the brain and functional decline, even in apparently healthy subjects. © 2014 American Heart Association, Inc.

Characterization of Heterozygous HTRA1 Mutations in Taiwanese Patients With Cerebral Small Vessel Disease.

PubMed

Lee, Yi-Chung; Chung, Chih-Ping; Chao, Nai-Chen; Fuh, Jong-Ling; Chang, Feng-Chi; Soong, Bing-Wing; Liao, Yi-Chu

2018-07-01

Homozygous and compound heterozygous mutations in the high temperature requirement serine peptidase A1 gene ( HTRA1 ) cause cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy. However, heterozygous HTRA1 mutations were recently identified to be associated with autosomal dominant cerebral small vessel disease (SVD). The present study aims at investigating the clinical features, frequency, and spectrum of HTRA1 mutations in a Taiwanese cohort with SVD. Mutational analyses of HTRA1 were performed by Sanger sequencing in 222 subjects, selected from a cohort of 337 unrelated patients with SVD after excluding those harboring a NOTCH3 mutation. The influence of these mutations on HTRA1 protease activities was characterized. Seven novel heterozygous mutations in HTRA1 were identified, including p.Gly120Asp, p.Ile179Asn, p.Ala182Profs*33, p.Ile256Thr, p.Gly276Ala, p.Gln289Ter, and p.Asn324Thr, and each was identified in 1 single index patient. All mutations significantly compromise the HTRA1 protease activities. For the 7 index cases and another 2 affected siblings carrying a heterozygous HTRA1 mutation, the common clinical presentations include lacunar infarction, intracerebral hemorrhage, cognitive decline, and spondylosis at the fifth to sixth decade of life. Among the 9 patients, 4 have psychiatric symptoms as delusion, depression, and compulsive behavior, 3 have leukoencephalopathy in anterior temporal poles, and 2 patients have alopecia. Heterozygous HTRA1 mutations account for 2.08% (7 of 337) of SVD in Taiwan. The clinical and neuroradiological features of HTRA1 -related SVD and sporadic SVD are similar. These findings broaden the mutational spectrum of HTRA1 and highlight the pathogenic role of heterozygous HTRA1 mutations in SVD. © 2018 American Heart Association, Inc.

Using SMOS brightness temperature and derived surface-soil moisture to characterize surface conditions and validate land surface models.

NASA Astrophysics Data System (ADS)

Polcher, Jan; Barella-Ortiz, Anaïs; Piles, Maria; Gelati, Emiliano; de Rosnay, Patricia

2017-04-01

The SMOS satellite, operated by ESA, observes the surface in the L-band. On continental surface these observations are sensitive to moisture and in particular surface-soil moisture (SSM). In this presentation we will explore how the observations of this satellite can be exploited over the Iberian Peninsula by comparing its results with two land surface models : ORCHIDEE and HTESSEL. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies. When comparing the surface-soil moisture of the models with the product derived operationally by ESA from SMOS observations similar results are found. The spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates is poor (ρ 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products. Other reasons have to be sought to explain the poor agreement in spatial patterns between satellite derived and modelled SSM. This presentation will hopefully contribute to the discussion of how SMOS and other observations can be used to prepare, carry-out and exploit a field campaign over the Iberian Peninsula which aims at improving our understanding of semi-arid land surface processes.

UTM TCL 2.0 Software Version Description (SVD) Document

NASA Technical Reports Server (NTRS)

Mcguirk, Patrick

2017-01-01

This is the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Technical Capability Level(TCL) 2.0 Software Version Description (SVD) document. This UTM TCL 2.0 SVD describes the following four topics: 1. Software Release Contents: A listing of the files comprising this release 2. Installation Instructions: How to install the release and get it running 3. Changes Since Previous Release: General updates since the previous UTM release 4. Known Issues: Known issues and limitations in this release

Cerebral small vessel disease: Capillary pathways to stroke and cognitive decline

PubMed Central

Engedal, Thorbjørn S; Moreton, Fiona; Hansen, Mikkel B; Wardlaw, Joanna M; Dalkara, Turgay; Markus, Hugh S; Muir, Keith W

2015-01-01

Cerebral small vessel disease (SVD) gives rise to one in five strokes worldwide and constitutes a major source of cognitive decline in the elderly. SVD is known to occur in relation to hypertension, diabetes, smoking, radiation therapy and in a range of inherited and genetic disorders, autoimmune disorders, connective tissue disorders, and infections. Until recently, changes in capillary patency and blood viscosity have received little attention in the aetiopathogenesis of SVD and the high risk of subsequent stroke and cognitive decline. Capillary flow patterns were, however, recently shown to limit the extraction efficacy of oxygen in tissue and capillary dysfunction therefore proposed as a source of stroke-like symptoms and neurodegeneration, even in the absence of physical flow-limiting vascular pathology. In this review, we examine whether capillary flow disturbances may be a shared feature of conditions that represent risk factors for SVD. We then discuss aspects of capillary dysfunction that could be prevented or alleviated and therefore might be of general benefit to patients at risk of SVD, stroke or cognitive decline. PMID:26661176

The natural oscillation of two types of ENSO events based on analyses of CMIP5 model control runs

NASA Astrophysics Data System (ADS)

Xu, Kang; Su, Jingzhi; Zhu, Congwen

2014-07-01

The eastern- and central-Pacific El Niño-Southern Oscillation (EP- and CP-ENSO) have been found to be dominant in the tropical Pacific Ocean, and are characterized by interannual and decadal oscillation, respectively. In the present study, we defined the EP- and CP-ENSO modes by singular value decomposition (SVD) between SST and sea level pressure (SLP) anomalous fields. We evaluated the natural features of these two types of ENSO modes as simulated by the pre-industrial control runs of 20 models involved in phase five of the Coupled Model Intercomparison Project (CMIP5). The results suggested that all the models show good skill in simulating the SST and SLP anomaly dipolar structures for the EP-ENSO mode, but only 12 exhibit good performance in simulating the tripolar CP-ENSO modes. Wavelet analysis suggested that the ensemble principal components in these 12 models exhibit an interannual and multi-decadal oscillation related to the EP- and CP-ENSO, respectively. Since there are no changes in external forcing in the pre-industrial control runs, such a result implies that the decadal oscillation of CP-ENSO is possibly a result of natural climate variability rather than external forcing.

Semi-quantitative assessment of pulmonary perfusion in children using dynamic contrast-enhanced MRI

NASA Astrophysics Data System (ADS)

Fetita, Catalin; Thong, William E.; Ou, Phalla

2013-03-01

This paper addresses the study of semi-quantitative assessment of pulmonary perfusion acquired from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in a study population mainly composed of children with pulmonary malformations. The automatic analysis approach proposed is based on the indicator-dilution theory introduced in 1954. First, a robust method is developed to segment the pulmonary artery and the lungs from anatomical MRI data, exploiting 2D and 3D mathematical morphology operators. Second, the time-dependent contrast signal of the lung regions is deconvolved by the arterial input function for the assessment of the local hemodynamic system parameters, ie. mean transit time, pulmonary blood volume and pulmonary blood flow. The discrete deconvolution method implements here a truncated singular value decomposition (tSVD) method. Parametric images for the entire lungs are generated as additional elements for diagnosis and quantitative follow-up. The preliminary results attest the feasibility of perfusion quantification in pulmonary DCE-MRI and open an interesting alternative to scintigraphy for this type of evaluation, to be considered at least as a preliminary decision in the diagnostic due to the large availability of the technique and to the non-invasive aspects.

Magnetic Diagnosis Upgrade and Analysis for MHD Instabilities on the J-TEXT

NASA Astrophysics Data System (ADS)

Guo, Daojing; Hu, Qiming; Zhuang, Ge; Wang, Nengchao; Ding, Yonghua; Tang, Yuejin; Yu, Qingquan; Huazhong University of Science; Technology Team; Max-Planck-Institut für Plasmaphysik Collaboration

2017-10-01

The magnetic diagnostic system on the J-TEXT tokamak has been upgraded to measure the magnetohydrodynamic (MHD) instabilities with diverse bands of frequencies. 12 saddle loop probes and 73 Mirnov probes are newly developed. The fabrication and installment of the new probes are elaborately designed, in consideration of higher spatial resolution and better amplitude-frequency characteristic. In this case, the probes utilize two kinds of novel fabrication craft, one of which is low temperature co-fired ceramics (LTCC), the other is flexible printed circuit (FPC). A great deal of experiments on the J-TEXT have validated the stability of the new system. Some typical discharges observed by the new diagnostic system are reviewed. In order to extract useful information from raw signals, several efficient signal processing methods are reviewed. An analytical model based on lumped eddy current circuits is used to compensate equilibrium flux and the corresponding eddy current fluxes, a visualization processing based on singular value decomposition (SVD) and cross-power spectrum are applied to detect the mode number. Fusion Science Program of China (Contract Nos. 2015GB111001 and 2014GB108000) and the National Natural Science Foundation of China (Contract Nos. 11505069 and 11405068).

Solar variability datalogger

DOE PAGES

Lave, Matthew; Stein, Joshua; Smith, Ryan

2016-07-28

To address the lack of knowledge of local solar variability, we have developed and deployed a low-cost solar variability datalogger (SVD). While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we present evaluation of various low-cost irradiance sensor types, describe the SVD, and present validation and comparison of the SVD collected data. In conclusion, the low cost and ease of use of the SVD will enable a greater understandingmore » of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic (PV) installations and thus will encourage greater penetrations of solar energy.« less

Mnemonic function in small vessel disease and associations with white matter tract microstructure.

PubMed

Metoki, Athanasia; Brookes, Rebecca L; Zeestraten, Eva; Lawrence, Andrew J; Morris, Robin G; Barrick, Thomas R; Markus, Hugh S; Charlton, Rebecca A

2017-09-01

Cerebral small vessel disease (SVD) is associated with deficits in working memory, with a relative sparing of long-term memory; function may be influenced by white matter microstructure. Working and long-term memory were examined in 106 patients with SVD and 35 healthy controls. Microstructure was measured in the uncinate fasciculi and cingula. Working memory was more impaired than long-term memory in SVD, but both abilities were reduced compared to controls. Regression analyses found that having SVD explained the variance in memory functions, with additional variance explained by the cingula (working memory) and uncinate (long-term memory). Performance can be explained in terms of integrity loss in specific white matter tract associated with mnemonic functions. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Does vacuum delivery carry a higher risk of shoulder dystocia? Review and meta-analysis of the literature.

PubMed

Dall'Asta, Andrea; Ghi, Tullio; Pedrazzi, Giuseppe; Frusca, Tiziana

2016-09-01

Vacuum extractor has been increasingly used over the last decades and is acknowledged as a risk factor for shoulder dystocia (SD). In this meta-analysis we assess the actual risk of SD following a vacuum delivery compared to spontaneous vaginal delivery (SVD) and forceps. Systematic literature search (English literature only) on MEDLINE, EMBASE, ScienceDirect, the Cochrane library and ClinicalTrials.gov conducted up to May 2015. Key search terms included: Operative/Vacuum/Forceps delivery [Mesh] and shoulder dystocia and subheadings. 2 stage-process study selection. We included only studies where data concerning the occurrence of SD following operative vaginal delivery were reported as adjusted odds ratio (AOR) and no significant difference in confounding factors for SD was recorded. Included trials clustered according to the delivery mode (1) vacuum vs. SVD, (2) forceps vs. vacuum. Methodological quality of each study evaluated with the Newcastle-Ottawa System (NOS). 87 potentially relevant papers. After applying inclusion and exclusion criteria only 7 were selected for the meta-analysis. Vacuum delivery appeared associated with a higher risk of SD than SVD in both fixed and random model (OR 2.87 and 2.98 respectively). No difference in the rate of SD was found between vacuum and forceps (p>0.05). Vacuum extractor carries an increased risk of SD compared with spontaneous vaginal delivery whereas the occurrence of SD does not seem to vary following vacuum or forceps. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

PubMed Central

Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

2015-01-01

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

Bioprosthetic Aortic Valve Durability: A Meta-Regression of Published Studies.

PubMed

Wang, Mansen; Furnary, Anthony P; Li, Hsin-Fang; Grunkemeier, Gary L

2017-09-01

To compare structural valve deterioration (SVD) among bioprosthetic aortic valve types, a PubMed search found 54 papers containing SVD-free curves extending to at least 10 years. The curves were digitized and fit to Weibull distributions, and the mean times to valve failure (MTTF) were calculated. Twelve valve models were collapsed into four valve types: Medtronic (Medtronic, Minneapolis, MN) and Edwards (Edwards Lifesciences, Irvine, CA) porcine; and Sorin (Sorin Group [now LivaNova], London, United Kingdom) and Edwards pericardial. Meta-regression found MTTF was associated with the patient's age, publication year, SVD definition, and valve type. Sorin pericardial valves had significantly lower risk-adjusted MTTF (higher SVD risk), and there were no significant differences in MTTF among the other three valve types. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

In-flight alignment using H ∞ filter for strapdown INS on aircraft.

PubMed

Pei, Fu-Jun; Liu, Xuan; Zhu, Li

2014-01-01

In-flight alignment is an effective way to improve the accuracy and speed of initial alignment for strapdown inertial navigation system (INS). During the aircraft flight, strapdown INS alignment was disturbed by lineal and angular movements of the aircraft. To deal with the disturbances in dynamic initial alignment, a novel alignment method for SINS is investigated in this paper. In this method, an initial alignment error model of SINS in the inertial frame is established. The observability of the system is discussed by piece-wise constant system (PWCS) theory and observable degree is computed by the singular value decomposition (SVD) theory. It is demonstrated that the system is completely observable, and all the system state parameters can be estimated by optimal filter. Then a H ∞ filter was designed to resolve the uncertainty of measurement noise. The simulation results demonstrate that the proposed algorithm can reach a better accuracy under the dynamic disturbance condition.

[Affine transformation-based automatic registration for peripheral digital subtraction angiography (DSA)].

PubMed

Kong, Gang; Dai, Dao-Qing; Zou, Lu-Min

2008-07-01

In order to remove the artifacts of peripheral digital subtraction angiography (DSA), an affine transformation-based automatic image registration algorithm is introduced here. The whole process is described as follows: First, rectangle feature templates are constructed with their centers of the extracted Harris corners in the mask, and motion vectors of the central feature points are estimated using template matching technology with the similarity measure of maximum histogram energy. And then the optimal parameters of the affine transformation are calculated with the matrix singular value decomposition (SVD) method. Finally, bilinear intensity interpolation is taken to the mask according to the specific affine transformation. More than 30 peripheral DSA registrations are performed with the presented algorithm, and as the result, moving artifacts of the images are removed with sub-pixel precision, and the time consumption is less enough to satisfy the clinical requirements. Experimental results show the efficiency and robustness of the algorithm.

Validation of PEP-II Resonantly Excited Turn-by-Turn BPM Data

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

Yan, Yiton T.; Cai, Yunhai; Colocho, William.

2007-06-28

For optics measurement and modeling of the PEP-II electron (HER) and position (LER) storage rings, we have been doing well with MIA [1] which requires analyzing turn-by-turn Beam Position Monitor (BPM) data that are resonantly excited at the horizontal, vertical, and longitudinal tunes. However, in anticipation that certain BPM buttons and even pins in the PEP-II IR region would be missing for the run starting in January 2007, we had been developing a data validation process to reduce the effect due to the reduced BPM data accuracy on PEP-II optics measurement and modeling. Besides the routine process for ranking BPMmore » noise level through data correlation among BPMs with a singular-value decomposition (SVD), we could also check BPM data symplecticity by comparing the invariant ratios. Results from PEP-II measurement will be presented.« less

Application of Improved 5th-Cubature Kalman Filter in Initial Strapdown Inertial Navigation System Alignment for Large Misalignment Angles.

PubMed

Wang, Wei; Chen, Xiyuan

2018-02-23

In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm.

Sparse Bayesian learning for DOA estimation with mutual coupling.

PubMed

Dai, Jisheng; Hu, Nan; Xu, Weichao; Chang, Chunqi

2015-10-16

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Acoustooptic linear algebra processors - Architectures, algorithms, and applications

NASA Technical Reports Server (NTRS)

Casasent, D.

1984-01-01

Architectures, algorithms, and applications for systolic processors are described with attention to the realization of parallel algorithms on various optical systolic array processors. Systolic processors for matrices with special structure and matrices of general structure, and the realization of matrix-vector, matrix-matrix, and triple-matrix products and such architectures are described. Parallel algorithms for direct and indirect solutions to systems of linear algebraic equations and their implementation on optical systolic processors are detailed with attention to the pipelining and flow of data and operations. Parallel algorithms and their optical realization for LU and QR matrix decomposition are specifically detailed. These represent the fundamental operations necessary in the implementation of least squares, eigenvalue, and SVD solutions. Specific applications (e.g., the solution of partial differential equations, adaptive noise cancellation, and optimal control) are described to typify the use of matrix processors in modern advanced signal processing.

Causes and consequences of cerebral small vessel disease. The RUN DMC study: a prospective cohort study. Study rationale and protocol.

PubMed

van Norden, Anouk Gw; de Laat, Karlijn F; Gons, Rob Ar; van Uden, Inge Wm; van Dijk, Ewoud J; van Oudheusden, Lucas Jb; Esselink, Rianne Aj; Bloem, Bastiaan R; van Engelen, Baziel Gm; Zwarts, Machiel J; Tendolkar, Indira; Olde-Rikkert, Marcel G; van der Vlugt, Maureen J; Zwiers, Marcel P; Norris, David G; de Leeuw, Frank-Erik

2011-02-28

Cerebral small vessel disease (SVD) is a frequent finding on CT and MRI scans of elderly people and is related to vascular risk factors and cognitive and motor impairment, ultimately leading to dementia or parkinsonism in some. In general, the relations are weak, and not all subjects with SVD become demented or get parkinsonism. This might be explained by the diversity of underlying pathology of both white matter lesions (WML) and the normal appearing white matter (NAWM). Both cannot be properly appreciated with conventional MRI. Diffusion tensor imaging (DTI) provides alternative information on microstructural white matter integrity. The association between SVD, its microstructural integrity, and incident dementia and parkinsonism has never been investigated. The RUN DMC study is a prospective cohort study on the risk factors and cognitive and motor consequences of brain changes among 503 non-demented elderly, aged between 50-85 years, with cerebral SVD. First follow up is being prepared for July 2011. Participants alive will be included and invited to the research centre to undergo a structured questionnaire on demographics and vascular risk factors, and a cognitive, and motor, assessment, followed by a MRI protocol including conventional MRI, DTI and resting state fMRI. The follow up of the RUN DMC study has the potential to further unravel the causes and possibly better predict the consequences of changes in white matter integrity in elderly with SVD by using relatively new imaging techniques. When proven, these changes might function as a surrogate endpoint for cognitive and motor function in future therapeutic trials. Our data could furthermore provide a better understanding of the pathophysiology of cognitive and motor disturbances in elderly with SVD. The execution and completion of the follow up of our study might ultimately unravel the role of SVD on the microstructural integrity of the white matter in the transition from "normal" aging to cognitive and motor decline and impairment and eventually to incident dementia and parkinsonism.

Statistical Analysis of the Ionosphere based on Singular Value Decomposition

NASA Astrophysics Data System (ADS)

Demir, Uygar; Arikan, Feza; Necat Deviren, M.; Toker, Cenk

2016-07-01

Ionosphere is made up of a spatio-temporally varying trend structure and secondary variations due to solar, geomagnetic, gravitational and seismic activities. Hence, it is important to monitor the ionosphere and acquire up-to-date information about its state in order both to better understand the physical phenomena that cause the variability and also to predict the effect of the ionosphere on HF and satellite communications, and satellite-based positioning systems. To charaterise the behaviour of the ionosphere, we propose to apply Singular Value Decomposition (SVD) to Total Electron Content (TEC) maps obtained from the TNPGN-Active (Turkish National Permanent GPS Network) CORS network. TNPGN-Active network consists of 146 GNSS receivers spread over Turkey. IONOLAB-TEC values estimated from each station are spatio-temporally interpolated using a Universal Kriging based algorithm with linear trend, namely IONOLAB-MAP, with very high spatial resolution. It is observed that the dominant singular value of TEC maps is an indicator of the trend structure of the ionosphere. The diurnal, seasonal and annual variability of the most dominant value is the representation of solar effect on ionosphere in midlatitude range. Secondary and smaller singular values are indicators of secondary variation which can have significance especially during geomagnetic storms or seismic disturbances. The dominant singular values are related to the physical basis vectors where ionosphere can be fully reconstructed using these vectors. Therefore, the proposed method can be used both for the monitoring of the current state of a region and also for the prediction and tracking of future states of ionosphere using singular values and singular basis vectors. This study is supported by by TUBITAK 115E915 and Joint TUBITAK 114E092 and AS CR14/001 projects.

Hyperhomocysteinemia Associates with Small Vessel Disease More Closely Than Large Vessel Disease

PubMed Central

Feng, Chao; Bai, Xue; Xu, Yu; Hua, Ting; Huang, Jing; Liu, Xue-Yuan

2013-01-01

Background: Hyperhomocysteinemia was believed to be an independent risk factor for stroke and associate with small vessel disease (SVD) related stroke and large vessel disease (LVD) related stroke differently. However it's still unclear which type of stroke associated with homocysteine (HCY) more strongly because the conclusions of previous studies were contradictory. In this study we focused on the subclinical angiopathies of stroke, i.e., SVD and LVD instead of stroke subtypes and sought to compare the associations between HCY level and different angiopathies. Methods: 324 non-stroke patients were enrolled. Sex, age, HCY level and other vascular risk factors were collected. MRI and angiographies were used to determine the type of angiopathies and their severity, i.e., the scores of leukoaraiosis (LA), plaques and numbers of silent brain infarctions (SBI). LVD was defined as the presence of atherosclerotic plaques of cerebral arteries. SVD was defined as the presence of either LA or SBI. 230 patients were deemed to have LVD; 180 patients were deemed to have SVD. Spearman's correlation test and logistic regression were used to analyze the association between HCY level and different angiopathies. Results: The correlation between HCY level and scores of plaques was weaker than that of the scores of LA and numbers of SBI. Hyperhomocysteinemia was an independent risk factor for SVD (OR = 1.315, P <0.001), whereas the association between HCY level and LVD was not that significant (OR = 1.058, P = 0.075). Conclusion: HCY level associated with SVD more strongly than LVD. PMID:23471237

Satellite imagery in the fight against Malaria, the case for Genetic Programming

NASA Astrophysics Data System (ADS)

Ssentongo, J. S.; Hines, E. L.

The analysis of multi-temporal data is a critical issue in the field of remote sensing and presents a constant challenge The approach used here relies primarily on utilising a method commonly used in statistics and signal processing Empirical Orthogonal Function EOF analysis Normalized Difference Vegetation Index NDVI and Rainfall Estimate RFE satellite images pertaining to the Sub-Saharan Africa region were obtained The images are derived from the Advanced Very High Resolution Radiometer AVHRR on the United States National Oceanic and Atmospheric Administration NOAA polar orbiting satellites spanning from January 2000 to December 2002 The region of interest was narrowed down to the Limpopo Province Northern Province of South Africa EOF analyses of the space-time-intensity series of dekadal mean NDVI values was been performed They reveal that NDVI can be accurately approximated by its principal component time series and contains a near sinusoidal oscillation pattern Peak greenness essentially what NDVI measures seasons last approximately 8 weeks This oscillation period is very similar to that of Malaria cases reported in the same period but lags behind by 4 dekads about 40 days Singular Value Decomposition SVD of Coupled Fields is performed on the spacetime-intensity series of dekadal mean NDVI and RFE values Correlation analyses indicate that both Malaria and greenness appear to be dependant on rainfall the onset of their seasonal highs always following an arrival of rain There is a greater

Optimal Electrode Selection for Electrical Resistance Tomography in Carbon Fiber Reinforced Polymer Composites

PubMed Central

Escalona Galvis, Luis Waldo; Diaz-Montiel, Paulina; Venkataraman, Satchi

2017-01-01

Electrical Resistance Tomography (ERT) offers a non-destructive evaluation (NDE) technique that takes advantage of the inherent electrical properties in carbon fiber reinforced polymer (CFRP) composites for internal damage characterization. This paper investigates a method of optimum selection of sensing configurations for delamination detection in thick cross-ply laminates using ERT. Reduction in the number of sensing locations and measurements is necessary to minimize hardware and computational effort. The present work explores the use of an effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations resulting from selecting sensing electrode pairs. Singular Value Decomposition (SVD) is applied to obtain a spectral representation of the resistance measurements in the laminate for subsequent EI based reduction to take place. The electrical potential field in a CFRP laminate is calculated using finite element analysis (FEA) applied on models for two different laminate layouts considering a set of specified delamination sizes and locations with two different sensing arrangements. The effectiveness of the EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of damage using the full set and the reduced set of resistance measurements. This investigation shows that the EI measure is effective for optimally selecting the electrode pairs needed for resistance measurements in ERT based damage detection. PMID:28772485

Optimal Electrode Selection for Electrical Resistance Tomography in Carbon Fiber Reinforced Polymer Composites.

PubMed

Escalona Galvis, Luis Waldo; Diaz-Montiel, Paulina; Venkataraman, Satchi

2017-02-04

Electrical Resistance Tomography (ERT) offers a non-destructive evaluation (NDE) technique that takes advantage of the inherent electrical properties in carbon fiber reinforced polymer (CFRP) composites for internal damage characterization. This paper investigates a method of optimum selection of sensing configurations for delamination detection in thick cross-ply laminates using ERT. Reduction in the number of sensing locations and measurements is necessary to minimize hardware and computational effort. The present work explores the use of an effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations resulting from selecting sensing electrode pairs. Singular Value Decomposition (SVD) is applied to obtain a spectral representation of the resistance measurements in the laminate for subsequent EI based reduction to take place. The electrical potential field in a CFRP laminate is calculated using finite element analysis (FEA) applied on models for two different laminate layouts considering a set of specified delamination sizes and locations with two different sensing arrangements. The effectiveness of the EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of damage using the full set and the reduced set of resistance measurements. This investigation shows that the EI measure is effective for optimally selecting the electrode pairs needed for resistance measurements in ERT based damage detection.

Characterizing Future El Niño Impacts to the Lower Colorado River Basin

NASA Astrophysics Data System (ADS)

Santos, N. I.; Miller, W. P.; Piechota, T. C.; Lakshmi, V.

2015-12-01

Past El Niño events in the Colorado River Basin, such as the 1982-1983 event, resulted in one of the basin's wettest years on record. Looking at past events and the current forecasts, which indicate Pacific Ocean conditions could lead to one of the strongest El Niño events on record this winter, it is no wonder that many water management agencies and their customers are expecting a relief in the southwestern United States (US) drought intensity as the probability of a strong El Niño becomes more significant. Despite the conditions in the Pacific Ocean, a strong El Niño is not a guarantee for wet conditions in the Colorado River basin - as can be seen from the 2002 event in which basin conditions were one of the driest on record. There is a great need to understand the range of possible conditions that could be observed under an El Niño event to better inform southwestern US water management agencies so that they may make well-guided decisions regarding their most valuable resource - the Colorado River. This study builds upon past research based on the Coupled Model Intercomparison Project 5 (CMIP5) climatology and hydrology projections and the analysis performed with singular variable decomposition (SVD) to identify climate models with high correlation between historical climate/hydrology in the CRB and sea surface temperature conditions in the Pacific Ocean. Past research methods were able to identify climate models which performed well using the SVD methodology. This current project seeks to analyze the well-performing climate models and identify future El Niño conditions in the Pacific Ocean and the resultant precipitation and temperature impacts in the lower CRB. This analysis will provide an objective, ensemble based outlook for potential climate change impacts under El Niño events.The results of the study can potentially assist lower CRB water management agencies in characterizing the range of future El Niño impacts, under climate change conditions. It is expected that this information could be beneficial in understanding the range of potential El Niño impacts and their effect on drought in the southwestern US.

Trend and Variability of China Precipitation in Spring and Summer: Linkage to Sea Surface Temperatures

NASA Technical Reports Server (NTRS)

Yang, Fanglin; Lau, K.-M.

2004-01-01

Observational records in the past 50 years show an upward trend of boreal-summer precipitation over central eastern China and a downward trend over northern China. During boreal spring, the trend is upward over southeastern China and downward over central eastern China. This study explores the forcing mechanism of these trends in association with the global sea-surface temperature (SST) variations on the interannual and inter-decadal timescales. Results based on Singular Value Decomposition analyses (SVD) show that the interannual variability of China precipitation in boreal spring and summer can be well defined by two centers of actions for each season, which are co-varying with two interannual modes of SSTs. The first SVD modes of precipitation in spring and summer, which are centered in southeastern China and northern China, respectively, are linked to an ENSO-like mode of SSTs. The second SVD modes of precipitation in both seasons are confined to central eastern China, and are primarily linked to SST variations over the warm pool and Indian Ocean. Features of the anomalous 850-hPa winds and 700-Wa geopotential height corresponding to these modes support a physical mechanism that explains the causal links between the modal variations of precipitation and SSTs. On the decadal and longer timescale, similar causal links are found between the same modes of precipitation and SSTs, except for the case of springtime precipitation over central eastern China. For this case, while the interannual mode of precipitation is positively correlated with the interannual variations of SSTs over the warm pool and Indian Ocean; the inter-decadal mode is negatively correlated with a different SST mode, the North Pacific mode. The later is responsible for the observed downward trend of springtime precipitation over central eastern China. For all other cases, both the interannual and inter-decadal variations of precipitation can be explained by the same mode of SSTs. The upward trend of springtime precipitation over southeastern China and downward trend of summertime precipitation over northern China are attributable to the warming trend of the ENSO-like mode. The recent frequent summertime floods over central eastern China are linked to the warming trend of SSTs over the warm pool and Indian Ocean.

Genomic Signal Processing: Predicting Basic Molecular Biological Principles

NASA Astrophysics Data System (ADS)

Alter, Orly

2005-03-01

Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of these two sets of states. Mapping genome-scale protein binding data using pseudoinverse projection onto patterns of RNA expression data that had been extracted by SVD and GSVD, a novel correlation between DNA replication initiation and RNA transcription during the cell cycle in yeast, that might be due to a previously unknown mechanism of regulation, is predicted. (1) Alter & Golub, Proc. Natl. Acad. Sci. USA 101, 16577 (2004). (2) Alter, Golub, Brown & Botstein, Miami Nat. Biotechnol. Winter Symp. 2004 (www.med.miami.edu/mnbws/alter-.pdf)

Spatial-frequency variability of the eddy kinetic energy in the South Atlantic Ocean

NASA Astrophysics Data System (ADS)

Cecilio, C. M.; Gherardi, D. F.; Souza, R.; Correa-Ramirez, M.

2013-05-01

In the South Atlantic Ocean (SAO) part of the inter-oceanic flow is accomplished through the issuance of anticyclonic eddies by the Agulhas Retroflection. This region, known as Agulhas Leakage (AL), is responsible by the intermittent shedding of eddies in the SAO. The propagation of these eddies into the SAO induces wave processes that allows the interaction between modes of variability of different basins, ranging from high to low frequency. Modelling studies suggests that the Indian-Atlantic inter-ocean exchange is strongly related to the structure of the wind field, in particular with the position of the maximum Southern Hemisphere westerly winds. This study aims to investigate the variations of the large-scale and regional mesoscale eddy field over the SAO using a frequency domain technique, Multiple Taper Method with Singular Value Decomposition (MTM-SVD). The MTM-SVD approach is applied to examine the individual and joint spatiotemporal variability modes of eddy kinetic energy (EKE) and winds stress. The EKE is estimated from geostrophic velocity anomalies data distributed by Aviso and winds stress from winds dataset of Cross-Calibrated Multi-Platform (CCMP) project from PO.DAAC. The impact of the AL in the SAO, was assessed first for the entire region and subsequently applied in the regions of higher mesoscale activity, which are the Brazil-Malvinas Confluence (BMC), the AL, and the Brazilian Current (BC) region. The results of local fractional variance (LFV) of EKE obtained by the MTM-SVD method show a strong significant annual variability in SAO and BC region while in BMC and in AL this frequency is weaker. In the most energetic mesoscale activity regions (BMC and AL) the pattern of variability is distinct. In the BMC region the interannual variability is dominated while in the AL region the most part of variability is associated by high frequency. The joint LFV spectrum of wind and EKE show an out-of-phase relationship between the AL region and BMC region in the interannual frequencies (3 to 5 years). The dominant frequencies can be seen in 1,5 to 3 years period band and in the intrasazonal frequencies, 0,3 to 0,5 years. The results suggests that the EKE variability patterns are different in the SAO wich might be related to the influence of eddies from AL.

Impact of Strategically Located White Matter Hyperintensities on Cognition in Memory Clinic Patients with Small Vessel Disease.

PubMed

Biesbroek, J Matthijs; Weaver, Nick A; Hilal, Saima; Kuijf, Hugo J; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Postma, Albert; Biessels, Geert Jan; Chen, Christopher P L H

2016-01-01

Studies on the impact of small vessel disease (SVD) on cognition generally focus on white matter hyperintensity (WMH) volume. The extent to which WMH location relates to cognitive performance has received less attention, but is likely to be functionally important. We examined the relation between WMH location and cognition in a memory clinic cohort of patients with sporadic SVD. A total of 167 patients with SVD were recruited from memory clinics. Assumption-free region of interest-based analyses based on major white matter tracts and voxel-wise analyses were used to determine the association between WMH location and executive functioning, visuomotor speed and memory. Region of interest-based analyses showed that WMHs located particularly within the anterior thalamic radiation and forceps minor were inversely associated with both executive functioning and visuomotor speed, independent of total WMH volume. Memory was significantly associated with WMH volume in the forceps minor, independent of total WMH volume. An independent assumption-free voxel-wise analysis identified strategic voxels in these same tracts. Region of interest-based analyses showed that WMH volume within the anterior thalamic radiation explained 6.8% of variance in executive functioning, compared to 3.9% for total WMH volume; WMH volume within the forceps minor explained 4.6% of variance in visuomotor speed and 4.2% of variance in memory, compared to 1.8% and 1.3% respectively for total WMH volume. Our findings identify the anterior thalamic radiation and forceps minor as strategic white matter tracts in which WMHs are most strongly associated with cognitive impairment in memory clinic patients with SVD. WMH volumes in individual tracts explained more variance in cognition than total WMH burden, emphasizing the importance of lesion location when addressing the functional consequences of WMHs.

Simplified Radiographic Damage Index for Affected Joints in Chronic Gouty Arthritis

PubMed Central

2016-01-01

The aim of this study was to develop and validate a new radiographic damage scoring method (DAmagE index of GoUt; DAEGU) in chronic gout using plain radiography. Two independent observers scored foot x-rays from 15 patients with chronic gout according to the DAEGU method and the modified Sharp/van der Heijde (SvdH) method. The 10 metatarsophalangeal (MTP) and 2 interphalangeal (IP) joints of the first toes of both feet were scored to assess the degrees of erosion and joint space narrowing (JSN). The intraobserver and interobserver reliabilities were analyzed by calculating the intraclass correlation coefficient (ICC) and minimal detectable change (MDC). The correlation between the DAEGU and SvdH methods was analyzed by calculating the Spearman's rho correlation coefficients and Kappa coefficients. The DAEGU method was found to be highly reproducible (0.945–0.987 for the intraobserver and 0.993–0.996 for the interobserver ICC values). The erosion, JSN, and total scores exhibited strong positive correlations between the DAEGU and SvdH methods and also within each method (r = 0.860–0.969, P < 0.001 for all parameters). The DAEGU and SvdH methods were in very good agreement as determined by Kappa coefficient analysis [0.732 (0.387–1.000) for erosion and 1.000 (1.000–1.000) for JSN]. In conclusion, this study revealed that DAEGU method was a reliable and feasible tool in the assessment of radiographic damage in chronic gout. The DAEGU method may provide a more easy assessment of structural damage in chronic gout in the real clinical practice. PMID:26955246

Tracking Energy Flow Using a Volumetric Acoustic Intensity Imager (VAIM)

NASA Technical Reports Server (NTRS)

Klos, Jacob; Williams, Earl G.; Valdivia, Nicolas P.

2006-01-01

A new measurement device has been invented at the Naval Research Laboratory which images instantaneously the intensity vector throughout a three-dimensional volume nearly a meter on a side. The measurement device consists of a nearly transparent spherical array of 50 inexpensive microphones optimally positioned on an imaginary spherical surface of radius 0.2m. Front-end signal processing uses coherence analysis to produce multiple, phase-coherent holograms in the frequency domain each related to references located on suspect sound sources in an aircraft cabin. The analysis uses either SVD or Cholesky decomposition methods using ensemble averages of the cross-spectral density with the fixed references. The holograms are mathematically processed using spherical NAH (nearfield acoustical holography) to convert the measured pressure field into a vector intensity field in the volume of maximum radius 0.4 m centered on the sphere origin. The utility of this probe is evaluated in a detailed analysis of a recent in-flight experiment in cooperation with Boeing and NASA on NASA s Aries 757 aircraft. In this experiment the trim panels and insulation were removed over a section of the aircraft and the bare panels and windows were instrumented with accelerometers to use as references for the VAIM. Results show excellent success at locating and identifying the sources of interior noise in-flight in the frequency range of 0 to 1400 Hz. This work was supported by NASA and the Office of Naval Research.

On the Comparison of the Global Surface Soil Moisture product and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Delorme, B., Jr.; Ottlé, C.; Peylin, P.; Polcher, J.

2016-12-01

Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve land surface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product. These results highlight that the role of other surface variables presenting a strong seasonal variability (like vegetation cover, possibly irrigation) is not accounted for similarly in both the model and the product, and that further work is needed to explore these discrepancies.

Understanding the role of the perivascular space in cerebral small vessel disease.

PubMed

Brown, Rosalind; Benveniste, Helene; Black, Sandra E; Charpak, Serge; Dichgans, Martin; Joutel, Anne; Nedergaard, Maiken; Smith, Kenneth J; Zlokovic, Berislav V; Wardlaw, Joanna M

2018-05-02

Small vessel diseases are a group of disorders that result from pathological alteration of the small blood vessels in the brain, including the small arteries, capillaries and veins. Of the 35-36 million people that are estimated to suffer from dementia worldwide, up to 65% have an SVD component. Furthermore, SVD causes 20-25% of strokes, worsens outcome after stroke and is a leading cause of disability, cognitive impairment and poor mobility. Yet the underlying cause(s) of SVD are not fully understood.Magnetic resonance imaging (MRI) has confirmed enlarged perivascular spaces (PVS) as a hallmark feature of SVD. In healthy tissue, these spaces are proposed to form part of a complex brain fluid drainage system which supports interstitial fluid exchange and may also facilitate clearance of waste products from the brain. The pathophysiological signature of PVS, and what this infers about their function and interaction with cerebral microcirculation, plus subsequent downstream effects on lesion development in the brain has not been established. Here we discuss the potential of enlarged PVS to be a unique biomarker for SVD and related brain disorders with a vascular component. We propose that widening of PVS suggests presence of peri-vascular cell debris and other waste products that forms part of a vicious cycle involving impaired cerebrovascular reactivity (CVR), blood-brain barrier (BBB) dysfunction, perivascular inflammation and ultimately impaired clearance of waste proteins from the interstitial fluid (ISF) space, leading to accumulation of toxins, hypoxia and tissue damage.Here, we outline current knowledge, questions and hypotheses regarding understanding the brain fluid dynamics underpinning dementia and stroke through the common denominator of SVD.

Increased Burden of Cerebral Small Vessel Disease in Patients With Type 2 Diabetes and Retinopathy.

PubMed

Sanahuja, Jordi; Alonso, Núria; Diez, Javier; Ortega, Emilio; Rubinat, Esther; Traveset, Alícia; Alcubierre, Núria; Betriu, Àngels; Castelblanco, Esmeralda; Hernández, Marta; Purroy, Francisco; Arcidiacono, Maria Vittoria; Jurjo, Carmen; Fernández, Elvira; Puig-Domingo, Manuel; Groop, Per-Henrik; Mauricio, Dídac

2016-09-01

We sought to examine the presence and severity of brain small vessel disease (SVD) in patients with type 2 diabetes and diabetic retinopathy (DR) compared with those without DR. We evaluated 312 patients with type 2 diabetes without previous cardiovascular disease (men 51%; mean age 57 years; age range 40-75 years); 153 patients (49%) had DR. MRI was performed to evaluate the presence and severity (age-related white matter changes scale) of white matter lesions (WMLs) and lacunes, and transcranial Doppler ultrasound was used to measure the Gosling pulsatility index (PI) of the middle cerebral artery (MCA). The prevalence of lesions of cerebral SVD (WML and/or lacunes) was higher in patients with DR (40.2% vs. 30.1% without DR, P = 0.04). Age (P < 0.01) and systolic blood pressure (P = 0.02) were associated with the presence of SVD. The severity of SVD was associated with age and the presence of DR (P < 0.01 and P = 0.01, respectively). Patients with DR showed a higher MCA PI compared with those without DR (P < 0.01). Age, systolic and diastolic blood pressure, and retinopathy and its severity were associated with an increased MCA PI (P < 0.01 for all variables). A positive correlation was found between MCA PI values and the presence and severity of SVD (P < 0.01 for both variables). Patients with type 2 diabetes who have DR have an increased burden of cerebral SVD compared with those without DR. Our findings suggest that the brain is a target organ for microangiopathy, similar to other classic target organs, like the retina. © 2016 by the American Diabetes Association.

Distinctive Resting State Network Disruptions Among Alzheimer's Disease, Subcortical Vascular Dementia, and Mixed Dementia Patients.

PubMed

Kim, Hee Jin; Cha, Jungho; Lee, Jong-Min; Shin, Ji Soo; Jung, Na-Yeon; Kim, Yeo Jin; Choe, Yearn Seong; Lee, Kyung Han; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Na, Duk L; Seo, Sang Won

2016-01-01

Recent advances in resting-state functional MRI have revealed altered functional networks in Alzheimer's disease (AD), especially those of the default mode network (DMN) and central executive network (CEN). However, few studies have evaluated whether small vessel disease (SVD) or combined amyloid and SVD burdens affect the DMN or CEN. The aim of this study was to evaluate whether SVD or combined amyloid and SVD burdens affect the DMN or CEN. In this cross-sectional study, we investigated the resting-state functional connectivity within DMN and CEN in 37 Pittsburgh compound-B (PiB)(+) AD, 37 PiB(-) subcortical vascular dementia (SVaD), 13 mixed dementia patients, and 65 normal controls. When the resting-state DMN of PiB(+) AD and PiB(-) SVaD patients were compared, the PiB(+) AD patients displayed lower functional connectivity in the inferior parietal lobule while the PiB(-) SVaD patients displayed lower functional connectivity in the medial frontal and superior frontal gyri. Compared to the PiB(-) SVaD or PiB(+) AD, the mixed dementia patients displayed lower functional connectivity within the DMN in the posterior cingulate gyrus. When the resting-state CEN connectivity of PiB(+) AD and PiB(-) SVaD patients were compared, the PiB(-) SVaD patients displayed lower functional connectivity in the anterior insular region. Compared to the PiB(-) SVaD or PiB(+) AD, the mixed dementia patients displayed lower functional connectivity within the CEN in the inferior frontal gyrus. Our findings suggest that in PiB(+) AD and PiB(-) SVaD, there is divergent disruptions in resting-state DMN and CEN. Furthermore, patients with combined amyloid and SVD burdens exhibited more disrupted resting-state DMN and CEN than patients with only amyloid or SVD burden.

Pathogenesis and neuroimaging of cerebral large and small vessel disease in type 2 diabetes: A possible link between cerebral and retinal microvascular abnormalities.

PubMed

Umemura, Toshitaka; Kawamura, Takahiko; Hotta, Nigishi

2017-03-01

Diabetes patients have more than double the risk of ischemic stroke compared with non-diabetic individuals, and its neuroimaging characteristics have important clinical implications. To understand the pathophysiology of ischemic stroke in diabetes, it is important to focus not only on the stroke subtype, but also on the size and location of the occlusive vessels. Specifically, ischemic stroke in diabetes patients might be attributed to both large and small vessels, and intracranial internal carotid artery disease and small infarcts of the posterior circulation often occur. An additional feature is that asymptomatic lacunar infarctions are often seen in the basal ganglia and brain stem on brain magnetic resonance imaging. In particular, cerebral small vessel disease (SVD), including lacunar infarctions, white matter lesions and cerebral microbleeds, has been shown to be associated not only with stroke incidence, but also with the development and progression of dementia and diabetic microangiopathy. However, the pathogenesis of cerebral SVD is not fully understood. In addition, data on the association between neuroimaging findings of the cerebral SVD and diabetes are limited. Recently, the clinical importance of the link between cerebral SVD and retinal microvascular abnormalities has been a topic of considerable interest. Several clinical studies have shown that retinal microvascular abnormalities are closely related to cerebral SVD, suggesting that retinal microvascular abnormalities might be pathophysiologically linked to ischemic cerebral SVD. We review the literature relating to the pathophysiology and neuroimaging of cerebrovascular disease in diabetes, and discuss the problems based on the concept of cerebral large and small vessel disease. © 2016 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Assessment of the Performance of a Scanning Wind Doppler Lidar at an Urban-Mountain Site in Seoul

NASA Astrophysics Data System (ADS)

Park, S.; Kim, S. W.

2017-12-01

Winds in the planetary boundary layer (PBL) are important factors for accurate modelling of air quality, numerical weather prediction and conversion of satellite measurements to near-surface air quality information (Seibert et al., AE, 2000; Emeis et al., Meteorol. Z., 2008). In this study, we (1) evaluate wind speed (WS) and direction (WD) retrieved from Wind Doppler Lidar (WDL) measurements by two methods [so called, `sine-fitting (SF) method' and `singular value decomposition (SVD) method'] and (2) analyze the WDL data at Seoul National University (SNU), Seoul, to investigate the diurnal evolution of winds and aerosol characteristics in PBL. Evaluation of the two methods used in retrieving wind from radial velocity was done through comparison with radiosonde soundings from the same site. Winds retrieved using the SVD method from mean radial velocity of 15 minutes showed good agreement with radiosonde profiles (i.e., bias of 0.03 m s-1 and root mean square of 1.70 m s-1 in WS). However, the WDL was found to have difficulty retrieving signals under clean conditions (i.e., too small signal to noise ratio) or under the presence of near-surface optically-thick aerosol/cloud layer (i.e., strong signal attenuation). Despite this shortcoming, the WDL was able to successfully capture the diurnal variation of PBL wind. Two major wind patterns were observed at SNU; first of all, when convective boundary layer was strongly developed, thermally induced winds with large variation of vertical WS in the afternoon and a diurnal variation in WD showing characteristics of mountain and valley winds were observed. Secondly, small variation in WS and WD throughout the day was a major characteristic of cases when wind was largely influenced by the synoptic weather pattern.

Systematic Constraint Selection Strategy for Rate-Controlled Constrained-Equilibrium Modeling of Complex Nonequilibrium Chemical Kinetics

NASA Astrophysics Data System (ADS)

Beretta, Gian Paolo; Rivadossi, Luca; Janbozorgi, Mohammad

2018-04-01

Rate-Controlled Constrained-Equilibrium (RCCE) modeling of complex chemical kinetics provides acceptable accuracies with much fewer differential equations than for the fully Detailed Kinetic Model (DKM). Since its introduction by James C. Keck, a drawback of the RCCE scheme has been the absence of an automatable, systematic procedure to identify the constraints that most effectively warrant a desired level of approximation for a given range of initial, boundary, and thermodynamic conditions. An optimal constraint identification has been recently proposed. Given a DKM with S species, E elements, and R reactions, the procedure starts by running a probe DKM simulation to compute an S-vector that we call overall degree of disequilibrium (ODoD) because its scalar product with the S-vector formed by the stoichiometric coefficients of any reaction yields its degree of disequilibrium (DoD). The ODoD vector evolves in the same (S-E)-dimensional stoichiometric subspace spanned by the R stoichiometric S-vectors. Next we construct the rank-(S-E) matrix of ODoD traces obtained from the probe DKM numerical simulation and compute its singular value decomposition (SVD). By retaining only the first C largest singular values of the SVD and setting to zero all the others we obtain the best rank-C approximation of the matrix of ODoD traces whereby its columns span a C-dimensional subspace of the stoichiometric subspace. This in turn yields the best approximation of the evolution of the ODoD vector in terms of only C parameters that we call the constraint potentials. The resulting order-C RCCE approximate model reduces the number of independent differential equations related to species, mass, and energy balances from S+2 to C+E+2, with substantial computational savings when C ≪ S-E.

Objective Assessment and Design Improvement of a Staring, Sparse Transducer Array by the Spatial Crosstalk Matrix for 3D Photoacoustic Tomography

PubMed Central

Kosik, Ivan; Raess, Avery

2015-01-01

Accurate reconstruction of 3D photoacoustic (PA) images requires detection of photoacoustic signals from many angles. Several groups have adopted staring ultrasound arrays, but assessment of array performance has been limited. We previously reported on a method to calibrate a 3D PA tomography (PAT) staring array system and analyze system performance using singular value decomposition (SVD). The developed SVD metric, however, was impractical for large system matrices, which are typical of 3D PAT problems. The present study consisted of two main objectives. The first objective aimed to introduce the crosstalk matrix concept to the field of PAT for system design. Figures-of-merit utilized in this study were root mean square error, peak signal-to-noise ratio, mean absolute error, and a three dimensional structural similarity index, which were derived between the normalized spatial crosstalk matrix and the identity matrix. The applicability of this approach for 3D PAT was validated by observing the response of the figures-of-merit in relation to well-understood PAT sampling characteristics (i.e. spatial and temporal sampling rate). The second objective aimed to utilize the figures-of-merit to characterize and improve the performance of a near-spherical staring array design. Transducer arrangement, array radius, and array angular coverage were the design parameters examined. We observed that the performance of a 129-element staring transducer array for 3D PAT could be improved by selection of optimal values of the design parameters. The results suggested that this formulation could be used to objectively characterize 3D PAT system performance and would enable the development of efficient strategies for system design optimization. PMID:25875177

Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

NASA Astrophysics Data System (ADS)

Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu

2018-05-01

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.

Portuguese version of Wechsler Memory Scale-3rd edition's utility with demented elderly adults.

PubMed

Gonçalves, Cátia; Pinho, Maria S; Cruz, Vítor; Gens, Helena; Oliveira, Fátima; Pais, Joana; Rente, José; Santana, Isabel; Santos, José M

2017-01-01

The purpose of this study is to analyze the utility of the Portuguese version of the Wechsler Memory Scale-3rd edition (WMS-III) with demented elderly people, namely its capacity to detect and discriminate between subcortical vascular dementia (SVD) and Alzheimer's disease (AD). We assessed early demented patients (SVD = 16; AD = 36) aged 65 or older who were compared to a control group (n = 40). Both clinical groups were adequately matched in terms of disease severity, overall cognitive functioning, depressive symptomatology, and pre-morbid intelligence. Between-group's differences were evaluated using the Quade's rank analysis of covariance. We also computed indexes and subtests optimal cut-off scores, and the corresponding sensitivity, specificity, and positive and negative predictive values, which were able to successfully discriminate between patients and healthy subjects. The SVD patients had a better overall memory performance than AD patients on the majority of the indexes and the delayed condition subtests of the WMS-III. The AD patients only showed a better performance on digit span subtest. Several measures discriminated patients from healthy subjects. This study suggests some recommendations for the diagnostic accuracy of the Portuguese version of WMS-III in dementia and about differential diagnosis between SVD and AD.

Optimal Tikhonov Regularization in Finite-Frequency Tomography

NASA Astrophysics Data System (ADS)

Fang, Y.; Yao, Z.; Zhou, Y.

2017-12-01

The last decade has witnessed a progressive transition in seismic tomography from ray theory to finite-frequency theory which overcomes the resolution limit of the high-frequency approximation in ray theory. In addition to approximations in wave propagation physics, a main difference between ray-theoretical tomography and finite-frequency tomography is the sparseness of the associated sensitivity matrix. It is well known that seismic tomographic problems are ill-posed and regularizations such as damping and smoothing are often applied to analyze the tradeoff between data misfit and model uncertainty. The regularizations depend on the structure of the matrix as well as noise level of the data. Cross-validation has been used to constrain data uncertainties in body-wave finite-frequency inversions when measurements at multiple frequencies are available to invert for a common structure. In this study, we explore an optimal Tikhonov regularization in surface-wave phase-velocity tomography based on minimization of an empirical Bayes risk function using theoretical training datasets. We exploit the structure of the sensitivity matrix in the framework of singular value decomposition (SVD) which also allows for the calculation of complete resolution matrix. We compare the optimal Tikhonov regularization in finite-frequency tomography with traditional tradeo-off analysis using surface wave dispersion measurements from global as well as regional studies.

Parallel solution of the symmetric tridiagonal eigenproblem. Research report

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

Jessup, E.R.

1989-10-01

This thesis discusses methods for computing all eigenvalues and eigenvectors of a symmetric tridiagonal matrix on a distributed-memory Multiple Instruction, Multiple Data multiprocessor. Only those techniques having the potential for both high numerical accuracy and significant large-grained parallelism are investigated. These include the QL method or Cuppen's divide and conquer method based on rank-one updating to compute both eigenvalues and eigenvectors, bisection to determine eigenvalues and inverse iteration to compute eigenvectors. To begin, the methods are compared with respect to computation time, communication time, parallel speed up, and accuracy. Experiments on an IPSC hypercube multiprocessor reveal that Cuppen's method ismore » the most accurate approach, but bisection with inverse iteration is the fastest and most parallel. Because the accuracy of the latter combination is determined by the quality of the computed eigenvectors, the factors influencing the accuracy of inverse iteration are examined. This includes, in part, statistical analysis of the effect of a starting vector with random components. These results are used to develop an implementation of inverse iteration producing eigenvectors with lower residual error and better orthogonality than those generated by the EISPACK routine TINVIT. This thesis concludes with adaptions of methods for the symmetric tridiagonal eigenproblem to the related problem of computing the singular value decomposition (SVD) of a bidiagonal matrix.« less

Parallel solution of the symmetric tridiagonal eigenproblem

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

Jessup, E.R.

1989-01-01

This thesis discusses methods for computing all eigenvalues and eigenvectors of a symmetric tridiagonal matrix on a distributed memory MIMD multiprocessor. Only those techniques having the potential for both high numerical accuracy and significant large-grained parallelism are investigated. These include the QL method or Cuppen's divide and conquer method based on rank-one updating to compute both eigenvalues and eigenvectors, bisection to determine eigenvalues, and inverse iteration to compute eigenvectors. To begin, the methods are compared with respect to computation time, communication time, parallel speedup, and accuracy. Experiments on an iPSC hyper-cube multiprocessor reveal that Cuppen's method is the most accuratemore » approach, but bisection with inverse iteration is the fastest and most parallel. Because the accuracy of the latter combination is determined by the quality of the computed eigenvectors, the factors influencing the accuracy of inverse iteration are examined. This includes, in part, statistical analysis of the effects of a starting vector with random components. These results are used to develop an implementation of inverse iteration producing eigenvectors with lower residual error and better orthogonality than those generated by the EISPACK routine TINVIT. This thesis concludes with adaptations of methods for the symmetric tridiagonal eigenproblem to the related problem of computing the singular value decomposition (SVD) of a bidiagonal matrix.« less

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

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

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significancemore » analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.« less

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish

2017-04-01

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

The anticipation of death by violence: a psychological profile.

PubMed

Mahoney, J; Kyle, D; Katz, G

1975-01-01

College students (n = 172) completed Cattell's personality factor questionnaire, Rotter's locus of control scale, Speilberger's trait anxiety measure, and Sabatini and Kastenbaum's self-completed death certificate. Comparison of profiles for subjects anticipating sudden violent death (SVD, n = 59) with those anticipating natural death (ND, n = 113) disclosed that the SVD group was characteristically more anxious and socially isolated. A sex-by-type of death interaction occurred for locus of control, with SVD females being the most external, suggesting that this group was more likely to "give up" in response to stress. The data support Shneidman's concept of subintentioned death in disclosing that several personality factors may be associated with violent death.

Intelligent Classification in Huge Heterogeneous Data Sets

DTIC Science & Technology

2015-06-01

Competencies DoD Department of Defense GMTI Ground Moving Target Indicator ISR Intelligence, Surveillance and Reconnaissance NCD Noncoherent Change...Detection OCR Optical Character Recognition PCA Principal Component Analysis SAR Synthetic Aperture Radar SVD Singular Value Decomponsition USPS United States Postal Service 8 Approved for Public Release; Distribution Unlimited.

Longitudinal decline in structural networks predicts dementia in cerebral small vessel disease

PubMed Central

Lawrence, Andrew J.; Zeestraten, Eva A.; Benjamin, Philip; Lambert, Christian P.; Morris, Robin G.; Barrick, Thomas R.

2018-01-01

Objective To determine whether longitudinal change in white matter structural network integrity predicts dementia and future cognitive decline in cerebral small vessel disease (SVD). To investigate whether network disruption has a causal role in cognitive decline and mediates the association between conventional MRI markers of SVD with both cognitive decline and dementia. Methods In the prospective longitudinal SCANS (St George's Cognition and Neuroimaging in Stroke) Study, 97 dementia-free individuals with symptomatic lacunar stroke were followed with annual MRI for 3 years and annual cognitive assessment for 5 years. Conversion to dementia was recorded. Structural networks were constructed from diffusion tractography using a longitudinal registration pipeline, and network global efficiency was calculated. Linear mixed-effects regression was used to assess change over time. Results Seventeen individuals (17.5%) converted to dementia, and significant decline in global cognition occurred (p = 0.0016). Structural network measures declined over the 3-year MRI follow-up, but the degree of change varied markedly between individuals. The degree of reductions in network global efficiency was associated with conversion to dementia (B = −2.35, odds ratio = 0.095, p = 0.00056). Change in network global efficiency mediated much of the association of conventional MRI markers of SVD with cognitive decline and progression to dementia. Conclusions Network disruption has a central role in the pathogenesis of cognitive decline and dementia in SVD. It may be a useful disease marker to identify that subgroup of patients with SVD who progress to dementia. PMID:29695593

Small vessel disease, but neither amyloid load nor metabolic deficit, is dependent on age at onset in Alzheimer's disease.

PubMed

Ortner, Marion; Kurz, Alexander; Alexopoulos, Panagiotis; Auer, Florian; Diehl-Schmid, Janine; Drzezga, Alexander; Förster, Stefan; Förstl, Hans; Perneczky, Robert; Sorg, Christian; Yousefi, Behrooz H; Grimmer, Timo

2015-04-15

There is controversy concerning whether Alzheimer's disease (AD) with early onset is distinct from AD with late onset with regard to amyloid pathology and neuronal metabolic deficit. We hypothesized that compared with patients with early-onset AD, patients with late-onset AD have more comorbid small vessel disease (SVD) contributing to clinical severity, whereas there are no differences in amyloid pathology and neuronal metabolic deficit. The study included two groups of patients with probable AD dementia with evidence of the AD pathophysiologic process: 24 patients with age at onset <60 years old and 36 patients with age at onset >70 years old. Amyloid deposition was assessed using carbon-11-labeled Pittsburgh compound B positron emission tomography, comorbid SVD was assessed using magnetic resonance imaging, and neuronal metabolic deficit was assessed using fluorodeoxyglucose positron emission tomography. Group differences of global and regional distribution of pathology were explored using region of interest and voxel-based analyses, respectively, carefully controlling for the influence of dementia severity, apolipoprotein E genotype, and in particular SVD. The pattern of cognitive impairment was determined using z scores of the subtests of the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery. Patients with late-onset AD showed a significantly greater amount of SVD. No statistically significant differences in global or regional amyloid deposition or neuronal metabolic deficit between the two groups were revealed. However, when not controlling for SVD, subtle differences in fluorodeoxyglucose uptake between early-onset AD and late-onset AD groups were detectable. There were no significant differences regarding cognitive functioning. Age at onset does not influence amyloid deposition or neuronal metabolic deficit in AD. The greater extent of SVD in late-onset AD influences the association between neuronal metabolic deficit and clinical symptoms. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Observations of EMIC Triggered Emissions off the Magnetic Equatorial Plane

NASA Astrophysics Data System (ADS)

Grison, B.; Breuillard, H.; Santolik, O.; Cornilleau-Wehrlin, N.

2016-12-01

On 19/08/2005 Cluster spacecraft had their perigee close to the dayside of the Earth magnetic equatorial plane, at about 14 hours Magnetic Local Time. The spacecraft crossed the equator from the southern hemisphere toward the northern hemisphere. In the Southern hemisphere, at about -23° magnetic latitude (MLAT) and at distance of 5.25 Earth Radii from Earth, Cluster 3 observes an EMIC triggered emission between the He+ and the proton local gyrofrequencies. The magnetic waveform (STAFF instrument data) is transformed into the Fourier space for a study based on single value decomposition (SVD) analysis. The emission lasts about 30s. The emission frequency rises from 1Hz up to 1.9Hz. The emission polarization is left-hand, its coherence value is high and the propagation angle is field aligned (lower than 30º). The Poynting flux orientation could not be established. Based on previous study results, these properties are indicative of an observation in vicinity of the source region of the triggered emission. From our knowledge this is the first time that EMIC triggered emission are observed off the magnetic equator. In order to identify the source region we study two possibilities: a source region at higher latitudes than the observations (and particles orbiting in "Shabansky" orbits) and a source region close to the magnetic equatorial plane, as reported in previous studies. We propose to identify the source region from ray tracing analysis and to compare the observed propagation angle in several frequency ranges to the ray tracing results.

In-Flight Alignment Using H ∞ Filter for Strapdown INS on Aircraft

PubMed Central

Pei, Fu-Jun; Liu, Xuan; Zhu, Li

2014-01-01

In-flight alignment is an effective way to improve the accuracy and speed of initial alignment for strapdown inertial navigation system (INS). During the aircraft flight, strapdown INS alignment was disturbed by lineal and angular movements of the aircraft. To deal with the disturbances in dynamic initial alignment, a novel alignment method for SINS is investigated in this paper. In this method, an initial alignment error model of SINS in the inertial frame is established. The observability of the system is discussed by piece-wise constant system (PWCS) theory and observable degree is computed by the singular value decomposition (SVD) theory. It is demonstrated that the system is completely observable, and all the system state parameters can be estimated by optimal filter. Then a H ∞ filter was designed to resolve the uncertainty of measurement noise. The simulation results demonstrate that the proposed algorithm can reach a better accuracy under the dynamic disturbance condition. PMID:24511300

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

SVD compression for magnetic resonance fingerprinting in the time domain.

PubMed

McGivney, Debra F; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A

2014-12-01

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Reduced order modeling of head related transfer functions for virtual acoustic displays

NASA Astrophysics Data System (ADS)

Willhite, Joel A.; Frampton, Kenneth D.; Grantham, D. Wesley

2003-04-01

The purpose of this work is to improve the computational efficiency in acoustic virtual applications by creating and testing reduced order models of the head related transfer functions used in localizing sound sources. State space models of varying order were generated from zero-elevation Head Related Impulse Responses (HRIRs) using Kungs Single Value Decomposition (SVD) technique. The inputs to the models are the desired azimuths of the virtual sound sources (from minus 90 deg to plus 90 deg, in 10 deg increments) and the outputs are the left and right ear impulse responses. Trials were conducted in an anechoic chamber in which subjects were exposed to real sounds that were emitted by individual speakers across a numbered speaker array, phantom sources generated from the original HRIRs, and phantom sound sources generated with the different reduced order state space models. The error in the perceived direction of the phantom sources generated from the reduced order models was compared to errors in localization using the original HRIRs.

Application of Improved 5th-Cubature Kalman Filter in Initial Strapdown Inertial Navigation System Alignment for Large Misalignment Angles

PubMed Central

Wang, Wei; Chen, Xiyuan

2018-01-01

In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm. PMID:29473912

Parallel Reconstruction Using Null Operations (PRUNO)

PubMed Central

Zhang, Jian; Liu, Chunlei; Moseley, Michael E.

2011-01-01

A novel iterative k-space data-driven technique, namely Parallel Reconstruction Using Null Operations (PRUNO), is presented for parallel imaging reconstruction. In PRUNO, both data calibration and image reconstruction are formulated into linear algebra problems based on a generalized system model. An optimal data calibration strategy is demonstrated by using Singular Value Decomposition (SVD). And an iterative conjugate- gradient approach is proposed to efficiently solve missing k-space samples during reconstruction. With its generalized formulation and precise mathematical model, PRUNO reconstruction yields good accuracy, flexibility, stability. Both computer simulation and in vivo studies have shown that PRUNO produces much better reconstruction quality than autocalibrating partially parallel acquisition (GRAPPA), especially under high accelerating rates. With the aid of PRUO reconstruction, ultra high accelerating parallel imaging can be performed with decent image quality. For example, we have done successful PRUNO reconstruction at a reduction factor of 6 (effective factor of 4.44) with 8 coils and only a few autocalibration signal (ACS) lines. PMID:21604290

Precipitation extremes and their relation to climatic indices in the Pacific Northwest USA

NASA Astrophysics Data System (ADS)

Zarekarizi, Mahkameh; Rana, Arun; Moradkhani, Hamid

2018-06-01

There has been focus on the influence of climate indices on precipitation extremes in the literature. Current study presents the evaluation of the precipitation-based extremes in Columbia River Basin (CRB) in the Pacific Northwest USA. We first analyzed the precipitation-based extremes using statistically (ten GCMs) and dynamically downscaled (three GCMs) past and future climate projections. Seven precipitation-based indices that help inform about the flood duration/intensity are used. These indices help in attaining first-hand information on spatial and temporal scales for different service sectors including energy, agriculture, forestry etc. Evaluation of these indices is first performed in historical period (1971-2000) followed by analysis of their relation to large scale tele-connections. Further we mapped these indices over the area to evaluate the spatial variation of past and future extremes in downscaled and observational data. The analysis shows that high values of extreme indices are clustered in either western or northern parts of the basin for historical period whereas the northern part is experiencing higher degree of change in the indices for future scenario. The focus is also on evaluating the relation of these extreme indices to climate tele-connections in historical period to understand their relationship with extremes over CRB. Various climate indices are evaluated for their relationship using Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Results indicated that, out of 13 climate tele-connections used in the study, CRB is being most affected inversely by East Pacific (EP), Western Pacific (WP), East Atlantic (EA) and North Atlaentic Oscillation (NAO).

Impact of Strategically Located White Matter Hyperintensities on Cognition in Memory Clinic Patients with Small Vessel Disease

PubMed Central

Hilal, Saima; Kuijf, Hugo J.; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Postma, Albert; Biessels, Geert Jan; Chen, Christopher P. L. H.

2016-01-01

Background and Purpose Studies on the impact of small vessel disease (SVD) on cognition generally focus on white matter hyperintensity (WMH) volume. The extent to which WMH location relates to cognitive performance has received less attention, but is likely to be functionally important. We examined the relation between WMH location and cognition in a memory clinic cohort of patients with sporadic SVD. Methods A total of 167 patients with SVD were recruited from memory clinics. Assumption-free region of interest-based analyses based on major white matter tracts and voxel-wise analyses were used to determine the association between WMH location and executive functioning, visuomotor speed and memory. Results Region of interest-based analyses showed that WMHs located particularly within the anterior thalamic radiation and forceps minor were inversely associated with both executive functioning and visuomotor speed, independent of total WMH volume. Memory was significantly associated with WMH volume in the forceps minor, independent of total WMH volume. An independent assumption-free voxel-wise analysis identified strategic voxels in these same tracts. Region of interest-based analyses showed that WMH volume within the anterior thalamic radiation explained 6.8% of variance in executive functioning, compared to 3.9% for total WMH volume; WMH volume within the forceps minor explained 4.6% of variance in visuomotor speed and 4.2% of variance in memory, compared to 1.8% and 1.3% respectively for total WMH volume. Conclusions Our findings identify the anterior thalamic radiation and forceps minor as strategic white matter tracts in which WMHs are most strongly associated with cognitive impairment in memory clinic patients with SVD. WMH volumes in individual tracts explained more variance in cognition than total WMH burden, emphasizing the importance of lesion location when addressing the functional consequences of WMHs. PMID:27824925

Microvascular Pathology and Morphometrics of Sporadic and Hereditary Small Vessel Diseases of the Brain

PubMed Central

Craggs, Lucinda JL; Yamamoto, Yumi; Deramecourt, Vincent; Kalaria, Raj N

2014-01-01

Small vessel diseases (SVDs) of the brain are likely to become increasingly common in tandem with the rise in the aging population. In recent years, neuroimaging and pathological studies have informed on the pathogenesis of sporadic SVD and several single gene (monogenic) disorders predisposing to subcortical strokes and diffuse white matter disease. However, one of the limitations toward studying SVD lies in the lack of consistent assessment criteria and lesion burden for both clinical and pathological measures. Arteriolosclerosis and diffuse white matter changes are the hallmark features of both sporadic and hereditary SVDs. The pathogenesis of the arteriopathy is the key to understanding the differential progression of disease in various SVDs. Remarkably, quantification of microvascular abnormalities in sporadic and hereditary SVDs has shown that qualitatively the processes involved in arteriolar degeneration are largely similar in sporadic SVD compared with hereditary disorders such as cerebral autosomal arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Important significant regional differences in lesion location within the brain may enable one to distinguish SVDs, where frontal lobe involvement appears consistently with almost every SVD, but others bear specific pathologies in other lobes, such as the temporal pole in CADASIL and the pons in pontine autosomal dominant microangiopathy and leukoencephalopathy or PADMAL. Additionally, degenerative changes in the vascular smooth muscle cells, the cerebral endothelium and the basal lamina are often rapid and more aggressive in genetic disorders. Further quantification of other microvascular elements and even neuronal cells is needed to fully characterize SVD pathogenesis and to differentiate the usefulness of vascular interventions and treatments on the resulting pathology. PMID:25323665

The Shock and Vibration Digest. Volume 13, Number 12

DTIC Science & Technology

1981-12-01

Resulting Unsteady Forces and Flow Phenomenon. Part III 26 BOOK REVIEWS STATISTICAL ENERGY ANALYSIS Chapter IV considers the problems of estimating J OF...stress, acceleration, modes. Statistical energy analysis (SEA), which is and pressure; estimations of the average system expressed in terms of random...by F.C. Nelson, SVD, 13 (8), pp 30-31 (Aug 1981) Lyons, R.H., Statistical Energy Analysis of Dynamic Systems, MIT Press, Cambridge, MA; Revieed by H

Persistence of symptoms in primary somatoform vertigo and dizziness: a disorder "lost" in health care?

PubMed

Tschan, Regine; Best, Christoph; Wiltink, Jörg; Beutel, Manfred E; Dieterich, Marianne; Eckhardt-Henn, Annegret

2013-04-01

The aim of this study was to perform a 3-year follow-up of primary somatoform vertigo and dizziness (SVD) regarding health care use and treatment. Ninety-two patients with dizziness underwent detailed vestibular neurophysiological testing and a Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Psychometric assessments comprised the Vertigo Symptom Scale, the Vertigo Handicap Questionnaire, the SCL-90-R, and the Short-Form-36 Health Survey. At the 3-year follow-up, 65 patients with primary SVD (anxiety, n = 29; depression, n = 14; somatoform disorders, n = 22) were reassessed (70.7% response). The patients improved in symptom severity (p < 0.05), handicap (p < 0.01), and physical quality of life (QoL; p < 0.05) but showed no change in emotional distress. A total of 63.1% (of n = 65) had ongoing SVD. A total of 69.2% (of n = 65) received different forms of treatments. A total of 46.1% (of n = 65) searched redundant medical diagnostic procedures. The patients with decreased coping capacity over time obtained the best prognosis. Primary SVD is an ineffectively treated disorder. Recommendations for specific complaint-oriented psychotherapy programs were given.

Perivascular Spaces, Glymphatic Dysfunction, and Small Vessel Disease

PubMed Central

Mestre, Humberto; Kostrikov, Serhii; Mehta, Rupal I.; Nedergaard, Maiken

2017-01-01

Cerebral small vessel diseases (SVD) range broadly in etiology but share a remarkably overlapping pathology. Features of SVD including enlarged perivascular spaces and formation of abluminal protein deposits cannot be completely explained by the putative pathophysiology. The recently discovered glymphatic system provides a new perspective to potentially address these gaps. This work provides a comprehensive review of the known factors that regulate glymphatic function and the disease mechanisms underlying glymphatic impairment emphasizing the role that aquaporin-4 (AQP4)-lined perivascular spaces, cerebrovascular pulsatility, and metabolite clearance play in normal CNS physiology. This review also discusses the implications that glymphatic impairment may have on SVD inception and progression with the aim of exploring novel therapeutic targets and highlighting the key questions that remain to be answered. PMID:28798076

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

PubMed

Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

2010-11-09

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Simultaneous and independent optical impairments monitoring using singular spectrum analysis of asynchronously sampled signal amplitudes

NASA Astrophysics Data System (ADS)

Guesmi, Latifa; Menif, Mourad

2015-09-01

Optical performance monitoring (OPM) becomes an inviting topic in high speed optical communication networks. In this paper, a novel technique of OPM based on a new elaborated computation approach of singular spectrum analysis (SSA) for time series prediction is presented. Indeed, various optical impairments among chromatic dispersion (CD), polarization mode dispersion (PMD) and amplified spontaneous emission (ASE) noise are a major factors limiting quality of transmission data in the systems with data rates lager than 40 Gbit/s. This technique proposed an independent and simultaneous multi-impairments monitoring, where we used SSA of time series analysis and forecasting. It has proven their usefulness in the temporal analysis of short and noisy time series in several fields, that it is based on the singular value decomposition (SVD). Also, advanced optical modulation formats (100 Gbit/s non-return-to zero dual-polarization quadrature phase shift keying (NRZ-DP-QPSK) and 160 Gbit/s DP-16 quadrature amplitude modulation (DP-16QAM)) offering high spectral efficiencies have been successfully employed by analyzing their asynchronously sampled amplitude. The simulated results proved that our method is efficient on CD, first-order PMD, Q-factor and OSNR monitoring, which enabled large monitoring ranges, the CD in the range of 170-1700 ps/nm.Km and 170-1110 ps/nm.Km for 100 Gbit/s NRZ-DP-QPSK and 160 Gbit/s DP-16QAM respectively, and also the DGD up to 20 ps is monitored. We could accurately monitor the OSNR in the range of 10-40 dB with monitoring error remains less than 1 dB in the presence of large accumulated CD.

A new classification scheme of plastic wastes based upon recycling labels

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

Özkan, Kemal, E-mail: kozkan@ogu.edu.tr; Ergin, Semih, E-mail: sergin@ogu.edu.tr; Işık, Şahin, E-mail: sahini@ogu.edu.tr

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize thesemore » materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.« less

Long-term surface EMG monitoring using K-means clustering and compressive sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

Removing non-stationary noise in spectrum sensing using matrix factorization

NASA Astrophysics Data System (ADS)

van Bloem, Jan-Willem; Schiphorst, Roel; Slump, Cornelis H.

2013-12-01

Spectrum sensing is key to many applications like dynamic spectrum access (DSA) systems or telecom regulators who need to measure utilization of frequency bands. The International Telecommunication Union (ITU) recommends a 10 dB threshold above the noise to decide whether a channel is occupied or not. However, radio frequency (RF) receiver front-ends are non-ideal. This means that the obtained data is distorted with noise and imperfections from the analog front-end. As part of the front-end the automatic gain control (AGC) circuitry mainly affects the sensing performance as strong adjacent signals lift the noise level. To enhance the performance of spectrum sensing significantly we focus in this article on techniques to remove the noise caused by the AGC from the sensing data. In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis. In addition, we use live measurement results to verify the performance and to remove the effects of the AGC from the sensing data using above mentioned techniques, i.e., applied on block-wise available spectrum data. In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum usage by 60%.

Attenuation of brain white matter lesions after lacunar stroke.

PubMed

Durand-Birchenall, Julia; Leclercq, Claire; Daouk, Joël; Monet, Pauline; Godefroy, Olivier; Bugnicourt, Jean-Marc

2012-02-01

White matter lesions (WMLs) are commonly observed in stroke patients with small vessel disease (SVD) and are thought to result from a progressive, irreversible disease process following arteriolosclerosis. In this study, we report a case of partial disappearance of WMLs 1 year after a lacunar stroke in a 69-year-old man with evidence of SVD. We also discuss possible mechanisms associated with this observation.

40 CFR Appendix 7 to Subpart A of... - Determination of the Amount of Non-Aqueous Drilling Fluid (NAF) Base Fluid From Drill Cuttings by...

Code of Federal Regulations, 2013 CFR

2013-07-01

... multiplying the density of the small volume NAF-cuttings discharges (ρsvd) times the volume of the small...-cuttings discharges (kg) ρsvd = density of the small volume NAF-cuttings discharges (kg/bbl) VSVD = volume of the small volume NAF-cuttings discharges (bbl) The density of the small volume NAF-cuttings...

40 CFR Appendix 7 to Subpart A of... - Determination of the Amount of Non-Aqueous Drilling Fluid (NAF) Base Fluid From Drill Cuttings by...

Code of Federal Regulations, 2014 CFR

2014-07-01

... multiplying the density of the small volume NAF-cuttings discharges (ρsvd) times the volume of the small...-cuttings discharges (kg) ρsvd = density of the small volume NAF-cuttings discharges (kg/bbl) VSVD = volume of the small volume NAF-cuttings discharges (bbl) The density of the small volume NAF-cuttings...

40 CFR Appendix 7 to Subpart A of... - Determination of the Amount of Non-Aqueous Drilling Fluid (NAF) Base Fluid From Drill Cuttings by...

Code of Federal Regulations, 2012 CFR

2012-07-01

... multiplying the density of the small volume NAF-cuttings discharges (ρsvd) times the volume of the small...-cuttings discharges (kg) ρsvd = density of the small volume NAF-cuttings discharges (kg/bbl) VSVD = volume of the small volume NAF-cuttings discharges (bbl) The density of the small volume NAF-cuttings...

Ten-year results of the Freedom Solo stentless heart valve: excellent haemodynamics but progressive valve dysfunction in the long term.

PubMed

Sponga, Sandro; Barbera, Mila Della; Pavoni, Daisy; Lechiancole, Andrea; Mazzaro, Enzo; Valente, Marialuisa; Nucifora, Gaetano; Thiene, Gaetano; Livi, Ugolino

2017-05-01

Freedom Solo (FS) is a pericardial stentless heart valve showing excellent haemodynamic performance at mid-term. The aim of this study was to evaluate the long-term performance of such bioprostheses. Between December 2004 and November 2009, 109 patients (31 men; mean age 76 ± 6 years) underwent aortic valve replacement with FS. Preoperatively, the mean NYHA class was 2.5 ± 0.7, the mean EuroSCORE II, 2.8 ± 2.5. Mean prosthesis size was 22.7 ± 1.9 mm; concomitant procedures were performed in 65 patients. Structural valve deterioration (SVD) was diagnosed according to the Valve Academic Research Consortium-2 definition. Two patients (1.8%) died within 30 days. Follow-up (72 ± 36 months) was 100% completed. The 1-, 5- and 10-year actuarial survival rates were 89, 73 and 42%, respectively, with 8 valve-related deaths; the actuarial freedom from SVD was 99, 93 and 76%. During 61 ± 39 months of follow-up, echocardiographic findings worsened progressively: At discharge, 3-5 and 7-9 years, the mean gradient was 8 ± 4, 12 ± 11 and 19 ± 19 mmHg ( P  < 0.01); the indexed effective orifice area was 1.0 ± 0.2, 0.9 ± 0.2 and 0.8 ± 0.3 cm 2 /m 2 ( P  < 0.01). Of the 13 patients who developed SVD, it was due to aortic stenosis in 11. SVD was a predictor of cardiovascular mortality at univariate analysis (HR 2.87, 1.12-7.29); 2 explanted prostheses showed massive calcium deposits with mean calcium and phosphorus contents of 234 ± 16 and 116 ± 7 mg/g dry weight, respectively. The FS bioprosthesis shows excellent mid-term clinical and haemodynamic results and offers an alternative to other valves, particularly in the case of a small aortic annulus. Worsening of FS performance was observed at late follow-up because of progressive SVD with stenosis, questioning whether it should be used in patients with a long life expectancy. © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Characteristics of stroke mechanisms in patients with medullary infarction.

PubMed

Lee, M J; Park, Y G; Kim, S J; Lee, J J; Bang, O Y; Kim, J S

2012-11-01

Few studies have focused on the mechanisms underlying medullary infarctions. Our aim in this study was to investigate stroke mechanisms in patients with medullary infarctions and to determine the clinical, radiological and laboratory characteristics of these patients with different underlying stroke etiologies. Consecutive patients with medullary infarction were analysed. Stroke mechanisms were classified as large artery disease (LAD), cardiogenic embolism (CE), small vessel disease (SVD), arterial dissection or undetermined etiology. Clinical, radiological and laboratory factors were analysed according to the location of the lesion and stroke mechanisms. A total of 77 patients were enrolled in this study. Amongst them, 53 (68.8%) patients had lateral medullary infarction (LMI), 22 (28.6%) had medial medullary infarction (MMI), and the remaining 2 (2.6%) had hemimedullary infarction. In both LMI and MMI patients, LAD was the most frequently encountered stroke mechanism. Arterial dissection was the second most common cause followed by SVD and CE in patients with LMI, whereas SVD was more frequently observed (P < 0.001) and dissection and CE were less prevalent (P < 0.001 and P = 0.024, respectively) in MMI than in LMI. Regarding differences amongst stroke etiologies, patients with dissection were younger and had a significantly lower incidence of metabolic syndrome (P = 0.002 and P = 0.009, respectively) than patients with LAD and SVD. Patients in the LAD (19/34, 60%) and dissection groups (12/14, 75%) had abnormal perfusion-weighted MRI (PWI) findings, whereas all patients with SVD (9/9) had normal PWI findings (P < 0.001). Stroke mechanisms in medullary infarction differ between LMI and MMI. Clinical and radiological characteristics, especially PWI features, are helpful in discriminating the etiologies of stroke in these patients. © 2012 The Author(s) European Journal of Neurology © 2012 EFNS.

Swine vesicular disease in northern Italy: diffusion through densely populated pig areas.

PubMed

Bellini, S; Alborali, L; Zanardi, G; Bonazza, V; Brocchi, E

2010-12-01

At the end of 2006, a recrudescence of swine vesicular disease (SVD) was recorded in Italy and the disease spread widely throughout the northern regions. Lombardy, a densely populated pig area, was most affected and the presence of the disease caused heavy economic losses to the entire pig industry. Although SVD is considered only moderately contagious, the epidemic in the north was characterised by a rapid spread of the condition. Numerous difficulties were encountered in eradicating it. Over the past decade, there has been a significant increase in the population of pigs in Lombardy, concentrated mainly in a few areas which were the most severely affected during the 2006 to 2007 SVD epidemic. Increases in both the pig population and animal movements, combined with weak biosecurity measures, increased the spread rate of the disease and hampered eradication activities.

Attenuation of Brain White Matter Lesions After Lacunar Stroke

PubMed Central

Durand-Birchenall, Julia; Leclercq, Claire; Daouk, Joël; Monet, Pauline; Godefroy, Olivier; Bugnicourt, Jean-Marc

2012-01-01

White matter lesions (WMLs) are commonly observed in stroke patients with small vessel disease (SVD) and are thought to result from a progressive, irreversible disease process following arteriolosclerosis. In this study, we report a case of partial disappearance of WMLs 1 year after a lacunar stroke in a 69-year-old man with evidence of SVD. We also discuss possible mechanisms associated with this observation. PMID:22347611

Could Better Phenotyping Small Vessel Disease Provide New Insights into Alzheimer Disease and Improve Clinical Trial Outcomes?

PubMed

Marnane, Michael; Hsiung, Ging-Yuek R

2016-01-01

Alzheimer Disease (AD) is the most common primary cause of dementia with a burgeoning epidemic as life expectancy and general medical care improve worldwide. Recent data from pathologic studies has shown that the cooccurrence of other neurodegenerative and vascular pathologies is in fact the rule rather than the exception. In late onset AD, cerebral small vessel disease (SVD) is almost invariably co-existent to a greater or lesser extent and is known to promote cognitive deterioration. Previous observational studies and clinical trials have largely sought to divide dementia based on predominant neurodegenerative or vascular mechanisms. Given the high degree of overlap, findings from such studies may be difficult to interpret and apply to population cohorts. Additionally opportunities may be lost for uncovering novel interventions that target interactions between co-existent vascular and neurodegenerative pathologies. In the current review, we consider potential pathophysiologic mechanisms through which SVD may be associated with and promote AD pathology. In particular we explore shared environmental and genetic associations and how these may converge via neuroinflammatory pathways potentially providing novel therapeutic targets. SVD has heterogenous manifestations on cerebral imaging and at pathology. We discuss how studying SVD topography may enable us to better identify those at risk for more rapid cognitive decline and improve future clinical trial design.

Comparison of two SVD-based color image compression schemes.

PubMed

Li, Ying; Wei, Musheng; Zhang, Fengxia; Zhao, Jianli

2017-01-01

Color image compression is a commonly used process to represent image data as few bits as possible, which removes redundancy in the data while maintaining an appropriate level of quality for the user. Color image compression algorithms based on quaternion are very common in recent years. In this paper, we propose a color image compression scheme, based on the real SVD, named real compression scheme. First, we form a new real rectangular matrix C according to the red, green and blue components of the original color image and perform the real SVD for C. Then we select several largest singular values and the corresponding vectors in the left and right unitary matrices to compress the color image. We compare the real compression scheme with quaternion compression scheme by performing quaternion SVD using the real structure-preserving algorithm. We compare the two schemes in terms of operation amount, assignment number, operation speed, PSNR and CR. The experimental results show that with the same numbers of selected singular values, the real compression scheme offers higher CR, much less operation time, but a little bit smaller PSNR than the quaternion compression scheme. When these two schemes have the same CR, the real compression scheme shows more prominent advantages both on the operation time and PSNR.

Comparison of two SVD-based color image compression schemes

PubMed Central

Li, Ying; Wei, Musheng; Zhang, Fengxia; Zhao, Jianli

2017-01-01

Color image compression is a commonly used process to represent image data as few bits as possible, which removes redundancy in the data while maintaining an appropriate level of quality for the user. Color image compression algorithms based on quaternion are very common in recent years. In this paper, we propose a color image compression scheme, based on the real SVD, named real compression scheme. First, we form a new real rectangular matrix C according to the red, green and blue components of the original color image and perform the real SVD for C. Then we select several largest singular values and the corresponding vectors in the left and right unitary matrices to compress the color image. We compare the real compression scheme with quaternion compression scheme by performing quaternion SVD using the real structure-preserving algorithm. We compare the two schemes in terms of operation amount, assignment number, operation speed, PSNR and CR. The experimental results show that with the same numbers of selected singular values, the real compression scheme offers higher CR, much less operation time, but a little bit smaller PSNR than the quaternion compression scheme. When these two schemes have the same CR, the real compression scheme shows more prominent advantages both on the operation time and PSNR. PMID:28257451

Phase unwinding for dictionary compression with multiple channel transmission in magnetic resonance fingerprinting.

PubMed

Lattanzi, Riccardo; Zhang, Bei; Knoll, Florian; Assländer, Jakob; Cloos, Martijn A

2018-06-01

Magnetic Resonance Fingerprinting reconstructions can become computationally intractable with multiple transmit channels, if the B 1 + phases are included in the dictionary. We describe a general method that allows to omit the transmit phases. We show that this enables straightforward implementation of dictionary compression to further reduce the problem dimensionality. We merged the raw data of each RF source into a single k-space dataset, extracted the transceiver phases from the corresponding reconstructed images and used them to unwind the phase in each time frame. All phase-unwound time frames were combined in a single set before performing SVD-based compression. We conducted synthetic, phantom and in-vivo experiments to demonstrate the feasibility of SVD-based compression in the case of two-channel transmission. Unwinding the phases before SVD-based compression yielded artifact-free parameter maps. For fully sampled acquisitions, parameters were accurate with as few as 6 compressed time frames. SVD-based compression performed well in-vivo with highly under-sampled acquisitions using 16 compressed time frames, which reduced reconstruction time from 750 to 25min. Our method reduces the dimensions of the dictionary atoms and enables to implement any fingerprint compression strategy in the case of multiple transmit channels. Copyright © 2018 Elsevier Inc. All rights reserved.

[Serum level of S100B as a marker of progression of vascular mild cognitive impairment into subcortical vascular dementia and therapy effectiveness].

PubMed

Levada, O A; Traïlin, A V

2012-01-01

We evaluated serum level of S100B in 11 patients with subcortical vascular dementia (SVD) and 19 patients with subcortical vascular mild cognitive impairment (SVMCI). Comparable groups were age-matched (79.18 +/- 7.76 in SVD group, 77.84 +/- 3.83 in SVMCI; P = 0.53). 22 patients were assessed after 1 month therapy. It was shown that the serum S100B level significantly increased--(0.065 +/- 0.020) micro/l (P = 0.0005) in SVD patients comparing to SVMCI ones - (0.043 +/- 0.010) microg/l. S100B level was significantly correlated with the clinical parameters: MMSE performance (r(s) = -0.61), CDR (r(s) = 0.58), attention task (r(s) = -0.46), pseudobulbar syndrome severity (r(s) = 0.37) and walking alteration (r(s)= 0.37). In patients with reduction of S100B level due to therapy (positive dynamics, n = 12) we registered significant improvement of some clinical parameters: MMSE, attention level, walking. In patients with increasing of S100B level (negative dynamics, n = 10) we didn't registered improvement of any clinical parameters. We made the conclusion that the serum level of S100B could be used as marker of progression SVMCI into SVD and therapy effectiveness.

Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC.

PubMed

Pacharawongsakda, Eakasit; Theeramunkong, Thanaruk

2013-12-01

Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.

Correlation analysis between the LDL-C in serum and the onset of transient ischemic attack caused by CSVD.

PubMed

Chen, Yaqi; Hu, Mei; Gong, Hongying

2017-08-01

The aim of this study was to investigate the correlation between the low-density lipoprotein cholesterol (LDL-C) in serum and the onset of transient ischemic attack caused by cerebral small vascular disease (CSVD). Between September 2012 and September 2015, 249 patients who were diagnosed as CSVD were randomly enrolled in this study. According to MRI results, patients were divided into the patient and control groups. In the patient group, the patients were further subdivided into the white matter lesion (WML) group (n=86) and lacunar infarction (LI) group (n=53). Head MRI and/or CT were conducted on all the patients. This included T1 and T2 phases, diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR). Additionally, mini-mental status examination (MMSE) test and Montreal cognitive assessment (MoCA) test were carried out on all the patients. As a result, the age, total cholesterol (TC) level and low-density lipoprotein (LDL) levels in the patient group were higher than those in the control group (p<0.05). The MMSE and MoCA scores in the patient group were significantly lower than those in the control group (p<0.05). With all the risk factors being set as independent variables and small vessel disease (SVD) as the dependent variable, we performed the logistic regression analysis and correlation analysis for paired data, and found that the increase in LDL was correlated to the onset of SVD, OR=1,321. After adjustment of other risk factors, we enrolled the level of triglyceride (TG) into the multivariable analysis and obtained a statistically significant difference (p<0.05). In conclusion, LDL is a major risk factor affecting the onset of transient ischemic attack (TIA) induced by CSVD. Patients with hyperlipidemia should receive head MRI or CT examination to eliminate the probability of the existence of CSVD. To reduce the occurrence of adverse events in clinical practice, we can perform early intervention in SVD by decreasing the level of LDL, improving the endothelial function of small vessels and applying the anti-inflammation and nerve-protection methods.

Hydrological signals in height and gravity in northeastern Italy inferred from principal components analysis

NASA Astrophysics Data System (ADS)

Zerbini, S.; Raicich, F.; Richter, B.; Gorini, V.; Errico, M.

2010-04-01

This work describes a study of GPS heights, gravity and hydrological time series collected by stations located in northeastern Italy. During the last 12 years, changes in the long-term behaviors of the GPS heights and gravity time series are observed. In particular, starting in 2004-2005, a height increase is observed over the whole area. The temporal and spatial variability of these parameters has been studied as well as those of key hydrological variables, namely precipitation, hydrological balance and water table by using the Empirical Orthogonal Functions (EOF) analysis. The coupled variability between the GPS heights and the hydrological balance and precipitation data has been investigated by means of the Singular Value Decomposition (SVD) approach. Significant common patterns in the spatial and temporal variability of these parameters have been recognized. In particular, hydrology-induced variations are clearly observable starting in 2002-2003 in the southern part of the Po Plain for the longest time series, and from 2004-2005 over the whole area. These findings, obtained by means of purely mathematical approaches, are supported by sound physical interpretation suggesting that the climate-related fluctuations in the regional/local hydrological regime are one of the main contributors to the observed variations. A regional scale signal has been identified in the GPS station heights; it is characterized by the opposite behavior of the southern and northern stations in response to the hydrological forcing. At Medicina, in the southern Po Plain, the EOF analysis has shown a marked common signal between the GPS heights and the Superconducting Gravimeter (SG) data both over the long and the short period.

Application of least-squares fitting of ellipse and hyperbola for two dimensional data

NASA Astrophysics Data System (ADS)

Lawiyuniarti, M. P.; Rahmadiantri, E.; Alamsyah, I. M.; Rachmaputri, G.

2018-01-01

Application of the least-square method of ellipse and hyperbola for two-dimensional data has been applied to analyze the spatial continuity of coal deposits in the mining field, by using the fitting method introduced by Fitzgibbon, Pilu, and Fisher in 1996. This method uses 4{a_0}{a_2} - a_12 = 1 as a constrain function. Meanwhile, in 1994, Gander, Golub and Strebel have introduced ellipse and hyperbola fitting methods using the singular value decomposition approach. This SVD approach can be generalized into a three-dimensional fitting. In this research we, will discuss about those two fitting methods and apply it to four data content of coal that is in the form of ash, calorific value, sulfur and thickness of seam so as to produce form of ellipse or hyperbola. In addition, we compute the error difference resulting from each method and from that calculation, we conclude that although the errors are not much different, the error of the method introduced by Fitzgibbon et al is smaller than the fitting method that introduced by Golub et al.

Monitoring of Surface Subsidence of the Mining Area Based on Sbas

NASA Astrophysics Data System (ADS)

Zhu, Y.; Zhou, S.; Zang, D.; Lu, T.

2018-05-01

This paper has collected 7 scenes of L band PALSAR sensor radar data of a mine in FengCheng city, jiangxi province, using the Small-baseline Subset (SBAS) method to invert the surface subsidence of the mine. Baselines of interference less than 800m has been chosen to constitute short baseline differential interference atlas, using pixels whose average coherent coefficient was larger than or equal to 0.3 as like high coherent point target, using singular value decomposition (SVD) method to calculate deformation phase sequence based on these high coherent points, and the accumulation of settlements of study area of different period had been obtained, so as to reflect the ground surface settlement evolution of the settlement of the area. The results of the study has showed that: SBAS technology has overcome coherent problem of the traditionality D-InSAR technique, continuous deformation field of surface mining in time dimension of time could been obtained, characteristics of ground surface settlement of mining subsidence in different period has been displayed, so to improve the accuracy and reliability of the monitoring results.

Limited angle tomographic breast imaging: A comparison of parallel beam and pinhole collimation

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

Wessell, D.E.; Kadrmas, D.J.; Frey, E.C.

1996-12-31

Results from clinical trials have suggested no improvement in lesion detection with parallel hole SPECT scintimammography (SM) with Tc-99m over parallel hole planar SM. In this initial investigation, we have elucidated some of the unique requirements of SPECT SM. With these requirements in mind, we have begun to develop practical data acquisition and reconstruction strategies that can reduce image artifacts and improve image quality. In this paper we investigate limited angle orbits for both parallel hole and pinhole SPECT SM. Singular Value Decomposition (SVD) is used to analyze the artifacts associated with the limited angle orbits. Maximum likelihood expectation maximizationmore » (MLEM) reconstructions are then used to examine the effects of attenuation compensation on the quality of the reconstructed image. All simulations are performed using the 3D-MCAT breast phantom. The results of these simulation studies demonstrate that limited angle SPECT SM is feasible, that attenuation correction is needed for accurate reconstructions, and that pinhole SPECT SM may have an advantage over parallel hole SPECT SM in terms of improved image quality and reduced image artifacts.« less

A New Adaptive Framework for Collaborative Filtering Prediction

PubMed Central

Almosallam, Ibrahim A.; Shang, Yi

2010-01-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix’s system. PMID:21572924

A New Adaptive Framework for Collaborative Filtering Prediction.

PubMed

Almosallam, Ibrahim A; Shang, Yi

2008-06-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.

A QR Code Based Zero-Watermarking Scheme for Authentication of Medical Images in Teleradiology Cloud

PubMed Central

Seenivasagam, V.; Velumani, R.

2013-01-01

Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT)—Singular Value Decomposition (SVD) domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR) code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu's invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks. PMID:23970943

A QR code based zero-watermarking scheme for authentication of medical images in teleradiology cloud.

PubMed

Seenivasagam, V; Velumani, R

2013-01-01

Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT)-Singular Value Decomposition (SVD) domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR) code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu's invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks.

AOFlagger: RFI Software

NASA Astrophysics Data System (ADS)

Offringa, A. R.

2010-10-01

The RFI software presented here can automatically flag data and can be used to analyze the data in a measurement. The purpose of flagging is to mark samples that are affected by interfering sources such as radio stations, airplanes, electrical fences or other transmitting interferers. The tools in the package are meant for offline use. The software package contains a graphical interface ("rfigui") that can be used to visualize a measurement set and analyze mitigation techniques. It also contains a console flagger ("rficonsole") that can execute a script of mitigation functions without the overhead of a graphical environment. All tools were written in C++. The software has been tested extensively on low radio frequencies (150 MHz or lower) produced by the WSRT and LOFAR telescopes. LOFAR is the Low Frequency Array that is built in and around the Netherlands. Higher frequencies should work as well. Some of the methods implemented are the SumThreshold, the VarThreshold and the singular value decomposition (SVD) method. Included also are several surface fitting algorithms. The software is published under the GNU General Public License version 3.

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

PubMed Central

Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang

2014-01-01

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method. PMID:24573313

Fixing Stellarator Magnetic Surfaces

NASA Astrophysics Data System (ADS)

Hanson, James D.

1999-11-01

Magnetic surfaces are a perennial issue for stellarators. The design heuristic of finding a magnetic field with zero perpendicular component on a specified outer surface often yields inner magnetic surfaces with very small resonant islands. However, magnetic fields in the laboratory are not design fields. Island-causing errors can arise from coil placement errors, stray external fields, and design inadequacies such as ignoring coil leads and incomplete characterization of current distributions within the coil pack. The problem addressed is how to eliminate such error-caused islands. I take a perturbation approach, where the zero order field is assumed to have good magnetic surfaces, and comes from a VMEC equilibrium. The perturbation field consists of error and correction pieces. The error correction method is to determine the correction field so that the sum of the error and correction fields gives zero island size at specified rational surfaces. It is particularly important to correctly calculate the island size for a given perturbation field. The method works well with many correction knobs, and a Singular Value Decomposition (SVD) technique is used to determine minimal corrections necessary to eliminate islands.

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

PubMed Central

Liu, Bo; Chen, Sanfeng; Li, Shuai; Liang, Yongsheng

2012-01-01

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces. PMID:22736969

A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.

PubMed

Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang

2014-02-25

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

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

Wang, Shaobu; Lu, Shuai; Zhou, Ning

In interconnected power systems, dynamic model reduction can be applied on generators outside the area of interest to mitigate the computational cost with transient stability studies. This paper presents an approach of deriving the reduced dynamic model of the external area based on dynamic response measurements, which comprises of three steps, dynamic-feature extraction, attribution and reconstruction (DEAR). In the DEAR approach, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highestmore » similarity, forming a suboptimal ‘basis’ of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original external system. Network model is un-changed in the DEAR method. Tests on several IEEE standard systems show that the proposed method gets better reduction ratio and response errors than the traditional coherency aggregation methods.« less

Strict vegetarian diet improves the risk factors associated with metabolic diseases by modulating gut microbiota and reducing intestinal inflammation.

PubMed

Kim, Min-Soo; Hwang, Seong-Soo; Park, Eun-Jin; Bae, Jin-Woo

2013-10-01

Low-grade inflammation of the intestine results in metabolic dysfunction, in which dysbiosis of the gut microbiota is intimately involved. Dietary fibre induces prebiotic effects that may restore imbalances in the gut microbiota; however, no clinical trials have been reported in patients with metabolic diseases. Here, six obese subjects with type 2 diabetes and/or hypertension were assigned to a strict vegetarian diet (SVD) for 1 month, and blood biomarkers of glucose and lipid metabolisms, faecal microbiota using 454-pyrosequencing of 16S ribosomal RNA genes, faecal lipocalin-2 and short-chain fatty acids were monitored. An SVD reduced body weight and the concentrations of triglycerides, total cholesterol, low-density lipoprotein cholesterol and haemoglobin A1c, and improved fasting glucose and postprandial glucose levels. An SVD reduced the Firmicutes-to-Bacteroidetes ratio in the gut microbiota, but did not alter enterotypes. An SVD led to a decrease in the pathobionts such as the Enterobacteriaceae and an increase in commensal microbes such as Bacteroides fragilis and Clostridium species belonging to clusters XIVa and IV, resulting in reduced intestinal lipocalin-2 and short-chain fatty acids levels. This study underscores the benefits of dietary fibre for improving the risk factors of metabolic diseases and shows that increased fibre intake reduces gut inflammation by changing the gut microbiota. © 2013 John Wiley & Sons Ltd and Society for Applied Microbiology.

Association of Chronic Kidney Disease With Small Vessel Disease in Patients With Hypertensive Intracerebral Hemorrhage.

PubMed

Tsai, Yuan-Hsiung; Lee, Meng; Lin, Leng-Chieh; Chang, Sheng-Wei; Weng, Hsu-Huei; Yang, Jen-Tsung; Huang, Yen-Chu; Lee, Ming-Hsueh

2018-01-01

Chronic kidney disease (CKD) has been closely associated with hypertension and stroke. Although studies have reported the relationship between CKD and cerebral small vessel disease (SVD), the link between CKD, hypertension, and SVD is uncertain. The aim of this study was to investigate the association between CKD and SVD in patients with strictly hypertensive intracerebral hemorrhage (ICH). 142 patients with acute hypertensive ICH were enrolled in this study. Magnetic resonance imaging was performed to assess imaging markers for SVD. Patients were categorized into three CKD groups based on the degree of kidney dysfunction [glomerular filtration rate (GFR) in milliliters per minute per 1.73 m 2 ]: normal kidney function (GFR ≥ 90), mild kidney disease (60 ≤ GFR < 90), and moderate to severe kidney disease (GFR < 60). The prevalence rate of mild and moderate to severe CKD was 50 and 14.8%, respectively. The stage of CKD was associated with history of chronic hypertension ( p  = 0.046) as well as the prevalence rate of overall and deep cerebral microbleed (CMB) ( p  = 0.001 and p  = 0.002, respectively). The stage of CKD was a significant risk factor for deep white matter hyperintensity (WMH) (OR 1.848; 95% CI 1.022-3.343, p  = 0.042), overall CMB (OR 2.628; 95% CI 1.462-4.724, p  = 0.001), lobar CMB (OR 2.106; 95% CI 1.119-3.963, p  = 0.021), and deep CMB (OR 2.237; 95% CI 1.263-3.960, p  = 0.006), even after adjustment for confounders. In patients with hypertensive ICH, the prevalence of CKD is high even at the early stage of renal function impairment and is associated with the prevalence of CMB and deep WMH. These results reinforce the notion of a link between hypertensive vasculopathy, renal function impairment, and cerebral SVD.

Dynamic CT myocardial perfusion imaging: detection of ischemia in a porcine model with FFR verification

NASA Astrophysics Data System (ADS)

Fahmi, Rachid; Eck, Brendan L.; Vembar, Mani; Bezerra, Hiram G.; Wilson, David L.

2014-03-01

Dynamic cardiac CT perfusion (CTP) is a high resolution, non-invasive technique for assessing myocardial blood ow (MBF), which in concert with coronary CT angiography enable CT to provide a unique, comprehensive, fast analysis of both coronary anatomy and functional ow. We assessed perfusion in a porcine model with and without coronary occlusion. To induce occlusion, each animal underwent left anterior descending (LAD) stent implantation and angioplasty balloon insertion. Normal ow condition was obtained with balloon completely de ated. Partial occlusion was induced by balloon in ation against the stent with FFR used to assess the extent of occlusion. Prospective ECG-triggered partial scan images were acquired at end systole (45% R-R) using a multi-detector CT (MDCT) scanner. Images were reconstructed using FBP and a hybrid iterative reconstruction (iDose4, Philips Healthcare). Processing included: beam hardening (BH) correction, registration of image volumes using 3D cubic B-spline normalized mutual-information, and spatio-temporal bilateral ltering to reduce partial scan artifacts and noise variation. Absolute blood ow was calculated with a deconvolutionbased approach using singular value decomposition (SVD). Arterial input function was estimated from the left ventricle (LV) cavity. Regions of interest (ROIs) were identi ed in healthy and ischemic myocardium and compared in normal and occluded conditions. Under-perfusion was detected in the correct LAD territory and ow reduction agreed well with FFR measurements. Flow was reduced, on average, in LAD territories by 54%.

Near-infrared incoherent broadband cavity enhanced absorption spectroscopy (NIR-IBBCEAS) for detection and quantification of natural gas components.

PubMed

Prakash, Neeraj; Ramachandran, Arun; Varma, Ravi; Chen, Jun; Mazzoleni, Claudio; Du, Ke

2018-06-28

The principle of near-infrared incoherent broadband cavity enhanced absorption spectroscopy was employed to develop a novel instrument for detecting natural gas leaks as well as for testing the quality of natural gas mixtures. The instrument utilizes the absorption features of methane, butane, ethane, and propane in the wavelength region of 1100 nm to 1250 nm. The absorption cross-section spectrum in this region for methane was adopted from the HITRAN database, and those for the other three gases were measured in the laboratory. A singular-value decomposition (SVD) based analysis scheme was employed for quantifying methane, butane, ethane, and propane by performing a linear least-square fit. The developed instrument achieved a detection limit of 460 ppm, 141 ppm, 175 ppm and 173 ppm for methane, butane, ethane, and propane, respectively, with a measurement time of 1 second and a cavity length of 0.59 m. These detection limits are less than 1% of the Lower Explosive Limit (LEL) for each gas. The sensitivity can be further enhanced by changing the experimental parameters (such as cavity length, lamp power etc.) and using longer averaging intervals. The detection system is a low-cost and portable instrument suitable for performing field monitorings. The results obtained on the gas mixture emphasize the instrument's potential for deployment at industrial facilities dealing with natural gas, where potential leaks pose a threat to public safety.

Landsat D Thematic Mapper image dimensionality reduction and geometric correction accuracy

NASA Technical Reports Server (NTRS)

Ford, G. E.

1986-01-01

To characterize and quantify the performance of the Landsat thematic mapper (TM), techniques for dimensionality reduction by linear transformation have been studied and evaluated and the accuracy of the correction of geometric errors in TM images analyzed. Theoretical evaluations and comparisons for existing methods for the design of linear transformation for dimensionality reduction are presented. These methods include the discrete Karhunen Loeve (KL) expansion, Multiple Discriminant Analysis (MDA), Thematic Mapper (TM)-Tasseled Cap Linear Transformation and Singular Value Decomposition (SVD). A unified approach to these design problems is presented in which each method involves optimizing an objective function with respect to the linear transformation matrix. From these studies, four modified methods are proposed. They are referred to as the Space Variant Linear Transformation, the KL Transform-MDA hybrid method, and the First and Second Version of the Weighted MDA method. The modifications involve the assignment of weights to classes to achieve improvements in the class conditional probability of error for classes with high weights. Experimental evaluations of the existing and proposed methods have been performed using the six reflective bands of the TM data. It is shown that in terms of probability of classification error and the percentage of the cumulative eigenvalues, the six reflective bands of the TM data require only a three dimensional feature space. It is shown experimentally as well that for the proposed methods, the classes with high weights have improvements in class conditional probability of error estimates as expected.

Estimation of arterial arrival time and cerebral blood flow from QUASAR arterial spin labeling using stable spline.

PubMed

Castellaro, Marco; Peruzzo, Denis; Mehndiratta, Amit; Pillonetto, Gianluigi; Petersen, Esben Thade; Golay, Xavier; Chappell, Michael A; Bertoldo, Alessandra

2015-12-01

QUASAR arterial spin labeling (ASL) permits the application of deconvolution approaches for the absolute quantification of cerebral perfusion. Currently, oscillation index regularized singular value decomposition (oSVD) combined with edge-detection (ED) is the most commonly used method. Its major drawbacks are nonphysiological oscillations in the impulse response function and underestimation of perfusion. The aim of this work is to introduce a novel method to overcome these limitations. A system identification method, stable spline (SS), was extended to address ASL peculiarities such as the delay in arrival of the arterial blood in the tissue. The proposed framework was compared with oSVD + ED in both simulated and real data. SS was used to investigate the validity of using a voxel-wise tissue T1 value instead of using a single global value (of blood T1 ). SS outperformed oSVD + ED in 79.9% of simulations. When applied to real data, SS exhibited a physiologically realistic range for perfusion and a higher mean value with respect to oSVD + ED (55.5 ± 9.5 SS, 34.9 ± 5.2 oSVD + ED mL/100 g/min). SS can represent an alternative to oSVD + ED for the quantification of QUASAR ASL data. Analysis of the retrieved impulse response function revealed that using a voxel wise tissue T1 might be suboptimal. © 2014 Wiley Periodicals, Inc.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Interactions and consequences of silicon, nitrogen, and Fusarium palustre on herbivory and DMSP levels of Spartina alterniflora

NASA Astrophysics Data System (ADS)

Bazzano, Magalí; Elmer, Wade

2017-11-01

Sudden Vegetation Dieback (SVD) has been associated with multiple factors affecting the health of Spartina alterniflora. These include altered nutrition (N, Si and various metals), herbivory from the purple marsh crab, and the association with a fungal pathogen (Fusarium palustre). A metabolite produced by Spartina alterniflora that has been associated with plant health is dimethylsulfoniopropionate (DMSP), but little information exist on how these biotic stressors and nutrition interact to affect DMSP levels. Understanding how these factors might be interrelated might provide insight into the etiology of SVD. Surveys of a marsh affected by SVD confirmed lower levels of DMSP and higher concentrations of Si and other metals were present in Sp. alterniflora when compared to plants from marsh that exhibited no signs of SVD. In repeated greenhouse experiments, the application of Si to Sp. alterniflora had no effect on DMSP concentrations. However, when plants were inoculated with the pathogenic fungus, Fusarium palustre, and then treated with Si, DMSP levels were elevated 27%. Inoculation alone had no effect on DMSP levels. Si application neither favor growth nor suppress the stunting effect of disease by F. palustre. Furthermore, grazing by Sesarma reticulatum, a herbivorous crab, was not affected by Si nutrition. Grazing was increased by nitrogen fertilization and inoculation with F. palustre. Deciphering the role of Si nutrition in Sp. alterniflora and dieback remains unresolved, but no evidence suggests enhancing Si nutrition would directly favor marsh health.

Drug‐coated balloons for de novo lesions in small coronary arteries: rationale and design of BASKET‐SMALL 2

PubMed Central

Gilgen, Nicole; Farah, Ahmed; Scheller, Bruno; Ohlow, Marc‐Alexander; Mangner, Norman; Weilenmann, Daniel; Wöhrle, Jochen; Jamshidi, Peiman; Leibundgut, Gregor; Möbius‐Winkler, Sven; Zweiker, Robert; Krackhardt, Florian; Butter, Christian; Bruch, Leonhard; Kaiser, Christoph; Hoffmann, Andreas; Rickenbacher, Peter; Mueller, Christian; Stephan, Frank‐Peter; Coslovsky, Michael

2018-01-01

The treatment of coronary small vessel disease (SVD) remains an unresolved issue. Drug‐eluting stents (DES) have limited efficacy due to increased rates of instent‐restenosis, mainly caused by late lumen loss. Drug‐coated balloons (DCB) are a promising technique because native vessels remain structurally unchanged. Basel Stent Kosten‐Effektivitäts Trial: Drug‐Coated Balloons vs. Drug‐Eluting Stents in Small Vessel Interventions (BASKET‐SMALL 2) is a multicenter, randomized, controlled, noninferiority trial of DCB vs DES in native SVD for clinical endpoints. Seven hundred fifty‐eight patients with de novo lesions in vessels <3 mm in diameter and an indication for percutaneous coronary intervention such as stable angina pectoris, silent ischemia, or acute coronary syndromes are randomized 1:1 to angioplasty with DCB vs implantation of a DES after successful initial balloon angioplasty. The primary endpoint is the combination of cardiac death, nonfatal myocardial infarction, and target‐vessel revascularization up to 1 year. Secondary endpoints include stent thrombosis, Bleeding Academic Research Consortium (BARC) type 3 to 5 bleeding, and long‐term outcome up to 3 years. Based on clinical endpoints after 1 year, we plan to assess the noninferiority of DCB compared to DES in patients undergoing primary percutaneous coronary intervention for SVD. Results will be available in the second half of 2018. This study will compare DCB and DES regarding long‐term safety and efficacy for the treatment of SVD in a large all‐comer population. PMID:29527709

Effect of dual-focus soft contact lens wear on axial myopia progression in children.

PubMed

Anstice, Nicola S; Phillips, John R

2011-06-01

To test the efficacy of an experimental Dual-Focus (DF) soft contact lens in reducing myopia progression. Prospective, randomized, paired-eye control, investigator-masked trial with cross-over. Forty children, 11-14 years old, with mean spherical equivalent refraction (SER) of -2.71 ± 1.10 diopters (D). Dual-Focus lenses had a central zone that corrected refractive error and concentric treatment zones that created 2.00 D of simultaneous myopic retinal defocus during distance and near viewing. Control was a single vision distance (SVD) lens with the same parameters but without treatment zones. Children wore a DF lens in 1 randomly assigned eye and an SVD lens in the fellow eye for 10 months (period 1). Lens assignment was then swapped between eyes, and lenses were worn for a further 10 months (period 2). Primary outcome was change in SER measured by cycloplegic autorefraction over 10 months. Secondary outcome was a change in axial eye length (AXL) measured by partial coherence interferometry over 10 months. Accommodation wearing DF lenses was assessed using an open-field autorefractor. In period 1, the mean change in SER with DF lenses (-0.44 ± 0.33 D) was less than with SVD lenses (-0.69 ± 0.38 D; P < 0.001); mean increase in AXL was also less with DF lenses (0.11 ± 0.09 mm) than with SVD lenses (0.22 ± 0.10 mm; P < 0.001). In 70% of the children, myopia progression was reduced by 30% or more in the eye wearing the DF lens relative to that wearing the SVD lens. Similar reductions in myopia progression and axial eye elongation were also observed with DF lens wear during period 2. Visual acuity and contrast sensitivity with DF lenses were not significantly different than with SVD lenses. Accommodation to a target at 40 cm was driven through the central distance-correction zone of the DF lens. Dual-Focus lenses provided normal acuity and contrast sensitivity and allowed accommodation to near targets. Myopia progression and eye elongation were reduced significantly in eyes wearing DF lenses. The data suggest that sustained myopic defocus, even when presented to the retina simultaneously with a clear image, can act to slow myopia progression without compromising visual function. Proprietary or commercial disclosure may be found after the references. Copyright © 2011 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Using a high-dimensional graph of semantic space to model relationships among words

PubMed Central

Jackson, Alice F.; Bolger, Donald J.

2014-01-01

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD). PMID:24860525

Using a high-dimensional graph of semantic space to model relationships among words.

PubMed

Jackson, Alice F; Bolger, Donald J

2014-01-01

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD).

Monitoring of surface deformation in open pit mine using DInSAR time-series: a case study in the N5W iron mine (Carajás, Brazil) using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Mura, José C.; Paradella, Waldir R.; Gama, Fabio F.; Santos, Athos R.; Galo, Mauricio; Camargo, Paulo O.; Silva, Arnaldo Q.; Silva, Guilherme G.

2014-10-01

We present an investigation of surface deformation using Differential SAR Interferometry (DInSAR) time-series carried out in an active open pit iron mine, the N5W, located in the Carajás Mineral Province (Brazilian Amazon region), using 33 TerraSAR-X (TSX-1) scenes. This mine has presented a historical of instability and surface monitoring measurements over sectors of the mine (pit walls) have been done based on ground based radar. Two complementary approaches were used: the standard DInSAR configuration, as an early warning of the slope instability conditions, and the DInSAR timeseries analysis. In order to decrease the topographic phase error a high resolution DEM was generated based on a stereo GeoEye-1 pair. Despite the fact that a DinSAR contains atmospheric and topographic phase artifacts and noise, it was possible to detect deformation in some interferometric pairs, covering pit benches, road ramps and waste piles. The timeseries analysis was performed using the 31 interferometric pairs, which were selected based on the highest mean coherence of a stack of 107 interferograms, presenting less phase unwrapping errors. The time-series deformation was retrieved by the Least-Squares (LS) solution using an extension of the Singular Value Decomposition (SVD), with a set of additional weighted constrain on the acceleration deformation. The atmospheric phase artifacts were filtered in the space-time domain and the DEM height errors were estimated based on the normal baseline diversity. The DInSAR time-series investigation showed good results for monitoring surface displacement in the N5W mine located in a tropical rainforest environment, providing very useful information about the ground movement for alarm, planning and risk assessment.

Plasma Amyloid-β Levels, Cerebral Small Vessel Disease, and Cognition: The Rotterdam Study.

PubMed

Hilal, Saima; Akoudad, Saloua; van Duijn, Cornelia M; Niessen, Wiro J; Verbeek, Marcel M; Vanderstichele, Hugo; Stoops, Erik; Ikram, M Arfan; Vernooij, Meike W

2017-01-01

Plasma amyloid-β (Aβ) levels are increasingly studied as a potential, accessible marker of cognitive impairment and dementia. The most common plasma Aβ isoforms, i.e., Aβ1-40 and Aβ1-42 have been linked with risk of Alzheimer's disease. However, it remains under-explored whether plasma Aβ levels including novel Aβ1-38 relate to vascular brain disease and cognition in a preclinical-phase of dementiaObjective:To examine the association of plasma Aβ levels (i.e., Aβ1-38, Aβ1-40, and Aβ1-42) with markers of cerebral small vessel disease (SVD) and cognition in a large population-based setting. We analyzed plasma Aβ1 levels in 1201 subjects from two independent cohorts of the Rotterdam Study. Markers of SVD [lacunes, white matter hyperintensity (WMH) volume] were assessed on brain MRI (1.5T). Cognition was assessed by a detailed neuropsychological battery. In each cohort, the association of Aβ levels with SVD and cognition was performed using regression models. Estimates were then pooled across cohorts using inverse variance meta-analysis with fixed effects. Higher levels of plasma Aβ1-38, Aβ1-40, Aβ1-42, and Aβ1-40/ Aβ1-42 ratio were associated with increasing lacunar and microbleeds counts. Moreover, higher levels of Aβ1-40 and Aβ1-40/ Aβ1-42 were significantly associated with larger WMH volumes. With regard to cognition, a higher level of Aβ1-38 Aβ1-40 and Aβ1-40/ Aβ1-42 was related to worse performance on cognitive test specifically in memory domain. Higher plasma levels of Aβ levels are associated with subclinical markers of vascular disease and poorer memory. Plasma Aβ levels thus mark the presence of vascular brain pathology.

Observational evidence of planetary wave influences on ozone enhancements over upper troposphere North Africa

NASA Astrophysics Data System (ADS)

Mengistu Tsidu, Gizaw; Ture, Kassahun; Sivakumar, V.

2013-07-01

MOZAIC instrument measured enhanced ozone on two occasions in February, 1996 and 1997 at cruise altitude over North Africa. The cause and source of ozone enhancements over the region are investigated using additional reanalysis data from ERA-Interim. The ERA-Interim reprocessed GOME ozone indicated existence of enhancement as well. Both observational data revealed that the increase in ozone has wider latitudinal coverage extending from North Europe upto North Africa. The geopotential heights and zonal wind from ERA-Interim have indicated existence of planetary-scale flow that allowed meridional airmass exchanges between subtropics and higher latitudes. The presence of troughs-ridge pattern are attributable to large amplitude waves of zonal wavenumber 1-5 propagating eastward in the winter hemisphere westerly current as determined from Hayashi spectra as well as local fractional variance spectra determined from Multitaper Method-Singular Value Decomposition (MTM-SVD) spectral method. MTM-SVD is also used to understand the role of these waves on ozone enhancement and variability during the observation period in a mechanistic approach. A joint analysis of driving field, such as wind and potential vorticity (PV) for which only signals of the dominant zonal wavenumbers of prevailing planetary waves are retained, has revealed strong linkage between wave activity and ozone enhancement over the region at a temporal cycle of 5.8 days. One of these features is the displacement of the polar vortex southward during the enhancements, allowing strong airmass, energy and momentum exchanges. Evidence of cutoff laws that are formed within the deep trough, characteristics of Rossby wave breaking, is also seen in the ozone horizontal distribution at different pressure levels during the events. The reconstruction of signals with the cycle of 5.8 days has shown that the time and strength of enhancement depend on the circulation patterns dictated by planetary-scale flow relative to the location of observation. The positive PV anomalies upstream or at the observation region bring ozone rich airmass to the region while a negative PV anomaly upstream does the opposite. The position of the anomalies with time changes in accordance with the period of the waves involved. The snap shot of coherent variation of PV and ozone at different time during half cycle of the 5.8-day period has indicated that a region could experience positive (enhancement) or negative (depletion) ozone anomalies of different degree as the wave propagates eastward.

Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

USGS Publications Warehouse

Marshall, Michael T.; Thenkabail, Prasad S.

2015-01-01

Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for alfalfa (R2 = 0.81); 722 and 732 nm for cotton (R2 = 0.97); and 529 and 895 nm for maize (R2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.

Drought assessment using multi-sattelite remote sensing in Brazil

NASA Astrophysics Data System (ADS)

Rebello, V.; Getirana, A.; Rotunno Filho, O. C.; Lakshmi, V.

2016-12-01

In this study, we investigated long-term Terra-MODIS Normalized Difference Vegetation Index (NDVI) response to a recent drought period in Brazil's Southeast (SE) and Northeast (NE) regions between 2012 and 2015. An analysis of precipitation anomaly from 1979 to 2015 suggests a dry period over NE in 2012-2013 and SE in 2014-2015. Through EOF analysis it was possible to note that the first two modes account for 76% of variability and depict the vegetation seasonal cycle. Moreover, the time series of the respective modes show a deviation of NDVI in 2012 when both regions had negative precipitation anomaly. The other EOF modes show a negative trend from 2012 until 2015 mainly in northeastern Brazil in the semiarid region named Caatinga. In order to examine the influence of hydro-meterological variables on vegetation changes, the SVD technique was used to identify coupled patterns between NDVI and precipitation, soil moisture and evapotranspiration. SVD results showed that the highest correlations are achieved between NDVI and precipitation, 0.81 and 0.83, respectively in the first two modes. Although less correlated than precipitation, significant coupling between NDVI and evapotranspiration was found for the second and third modes, the correlation between their expanded coefficients was respectively 0.82 and 0.90.

Viromes of one year old infants reveal the impact of birth mode on microbiome diversity.

PubMed

McCann, Angela; Ryan, Feargal J; Stockdale, Stephen R; Dalmasso, Marion; Blake, Tony; Ryan, C Anthony; Stanton, Catherine; Mills, Susan; Ross, Paul R; Hill, Colin

2018-01-01

Establishing a diverse gut microbiota after birth is being increasingly recognised as important for preventing illnesses later in life. It is well established that bacterial diversity rapidly increases post-partum; however, few studies have examined the infant gut virome/phageome during this developmental period. We performed a metagenomic analysis of 20 infant faecal viromes at one year of age to determine whether spontaneous vaginal delivery (SVD) or caesarean section (CS) influenced viral composition. We find that birth mode results in distinctly different viral communities, with SVD infants having greater viral and bacteriophage diversity. We demonstrate that CrAssphage is acquired early in life, both in this cohort and two others, although no difference in birth mode is detected. A previous study has shown that bacterial OTU's (operational taxonomic units) identified in the same infants could not discriminate between birth mode at 12 months of age. Therefore, our results indicate that vertical transmission of viral communities from mother to child may play a role in shaping the early life microbiome, and that birth mode should be considered when studying the early life gut virome.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

PubMed Central

2010-01-01

Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data. PMID:21062443

Association of Key Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease With Hematoma Volume and Expansion in Patients With Lobar and Deep Intracerebral Hemorrhage

PubMed Central

Boulouis, Gregoire; van Etten, Ellis S.; Charidimou, Andreas; Auriel, Eitan; Morotti, Andrea; Pasi, Marco; Haley, Kellen E.; Brouwers, H. Bart; Ayres, Alison M.; Vashkevich, Anastasia; Jessel, Michael J.; Schwab, Kristin M.; Viswanathan, Anand; Greenberg, Steven M.; Rosand, Jonathan; Goldstein, Joshua N.; Gurol, M. Edip

2017-01-01

IMPORTANCE Hematoma expansion is an important determinant of outcome in spontaneous intracerebral hemorrhage (ICH) due to small vessel disease (SVD), but the association between the severity of the underlying SVD and the extent of bleeding at the acute phase is unknown to date. OBJECTIVE To investigate the association between key magnetic resonance imaging (MRI) markers of SVD (as per the Standards for Reporting Vascular Changes on Neuroimaging [STRIVE] guidelines) and hematoma volume and expansion in patients with lobar or deep ICH. DESIGN, SETTING, AND PARTICIPANTS Analysis of data collected from 418 consecutive patients admitted with primary lobar or deep ICH to a single tertiary care medical center between January 1, 2000, and October 1, 2012. Data were analyzed on March 4, 2016. Participants were consecutive patients with computed tomographic images allowing ICH volume calculation and MRI allowing imaging markers of SVD assessment. MAIN OUTCOMES AND MEASURES The ICH volumes at baseline and within 48 hours after symptom onset were measured in 418 patients with spontaneous ICH without anticoagulant therapy, and hematoma expansion was calculated. Cerebral microbleeds, cortical superficial siderosis, and white matter hyperintensity volume were assessed on MRI. The associations between these SVD markers and ICH volume, as well as hematoma expansion, were investigated using multivariable models. RESULTS This study analyzed 254 patients with lobar ICH (mean [SD] age, 75 [11] years and 140 [55.1%] female) and 164 patients with deep ICH (mean [SD] age 67 [14] years and 71 [43.3%] female). The presence of cortical superficial siderosis was an independent variable associated with larger ICH volume in the lobar ICH group (odds ratio per quintile increase in final ICH volume, 1.49; 95% CI, 1.14–1.94; P = .004). In multivariable models, the absence of cerebral microbleeds was associated with larger ICH volume for both the lobar and deep ICH groups (odds ratios per quintile increase in final ICH volume, 1.41; 95% CI, 1.11–1.81; P = .006 and 1.43; 95% CI, 1.04–1.99; P = .03; respectively) and with hematoma expansion in the lobar ICH group (odds ratio, 1.70; 95% CI, 1.07–2.92; P = .04). The white matter hyperintensity volumes were not associated with either hematoma volume or expansion. CONCLUSIONS AND RELEVANCE In patients admitted with primary lobar or deep ICH to a single tertiary care medical center, the presence of cortical superficial siderosis was an independent variable associated with larger lobar ICH volume, and the absence of cerebral microbleeds was associated with larger lobar and deep ICHs. The absence of cerebral microbleeds was independently associated with more frequent hematoma expansion in patients with lobar ICH. We provide an analytical framework for future studies aimed at limiting hematoma expansion. PMID:27723863

[Immediate fetal-maternal morbidity of first instrumental vaginal delivery using Thierry's spatulas. A prospective continuous study of 195 fetal extractions].

PubMed

Parant, O; Simon-Toulza, C; Capdet, J; Fuzier, V; Arnaud, C; Rème, J-M

2009-10-01

To investigate the immediate fetal-maternal morbidity related to Thierry's spatula for first instrumental vaginal delivery. We conducted a prospective observational study in Toulouse university hospital, including primiparas who vaginally delivered a live singleton cephalic infant>36 WG, between December 2005 and June 2006. Instrumental deliveries were performed using short spatulas in all cases. Outcome measures were: perineal complications (episiotomy, laceration and associated lesions, urinary retention, pain at H48), neonatal morbidity (cutaneous injuries, neonatal transfer, cord pH, Apgar score). Instrumental deliveries were compared with spontaneous vaginal deliveries (SVD). Six hundred and eight primiparas were included, distributed in 195 extractions (32%) and 413 SVD (68%). Spatulas allowed fetal extraction in all cases. Main differences between the two groups were: length of labour, occiput posterior position (12.8% for spatulas vs 1.7% for SVD; p<0.0001), episiotomy rate (97.9% vs 51.3%; p<0.0001), severe perineal lacerations (3.6% vs 0.2%; p=0.0007), post-partum morbidity (pain, hematoma, and urinary retention). No case of early severe neonatal complication was related to the use of the spatulas. Perineal complications (severe lacerations) associated with spatulas are increased with regard to SVD, but comparable to that reported with forceps. The main disadvantage is the high frequency of episiotomy, which should not be systematic. Neonatal morbidity is reduced. Comparative studies (spatulas vs. other procedures) are needed to confirm these data, but spatulas remain a multipurpose instrument which should continue to be taught.

Extracellular matrix inflammation in vascular cognitive impairment and dementia.

PubMed

Rosenberg, Gary A

2017-03-01

Vascular cognitive impairment and dementia (VCID) include a wide spectrum of chronic manifestations of vascular disease related to large vessel strokes and small vessel disease (SVD). Lacunar strokes and white matter (WM) injury are consequences of SVD. The main vascular risk factor for SVD is brain hypoperfusion from cerebral blood vessel narrowing due to chronic hypertension. The hypoperfusion leads to activation and degeneration of astrocytes with the resulting fibrosis of the extracellular matrix (ECM). Elasticity is lost in fibrotic cerebral vessels, reducing the response of stiffened blood vessels in times of increased metabolic need. Intermittent hypoxia/ischaemia activates a molecular injury cascade, producing an incomplete infarction that is most damaging to the deep WM, which is a watershed region for cerebral blood flow. Neuroinflammation caused by hypoxia activates microglia/macrophages to release proteases and free radicals that perpetuate the damage over time to molecules in the ECM and the neurovascular unit (NVU). Matrix metalloproteinases (MMPs) secreted in an attempt to remodel the blood vessel wall have the undesired consequences of opening the blood-brain barrier (BBB) and attacking myelinated fibres. This dual effect of the MMPs causes vasogenic oedema in WM and vascular demyelination, which are the hallmarks of the subcortical ischaemic vascular disease (SIVD), which is the SVD form of VCID also called Binswanger's disease (BD). Unravelling the complex pathophysiology of the WM injury-related inflammation in the small vessel form of VCID could lead to novel therapeutic strategies to reduce damage to the ECM, preventing the progressive damage to the WM. © 2017 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Drug-coated balloons for de novo lesions in small coronary arteries: rationale and design of BASKET-SMALL 2.

PubMed

Gilgen, Nicole; Farah, Ahmed; Scheller, Bruno; Ohlow, Marc-Alexander; Mangner, Norman; Weilenmann, Daniel; Wöhrle, Jochen; Jamshidi, Peiman; Leibundgut, Gregor; Möbius-Winkler, Sven; Zweiker, Robert; Krackhardt, Florian; Butter, Christian; Bruch, Leonhard; Kaiser, Christoph; Hoffmann, Andreas; Rickenbacher, Peter; Mueller, Christian; Stephan, Frank-Peter; Coslovsky, Michael; Jeger, Raban

2018-05-01

The treatment of coronary small vessel disease (SVD) remains an unresolved issue. Drug-eluting stents (DES) have limited efficacy due to increased rates of instent-restenosis, mainly caused by late lumen loss. Drug-coated balloons (DCB) are a promising technique because native vessels remain structurally unchanged. Basel Stent Kosten-Effektivitäts Trial: Drug-Coated Balloons vs. Drug-Eluting Stents in Small Vessel Interventions (BASKET-SMALL 2) is a multicenter, randomized, controlled, noninferiority trial of DCB vs DES in native SVD for clinical endpoints. Seven hundred fifty-eight patients with de novo lesions in vessels <3 mm in diameter and an indication for percutaneous coronary intervention such as stable angina pectoris, silent ischemia, or acute coronary syndromes are randomized 1:1 to angioplasty with DCB vs implantation of a DES after successful initial balloon angioplasty. The primary endpoint is the combination of cardiac death, nonfatal myocardial infarction, and target-vessel revascularization up to 1 year. Secondary endpoints include stent thrombosis, Bleeding Academic Research Consortium (BARC) type 3 to 5 bleeding, and long-term outcome up to 3 years. Based on clinical endpoints after 1 year, we plan to assess the noninferiority of DCB compared to DES in patients undergoing primary percutaneous coronary intervention for SVD. Results will be available in the second half of 2018. This study will compare DCB and DES regarding long-term safety and efficacy for the treatment of SVD in a large all-comer population. © 2018 The Authors. Clinical Cardiology published by Wiley Periodicals, Inc.

A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

NASA Astrophysics Data System (ADS)

Vijverberg, Koen; Ghafoorian, Mohsen; van Uden, Inge W. M.; de Leeuw, Frank-Erik; Platel, Bram; Heskes, Tom

2016-03-01

Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.

The effect of caesarean section on self-esteem amongst primiparous women in South-Western Nigeria: a case-control study.

PubMed

Loto, Olabisi M; Adewuya, Abiodun O; Ajenifuja, Olusegun K; Orji, Ernest O; Owolabi, Alexander T; Ogunniyi, Solomon O

2009-09-01

This study aims to assess the level of self-esteem of newly delivered mothers who had caesarean section (CS) and evaluate the sociodemographic and obstetrics correlates of low self-esteem in them. Newly delivered mothers who had CS (n = 109) and who had spontaneous vaginal delivery (SVD) (n = 97) completed questionnaires on sociodemographic and obstetrics variables within 1 week of delivery. They also completed the Rosenberg self-esteem scale. RESULTS. Women with CS had statistically significant lower scores on the self-esteem scale than women with SVD (p = 0.006). Thirty (27.5%) of the CS group were classified as having low self-esteem compared with 11 (11.3%) of the SVD group (p = 004). The correlates of low self-esteem in the CS group included polygamy (odd ratio (OR) 4.99, 95% confidence interval (95% CI) 1.62-15.33) and emergency CS (OR 4.66, 95% CI 1.55-16.75). CS in South-Western Nigerian women is associated with lowered self-esteem in the mothers.

A parameterization method and application in breast tomosynthesis dosimetry

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

Li, Xinhua; Zhang, Da; Liu, Bob

2013-09-15

Purpose: To present a parameterization method based on singular value decomposition (SVD), and to provide analytical parameterization of the mean glandular dose (MGD) conversion factors from eight references for evaluating breast tomosynthesis dose in the Mammography Quality Standards Act (MQSA) protocol and in the UK, European, and IAEA dosimetry protocols.Methods: MGD conversion factor is usually listed in lookup tables for the factors such as beam quality, breast thickness, breast glandularity, and projection angle. The authors analyzed multiple sets of MGD conversion factors from the Hologic Selenia Dimensions quality control manual and seven previous papers. Each data set was parameterized usingmore » a one- to three-dimensional polynomial function of 2–16 terms. Variable substitution was used to improve accuracy. A least-squares fit was conducted using the SVD.Results: The differences between the originally tabulated MGD conversion factors and the results computed using the parameterization algorithms were (a) 0.08%–0.18% on average and 1.31% maximum for the Selenia Dimensions quality control manual, (b) 0.09%–0.66% on average and 2.97% maximum for the published data by Dance et al. [Phys. Med. Biol. 35, 1211–1219 (1990); ibid. 45, 3225–3240 (2000); ibid. 54, 4361–4372 (2009); ibid. 56, 453–471 (2011)], (c) 0.74%–0.99% on average and 3.94% maximum for the published data by Sechopoulos et al. [Med. Phys. 34, 221–232 (2007); J. Appl. Clin. Med. Phys. 9, 161–171 (2008)], and (d) 0.66%–1.33% on average and 2.72% maximum for the published data by Feng and Sechopoulos [Radiology 263, 35–42 (2012)], excluding one sample in (d) that does not follow the trends in the published data table.Conclusions: A flexible parameterization method is presented in this paper, and was applied to breast tomosynthesis dosimetry. The resultant data offer easy and accurate computations of MGD conversion factors for evaluating mean glandular breast dose in the MQSA protocol and in the UK, European, and IAEA dosimetry protocols. Microsoft Excel™ spreadsheets are provided for the convenience of readers.« less

Multi-channel System for Beat to Beat QT Interval Variability and its Use in Screening for Coronary Artery Disease and Cardiomyopathy

NASA Technical Reports Server (NTRS)

Starc, V.; Schlegel, T. T.; Arenare, B.; Greco, E. C.; DePalma, J. L.; Nunez, T.; Medina, R.; Jugo, D.; Rahman, M. A.; Delgado, R.

2007-01-01

We investigated the ability of beat-to-beat QT interval variability (QTV) and related parameters to differentiate healthy individuals from patients with obstructive coronary artery disease (CAD) and cardiomyopathy (CM). For this purpose we developed a PC-based ECG software program that in real time, acquires, analyzes and displays QTV in each of the eight independent channels that constitute the 12-lead conventional ECG. The system also analyzes and displays the QTV from QT interval signals that are derived from multiple channels and from singular value decomposition (SVD) to substantially reduce the effect of noise and other artifacts on the QTV results. It also provides other useful SVD-related parameters such as the normalized 3-dimensional volume of the T wave (nTV) = 100*(rho(sub 2)*rho(sub 3)rho(sub 1^2). Advanced high-fidelity 12-lead ECG tests (approx. 5-min supine) were first performed on a "training set" of 99 individuals: 33 with ischemic or dilated CM and low ejection fraction (EF less than 40%); 33 with catheterization-proven obstructive CAD but normal EF; and 33 age-/gender-matched healthy controls. All QTV parameters that were studied for their accuracy in detecting CM and CAD significantly differentiated both CM and CAD from controls (p less than 0.0001). Retrospective areas under the ROC curve (AUC) of SDNN-QTV, rmsSD-QTV, and QTV Index (QTVI) for CM vs. controls in the lead V5 were 0.85, 0.90, and 0.99, respectively, and those for CAD vs. controls in the lead II were 0.82, 0.82, and 0.89. Other advanced ECG parameters, such as HFQRS RAZ score, LF Lomb of RRV or QRS-T angle, differentiated both CM and CAD from controls less significantly, with the respective AUC values of 0.89, 0.88 and 0.98 for CM vs. controls, and 0.73, 0.71 and 0.80 for CAD vs. controls. QTV parameters (especially QTVI, which is QTV as indexed to RRV) were, diagnostically speaking, amongst the best performing of the advanced ECG techniques studied thus far.

Construction and Use of Resting 12-Lead High Fidelity ECG "SuperScores" in Screening for Heart Disease

NASA Technical Reports Server (NTRS)

Schlegel, T. T.; Arenare, B.; Greco, E. C.; DePalma, J. L.; Starc, V.; Nunez, T.; Medina, R.; Jugo, D.; Rahman, M.A.; Delgado, R.

2007-01-01

We investigated the accuracy of several conventional and advanced resting ECG parameters for identifying obstructive coronary artery disease (CAD) and cardiomyopathy (CM). Advanced high-fidelity 12-lead ECG tests (approx. 5-min supine) were first performed on a "training set" of 99 individuals: 33 with ischemic or dilated CM and low ejection fraction (EF less than 40%); 33 with catheterization-proven obstructive CAD but normal EF; and 33 age-/gender-matched healthy controls. Multiple conventional and advanced ECG parameters were studied for their individual and combined retrospective accuracies in detecting underlying disease, the advanced parameters falling within the following categories: 1) Signal averaged ECG, including 12-lead high frequency QRS (150-250 Hz) plus multiple filtered and unfiltered parameters from the derived Frank leads; 2) 12-lead P, QRS and T-wave morphology via singular value decomposition (SVD) plus signal averaging; 3) Multichannel (12-lead, derived Frank lead, SVD lead) beat-to-beat QT interval variability; 4) Spatial ventricular gradient (and gradient component) variability; and 5) Heart rate variability. Several multiparameter ECG SuperScores were derivable, using stepwise and then generalized additive logistic modeling, that each had 100% retrospective accuracy in detecting underlying CM or CAD. The performance of these same SuperScores was then prospectively evaluated using a test set of another 120 individuals (40 new individuals in each of the CM, CAD and control groups, respectively). All 12-lead ECG SuperScores retrospectively generated for CM continued to perform well in prospectively identifying CM (i.e., areas under the ROC curve greater than 0.95), with one such score (containing just 4 components) maintaining 100% prospective accuracy. SuperScores retrospectively generated for CAD performed somewhat less accurately, with prospective areas under the ROC curve typically in the 0.90-0.95 range. We conclude that resting 12-lead high-fidelity ECG employing and combining the results of several advanced ECG software techniques shows great promise as a rapid and inexpensive tool for screening of heart disease.

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

NASA Astrophysics Data System (ADS)

Vishnukumar, S.; Wilscy, M.

2017-12-01

In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

Singular spectrum and singular entropy used in signal processing of NC table

NASA Astrophysics Data System (ADS)

Wang, Linhong; He, Yiwen

2011-12-01

NC (numerical control) table is a complex dynamic system. The dynamic characteristics caused by backlash, friction and elastic deformation among each component are so complex that they have become the bottleneck of enhancing the positioning accuracy, tracking accuracy and dynamic behavior of NC table. This paper collects vibration acceleration signals from NC table, analyzes the signals with SVD (singular value decomposition) method, acquires the singular spectrum and calculates the singular entropy of the signals. The signal characteristics and their regulations of NC table are revealed via the characteristic quantities such as singular spectrum, singular entropy etc. The steep degrees of singular spectrums can be used to discriminate complex degrees of signals. The results show that the signals in direction of driving axes are the simplest and the signals in perpendicular direction are the most complex. The singular entropy values can be used to study the indetermination of signals. The results show that the signals of NC table are not simple signal nor white noise, the entropy values in direction of driving axe are lower, the entropy values increase along with the increment of driving speed and the entropy values at the abnormal working conditions such as resonance or creeping etc decrease obviously.

BEAM DIAGNOSTICS USING BPM SIGNALS FROM INJECTED AND STORED BEAMS IN A STORAGE RING

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

Wang, G.M.; Shaftan; T.

2011-03-28

Many modern light sources are operating in top-off injection mode or are being upgraded to top-off injection mode. The storage ring always has the stored beam and injected beam for top-off injection mode. So the BPM data is the mixture of both beam positions and the injected beam position cannot be measured directly. We propose to use dedicated wide band BPM electronics in the NSLS II storage ring to retrieve the injected beam trajectory with the singular value decomposition (SVD) method. The beam position monitor (BPM) has the capability to measure bunch-by-bunch beam position. Similar electronics can be used tomore » measure the bunch-by-bunch beam current which is necessary to get the injection beam position. The measurement precision of current needs to be evaluated since button BPM sum signal has position dependence. The injected beam trajectory can be measured and monitored all the time without dumping the stored beam. We can adjust and optimize the injected beam trajectory to maximize the injection efficiency. We can also measure the storage ring acceptance by mapping the injected beam trajectory.« less

Complex mode indication function and its applications to spatial domain parameter estimation

NASA Astrophysics Data System (ADS)

Shih, C. Y.; Tsuei, Y. G.; Allemang, R. J.; Brown, D. L.

1988-10-01

This paper introduces the concept of the Complex Mode Indication Function (CMIF) and its application in spatial domain parameter estimation. The concept of CMIF is developed by performing singular value decomposition (SVD) of the Frequency Response Function (FRF) matrix at each spectral line. The CMIF is defined as the eigenvalues, which are the square of the singular values, solved from the normal matrix formed from the FRF matrix, [ H( jω)] H[ H( jω)], at each spectral line. The CMIF appears to be a simple and efficient method for identifying the modes of the complex system. The CMIF identifies modes by showing the physical magnitude of each mode and the damped natural frequency for each root. Since multiple reference data is applied in CMIF, repeated roots can be detected. The CMIF also gives global modal parameters, such as damped natural frequencies, mode shapes and modal participation vectors. Since CMIF works in the spatial domain, uneven frequency spacing data such as data from spatial sine testing can be used. A second-stage procedure for accurate damped natural frequency and damping estimation as well as mode shape scaling is also discussed in this paper.

Quasi-biennial modulation of the Northern Hemisphere tropopause height and temperature

NASA Astrophysics Data System (ADS)

Ribera, P.; PeñA-Ortiz, C.; AñEl, J. A.; Gimeno, L.; de la Torre, L.; Gallego, D.

2008-04-01

The influence of the quasi-biennial oscillation (QBO) on the tropopause pressure and temperature is studied through the application of the multitaper-singular value decomposition method (MTM-SVD). Reanalysis data (ERA-40) from the European Centre for Medium-Range Weather Forecasts (ECMWF) and radiosonde data from the Integrated Global Radiosonde Archive (IGRA) covering the period 1979-1999 are used. The results show a strong response of the height and temperature of the tropopause to the QBO not limited to the equatorial latitudes but affecting the entire Northern Hemisphere. A cooling (warming) of the tropopause temperature over polar (equatorial) latitudes during a QBO positive phase is observed, being particularly noticeable over polar latitudes. The anomalies in the tropopause height confirm these results, with the tropopause being at higher (lower) levels in polar (equatorial) latitudes during QBO positive phase. Results for the QBO negative phase are of opposite sign. We also found that the results obtained using raw radiosonde data and reanalysis are in very good agreement. Finally, the evolution of the mass stream function through a QBO cycle is used to justify the differences observed in the evolution of the tropopause characteristics at low and high latitudes through the QBO cycle.

XRF map identification problems based on a PDE electrodeposition model

NASA Astrophysics Data System (ADS)

Sgura, Ivonne; Bozzini, Benedetto

2017-04-01

In this paper we focus on the following map identification problem (MIP): given a morphochemical reaction-diffusion (RD) PDE system modeling an electrodepostion process, we look for a time t *, belonging to the transient dynamics and a set of parameters \\mathbf{p} , such that the PDE solution, for the morphology h≤ft(x,y,{{t}\\ast};\\mathbf{p}\\right) and for the chemistry θ ≤ft(x,y,{{t}\\ast};\\mathbf{p}\\right) approximates a given experimental map M *. Towards this aim, we introduce a numerical algorithm using singular value decomposition (SVD) and Frobenius norm to give a measure of error distance between experimental maps for h and θ and simulated solutions of the RD-PDE system on a fixed time integration interval. The technique proposed allows quantitative use of microspectroscopy images, such as XRF maps. Specifically, in this work we have modelled the morphology and manganese distributions of nanostructured components of innovative batteries and we have followed their changes resulting from ageing under operating conditions. The availability of quantitative information on space-time evolution of active materials in terms of model parameters will allow dramatic improvements in knowledge-based optimization of battery fabrication and operation.

Empirical seasonal forecasts of the NAO

NASA Astrophysics Data System (ADS)

Sanchezgomez, E.; Ortizbevia, M.

2003-04-01

We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

PubMed

Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

2014-01-01

An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

Effect of Facet Displacement on Radiation Field and Its Application for Panel Adjustment of Large Reflector Antenna

NASA Astrophysics Data System (ADS)

Wang, Wei; Lian, Peiyuan; Zhang, Shuxin; Xiang, Binbin; Xu, Qian

2017-05-01

Large reflector antennas are widely used in radars, satellite communication, radio astronomy, and so on. The rapid developments in these fields have created demands for development of better performance and higher surface accuracy. However, low accuracy and low efficiency are the common disadvantages for traditional panel alignment and adjustment. In order to improve the surface accuracy of large reflector antenna, a new method is presented to determinate panel adjustment values from far field pattern. Based on the method of Physical Optics (PO), the effect of panel facet displacement on radiation field value is derived. Then the linear system is constructed between panel adjustment vector and far field pattern. Using the method of Singular Value Decomposition (SVD), the adjustment value for all panel adjustors are obtained by solving the linear equations. An experiment is conducted on a 3.7 m reflector antenna with 12 segmented panels. The results of simulation and test are similar, which shows that the presented method is feasible. Moreover, the discussion about validation shows that the method can be used for many cases of reflector shape. The proposed research provides the instruction to adjust surface panels efficiently and accurately.

Implementation of the RCOG guidelines for prevention of obstetric anal sphincter injuries (OASIS) at two London Hospitals: A time series analysis.

PubMed

Mohiudin, Henna; Ali, Sajjad; Pisal, Pradyna N; Villar, Rose

2018-05-01

To audit the impact of implementation of the RCOG guidelines for prevention of Obstetric anal sphincter injuries (OASIS) by introducing antenatal perineal massage, manual perineal protection, and cutting episiotomies at 60° to the midline at the time of crowning. Time series analysis; Setting - Two London teaching hospitals; Royal Free London (RFL) and Barnet; Population or Sample - All nulliparous women undergoing vaginal birth; Methods - Training was provided for above techniques. EPISCISSORS-60 were introduced to perform 60° episiotomies. Data were extracted from maternity databases and dashboards; Main Outcome Measures - OASIS rates before and after implementation. Data from 2566 births were analysed. In operative vaginal deliveries (OVD), OASIS declined from 9.6% to 2% (p = 0.001) at Barnet and from 5.6% to 4.2% (p = 0.4) at RFL. OASIS reduced in nulliparous OVD's given episiotomies from 6.3% in the 'before' period to 0.6% in the 'after' period [p = 0.01] at Barnet. Before introduction of the EPISCISSORS-60, OASIS rate was 6.3% with episiotomies and 30% without episiotomies (p = 0.000). After introduction of the EPISCISSORS-60, OASIS rate was 0.63% with episiotomies v 16% without episiotomies (p = 0.000) at Barnet. At RFL, OASIS rate was 2.6% with episiotomies, and 42% without episiotomy (p = 0.000). In SVD's at Barnet, OASIS declined from 6.6% before to 0% after (p = 0.000) in women given episiotomies while it declined from 5.4% to 3% (p = 0.12) in those not given episiotomies. After introduction of the EPISCISSORS-60, OASIS was 0% in women with episiotomies and 3% in those without episiotomies (p = 0.04). In SVD's at RFL, OASIS was 0% in women given episiotomy v 4.7% without episiotomy (p = 0.03). Deliveries with EPISCISSORS-60 episiotomies had lesser OASIS than those without episiotomies in both nulliparous OVD's and SVD's. OASIS was lower with EPISCISSORS-60 episiotomies than those with eyeballed episiotomies. Copyright © 2018 Elsevier B.V. All rights reserved.

Stentless vs. stented bioprosthesis for aortic valve replacement: A case matched comparison of long-term follow-up and subgroup analysis of patients with native valve endocarditis.

PubMed

Schaefer, Andreas; Dickow, Jannis; Schoen, Gerhard; Westhofen, Sumi; Kloss, Lisa; Al-Saydali, Tarik; Reichenspurner, Hermann; Philipp, Sebastian A; Detter, Christian

2018-01-01

Current retrospective evidence suggests similar clinical and superior hemodynamic outcomes of the Sorin Freedom Solo stentless aortic valve (SFS) (LivaNova PLC, London, UK) compared to the Carpentier Edwards Perimount stented aortic valve (CEP) (Edwards Lifesciences Inc., Irvine, California, USA). To date, no reports exist describing case-matched long-term outcomes and analysis for treatment of native valve endocarditis (NVE). From 2004 through 2014, 77 consecutive patients (study group, 59.7% male, 68.9 ± 12.5 years, logEuroSCORE II 7.6 ± 12.3%) received surgical aortic valve replacement (SAVR) with the SFS. A control group of patients after SAVR with the CEP was retrieved from our database and matched to the study group regarding 15 parameters including preoperative endocarditis. Acute perioperative outcomes and follow-up data (mean follow-up time 48.7±29.8 months, 95% complete) were retrospectively analyzed. No differences in early mortality occurred during 30-day follow up (3/77; 3.9% vs. 4/77; 5.2%; p = 0.699). Echocardiographic findings revealed lower postprocedural transvalvular pressure gradients (max. 17.0 ± 8.2 vs. 24.5 ± 9.2 mmHg, p< 0.001/ mean pressure of 8.4 ± 4.1 vs. 13.1 ± 5.9 mmHg, p< 0.001) in the SFS group. Structural valve degeneration (SVD) (5.2% vs. 0%; p = 0.04) and valve explantation due to SVD or prosthetic valve endocarditis (PVE) (9.1% vs. 1.3%; p = 0.04) was more frequent in the SFS group. All-cause mortality during follow-up was 20.8% vs. 14.3% (p = 0.397). When patients were divided into subgroups of NVE and respective utilized bioprosthesis, the SFS presented impaired outcomes regarding mortality in NVE cases (p = 0.031). The hemodynamic superiority of the SFS was confirmed in this comparison. However, clinical outcomes in terms of SVD and PVE rates, as well as survival after NVE, were inferior in this study. Therefore, we are reluctant to recommend utilization of the SFS for treatment of NVE.

Attenuation of Lg waves in the New Madrid seismic zone of the central United States using the coda normalization method

NASA Astrophysics Data System (ADS)

Nazemi, Nima; Pezeshk, Shahram; Sedaghati, Farhad

2017-08-01

Unique properties of coda waves are employed to evaluate the frequency dependent quality factor of Lg waves using the coda normalization method in the New Madrid seismic zone of the central United States. Instrument and site responses are eliminated and source functions are isolated to construct the inversion problem. For this purpose, we used 121 seismograms from 37 events with moment magnitudes, M, ranging from 2.5 to 5.2 and hypocentral distances from 120 to 440 km recorded by 11 broadband stations. A singular value decomposition (SVD) algorithm is used to extract Q values from the data, while the geometric spreading exponent is assumed to be a constant. Inversion results are then fitted with a power law equation from 3 to 12 Hz to derive the frequency dependent quality factor function. The final results of the analysis are QVLg (f) = (410 ± 38) f0.49 ± 0.05 for the vertical component and QHLg (f) = (390 ± 26) f0.56 ± 0.04 for the horizontal component, where the term after ± sign represents one standard error. For stations within the Mississippi embayment with an average sediment depth of 1 km around the Memphis metropolitan area, estimation of quality factor using the coda normalization method is not well-constrained at low frequencies (f < 3 Hz). There may be several reasons contributing to this issue, such as low frequency surface wave contamination, site effects, or even a change in coda wave scattering regime which can exacerbate the scatter of the data.

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

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

Chowdhary, Kenny; Najm, Habib N.

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition)more » on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.« less

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

DOE PAGES

Chowdhary, Kenny; Najm, Habib N.

2016-04-13

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition)more » on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.« less

Prevalence of CADASIL and Fabry Disease in a Cohort of MRI Defined Younger Onset Lacunar Stroke

PubMed Central

Kilarski, Laura L.; Rutten-Jacobs, Loes C. A.; Bevan, Steve; Baker, Rob; Hassan, Ahamad; Hughes, Derralynn A.; Markus, Hugh S.

2015-01-01

Background and Purpose Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), caused by mutations in the NOTCH3 gene, is the most common monogenic disorder causing lacunar stroke and cerebral small vessel disease (SVD). Fabry disease (FD) due to mutations in the GLA gene has been suggested as an underdiagnosed cause of stroke, and one feature is SVD. Previous studies reported varying prevalence of CADASIL and FD in stroke, likely due to varying subtypes studied; no studies have looked at a large cohort of younger onset SVD. We determined the prevalence in a well-defined, MRI-verified cohort of apparently sporadic patients with lacunar infarct. Methods Caucasian patients with lacunar infarction, aged ≤70 years (mean age 56.7 (SD8.6)), were recruited from 72 specialist stroke centres throughout the UK as part of the Young Lacunar Stroke DNA Resource. Patients with a previously confirmed monogenic cause of stroke were excluded. All MRI’s and clinical histories were reviewed centrally. Screening was performed for NOTCH3 and GLA mutations. Results Of 994 subjects five had pathogenic NOTCH3 mutations (R169C, R207C, R587C, C1222G and C323S) all resulting in loss or gain of a cysteine in the NOTCH3 protein. All five patients had confluent leukoaraiosis (Fazekas grade ≥2). CADASIL prevalence overall was 0.5% (95% CI 0.2%-1.1%) and among cases with confluent leukoaraiosis 1.5% (95% CI 0.6%-3.3%). No classic pathogenic FD mutations were found; one patient had a missense mutation (R118C), associated with late-onset FD. Conclusion CADASIL cases are rare and only detected in SVD patients with confluent leukoaraiosis. No definite FD cases were detected. PMID:26305465

Identification of substance in complicated mixture of simulants under the action of THz radiation on the base of SDA (spectral dynamics analysis) method

NASA Astrophysics Data System (ADS)

Trofimov, Vyacheslav A.; Varentsova, Svetlana A.; Krotkus, Arunas; Molis, Gediminas

2010-10-01

The SDA (Spectral Dynamics Analysis) - method (method of THz spectrum dynamics analysis in THz range of frequencies) is used for the detection and identification of substances with similar THz Fourier spectra (such substances are named usually as the simulants) in the two- or three-component medium. This method allows us to obtain the unique 2D THz signature of the substance - the spectrogram- and to analyze the dynamics of many spectral lines of the THz signal, passed through or reflected from substance, by one set of its integral measurements simultaneously; even measurements are made on short-term intervals (less than 20 ps). For long-term intervals (100 ps and more) the SDA method gives an opportunity to define the relaxation time for excited energy levels of molecules. This information gives new opportunity to identify the substance because the relaxation time is different for molecules of different substances. The restoration of the signal by its integral values is made on the base of SVD - Single Value Decomposition - technique. We consider three examples for PTFE mixed with small content of the L-Tartaric Acid and the Sucrose in pellets. A concentration of these substances is about 5%-10%. Our investigations show that the spectrograms and dynamics of spectral lines of THz pulse passed through the pure PTFE differ from the spectrograms of the compound medium containing PTFE and the L-Tartaric Acid or the Sucrose or both these substances together. So, it is possible to detect the presence of a small amount of the additional substances in the sample even their THz Fourier spectra are practically identical. Therefore, the SDA method can be very effective for the defense and security applications and for quality control in pharmaceutical industry. We also show that in the case of substances-simulants the use of auto- and correlation functions has much worse resolvability in a comparison with the SDA method.

HGVS Recommendations for the Description of Sequence Variants: 2016 Update.

PubMed

den Dunnen, Johan T; Dalgleish, Raymond; Maglott, Donna R; Hart, Reece K; Greenblatt, Marc S; McGowan-Jordan, Jean; Roux, Anne-Francoise; Smith, Timothy; Antonarakis, Stylianos E; Taschner, Peter E M

2016-06-01

The consistent and unambiguous description of sequence variants is essential to report and exchange information on the analysis of a genome. In particular, DNA diagnostics critically depends on accurate and standardized description and sharing of the variants detected. The sequence variant nomenclature system proposed in 2000 by the Human Genome Variation Society has been widely adopted and has developed into an internationally accepted standard. The recommendations are currently commissioned through a Sequence Variant Description Working Group (SVD-WG) operating under the auspices of three international organizations: the Human Genome Variation Society (HGVS), the Human Variome Project (HVP), and the Human Genome Organization (HUGO). Requests for modifications and extensions go through the SVD-WG following a standard procedure including a community consultation step. Version numbers are assigned to the nomenclature system to allow users to specify the version used in their variant descriptions. Here, we present the current recommendations, HGVS version 15.11, and briefly summarize the changes that were made since the 2000 publication. Most focus has been on removing inconsistencies and tightening definitions allowing automatic data processing. An extensive version of the recommendations is available online, at http://www.HGVS.org/varnomen. © 2016 WILEY PERIODICALS, INC.

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.

PubMed

Fang, Chunying; Li, Haifeng; Ma, Lin; Zhang, Mancai

2017-01-01

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S -transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S -transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F -score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Are there still roles for exocrine bladder drainage and portal venous drainage for pancreatic allografts?

PubMed

Young, Carlton J

2009-02-01

Controversy remains regarding the best methodology of handling exocrine pancreatic fluid and pancreatic venous effluent. Bladder drainage has given way to enteric drainage. However, is there an instance in which bladder drainage is preferable? Also, hyperinsulinemia, as a result of systemic venous drainage (SVD), is claimed to be proatherosclerotic, whereas portal venous drainage (PVD) is more physiologic and less atherosclerotic. Bladder drainage remains a viable method of exocrine pancreas drainage, but evidence is sparse that measuring urinary amylase has a substantial benefit in the early detection of acute rejection in all types of pancreas transplants. Currently, there is no incontrovertible evidence that systemic hyperinsulinemia is proatherosclerotic, whereas recent metabolic studies on SVD and PVD showed that there was no benefit to PVD. Given the advent of newer immunosuppressive agents and overall lower acute rejection rates, the perceived benefit of bladder drainage as a means to measure urinary amylase as an early marker of rejection has not been substantiated. However, there may be a selective role for bladder drainage in 'high risk' pancreases. Also, without a clear-cut metabolic benefit to PVD over SVD, it remains the surgeon's choice as to which method to use.

Muscle and eye movement artifact removal prior to EEG source localization.

PubMed

Hallez, Hans; Vergult, Anneleen; Phlypo, Ronald; Van Hese, Peter; De Clercq, Wim; D'Asseler, Yves; Van de Walle, Rik; Vanrumste, Bart; Van Paesschen, Wim; Van Huffel, Sabine; Lemahieu, Ignace

2006-01-01

Muscle and eye movement artifacts are very prominent in the ictal EEG of patients suffering from epilepsy, thus making the dipole localization of ictal activity very unreliable. Recently, two techniques (BSS-CCA and pSVD) were developed to remove those artifacts. The purpose of this study is to assess whether the removal of muscle and eye movement artifacts improves the EEG dipole source localization. We used a total of 8 EEG fragments, each from another patient, first unfiltered, then filtered by the BSS-CCA and pSVD. In both the filtered and unfiltered EEG fragments we estimated multiple dipoles using RAP-MUSIC. The resulting dipoles were subjected to a K-means clustering algorithm, to extract the most prominent cluster. We found that the removal of muscle and eye artifact results to tighter and more clear dipole clusters. Furthermore, we found that localization of the filtered EEG corresponded with the localization derived from the ictal SPECT in 7 of the 8 patients. Therefore, we can conclude that the BSS-CCA and pSVD improve localization of ictal activity, thus making the localization more reliable for the presurgical evaluation of the patient.

Delayed-onset dementia after stroke or transient ischemic attack.

PubMed

Mok, Vincent C T; Lam, Bonnie Y K; Wang, Zhaolu; Liu, Wenyan; Au, Lisa; Leung, Eric Y L; Chen, Sirong; Yang, Jie; Chu, Winnie C W; Lau, Alexander Y L; Chan, Anne Y Y; Shi, Lin; Fan, Florence; Ma, Sze H; Ip, Vincent; Soo, Yannie O Y; Leung, Thomas W H; Kwok, Timothy C Y; Ho, Chi L; Wong, Lawrence K S; Wong, Adrian

2016-11-01

Patients surviving stroke without immediate dementia are at high risk of delayed-onset dementia. Mechanisms underlying delayed-onset dementia are complex and may involve vascular and/or neurodegenerative diseases. Dementia-free patients with stroke and/or transient ischemic attack (TIA; n = 919) were studied for 3 years prospectively, excluding those who developed dementia 3 to 6 months after stroke and/or TIA. Forty subjects (4.4%) developed dementia during the study period. Imaging markers of severe small vessel disease (SVD), namely presence of ≥3 lacunes and confluent white matter changes; history of hypertension and diabetes mellitus independently predicted delayed-onset dementia after adjustment for age, gender, and education. Only 6 of 31 (19.4%) subjects with delayed cognitive decline harbored Alzheimer's disease-like Pittsburg compound B (PiB) retention. Most PiB cases (16/25, 64%) had evidence of severe SVD. Severe SVD contributes importantly to delayed-onset dementia after stroke and/or TIA. Future clinical trials aiming to prevent delayed-onset dementia after stroke and/or TIA should target this high-risk group. Copyright © 2016 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Time-frequency analysis : mathematical analysis of the empirical mode decomposition.

DOT National Transportation Integrated Search

2009-01-01

Invented over 10 years ago, empirical mode : decomposition (EMD) provides a nonlinear : time-frequency analysis with the ability to successfully : analyze nonstationary signals. Mathematical : Analysis of the Empirical Mode Decomposition : is a...

Coronary Angiography Findings and Its Determinants in Patients Presenting With Acute Coronary Syndrome: A Descriptive Analysis from Asian Population.

PubMed

Chourasiya, M; Satheesh, S; Selvaraj, R; Jayaraman, B; Pillai, A A

2017-10-01

The aim was to study the angiographic profile in patients presented as acute coronary syndrome and its relation with risk factors and comparison between genders. This prospective observational study was performed on total 352 patients of acute coronary syndrome were analyzed for various risk factors, angiographic pattern in Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, South India from January 2015 to July 2016. Mean age of presentation was 52.62±11.63 years. Male were 271(77.0%) and female were 81(23.0%). Majority of patients were STEMI (67.6%) followed by UA (24.4%) and NSTEMI (8%). Smoker was 117(33.3%) patients. Hypertensive were 124(35.2%) of patients and 149(42.3%) were diabetics. Family history of CAD was positive in 45(12.8%). On angiographic evaluation left main reference diameter was lower in females (4.02±0.72) than males (4.07±0.82). LAD was most commonly involved followed by RCA and LCX among all three group of acute coronary syndrome. Left main was least involved (8.3%). In STEMI SVD (40.3%) was most common presentation, after that DVD was seen in 22.3%, TVD in 10.5%, non-obstructive coronary was seen in 16% of patients and normal coronary was seen in 11% of patients. In UA 28%, 22.8%, 13.2%, 15.8%, 20.2% was seen in SVD, DVD, TVD, non-obstructive and normal coronary respectively. Long length coronary lesions (>20mm) were seen in majority in all type of acute coronary syndrome. Coronary lesion length was not associated with presentation acute coronary syndrome and genders. Male were most commonly presented as acute coronary syndrome. STEMI was most common presentation. Diabetic was most prevalent risk factor. SVD was most common angiographic pattern and LAD was most common involved arteries.

Subvalvular Pannus Overgrowth after Mosaic Bioprosthesis Implantation in the Aortic Position

PubMed Central

Isomura, Tadashi; Yoshida, Minoru; Katsumata, Chieko; Ito, Fusahiko; Watanabe, Masazumi

2015-01-01

Purpose: Although pannus overgrowth by itself was not the pathology of structural valve deterioration (SVD), it might be related to reoperation for SVD of the bioprostheses. Methods: We retrospectively reviewed patients undergoing reoperation for SVD after implantation of the third-generation Mosaic aortic bioprosthesis and macroscopic appearance of the explanted valves was examined to detect the presence of pannus. Results: There were 10 patients and the age for the initial aortic valve replacement was 72 ± 10 years old. The duration of durability was 9.9 ± 2.0 years. Deteriorated valve presented stenosis (valvular area of 0.96 ± 0.20 cm2; pressure gradient of 60 ± 23 mmHg). Coexisting regurgitant flow was detected in two cases. Macroscopically, subvalvular pannus overgrowth was detected in 8 cases (80%). The proportion of overgrowth from the annulus was almost even and pannus overgrowth created subvalvular membrane, which restricted the area especially for each commissure. In contrast, opening and mobility of each leaflet was not severely limited and pannus overgrowth would restrict the area, especially for each commissure. In other two cases with regurgitation, tear of the leaflet on the stent strut was detected and mild calcification of each leaflet restricted opening. Conclusion: In patients with the Mosaic aortic bioprosthesis, pannus overgrowth was the major cause for reoperation. PMID:26633541

Subvalvular Pannus Overgrowth after Mosaic Bioprosthesis Implantation in the Aortic Position.

PubMed

Hirota, Masanori; Isomura, Tadashi; Yoshida, Minoru; Katsumata, Chieko; Ito, Fusahiko; Watanabe, Masazumi

2016-01-01

Although pannus overgrowth by itself was not the pathology of structural valve deterioration (SVD), it might be related to reoperation for SVD of the bioprostheses. We retrospectively reviewed patients undergoing reoperation for SVD after implantation of the third-generation Mosaic aortic bioprosthesis and macroscopic appearance of the explanted valves was examined to detect the presence of pannus. There were 10 patients and the age for the initial aortic valve replacement was 72 ± 10 years old. The duration of durability was 9.9 ± 2.0 years. Deteriorated valve presented stenosis (valvular area of 0.96 ± 0.20 cm(2); pressure gradient of 60 ± 23 mmHg). Coexisting regurgitant flow was detected in two cases. Macroscopically, subvalvular pannus overgrowth was detected in 8 cases (80%). The proportion of overgrowth from the annulus was almost even and pannus overgrowth created subvalvular membrane, which restricted the area especially for each commissure. In contrast, opening and mobility of each leaflet was not severely limited and pannus overgrowth would restrict the area, especially for each commissure. In other two cases with regurgitation, tear of the leaflet on the stent strut was detected and mild calcification of each leaflet restricted opening. In patients with the Mosaic aortic bioprosthesis, pannus overgrowth was the major cause for reoperation.

Automated detection of microaneurysms using robust blob descriptors

NASA Astrophysics Data System (ADS)

Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

2013-03-01

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

Structure Calculation and Reconstruction of Discrete-State Dynamics from Residual Dipolar Couplings.

PubMed

Cole, Casey A; Mukhopadhyay, Rishi; Omar, Hanin; Hennig, Mirko; Valafar, Homayoun

2016-04-12

Residual dipolar couplings (RDCs) acquired by nuclear magnetic resonance (NMR) spectroscopy are an indispensable source of information in investigation of molecular structures and dynamics. Here, we present a comprehensive strategy for structure calculation and reconstruction of discrete-state dynamics from RDC data that is based on the singular value decomposition (SVD) method of order tensor estimation. In addition to structure determination, we provide a mechanism of producing an ensemble of conformations for the dynamical regions of a protein from RDC data. The developed methodology has been tested on simulated RDC data with ±1 Hz of error from an 83 residue α protein (PDB ID 1A1Z ) and a 213 residue α/β protein DGCR8 (PDB ID 2YT4 ). In nearly all instances, our method reproduced the structure of the protein including the conformational ensemble to within less than 2 Å. On the basis of our investigations, arc motions with more than 30° of rotation are identified as internal dynamics and are reconstructed with sufficient accuracy. Furthermore, states with relative occupancies above 20% are consistently recognized and reconstructed successfully. Arc motions with a magnitude of 15° or relative occupancy of less than 10% are consistently unrecognizable as dynamical regions within the context of ±1 Hz of error.

The rehabilitation of attention in patients with mild cognitive impairment and brain subcortical vascular changes using the Attention Process Training-II. The RehAtt Study: rationale, design and methodology.

PubMed

Salvadori, Emilia; Poggesi, Anna; Valenti, Raffaella; Della Rocca, Eleonora; Diciotti, Stefano; Mascalchi, Mario; Inzitari, Domenico; Pantoni, Leonardo

2016-10-01

Cerebral small vessel disease (SVD) may cause attentional and executive cognitive deficits. No drug is currently available to improve cognitive performance or to prevent dementia in SVD patients, and cognitive rehabilitation could be a promising approach. We aimed to investigate: (1) the effectiveness of the Attention Process Training-II program in the rehabilitation of patients with mild cognitive impairment (MCI) and SVD; (2) the impact of the induced cognitive improvement on functionality and quality of life; (3) the effect of training on brain activity at rest and the possibility of a training-induced plasticity effect. The RehAtt study is designed as a 3-year prospective, single-blinded, randomized clinical trial. Inclusion criteria were: (1) MCI defined according to Winblad et al. criteria; (2) evidence of impairment across attention neuropsychological tests; (3) evidence on MRI of moderate/severe white matter hyperintensities. All enrolled patients are evaluated at baseline, and after 6 and 12 months, according to an extensive clinical, functional, MRI and neuropsychological protocol. The baseline RehAtt cohort includes 44 patients (66 % males, mean ± SD age and years of education 75.3 ± 6.8 and 8.3 ± 4.3, respectively). After baseline assessment, patients have been randomly assigned to 'attention training' or 'standard care'. Treatments and follow-up visits at 6 months are completed, while follow-up visits at 12 months are ongoing. This study is the first attempt to reduce attention deficits in patients affected by MCI with SVD. The results of this pilot experience will represent an essential background for designing larger multicenter, prospective, double-blinded, randomized and controlled clinical trials. NCT02033850 (ClinicalTrials.gov Identifier).

Sex differences in associations between blood lipids and cerebral small vessel disease.

PubMed

Yin, Z-G; Wang, Q-S; Yu, K; Wang, W-W; Lin, H; Yang, Z-H

2018-01-01

Dyslipidemia predicts higher risk of coronary events and stroke and might be associated with cerebral small vessel disease (SVD). Previous studies linking blood lipids and SVD have yielded inconsistent results, which may be attributable to sex differences in lipids metabolism. The aim of this study was to investigate the relationships between blood lipids and SVD in neurologically healthy men and women. Consecutive 817 people aged 50 years or more were enrolled and underwent magnetic resonance imaging scans to evaluate the periventricular white matter lesions (PVWMLs), deep white matter lesions (DWMLs) and silent brain infarction (SBI). Fasting total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol, apolipoprotein A-1 (apoA-1) and apolipoprotein B were assessed. Multivariable logistic regression analyses were performed to determine the associations of blood lipids with PVWMLs, DWMLs and SBI. HDL-C (for PVWMLs: OR 0.36, 95% CI 0.19-0.71; for DWMLs: OR 0.35, 95% CI 0.20-0.63) and apoA-1 (for PVWMLs: OR 0.27, 95% CI 0.11-0.66; for DWMLs: OR 0.22, 95% CI 0.10-0.48) were inversely associated with the severity of PVWMLs and DWMLs in women but not in men after adjustment for age, hypertension, diabetes, current smoking, daily drinking, body mass index and uric acid. Additionally, no blood lipids were significantly associated with SBI. Our findings demonstrate that sex differences may exist in the associations between lipids and SVD. HDL-C and apoA-1 levels were inversely associated with the severity of PVWMLs and DWMLs in women. Copyright © 2017 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images

NASA Astrophysics Data System (ADS)

Fang, Li; Xu, Yusheng; Yao, Wei; Stilla, Uwe

2016-11-01

For monitoring of glacier surface motion in pole and alpine areas, radar remote sensing is becoming a popular technology accounting for its specific advantages of being independent of weather conditions and sunlight. In this paper we propose a method for glacier surface motion monitoring using phase correlation (PC) based on point-like features (PLF). We carry out experiments using repeat-pass TerraSAR X-band (TSX) and Sentinel-1 C-band (S1C) intensity images of the Taku glacier in Juneau icefield located in southeast Alaska. The intensity imagery is first filtered by an improved adaptive refined Lee filter while the effect of topographic reliefs is removed via SRTM-X DEM. Then, a robust phase correlation algorithm based on singular value decomposition (SVD) and an improved random sample consensus (RANSAC) algorithm is applied to sequential PLF pairs generated by correlation using a 2D sinc function template. The approaches for glacier monitoring are validated by both simulated SAR data and real SAR data from two satellites. The results obtained from these three test datasets confirm the superiority of the proposed approach compared to standard correlation-like methods. By the use of the proposed adaptive refined Lee filter, we achieve a good balance between the suppression of noise and the preservation of local image textures. The presented phase correlation algorithm shows the accuracy of better than 0.25 pixels, when conducting matching tests using simulated SAR intensity images with strong noise. Quantitative 3D motions and velocities of the investigated Taku glacier during a repeat-pass period are obtained, which allows a comprehensive and reliable analysis for the investigation of large-scale glacier surface dynamics.

Assessment of the 1997-1998 Asian Monsoon Anomalies

NASA Technical Reports Server (NTRS)

Lau, William K.-M.; Wu, H.-T.

1999-01-01

Using State-of-the-art satellite-gauge monthly rainfall estimate and optimally interpolated sea surface temperature (SST) data, we have assessed the 1997-98 Asian monsoon anomalies in terms of three basic causal factors: basin-scale SST, regional coupling, and internal variability. Singular Value Decomposition analysis of rainfall and SST are carried out globally over the entire tropics and regionally over the Asian monsoon domain. Contributions to monsoon rainfall predictability by various factors are evaluated from cumulative anomaly correlation with dominant regional SVD modes. Results reveal a dominant, large-scale monsoon-El Nino coupled mode with well-defined centers of action in the near-equatorial monsoon regions. it is noted that some subcontinental regions such as all-India, or arbitrarily chosen land regions over East Asia, while important socio-economically, are not near the centers of influence from El Nino, hence are not necessarily representative of the response of the entire monsoon region to El Nino. The observed 1997-98 Asian monsoon anomalies are found to be very complex with approximately 34% of the anomalies attributable to basin- scale SST influence associated with El Nino. Regional coupled processes contribute an additional 19%, leaving about 47% due to internal dynamics. Also noted is that the highest monsoon predictability is not necessary associated with major El Nino events (e.g. 1997, 1982) but rather in non-El Nino years (e.g. 1980, 1988) when contributions from the regional coupled modes far exceed those from the basin-scale SST. The results suggest that in order to improve monsoon seasonal-to-interannual predictability, there is a need to exploit not only monsoon-El Nino relationship, but also monsoon regional coupled processes and their modulation by long-term climate change.

Direct and Indirect Effects of UV-B Exposure on Litter Decomposition: A Meta-Analysis

PubMed Central

Song, Xinzhang; Peng, Changhui; Jiang, Hong; Zhu, Qiuan; Wang, Weifeng

2013-01-01

Ultraviolet-B (UV-B) exposure in the course of litter decomposition may have a direct effect on decomposition rates via changing states of photodegradation or decomposer constitution in litter while UV-B exposure during growth periods may alter chemical compositions and physical properties of plants. Consequently, these changes will indirectly affect subsequent litter decomposition processes in soil. Although studies are available on both the positive and negative effects (including no observable effects) of UV-B exposure on litter decomposition, a comprehensive analysis leading to an adequate understanding remains unresolved. Using data from 93 studies across six biomes, this introductory meta-analysis found that elevated UV-B directly increased litter decomposition rates by 7% and indirectly by 12% while attenuated UV-B directly decreased litter decomposition rates by 23% and indirectly increased litter decomposition rates by 7%. However, neither positive nor negative effects were statistically significant. Woody plant litter decomposition seemed more sensitive to UV-B than herbaceous plant litter except under conditions of indirect effects of elevated UV-B. Furthermore, levels of UV-B intensity significantly affected litter decomposition response to UV-B (P<0.05). UV-B effects on litter decomposition were to a large degree compounded by climatic factors (e.g., MAP and MAT) (P<0.05) and litter chemistry (e.g., lignin content) (P<0.01). Results suggest these factors likely have a bearing on masking the important role of UV-B on litter decomposition. No significant differences in UV-B effects on litter decomposition were found between study types (field experiment vs. laboratory incubation), litter forms (leaf vs. needle), and decay duration. Indirect effects of elevated UV-B on litter decomposition significantly increased with decay duration (P<0.001). Additionally, relatively small changes in UV-B exposure intensity (30%) had significant direct effects on litter decomposition (P<0.05). The intent of this meta-analysis was to improve our understanding of the overall effects of UV-B on litter decomposition. PMID:23818993

Time series analysis of Mexico City subsidence constrained by radar interferometry

NASA Astrophysics Data System (ADS)

López-Quiroz, Penélope; Doin, Marie-Pierre; Tupin, Florence; Briole, Pierre; Nicolas, Jean-Marie

2009-09-01

In Mexico City, subsidence rates reach up to 40 cm/yr mainly due to soil compaction led by the over exploitation of the Mexico Basin aquifer. In this paper, we map the spatial and temporal patterns of the Mexico City subsidence by differential radar interferometry, using 38 ENVISAT images acquired between end of 2002 and beginning of 2007. We present the severe interferogram unwrapping problems partly due to the coherence loss but mostly due to the high fringe rates. These difficulties are overcome by designing a new methodology that helps the unwrapping step. Our approach is based on the fact that the deformation shape is stable for similar time intervals during the studied period. As a result, a stack of the five best interferograms can be used to compute an average deformation rate for a fixed time interval. Before unwrapping, the number of fringes is then decreased in wrapped interferograms using a scaled version of the stack together with the estimation of the atmospheric phase contribution related with the troposphere vertical stratification. The residual phase, containing less fringes, is more easily unwrapped than the original interferogram. The unwrapping procedure is applied in three iterative steps. The 71 small baseline unwrapped interferograms are inverted to obtain increments of radar propagation delays between the 38 acquisition dates. Based on the redundancy of the interferometric data base, we quantify the unwrapping errors and show that they are strongly decreased by iterations in the unwrapping process. A map of the RMS interferometric system misclosure allows to define the unwrapping reliability for each pixel. Finally, we present a new algorithm for time series analysis that differs from classical SVD decomposition and is best suited to the present data base. Accurate deformation time series are then derived over the metropolitan area of the city with a spatial resolution of 30 × 30 m.

Optimum electrode configuration selection for electrical resistance change based damage detection in composites using an effective independence measure

NASA Astrophysics Data System (ADS)

Escalona, Luis; Díaz-Montiel, Paulina; Venkataraman, Satchi

2016-04-01

Laminated carbon fiber reinforced polymer (CFRP) composite materials are increasingly used in aerospace structures due to their superior mechanical properties and reduced weight. Assessing the health and integrity of these structures requires non-destructive evaluation (NDE) techniques to detect and measure interlaminar delamination and intralaminar matrix cracking damage. The electrical resistance change (ERC) based NDE technique uses the inherent changes in conductive properties of the composite to characterize internal damage. Several works that have explored the ERC technique have been limited to thin cross-ply laminates with simple linear or circular electrode arrangements. This paper investigates a method of optimum selection of electrode configurations for delamination detection in thick cross-ply laminates using ERC. Inverse identification of damage requires numerical optimization of the measured response with a model predicted response. Here, the electrical voltage field in the CFRP composite laminate is calculated using finite element analysis (FEA) models for different specified delamination size and locations, and location of ground and current electrodes. Reducing the number of sensor locations and measurements is needed to reduce hardware requirements, and computational effort needed for inverse identification. This paper explores the use of effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations of selecting a pair of electrodes among the n electrodes. To enable use of EI to ERC required, it is proposed in this research a singular value decomposition SVD to obtain a spectral representation of the resistance measurements in the laminate. The effectiveness of EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of damage using the full set of resistance measurements and the reduced set of measurements. The investigation shows that the EI measure is effective for optimally selecting the electrode pairs needed for resistance measurements in ERC based damage detection.

High Resolution Digital Surface Model For Production Of Airport Obstruction Charts Using Spaceborne SAR Sensors

NASA Astrophysics Data System (ADS)

Oliveira, Henrique; Rodrigues, Marco; Radius, Andrea

2012-01-01

Airport Obstruction Charts (AOCs) are graphical representations of natural or man-made obstructions (its locations and heights) around airfields, according to International Civil Aviation Organization (ICAO) Annexes 4, 14 and 15. One of the most important types of data used in AOCs production/update tasks is a Digital Surface Model (first reflective surface) of the surveyed area. The development of advanced remote sensing technologies provide the available tools for obstruction data acquisition, while Geographic Information Systems (GIS) present the perfect platform for storing and analyzing this type of data, enabling the production of digital ACOs, greatly contributing to the increase of the situational awareness of pilots and enhancing the air navigation safety level [1]. Data acquisition corresponding to the first reflective surface can be obtained through the use of Airborne Laser-Scanning and Light Detection and Ranging (ALS/LIDAR) or Spaceborne SAR Systems. The need of surveying broad areas, like the entire territory of a state, shows that Spaceborne SAR systems are the most adequate in economic and feasibility terms of the process, to perform the monitoring and producing a high resolution Digital Surface Model (DSM). The high resolution DSM generation depends on many factors: the available data set, the used technique and the setting parameters. To increase the precision and obtain high resolution products, two techniques are available using a stack of data: the PS (Permanent Scatterers) technique [2], that uses large stack of data to identify many stable and coherent targets through multi- temporal analysis, removing the atmospheric contribution and to minimize the estimation errors, and the Small Baseline Subset (SBAS) technique ([3],[4]), that relies on the use of small baseline SAR interferograms and on the application of the so called singular value decomposition (SVD) method, in order to link independent SAR acquisition data sets, separated by large baselines, thus increasing the number of data used for the analysis.

Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values.

PubMed

Rasouli, Zolaikha; Ghavami, Raouf

2016-08-05

Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] μgmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Spatial-spectral preprocessing for endmember extraction on GPU's

NASA Astrophysics Data System (ADS)

Jimenez, Luis I.; Plaza, Javier; Plaza, Antonio; Li, Jun

2016-10-01

Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been developed for automatic or semi-automatic identification of endmembers using spatial and spectral information, including the spectral-spatial endmember extraction (SSEE) where, within a preprocessing step in the technique, both sources of information are extracted from the hyperspectral image and equally used for this purpose. Previous works have implemented the SSEE technique in four main steps: 1) local eigenvectors calculation in each sub-region in which the original hyperspectral image is divided; 2) computation of the maxima and minima projection of all eigenvectors over the entire hyperspectral image in order to obtain a candidates pixels set; 3) expansion and averaging of the signatures of the candidate set; 4) ranking based on the spectral angle distance (SAD). The result of this method is a list of candidate signatures from which the endmembers can be extracted using various spectral-based techniques, such as orthogonal subspace projection (OSP), vertex component analysis (VCA) or N-FINDR. Considering the large volume of data and the complexity of the calculations, there is a need for efficient implementations. Latest- generation hardware accelerators such as commodity graphics processing units (GPUs) offer a good chance for improving the computational performance in this context. In this paper, we develop two different implementations of the SSEE algorithm using GPUs. Both are based on the eigenvectors computation within each sub-region of the first step, one using the singular value decomposition (SVD) and another one using principal component analysis (PCA). Based on our experiments with hyperspectral data sets, high computational performance is observed in both cases.

Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values

NASA Astrophysics Data System (ADS)

Rasouli, Zolaikha; Ghavami, Raouf

2016-08-01

Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD = 0.12], 0.67-23.19 [LOD = 0.13] and 0.73-25.12 [LOD = 0.15] μg mL- 1 for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.

ApoE and Sex Bias in Cerebrovascular Aging of Men and Mice

PubMed Central

Finch, Caleb E.; Shams, Sara

2016-01-01

Alzheimer disease (AD) research has mainly focused on neurodegenerative processes associated with the classic neuropathologic markers of senile plaques and neurofibrillary tangles. Additionally, cerebrovascular contributions to dementia are increasingly recognized, particularly from cerebral small vessel disease (SVD). Remarkably, in AD brains, the ApoE ε4 allele shows male excess for cerebral microbleeds (CMB), a marker of SVD, which is opposite to the female excess of plaques and tangles. Mouse transgenic models add further complexities to sex-ApoE ε4 allele interactions, with female excess of CMBs and brain amyloid. We conclude that brain aging and AD pathogenesis cannot be understood in humans without addressing major gaps in the extent of sex differences in cerebrovascular pathology. PMID:27546867

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process.

PubMed

Yao, Shengnan; Zeng, Weiming; Wang, Nizhuan; Chen, Lei

2013-07-01

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA. Copyright © 2013 Elsevier Inc. All rights reserved.

Grouping Parturients by Parity, Previous-Cesarean, and Mode of Delivery (P-C-MoD Classification) Better Identifies Groups at Risk for Postpartum Hemorrhage.

PubMed

Reichman, Orna; Gal, Micahel; Sela, Hen Y; Khayyat, Izzat; Emanuel, Michael; Samueloff, Arnon

2016-10-01

Objective We aimed to create a clinical classification to better identify parturients at risk for postpartum hemorrhage (PPH). Method A retrospective cohort, including all women who delivered at a single tertiary care medical center, between 2006 and 2014. Parturients were grouped by parity and history of cesarean delivery (CD): primiparas, multipara, and multipara with previous CD. Each were further subgrouped by mode of delivery (spontaneous vaginal delivery [SVD], operative vaginal delivery [OVD], emergency or elective CD). In all, 12 subgroups, based on parity, previous cesarean, and mode of delivery, formed the P-C-MoD classification. PPH was defined as a decrease of ≥3 gram% hemoglobin from admission and/or transfusion of blood products. Univariate analysis followed by multivariate analysis was performed to assess risk for PPH, controlling for confounders. Results The crude rate of PPH among 126,693 parturients was 7%. The prevalence differed significantly among independent risk factors: primiparity, 14%; multiparity, 4%; OVD, 22%; and CD, 15%. The P-C-MoD classification, segregated better between parturients at risk for PPH. The prevalence of PPH was highest for primiparous undergoing OVD (27%) compared with multiparous with SVD (3%), odds ratio [OR] = 12.8 (95% confidence interval [CI],11.9-13.9). These finding were consistent in the multivariate analysis OR = 13.1 (95% CI,12.1-14.3). Conclusion Employing the P-C-MoD classification more readily identifies parturients at risk for PPH and is superior to estimations based on single risk factors. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Assessment of structural valve deterioration of transcatheter aortic bioprosthetic balloon-expandable valves using the new European consensus definition.

PubMed

Eltchaninoff, Hélène; Durand, Eric; Avinée, Guillaume; Tron, Christophe; Litzler, Pierre-Yves; Bauer, Fabrice; Dacher, Jean-Nicolas; Werhlin, Camille; Bouhzam, Najime; Bettinger, Nicolas; Candolfi, Pascal; Cribier, Alain

2018-03-30

Durability of transcatheter aortic bioprosthetic valves remains a major issue. Standardised definitions of deterioration and failure of bioprosthetic valves have recently been proposed. The aim of this study was to assess structural transcatheter valve deterioration (SVD) and bioprosthetic valve failure (BVF) using these new definitions. All TAVI patients implanted up to September 2012 with a minimal theoretical five-year follow-up were included. Systematic clinical and echocardiographic follow-up was performed annually. New standardised definitions were used to assess durability of transcatheter aortic bioprosthetic valves. From 2002 to 2012, 378 patients were included. Mean age and logistic EuroSCORE were 83.3±6.8 years and 22.8±13.1%. Thirty-day mortality was 13.2%. Nine patients had SVD including two severe forms and two patients had definite late BVF. The incidence of SVD and BVF at eight years was 3.2% (95% CI: 1.45-6.11) and 0.58% (95% CI: 0.15-2.75), respectively. Even though limited by the poor survival of the very high-risk/compassionate early population, our data do not demonstrate any alarm concerning transcatheter aortic valve durability. Careful prospective assessment in younger and lower-risk patients and comparison with surgical bioprosthetic valves are required for further assessment of the long-term durability of transcatheter valves.

Obstructive Sleep Apnea is Linked to Depression and Cognitive Impairment: Evidence and Potential Mechanisms

PubMed Central

Kerner, Nancy A.; Roose, Steven P.

2017-01-01

Obstructive sleep apnea (OSA) is highly prevalent but very frequently undiagnosed. OSA is an independent risk factor for depression and cognitive impairment/dementia. Herein the authors review studies in the literature pertinent to the effects of OSA on the cerebral microvascular and neurovascular systems and present a model to describe the key pathophysiologic mechanisms that may underlie the associations, including hypoperfusion, endothelial dysfunction, and neuroinflammation. Intermittent hypoxia plays a critical role in initiating and amplifying these pathologic processes. Hypoperfusion and impaired cerebral vasomotor reactivity lead to the development or progression of cerebral small vessel disease (C-SVD). Hypoxemia exacerbates these processes, resulting in white matter lesions, white matter integrity abnormalities, and gray matter loss. Blood–brain barrier (BBB) hyperpermeability and neuroinflammation lead to altered synaptic plasticity, neuronal damage, and worsening C-SVD. Thus, OSA may initiate or amplify the pathologic processes of C-SVD and BBB dysfunction, resulting in the development or exacerbation of depressive symptoms and cognitive deficits. Given the evidence that adequate treatment of OSA with continuous positive airway pressure improves depression and neurocognitive functions, it is important to identify OSA when assessing patients with depression or cognitive impairment. Whether treatment of OSA changes the deteriorating trajectory of elderly patients with already-diagnosed vascular depression and cognitive impairment/dementia remains to be determined in randomized controlled trials. PMID:27139243

Passive forensics for copy-move image forgery using a method based on DCT and SVD.

PubMed

Zhao, Jie; Guo, Jichang

2013-12-10

As powerful image editing tools are widely used, the demand for identifying the authenticity of an image is much increased. Copy-move forgery is one of the tampering techniques which are frequently used. Most existing techniques to expose this forgery need to improve the robustness for common post-processing operations and fail to precisely locate the tampering region especially when there are large similar or flat regions in the image. In this paper, a robust method based on DCT and SVD is proposed to detect this specific artifact. Firstly, the suspicious image is divided into fixed-size overlapping blocks and 2D-DCT is applied to each block, then the DCT coefficients are quantized by a quantization matrix to obtain a more robust representation of each block. Secondly, each quantized block is divided non-overlapping sub-blocks and SVD is applied to each sub-block, then features are extracted to reduce the dimension of each block using its largest singular value. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks will be matched by predefined shift frequency threshold. Experiment results demonstrate that our proposed method can effectively detect multiple copy-move forgery and precisely locate the duplicated regions, even when an image was distorted by Gaussian blurring, AWGN, JPEG compression and their mixed operations. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Risk and Determinants of Dementia in Patients with Mild Cognitive Impairment and Brain Subcortical Vascular Changes: A Study of Clinical, Neuroimaging, and Biological Markers—The VMCI-Tuscany Study: Rationale, Design, and Methodology

PubMed Central

Poggesi, Anna; Salvadori, Emilia; Pantoni, Leonardo; Pracucci, Giovanni; Cesari, Francesca; Chiti, Alberto; Ciolli, Laura; Cosottini, Mirco; Del Bene, Alessandra; De Stefano, Nicola; Diciotti, Stefano; Dotti, Maria Teresa; Ginestroni, Andrea; Giusti, Betti; Gori, Anna Maria; Nannucci, Serena; Orlandi, Giovanni; Pescini, Francesca; Valenti, Raffaella; Abbate, Rosanna; Federico, Antonio; Mascalchi, Mario; Murri, Luigi; Inzitari, Domenico

2012-01-01

Dementia is one of the most disabling conditions. Alzheimer's disease and vascular dementia (VaD) are the most frequent causes. Subcortical VaD is consequent to deep-brain small vessel disease (SVD) and is the most frequent form of VaD. Its pathological hallmarks are ischemic white matter changes and lacunar infarcts. Degenerative and vascular changes often coexist, but mechanisms of interaction are incompletely understood. The term mild cognitive impairment defines a transitional state between normal ageing and dementia. Pre-dementia stages of VaD are also acknowledged (vascular mild cognitive impairment, VMCI). Progression relates mostly to the subcortical VaD type, but determinants of such transition are unknown. Variability of phenotypic expression is not fully explained by severity grade of lesions, as depicted by conventional MRI that is not sensitive to microstructural and metabolic alterations. Advanced neuroimaging techniques seem able to achieve this. Beside hypoperfusion, blood-brain-barrier dysfunction has been also demonstrated in subcortical VaD. The aim of the Vascular Mild Cognitive Impairment Tuscany Study is to expand knowledge about determinants of transition from mild cognitive impairment to dementia in patients with cerebral SVD. This paper summarizes the main aims and methodological aspects of this multicenter, ongoing, observational study enrolling patients affected by VMCI with SVD. PMID:22550606

Neuroimaging Characteristics of Small-Vessel Disease in Older Adults with Normal Cognition, Mild Cognitive Impairment, and Alzheimer Disease.

PubMed

Mimenza-Alvarado, Alberto; Aguilar-Navarro, Sara G; Yeverino-Castro, Sara; Mendoza-Franco, César; Ávila-Funes, José Alberto; Román, Gustavo C

2018-01-01

Cerebral small-vessel disease (SVD) represents the most frequent type of vascular brain lesions, often coexisting with Alzheimer disease (AD). By quantifying white matter hyperintensities (WMH) and hippocampal and parietal atrophy, we aimed to describe the prevalence and severity of SVD among older adults with normal cognition (NC), mild cognitive impairment (MCI), and probable AD and to describe associated risk factors. This study included 105 older adults evaluated with magnetic resonance imaging and clinical and neuropsychological tests. We used the Fazekas scale (FS) for quantification of WMH, the Scheltens scale (SS) for hippocampal atrophy, and the Koedam scale (KS) for parietal atrophy. Logistic regression models were performed to determine the association between FS, SS, and KS scores and the presence of NC, MCI, or probable AD. Compared to NC subjects, SVD was more prevalent in MCI and probable AD subjects. After adjusting for confounding factors, logistic regression showed a positive association between higher scores on the FS and probable AD (OR = 7.6, 95% CI 2.7-20, p < 0.001). With the use of the SS and KS (OR = 4.5, 95% CI 3.5-58, p = 0.003 and OR = 8.9, 95% CI 1-72, p = 0.04, respectively), the risk also remained significant for probable AD. These results suggest an association between severity of vascular brain lesions and neurodegeneration.

Perivascular spaces on 7 Tesla brain MRI are related to markers of small vessel disease but not to age or cardiovascular risk factors

PubMed Central

Zwanenburg, Jaco JM; Reinink, Rik; Wisse, Laura EM; Luijten, Peter R; Kappelle, L Jaap; Geerlings, Mirjam I; Biessels, Geert Jan

2016-01-01

Cerebral perivascular spaces (PVS) are small physiological structures around blood vessels in the brain. MRI visible PVS are associated with ageing and cerebral small vessel disease (SVD). 7 Tesla (7T) MRI improves PVS detection. We investigated the association of age, vascular risk factors, and imaging markers of SVD with PVS counts on 7 T MRI, in 50 persons aged ≥ 40. The average PVS count ± SD in the right hemisphere was 17 ± 6 in the basal ganglia and 71 ± 28 in the semioval centre. We observed no relation between age or vascular risk factors and PVS counts. The presence of microbleeds was related to more PVS in the basal ganglia (standardized beta 0.32; p = 0.04) and semioval centre (standardized beta 0.39; p = 0.01), and white matter hyperintensity volume to more PVS in the basal ganglia (standardized beta 0.41; p = 0.02). We conclude that PVS counts on 7T MRI are high and are related SVD markers, but not to age and vascular risk factors. This latter finding may indicate that due to the high sensitivity of 7T MRI, the correlation of PVS counts with age or vascular risk factors may be attenuated by the detection of “normal”, non-pathological PVS. PMID:27154503

Subsurface structure identification uses derivative analyses of the magnetic data in Candi Umbul-Telomoyo geothermal prospect area

NASA Astrophysics Data System (ADS)

Septyasari, U.; Niasari, S. W.; Maghfira, P. D.

2018-04-01

Telomoyo geothermal prospect area is located in Central Java, Indonesia. One of the manifestations around Telomoyo is a warm spring, called Candi Umbul. The hydrothermal fluids from the manifestation could be from the subsurface flowing up through geological structures. The previous research about 2D magnetic modeling in Candi Umbul showed that there was a normal fault with strike/dip N60°E/45° respectively. This research aims to know the distance boundary and the kind of the geological structure in the study area. We also compared the geological structure direction based on the geologic map and the derivative maps. We used derivative analyses of the magnetic data, i.e. First Horizontal Derivative (FHD) which is the rate of change of the horizontal gradient in the horizontal direction. FHD indicates the boundaries of the geological structure. We also used Second Vertical Derivative (SVD) which is the rate of change of the vertical gradient in the vertical direction. SVD can reveal normal fault or thrust fault. The FHD and SVD maps show that the geological structure boundary has the same direction with the north west-south east geological structure. The geological structure boundary is in 486 m of the local distance. Our result confirms that there is a normal fault in the study area.

Technical brief: Pump-probe paradigm in an integrating cavity to study photodecomposition processes

PubMed Central

Betts-Obregon, Brandi; Tsin, Andrew T.; DeSa, Richard J.

2016-01-01

Purpose Assaying photodecomposition is challenging because light must be used to initiate the photodamage and light must be used to monitor the photodecomposition. The experimental requirements are as follows: 1) During exposure of the actinic beam, continuously monitor the spectral characteristics of the sample, 2) uniformly expose the reactants to the actinic source, 3) obtain informative spectra in the presence of light scatter, and 4) achieve sufficient sensitivity for dilute reactants. Traditional spectrophotometers cannot address these issues due to sample turbidity, the inability to uniformly expose the cuvette contents to the incident beam, the inability to simultaneously perform spectral scans, and inherent low sensitivity. Here, we describe a system that meets these challenges in a practical way. Methods Light access to a 8.6 ml quartz integrating sphere containing 10 µM all-trans retinol in PBS was provided by three ports at right angles allowing for the following: 1) actinic light delivery from light-emitting diodes (LEDs) firing at 100 pulses/sec, 2) entry of a separate scanning beam at 100 scans/sec (10,000 µsec scan time) via an OLIS RSM 1000 ultraviolet/visual (UV/Vis) rapid-scanning spectrophotometer (RSM), and 3) light exit to the detector photomultiplier. The RSM spectral intermediate slit was partially covered to allow for a “dark” period of 2,000 µsec when no scanning light was admitted to the cuvette. During that interval, the LED was flashed, and the photomultiplier was temporarily blocked by a perforated spinning shutter disk. The absorbance per centimeter, which is increased due to the internal reflectance of the integrating sphere compared to a standard 1 cm rectangular cuvette, was calculated according to Fry et al. (2010) Applied Optics 49:575. Retinoid photodecomposition was confirmed with high-performance liquid chromatography (HPLC). Results Using the RSM to trigger the LED flash and photomultiplier shutter closure during the “dark” period allowed actinic flashes to be placed between scans. Exposure of the all-trans retinol to 366 nm flashes resulted in marked reduction in absorbance and a blue shift of the λmax. A white LED, despite its higher photon output, did not support all-trans retinol photolysis. Singular value decomposition (SVD) analysis revealed three spectral intermediates with mechanism, I -> II -> III. HPLC analysis of the reactants at the beginning and the conclusion of the light exposure confirmed the retinol photodecomposition. Conclusions The highly reflecting cavity acts as a multipass cuvette that markedly increased the light path length and, thus, sensitivity. Triggering the LED during a dark period within the scan time allowed the actinic flashes to be interleafed between scans in a pump-probe paradigm. Furthermore, the entire sample was exposed to scan beam and actinic flashes, which is not possible in traditional spectrophotometers. Finally, the integrating cavity cuvette allowed use of turbid samples. SVD was useful for resolving spectral intermediates. Although the identity of the intermediates was not determined here, the ability to define molecular intermediates during photodecomposition reactions will allow future studies to isolate and identify the degradation products and determine the mechanism of light-induced retinoid degradation and that of retinoid-binding protein-mediated photoprotection. PMID:27559291

Apolipoprotein E and Sex Bias in Cerebrovascular Aging of Men and Mice.

PubMed

Finch, Caleb E; Shams, Sara

2016-09-01

Alzheimer disease (AD) research has mainly focused on neurodegenerative processes associated with the classic neuropathologic markers of senile plaques and neurofibrillary tangles. Additionally, cerebrovascular contributions to dementia are increasingly recognized, particularly from cerebral small vessel disease (SVD). Remarkably, in AD brains, the apolipoprotein E (ApoE) ɛ4 allele shows male excess for cerebral microbleeds (CMBs), a marker of SVD, which is opposite to the female excess of plaques and tangles. Mouse transgenic models add further complexities to sex-ApoE ɛ4 allele interactions, with female excess of both CMBs and brain amyloid. We conclude that brain aging and AD pathogenesis cannot be understood in humans without addressing major gaps in the extent of sex differences in cerebrovascular pathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multilayer neural networks for reduced-rank approximation.

PubMed

Diamantaras, K I; Kung, S Y

1994-01-01

This paper is developed in two parts. First, the authors formulate the solution to the general reduced-rank linear approximation problem relaxing the invertibility assumption of the input autocorrelation matrix used by previous authors. The authors' treatment unifies linear regression, Wiener filtering, full rank approximation, auto-association networks, SVD and principal component analysis (PCA) as special cases. The authors' analysis also shows that two-layer linear neural networks with reduced number of hidden units, trained with the least-squares error criterion, produce weights that correspond to the generalized singular value decomposition of the input-teacher cross-correlation matrix and the input data matrix. As a corollary the linear two-layer backpropagation model with reduced hidden layer extracts an arbitrary linear combination of the generalized singular vector components. Second, the authors investigate artificial neural network models for the solution of the related generalized eigenvalue problem. By introducing and utilizing the extended concept of deflation (originally proposed for the standard eigenvalue problem) the authors are able to find that a sequential version of linear BP can extract the exact generalized eigenvector components. The advantage of this approach is that it's easier to update the model structure by adding one more unit or pruning one or more units when the application requires it. An alternative approach for extracting the exact components is to use a set of lateral connections among the hidden units trained in such a way as to enforce orthogonality among the upper- and lower-layer weights. The authors call this the lateral orthogonalization network (LON) and show via theoretical analysis-and verify via simulation-that the network extracts the desired components. The advantage of the LON-based model is that it can be applied in a parallel fashion so that the components are extracted concurrently. Finally, the authors show the application of their results to the solution of the identification problem of systems whose excitation has a non-invertible autocorrelation matrix. Previous identification methods usually rely on the invertibility assumption of the input autocorrelation, therefore they can not be applied to this case.

Differential Decomposition Among Pig, Rabbit, and Human Remains.

PubMed

Dautartas, Angela; Kenyhercz, Michael W; Vidoli, Giovanna M; Meadows Jantz, Lee; Mundorff, Amy; Steadman, Dawnie Wolfe

2018-03-30

While nonhuman animal remains are often utilized in forensic research to develop methods to estimate the postmortem interval, systematic studies that directly validate animals as proxies for human decomposition are lacking. The current project compared decomposition rates among pigs, rabbits, and humans at the University of Tennessee's Anthropology Research Facility across three seasonal trials that spanned nearly 2 years. The Total Body Score (TBS) method was applied to quantify decomposition changes and calculate the postmortem interval (PMI) in accumulated degree days (ADD). Decomposition trajectories were analyzed by comparing the estimated and actual ADD for each seasonal trial and by fuzzy cluster analysis. The cluster analysis demonstrated that the rabbits formed one group while pigs and humans, although more similar to each other than either to rabbits, still showed important differences in decomposition patterns. The decomposition trends show that neither nonhuman model captured the pattern, rate, and variability of human decomposition. © 2018 American Academy of Forensic Sciences.

Contribution of deletion in angiotensin-converting enzyme but not A1166C angiotensin II type-1 receptor gene polymorphisms to clinical outcomes in atherothrombotic disease.

PubMed

Le Hello, Claire; Fradin, Sabine; Morello, Rémy; Coffin, Olivier; Maïza, Dominique; Hamon, Martial

2011-04-01

Angiotensin-converting enzyme insertion/deletion (rs4340) and angiotensin II type 1 receptor A1166C (rs5186) gene polymorphisms may be involved in coronary heart disease (CHD). This study was designed to evaluate potential relationships between these polymorphisms and the risk of long-term all-cause mortality and major adverse cardiovascular events (MACE) in patients requiring revascularization for atherothrombotic disease (ATD) lesions. This prospective observational study concerned patients referred for supra-aortic vessel disease (SVD), CHD, peripheral artery occlusive disease (PAOD) or visceral artery disease (VAD). Collected data included ATD referral site, ATD symptoms, personal and familial medical histories, ATD extent, vascular risk factors, biological values, medication use and rs4340 and rs5186 polymorphisms. The primary end point was all-cause mortality. The secondary end point, MACE, included cardiovascular death, clinical ischemic event related to SVD, CHD, PAOD or VAD. The cohort comprised 956 patients of whom 872 (91.2%) were genotyped and followed for 21.1 ± 9.9 months. Patients were referred for SVD (25.9%), CHD (42.3%), PAOD (35.2%) or VAD (1.6%). All-cause mortality and MACE rates were 7.6 and 27.2%, respectively. When comparing I/D + D/D vs. I/I genotypes, rs4340 polymorphism was associated with higher all-cause mortality rates according to uni- and multivariate analyses (p=0.008 and 0.011, respectively). Other differences were not significant (rs4340 polymorphism and MACE, rs5186 polymorphism and all-cause mortality and MACE). No interaction was found between the polymorphisms. Other independent predictors of all-cause mortality included PAOD history, SVD history, body mass index <25 kg/m(2), HbA(1c) ≥6.5%, absence of dyslipidemia and no use of aspirin. rs4340 polymorphism is associated with long-term all-cause mortality in advanced ATD patients requiring revascularization, whereas rs5186 polymorphism does not. Copyright © 2011 IMSS. Published by Elsevier Inc. All rights reserved.

Plasma homocysteine and cerebral small vessel disease as possible mediators between kidney and cognitive functions in patients with diabetes mellitus.

PubMed

Sonoda, Mika; Shoji, Tetsuo; Kuwamura, Yukinobu; Okute, Yujiro; Naganuma, Toshihide; Shima, Hideaki; Motoyama, Koka; Morioka, Tomoaki; Mori, Katsuhito; Fukumoto, Shinya; Shioi, Atsushi; Shimono, Taro; Fujii, Hisako; Kabata, Daijiro; Shintani, Ayumi; Emoto, Masanori; Inaba, Masaaki

2017-06-29

Cognitive impairment is more prevalent in those with decreased kidney function. We tested a hypothesis that an increased homocysteine and/or cerebral small vessel diseases (SVDs) mediate the link between kidney and cognitive functions in a cross-sectional study in 143 type 2 diabetes patients without diagnosis of dementia or prior stroke. The exposure and outcome variables were estimated glomerular filtration rate (eGFR) and cognitive performance evaluated with Modified Mini-Mental State (3 MS) examination, respectively. The candidate mediators were plasma homocysteine concentration, and SVDs including silent cerebral infarction, cerebral microbleed, periventricular hyperintensity, and deep and subcortical white matter hyperintensity by magnetic resonance imaging. In multiple regression models adjusted for 12 potential confounders, eGFR was positively associated with 3 MS score, inversely with homocysteine, but not significantly with the presence of any type of SVD. The association of eGFR with 3 MS remained significant when each of the SVDs was added to the model, whereas it disappeared when homocysteine was included in place of SVD. Mediation analysis indicated nearly significant mediation of homocysteine (P = 0.062) but no meaningful mediations of SVDs (P = 0.842-0.930). Thus, homocysteine, not SVDs, was shown to be the possible mediator between kidney and cognitive functions in patients with type 2 diabetes mellitus.

ENSO related variability in the Southern Hemisphere, 1948-2000

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Mann, Michael E.

2003-01-01

The spatiotemporal evolution of Southern Hemisphere climate variability is diagnosed based on the use of the NCEP reanalysis (1948-2000) dataset. Using the MTM-SVD analysis method, significant narrowband variability is isolated from the multi-variate dataset. It is found that the ENSO signal exhibits statistically significant behavior at quasiquadrennial (3-6 yr) timescales for the full time-period. A significant quasibiennial (2-3 yr) timescales emerges only for the latter half of period. Analyses of the spatial evolution of the two reconstructed signals shed additional light on linkages between low and high-latitude Southern Hemisphere climate anomalies.

Entanglement entropy from tensor network states for stabilizer codes

NASA Astrophysics Data System (ADS)

He, Huan; Zheng, Yunqin; Bernevig, B. Andrei; Regnault, Nicolas

2018-03-01

In this paper, we present the construction of tensor network states (TNS) for some of the degenerate ground states of three-dimensional (3D) stabilizer codes. We then use the TNS formalism to obtain the entanglement spectrum and entropy of these ground states for some special cuts. In particular, we work out examples of the 3D toric code, the X-cube model, and the Haah code. The latter two models belong to the category of "fracton" models proposed recently, while the first one belongs to the conventional topological phases. We mention the cases for which the entanglement entropy and spectrum can be calculated exactly: For these, the constructed TNS is a singular value decomposition (SVD) of the ground states with respect to particular entanglement cuts. Apart from the area law, the entanglement entropies also have constant and linear corrections for the fracton models, while the entanglement entropies for the toric code models only have constant corrections. For the cuts we consider, the entanglement spectra of these three models are completely flat. We also conjecture that the negative linear correction to the area law is a signature of extensive ground-state degeneracy. Moreover, the transfer matrices of these TNSs can be constructed. We show that the transfer matrices are projectors whose eigenvalues are either 1 or 0. The number of nonzero eigenvalues is tightly related to the ground-state degeneracy.

Dedicated cardiac rehabilitation wearable sensor and its clinical potential.

PubMed

Lee, Hooseok; Chung, Heewon; Ko, Hoon; Jeong, Changwon; Noh, Se-Eung; Kim, Chul; Lee, Jinseok

2017-01-01

We describe a wearable sensor developed for cardiac rehabilitation (CR) exercise. To effectively guide CR exercise, the dedicated CR wearable sensor (DCRW) automatically recommends the exercise intensity to the patient by comparing heart rate (HR) measured in real time with a predefined target heart rate zone (THZ) during exercise. The CR exercise includes three periods: pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up through a smartphone application we developed for iPhones and Android devices. The set-up information is transmitted to the DCRW via Bluetooth communication. In the period of exercise with intensity guidance, the DCRW continuously estimates HR using a reflected pulse signal in the wrist. To achieve accurate HR measurements, we used multichannel photo sensors and increased the chances of acquiring a clean signal. Subsequently, we used singular value decomposition (SVD) for de-noising. For the median and variance of RMSEs in the measured HRs, our proposed method with DCRW provided lower values than those from a single channel-based method and template-based multiple-channel method for the entire exercise stage. In the post-exercise period, the DCRW transmits all the measured HR data to the smartphone application via Bluetooth communication, and the patient can monitor his/her own exercise history.

Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation.

PubMed

Subudhi, Badri Narayan; Thangaraj, Veerakumar; Sankaralingam, Esakkirajan; Ghosh, Ashish

2016-11-01

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Extracting DEM from airborne X-band data based on PolInSAR

NASA Astrophysics Data System (ADS)

Hou, X. X.; Huang, G. M.; Zhao, Z.

2015-06-01

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) is a new trend of SAR remote sensing technology which combined polarized multichannel information and Interferometric information. It is of great significance for extracting DEM in some regions with low precision of DEM such as vegetation coverage area and building concentrated area. In this paper we describe our experiments with high-resolution X-band full Polarimetric SAR data acquired by a dual-baseline interferometric airborne SAR system over an area of Danling in southern China. Pauli algorithm is used to generate the double polarimetric interferometry data, Singular Value Decomposition (SVD), Numerical Radius (NR) and Phase diversity (PD) methods are used to generate the full polarimetric interferometry data. Then we can make use of the polarimetric interferometric information to extract DEM with processing of pre filtering , image registration, image resampling, coherence optimization, multilook processing, flat-earth removal, interferogram filtering, phase unwrapping, parameter calibration, height derivation and geo-coding. The processing system named SARPlore has been exploited based on VC++ led by Chinese Academy of Surveying and Mapping. Finally compared optimization results with the single polarimetric interferometry, it has been observed that optimization ways can reduce the interferometric noise and the phase unwrapping residuals, and improve the precision of DEM. The result of full polarimetric interferometry is better than double polarimetric interferometry. Meanwhile, in different terrain, the result of full polarimetric interferometry will have a different degree of increase.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns.

PubMed

Haghnegahdar, A A; Kolahi, S; Khojastepour, L; Tajeripour, F

2018-03-01

Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.

Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.

PubMed

Behl, Nicolas G R; Gnahm, Christine; Bachert, Peter; Ladd, Mark E; Nagel, Armin M

2016-04-01

To reduce noise and artifacts in (23)Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial (23)Na data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo (23)Na MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. © 2015 Wiley Periodicals, Inc.

Cerebrospinal fluid PCR analysis and biochemistry in bodies with severe decomposition.

PubMed

Palmiere, Cristian; Vanhaebost, Jessica; Ventura, Francesco; Bonsignore, Alessandro; Bonetti, Luca Reggiani

2015-02-01

The aim of this study was to assess whether Neisseria meningitidis, Listeria monocytogenes, Streptococcus pneumoniae and Haemophilus influenzae can be identified using the polymerase chain reaction technique in the cerebrospinal fluid of severely decomposed bodies with known, noninfectious causes of death or whether postmortem changes can lead to false positive results and thus erroneous diagnostic information. Biochemical investigations, postmortem bacteriology and real-time polymerase chain reaction analysis in cerebrospinal fluid were performed in a series of medico-legal autopsies that included noninfectious causes of death with decomposition, bacterial meningitis without decomposition, bacterial meningitis with decomposition, low respiratory tract infections with decomposition and abdominal infections with decomposition. In noninfectious causes of death with decomposition, postmortem investigations failed to reveal results consistent with generalized inflammation or bacterial infections at the time of death. Real-time polymerase chain reaction analysis in cerebrospinal fluid did not identify the studied bacteria in any of these cases. The results of this study highlight the usefulness of molecular approaches in bacteriology as well as the use of alternative biological samples in postmortem biochemistry in order to obtain suitable information even in corpses with severe decompositional changes. Copyright © 2014 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Can Chinese Herbal Medicine Adjunctive Therapy Improve Outcomes of Senile Vascular Dementia? Systematic Review with Meta-analysis of Clinical Trials.

PubMed

Zeng, Lingfeng; Zou, Yuanping; Kong, Lingshuo; Wang, Ningsheng; Wang, Qi; Wang, Lu; Cao, Ye; Wang, Kezhu; Chen, Yunbo; Mi, Suiqing; Zhao, Wei; Wu, Haitao; Cheng, Shuyi; Xu, Weihua; Liang, Weixiong

2015-12-01

Many publications have reported the growing application of complementary and alternative medicine, particularly the use of Chinese herbal medicine (CHM) in combination with routine pharmacotherapy (RP) for senile vascular dementia (SVD), but its efficacy remains largely unexplored. The purpose of this study is to evaluate the efficacy of CHM adjunctive therapy (CHMAT), which is CHM combined with RP, in the treatment of SVD. Publications in seven electronic databases were searched extensively, and 27 trials with a total of 1961 patients were included for analysis. Compared with RP alone, CHMAT significantly increased the effective rate [odds ratio (OR) 2.98, 95% confidence interval (CI) 2.30, 3.86]. In addition, CHMAT showed benefits in detailed subgroups of the Mini-Mental State Exam (MMSE) score from time of onset to 4 weeks (WMD 3.01, 95% CI 2.15, 3.87), 8 weeks (weighted mean difference (WMD) 2.30, 95% CI 1.28, 3.32), 12 weeks (WMD 2.93, 95% CI 2.17, 3.69), and 24 weeks (WMD 3.25, 95% CI 2.61, 3.88), and in the activity of daily living scale score from time of onset to 4 weeks (WMD -4.64, 95% CI -6.12, -3.17), 8 weeks (WMD -4.30, 95% CI -6.04, -2.56), 12 weeks (WMD -3.89, 95% CI -4.68, -3.09), and 24 weeks (WMD -4.04, 95% CI -6.51, -1.57). Moreover, CHMAT had positive effects on changes in the Hasegawa dementia scale, National Institutes of Health Stroke Scale, Clinical Dementia Rating, and Montreal Cognitive Assessment scores, as well as blood fat levels (total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and apolipoprotein E), platelet aggregation rate (1-min platelet aggregation rate, 5-min platelet aggregation rate, and maximal platelet aggregation rate), and blood rheology (whole-blood viscosity and hematocrit). No serious or frequently occurring adverse effects were reported. Weaknesses of methodological quality in most trials were assessed using the Cochrane risk of bias tool, while the quality level of Grades of Recommendations Assessment Development and Evaluation (GRADE) evidence classification indicated 'very low'. This systematic review suggests that CHM as an adjunctive therapy can improve cognitive impairment and enhance immediate response and quality of life in SVD patients. However, because of limitations of methodological quality in the included studies, further research of rigorous design is needed. Copyright © 2015 John Wiley & Sons, Ltd.

Perivascular spaces, glymphatic dysfunction, and small vessel disease.

PubMed

Mestre, Humberto; Kostrikov, Serhii; Mehta, Rupal I; Nedergaard, Maiken

2017-09-01

Cerebral small vessel diseases (SVDs) range broadly in etiology but share remarkably overlapping pathology. Features of SVD including enlarged perivascular spaces (EPVS) and formation of abluminal protein deposits cannot be completely explained by the putative pathophysiology. The recently discovered glymphatic system provides a new perspective to potentially address these gaps. This work provides a comprehensive review of the known factors that regulate glymphatic function and the disease mechanisms underlying glymphatic impairment emphasizing the role that aquaporin-4 (AQP4)-lined perivascular spaces (PVSs), cerebrovascular pulsatility, and metabolite clearance play in normal CNS physiology. This review also discusses the implications that glymphatic impairment may have on SVD inception and progression with the aim of exploring novel therapeutic targets and highlighting the key questions that remain to be answered. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

The Study of Fault Lineament Pattern of the Lamongan Volcanic Field Using Gravity Data

NASA Astrophysics Data System (ADS)

Aziz, K. N.; Hartantyo, E.; Niasari, S. W.

2018-04-01

Lamongan Volcano located in Tiris, East Java, possesses geothermal potential energy. The geothermal potential was indicated by the presence of geothermal manifestations such as hot springs. We usedsecondary gravity data from GGMplus. The result of gravity anomaly map shows that there is the lowest gravity anomaly in the center of the study area coinciding with the hot spring location. Gravity data were analyzed using SVD method to identify fault structures. It controls the geothermal fluid pathways. The result of this research shows thatthe type of fault in hot springsisanormal fault with direction NW-SE. The fault lineament pattern along maaris NW-SE.Maar indicates anormal fault. As the result we know that gravity data from GGMplus which analyzed with SVD can be used to determine the type and trend of fault.

Slavic Village: incorporating active living into community development through partnerships.

PubMed

Miller, Emily K; Scofield, Jennifer L

2009-12-01

The Slavic Village neighborhood in Cleveland, Ohio, is a diverse community of 30,524 residents that is struggling economically yet strong in tradition. The neighborhood is located just south of downtown and adjacent to the city's industrial valley. Slavic Village Development (SVD) works with local and state partners to improve the quality of life for its residents, including low-income and market-rate housing developments, economic development, community organizing, and greenspace planning. Using the Active Living by Design framework (ALbD), SVD developed strong partnerships to address preparation, promotions, programs, policy, and physical projects. Efforts were focused on Safe Routes to School, neighborhood activities, asset mapping, worksite wellness, and social marketing. The ALbD project changed both the physical environment of Slavic Village and its marketed image. The initiative built cross-disciplinary partnerships that leveraged individual strengths to implement strategies to make Slavic Village a vibrant, healthy, family-friendly neighborhood that promotes active living. There is a strong connection between health and community development. When partners from multiple disciplines work together on a common goal, it is easier to leverage resources and create change. Resource development will always be a challenge. Through the leadership of SVD and its strong ties in the community, the ALbD initiative has re-engaged residents and businesses in efforts to restore the vitality of the community. The partnership in Cleveland has successfully incorporated health into community development, a model of collaboration that can be replicated in other communities.

Thermal decomposition characteristics of microwave liquefied rape straw residues using thermogravimetric analysis

Treesearch

Xingyan Huang; Cornelis F. De Hoop; Jiulong Xie; Chung-Yun Hse; Jinqiu Qi; Yuzhu Chen; Feng Li

2017-01-01

The thermal decomposition characteristics of microwave liquefied rape straw residues with respect to liquefaction condition and pyrolysis conversion were investigated using a thermogravimetric (TG) analyzer at the heating rates of 5, 20, 50 °C min-1. The hemicellulose decomposition peak was absent at the derivative thermogravimetric analysis (DTG...

Understanding Singular Vectors

ERIC Educational Resources Information Center

James, David; Botteron, Cynthia

2013-01-01

matrix yields a surprisingly simple, heuristical approximation to its singular vectors. There are correspondingly good approximations to the singular values. Such rules of thumb provide an intuitive interpretation of the singular vectors that helps explain why the SVD is so…

Domain decomposition for aerodynamic and aeroacoustic analyses, and optimization

NASA Technical Reports Server (NTRS)

Baysal, Oktay

1995-01-01

The overarching theme was the domain decomposition, which intended to improve the numerical solution technique for the partial differential equations at hand; in the present study, those that governed either the fluid flow, or the aeroacoustic wave propagation, or the sensitivity analysis for a gradient-based optimization. The role of the domain decomposition extended beyond the original impetus of discretizing geometrical complex regions or writing modular software for distributed-hardware computers. It induced function-space decompositions and operator decompositions that offered the valuable property of near independence of operator evaluation tasks. The objectives have gravitated about the extensions and implementations of either the previously developed or concurrently being developed methodologies: (1) aerodynamic sensitivity analysis with domain decomposition (SADD); (2) computational aeroacoustics of cavities; and (3) dynamic, multibody computational fluid dynamics using unstructured meshes.

Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques

DTIC Science & Technology

2018-04-30

Title: Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques Subject: Monthly Progress Report Period of...Resources: N/A TOTAL: $18,687 2 TECHNICAL STATUS REPORT Abstract The program goal is analysis of sea ice dynamical behavior using Koopman Mode Decompo...sition (KMD) techniques. The work in the program’s first month consisted of improvements to data processing code, inclusion of additional arctic sea ice

Analysis of cured carbon-phenolic decomposition products to investigate the thermal decomposition of nozzle materials

NASA Technical Reports Server (NTRS)

Thompson, James M.; Daniel, Janice D.

1989-01-01

The development of a mass spectrometer/thermal analyzer/computer (MS/TA/Computer) system capable of providing simultaneous thermogravimetry (TG), differential thermal analysis (DTA), derivative thermogravimetry (DTG) and evolved gas detection and analysis (EGD and EGA) under both atmospheric and high pressure conditions is described. The combined system was used to study the thermal decomposition of the nozzle material that constitutes the throat of the solid rocket boosters (SRB).

Parallel transformation of K-SVD solar image denoising algorithm

NASA Astrophysics Data System (ADS)

Liang, Youwen; Tian, Yu; Li, Mei

2017-02-01

The images obtained by observing the sun through a large telescope always suffered with noise due to the low SNR. K-SVD denoising algorithm can effectively remove Gauss white noise. Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. In this paper, an OpenMP parallel programming language is proposed to transform the serial algorithm to the parallel version. Data parallelism model is used to transform the algorithm. Not one atom but multiple atoms updated simultaneously is the biggest change. The denoising effect and acceleration performance are tested after completion of the parallel algorithm. Speedup of the program is 13.563 in condition of using 16 cores. This parallel version can fully utilize the multi-core CPU hardware resources, greatly reduce running time and easily to transplant in multi-core platform.

Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD.

PubMed

Bhandari, A K; Soni, V; Kumar, A; Singh, G K

2014-07-01

This paper presents a new contrast enhancement approach which is based on Cuckoo Search (CS) algorithm and DWT-SVD for quality improvement of the low contrast satellite images. The input image is decomposed into the four frequency subbands through Discrete Wavelet Transform (DWT), and CS algorithm used to optimize each subband of DWT and then obtains the singular value matrix of the low-low thresholded subband image and finally, it reconstructs the enhanced image by applying IDWT. The singular value matrix employed intensity information of the particular image, and any modification in the singular values changes the intensity of the given image. The experimental results show superiority of the proposed method performance in terms of PSNR, MSE, Mean and Standard Deviation over conventional and state-of-the-art techniques. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Channel Training for Analog FDD Repeaters: Optimal Estimators and Cramér-Rao Bounds

NASA Astrophysics Data System (ADS)

Wesemann, Stefan; Marzetta, Thomas L.

2017-12-01

For frequency division duplex channels, a simple pilot loop-back procedure has been proposed that allows the estimation of the UL & DL channels at an antenna array without relying on any digital signal processing at the terminal side. For this scheme, we derive the maximum likelihood (ML) estimators for the UL & DL channel subspaces, formulate the corresponding Cram\\'er-Rao bounds and show the asymptotic efficiency of both (SVD-based) estimators by means of Monte Carlo simulations. In addition, we illustrate how to compute the underlying (rank-1) SVD with quadratic time complexity by employing the power iteration method. To enable power control for the data transmission, knowledge of the channel gains is needed. Assuming that the UL & DL channels have on average the same gain, we formulate the ML estimator for the channel norm, and illustrate its robustness against strong noise by means of simulations.

Assessment of a new method for the analysis of decomposition gases of polymers by a combining thermogravimetric solid-phase extraction and thermal desorption gas chromatography mass spectrometry.

PubMed

Duemichen, E; Braun, U; Senz, R; Fabian, G; Sturm, H

2014-08-08

For analysis of the gaseous thermal decomposition products of polymers, the common techniques are thermogravimetry, combined with Fourier transformed infrared spectroscopy (TGA-FTIR) and mass spectrometry (TGA-MS). These methods offer a simple approach to the decomposition mechanism, especially for small decomposition molecules. Complex spectra of gaseous mixtures are very often hard to identify because of overlapping signals. In this paper a new method is described to adsorb the decomposition products during controlled conditions in TGA on solid-phase extraction (SPE) material: twisters. Subsequently the twisters were analysed with thermal desorption gas chromatography mass spectrometry (TDS-GC-MS), which allows the decomposition products to be separated and identified using an MS library. The thermoplastics polyamide 66 (PA 66) and polybutylene terephthalate (PBT) were used as example polymers. The influence of the sample mass and of the purge gas flow during the decomposition process was investigated in TGA. The advantages and limitations of the method were presented in comparison to the common analysis techniques, TGA-FTIR and TGA-MS. Copyright © 2014 Elsevier B.V. All rights reserved.

Thermal decomposition kinetics of hydrazinium cerium 2,3-Pyrazinedicarboxylate hydrate: a new precursor for CeO2.

PubMed

Premkumar, Thathan; Govindarajan, Subbiah; Coles, Andrew E; Wight, Charles A

2005-04-07

The thermal decomposition kinetics of N(2)H(5)[Ce(pyrazine-2,3-dicarboxylate)(2)(H(2)O)] (Ce-P) have been studied by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), for the first time; TGA analysis reveals an oxidative decomposition process yielding CeO(2) as the final product with an activation energy of approximately 160 kJ mol(-1). This complex may be used as a precursor to fine particle cerium oxides due to its low temperature of decomposition.

On the Possibility of Studying the Reactions of the Thermal Decomposition of Energy Substances by the Methods of High-Resolution Terahertz Spectroscopy

NASA Astrophysics Data System (ADS)

Vaks, V. L.; Domracheva, E. G.; Chernyaeva, M. B.; Pripolzin, S. I.; Revin, L. S.; Tretyakov, I. V.; Anfertyev, V. A.; Yablokov, A. A.; Lukyanenko, I. A.; Sheikov, Yu. V.

2018-02-01

We show prospects for using the method of high-resolution terahertz spectroscopy for a continuous analysis of the decomposition products of energy substances in the gas phase (including short-lived ones) in a wide temperature range. The experimental setup, which includes a terahertz spectrometer for studying the thermal decomposition reactions, is described. The results of analysis of the gaseous decomposition products of energy substances by the example of ammonium nitrate heated from room temperature to 167°C are presented.

Reaction Time is a Marker of Early Cognitive and Behavioral Alterations in Pure Cerebral Small Vessel Disease.

PubMed

Jouvent, Eric; Reyes, Sonia; De Guio, François; Chabriat, Hugues

2015-01-01

The assessment of early and subtle cognitive and behavioral effects of cerebral small vessel disease (SVD) requires specific and long-lasting evaluations performed by experienced neuropsychologists. Simpler tools would be helpful for daily clinical practice. To determine whether a simple reaction time task that lasts 5 minutes and can be performed without external supervision on any tablet or laptop can be used as a proxy of early cognitive and behavioral alterations in CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy), a monogenic form of pure SVD related to NOTCH3 mutations. Twenty-two genetically confirmed patients with CADASIL having preserved global cognitive abilities and without disability (MMSE >24 and modified Rankin's scale ≤1) were compared to 29 age-and-gender matched controls to determine group differences according to: 1) conventional neuropsychological and behavioral testing; 2) a computerized battery evaluating reaction time, processing speed, and executive functions. In a second step, correlations between reaction time and cognitive and behavioral alterations detected using both conventional and computerized testing were tested in patients. Reaction time was significantly higher in patients than in controls (mean in patients: 283 ms - in controls: 254 ms, p = 0.03). In patients, reaction time was significantly associated with conventional and chronometric tests of executive functions, working memory, and apathy. Reaction time obtained using a very simple task may serve as a proxy of early cognitive and behavioral alterations in SVD and could be easily used in daily clinical practice.

The pathology and pathophysiology of vascular dementia.

PubMed

Kalaria, Raj N

2017-12-19

Vascular dementia (VaD) is widely recognised as the second most common type of dementia. Consensus and accurate diagnosis of clinically suspected VaD relies on wide-ranging clinical, neuropsychological and neuroimaging measures in life but more importantly pathological confirmation. Factors defining subtypes of VaD include the nature and extent of vascular pathologies, degree of involvement of extra and intracranial vessels and the anatomical location of tissue changes as well as time after the initial vascular event. Atherosclerotic and cardioembolic diseases combined appear the most common subtypes of vascular brain injury. In recent years, cerebral small vessel disease (SVD) has gained prominence worldwide as an important substrate of cognitive impairment. SVD is characterised by arteriolosclerosis, lacunar infarcts and cortical and subcortical microinfarcts and diffuse white matter changes, which involve myelin loss and axonal abnormalities. Global brain atrophy and focal degeneration of the cerebrum including medial temporal lobe atrophy are also features of VaD similar to Alzheimer's disease. Hereditary arteriopathies have provided insights into the mechanisms of dementia particularly how arteriolosclerosis, a major contributor of SVD promotes cognitive impairment. Recently developed and validated neuropathology guidelines indicated that the best predictors of vascular cognitive impairment were small or lacunar infarcts, microinfarcts, perivascular space dilation, myelin loss, arteriolosclerosis and leptomeningeal cerebral amyloid angiopathy. While these substrates do not suggest high specificity, VaD is likely defined by key neuronal and dendro-synaptic changes resulting in executive dysfunction and related cognitive deficits. Greater understanding of the molecular pathology is needed to clearly define microvascular disease and vascular substrates of dementia. Copyright © 2017 Elsevier Ltd. All rights reserved.

Medial medullary infarction: clinical, imaging, and outcome study in 86 consecutive patients.

PubMed

Kim, Jong S; Han, Young S

2009-10-01

Clinical-imaging correlation and long-term clinical outcomes remain to be investigated in medial medullary infarction (MMI). We studied clinical, MRI, and angiographic data of 86 consecutive MMI patients. The lesions were correlated with clinical findings, and long-term outcomes, divided into mild and severe (modified Rankin scale >3), were assessed by telephone interview. Central poststroke pain (CPSP) was defined as persistent pain with visual numeric scale > or = 4. The lesions were located mostly in the rostral medulla (rostral 76%, rostral+middle 16%), while ventro-dorsal lesion patterns include ventral (V, 20%), ventral+middle (VM, 33%), and ventral+middle+dorsal (VMD, 41%). Clinical manifestations included motor dysfunction in 78 patients (91%), sensory dysfunction in 59 (73%), and vertigo/dizziness in 51 (59%), each closely related to involvement of ventral, middle, and dorsal portions, respectively (P<0.001, each). Vertebral artery (VA) atherosclerotic disease relevant to the infarction occurred in 53 (62%) patients, mostly producing atheromatous branch occlusion (ABO). Small vessel disease (SVD) occurred in 24 (28%) patients. ABO was more closely related to VMD (versus V+VM) than was SVD (P=0.035). During follow-up (mean 71 months), 11 patients died, and recurrent strokes occurred in 11. Old age (P=0.001) and severe motor dysfunction at admission (P=0.001) were factors predicting poor prognosis. CPSP, occurring in 21 patients, was closely (P=0.013) related to poor clinical outcome. MMI usually presents with a rostral medullary lesion, with a good clinical ventro-dorsal imaging correlation, caused most frequently by ABO followed by SVD. A significant proportion of patients remain dependent or have CPSP.

Vitamin D status and vascular dementia due to cerebral small vessel disease in the elderly Asian Indian population.

PubMed

Prabhakar, Puttachandra; Chandra, Sadanandavalli Retnaswami; Supriya, Manjunath; Issac, Thomas Gregor; Prasad, Chandrajit; Christopher, Rita

2015-12-15

Vitamin D plays vital roles in human health and recent studies have shown its beneficial effect on brain functioning. The present study was designed to evaluate the association of vitamin D with vascular dementia (VaD) due to cerebral small vessel disease (SVD) in Asian Indian population. 140 VaD patients aged ≥ 60 years with neuroimaging evidence of SVD, and 132 age and gender-matched controls, were investigated. Vitamin D status was estimated by measuring serum 25-hydroxy vitamin D. Logistic regression model revealed that deficient levels of vitamin D (<12 ng/ml) were associated with 2.2-fold increase in odds of VaD after adjustment with covariates. Hypertension was independently associated with 11.3-fold increased odds of VaD. In hypertensives with vitamin D deficiency and insufficiency (12-20 ng/ml), the odds were increased to 31.6-fold and 14.4-fold, respectively. However, in hypertensives with vitamin D sufficiency (>20 ng/ml), the odds of VaD were increased by 3.8-fold only. Pearson correlation showed that serum vitamin D was inversely associated with systolic and diastolic blood pressure (r=-0.401 and -0.411, p<0.01, respectively) in vitamin D-deficient subjects. Since the combined presence of hypertension and vitamin D deficiency increases the probability of developing VaD, screening for vitamin D status in addition to regular monitoring of blood pressure, could reduce the risk of VaD associated with cerebral SVD in the elderly Asian Indian subjects. Copyright © 2015 Elsevier B.V. All rights reserved.

Detection of white matter lesions in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Riad, Medhat M.; Platel, Bram; de Leeuw, Frank-Erik; Karssemeijer, Nico

2013-02-01

White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (Az) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

NASA Astrophysics Data System (ADS)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

Scalable parallel elastic-plastic finite element analysis using a quasi-Newton method with a balancing domain decomposition preconditioner

NASA Astrophysics Data System (ADS)

Yusa, Yasunori; Okada, Hiroshi; Yamada, Tomonori; Yoshimura, Shinobu

2018-04-01

A domain decomposition method for large-scale elastic-plastic problems is proposed. The proposed method is based on a quasi-Newton method in conjunction with a balancing domain decomposition preconditioner. The use of a quasi-Newton method overcomes two problems associated with the conventional domain decomposition method based on the Newton-Raphson method: (1) avoidance of a double-loop iteration algorithm, which generally has large computational complexity, and (2) consideration of the local concentration of nonlinear deformation, which is observed in elastic-plastic problems with stress concentration. Moreover, the application of a balancing domain decomposition preconditioner ensures scalability. Using the conventional and proposed domain decomposition methods, several numerical tests, including weak scaling tests, were performed. The convergence performance of the proposed method is comparable to that of the conventional method. In particular, in elastic-plastic analysis, the proposed method exhibits better convergence performance than the conventional method.

Interannual coherent variability of SSTA and SSHA in the Tropical Indian Ocean

NASA Astrophysics Data System (ADS)

Feng, J. Q.

2012-01-01

Sea surface height derived from the multiple ocean satellite altimeter missions (TOPEX/Poseidon, Jason-1, ERS, Envisat et al.) and sea surface temperature from National Centers for Environmental Prediction (NCEP) over 1993-2008 are analyzed to investigate the coherent patterns between the interannual variability of the sea surface and subsurface in the Tropical Indian Ocean, by jointly adopting Singular Value Decomposition (SVD) and Extended Associate Pattern Analysis (EAPA) methods. Results show that there are two dominant coherent modes with the nearly same main period of about 3-5 yr, accounting for 86 % of the total covariance in all, but 90° phase difference between them. The primary pattern is characterized by a east-west dipole mode associated with the mature phase of ENSO, and the second presents a sandwich mode having one sign anomalies along Sumatra-Java coast and northeast of Madagascar, whilst an opposite sign between the two regions. The robust correlations of the sea surface height anomaly (SSHA) with sea surface temperature anomaly (SSTA) in the leading modes indicate a strong interaction between them, though the highest correlation coefficient appears with a time lag. And there may be some physical significance with respect to ocean dynamics implied in SSHA variability. Analyzing results show that the features of oceanic waves with basin scale, of which the Rossby wave is prominent, are apparent in the dominant modes. It is further demonstrated from the EAPA that the equatorial eastward Kelvin wave and off-equatorial westward Rossby wave as well as their reflection in the east and west boundary, respectively, are important dynamic mechanisms in the evolution of the two leading coherent patterns. Results of the present study suggest that the upper ocean thermal variations on the timescale of interannual coherent with the ocean dynamics in spatial structure and temporal evolution are mainly attributed to the ocean waves.

Online Low-Rank Representation Learning for Joint Multi-subspace Recovery and Clustering.

PubMed

Li, Bo; Liu, Risheng; Cao, Junjie; Zhang, Jie; Lai, Yu-Kun; Liua, Xiuping

2017-10-06

Benefiting from global rank constraints, the lowrank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank minimization for the entire data set whenever new samples are dynamically added, making it prohibitively expensive. Existing attempts to online LRR either take a stochastic approach or build the representation purely based on a small sample set and treat new input as out-of-sample data. The former often requires multiple runs for good performance and thus takes longer time to run, and the latter formulates online LRR as an out-ofsample classification problem and is less robust to noise. In this paper, a novel online low-rank representation subspace learning method is proposed for both large-scale and dynamic data. The proposed algorithm is composed of two stages: static learning and dynamic updating. In the first stage, the subspace structure is learned from a small number of data samples. In the second stage, the intrinsic principal components of the entire data set are computed incrementally by utilizing the learned subspace structure, and the low-rank representation matrix can also be incrementally solved by an efficient online singular value decomposition (SVD) algorithm. The time complexity is reduced dramatically for large-scale data, and repeated computation is avoided for dynamic problems. We further perform theoretical analysis comparing the proposed online algorithm with the batch LRR method. Finally, experimental results on typical tasks of subspace recovery and subspace clustering show that the proposed algorithm performs comparably or better than batch methods including the batch LRR, and significantly outperforms state-of-the-art online methods.

Transportation Network Analysis and Decomposition Methods

DOT National Transportation Integrated Search

1978-03-01

The report outlines research in transportation network analysis using decomposition techniques as a basis for problem solutions. Two transportation network problems were considered in detail: a freight network flow problem and a scheduling problem fo...

Electrochemical and Infrared Absorption Spectroscopy Detection of SF₆ Decomposition Products.

PubMed

Dong, Ming; Zhang, Chongxing; Ren, Ming; Albarracín, Ricardo; Ye, Rixin

2017-11-15

Sulfur hexafluoride (SF₆) gas-insulated electrical equipment is widely used in high-voltage (HV) and extra-high-voltage (EHV) power systems. Partial discharge (PD) and local heating can occur in the electrical equipment because of insulation faults, which results in SF₆ decomposition and ultimately generates several types of decomposition products. These SF₆ decomposition products can be qualitatively and quantitatively detected with relevant detection methods, and such detection contributes to diagnosing the internal faults and evaluating the security risks of the equipment. At present, multiple detection methods exist for analyzing the SF₆ decomposition products, and electrochemical sensing (ES) and infrared (IR) spectroscopy are well suited for application in online detection. In this study, the combination of ES with IR spectroscopy is used to detect SF₆ gas decomposition. First, the characteristics of these two detection methods are studied, and the data analysis matrix is established. Then, a qualitative and quantitative analysis ES-IR model is established by adopting a two-step approach. A SF₆ decomposition detector is designed and manufactured by combining an electrochemical sensor and IR spectroscopy technology. The detector is used to detect SF₆ gas decomposition and is verified to reliably and accurately detect the gas components and concentrations.

Critical analysis of nitramine decomposition data: Activation energies and frequency factors for HMX and RDX decomposition

NASA Technical Reports Server (NTRS)

Schroeder, M. A.

1980-01-01

A summary of a literature review on thermal decomposition of HMX and RDX is presented. The decomposition apparently fits first order kinetics. Recommended values for Arrhenius parameters for HMX and RDX decomposition in the gaseous and liquid phases and for decomposition of RDX in solution in TNT are given. The apparent importance of autocatalysis is pointed out, as are some possible complications that may be encountered in interpreting extending or extrapolating kinetic data for these compounds from measurements carried out below their melting points to the higher temperatures and pressure characteristic of combustion.

Tracking spin-axis orbital alignment in selected binary systems: the Torun Rossiter-McLaughlin effect survey

NASA Astrophysics Data System (ADS)

Sybilski, P.; Pawłaszek, R. K.; Sybilska, A.; Konacki, M.; Hełminiak, K. G.; Kozłowski, S. K.; Ratajczak, M.

2018-07-01

We have obtained high-resolution spectra of four eclipsing binary systems (FM Leo, NN Del, V963 Cen and AI Phe) with the view to gaining an insight into the relative orientations of their stellar spin axes and orbital axes. The so-called Rossiter-McLaughlin (RM) effect, i.e. the fact that the broadening and the amount of blue or redshift in the spectra during an eclipse depends on the tilt of the spin axis of the background star, has the potential of reconciling observations and theoretical models if such a tilt is found. We analyse the RM effect by disentangling the spectra, removing the front component and measuring the remaining, distorted lines with a broadening function (BF) obtained from single-value decomposition (SVD), weighting by the intensity centre of the BF in the eclipse. All but one of our objects show no significant misalignment, suggesting that aligned systems are dominant. We provide stellar as well as orbital parameters for our systems. With five measured spin-orbit angles, we increase significantly (from 9 to 14) the number of stars for which it has been measured. The spin-orbit angle β calculated for AI Phe's secondary component shows a misalignment of 87±17°. NN Del, with a large separation of components and a long dynamical time-scale for circularization and synchronization, is an example of a close to primordial spin-orbit angle measurement.

An advanced algorithm for deformation estimation in non-urban areas

NASA Astrophysics Data System (ADS)

Goel, Kanika; Adam, Nico

2012-09-01

This paper presents an advanced differential SAR interferometry stacking algorithm for high resolution deformation monitoring in non-urban areas with a focus on distributed scatterers (DSs). Techniques such as the Small Baseline Subset Algorithm (SBAS) have been proposed for processing DSs. SBAS makes use of small baseline differential interferogram subsets. Singular value decomposition (SVD), i.e. L2 norm minimization is applied to link independent subsets separated by large baselines. However, the interferograms used in SBAS are multilooked using a rectangular window to reduce phase noise caused for instance by temporal decorrelation, resulting in a loss of resolution and the superposition of topography and deformation signals from different objects. Moreover, these have to be individually phase unwrapped and this can be especially difficult in natural terrains. An improved deformation estimation technique is presented here which exploits high resolution SAR data and is suitable for rural areas. The implemented method makes use of small baseline differential interferograms and incorporates an object adaptive spatial phase filtering and residual topography removal for an accurate phase and coherence estimation, while preserving the high resolution provided by modern satellites. This is followed by retrieval of deformation via the SBAS approach, wherein, the phase inversion is performed using an L1 norm minimization which is more robust to the typical phase unwrapping errors encountered in non-urban areas. Meter resolution TerraSAR-X data of an underground gas storage reservoir in Germany is used for demonstrating the effectiveness of this newly developed technique in rural areas.

Dominant covarying climate signals in the Southern Ocean and Antarctic Sea Ice influence during last three decades

NASA Astrophysics Data System (ADS)

Cerrone, Dario; Fusco, Giannetta; Simmonds, Ian; Aulicino, Giuseppe; Budillon, Giorgio

2017-04-01

A composite dataset (comprising geopotential height, sea surface temperature, zonal and meridional surface winds, precipitation, cloud cover, surface air temperature, latent plus sensible heat fluxes , and sea ice concentration) has been investigated with the aim of revealing the dominant timescales of variability from 1982 to 2013. Three covarying climate signals associated with variations in the sea ice distribution around Antarctica have been detected through the application of the Multiple-Taper Method with Singular Value Decomposition (MTM-SVD). Features of the established patterns of variation over the Southern Hemisphere (SH) extratropics have been identified in each of these three climate signals in the form of coupled or individual oscillations. The climate patterns considered here are the Southern Annular Mode (SAM), the Pacific-South America (PSA) teleconnection, the Semi-Annual Oscillation (SAO) and Zonal Wavenumber-3 (ZW3) mode. It is shown that most of the sea ice temporal variance is concentrated at the quasi-triennial scale resulting from the constructive superposition of the PSA and ZW3 patterns. In addition the combination of the SAM and SAO patterns is found to promote the interannual sea ice variations underlying a general change in the Southern Ocean atmospheric and oceanic circulations. These two modes of variability are also found consistent with the occurrence of the SAM+/PSA- or SAM-/PSA+ combinations, which could have favored the cooling of the sub-Antarctic and important changes in the Antarctic sea ice distribution since 2000.

Interannual hydroclimatic variability and the 2009-2011 extreme ENSO phases in Colombia: from Andean glaciers to Caribbean lowlands

NASA Astrophysics Data System (ADS)

Bedoya-Soto, Juan Mauricio; Poveda, Germán; Trenberth, Kevin E.; Vélez-Upegui, Jorge Julián

2018-03-01

During 2009-2011, Colombia experienced extreme hydroclimatic events associated with the extreme phases of El Niño-Southern Oscillation (ENSO). Here, we study the dynamics of diverse land-atmosphere phenomena involved in such anomalous events at continental, regional, and local scales. Standardized anomalies of precipitation, 2-m temperature, total column water (TCW), volumetric soil water (VSW), temperature at 925 hPa, surface sensible heat (SSH), latent heat (SLH), evaporation (EVP), and liquid water equivalent thickness (LWET) are analyzed to assess atmosphere-land controls and relationships over tropical South America (TropSA) during 1986-2013 (long term) and 2009-2011 (ENSO extreme phases). An assessment of the interannual covariability between precipitation and 2-m temperature is performed using singular value decomposition (SVD) to identify the dominant spatiotemporal modes of hydroclimatic variability over the region's largest river basins (Amazon, Orinoco, Tocantins, Magdalena-Cauca, and Essequibo). ENSO, its evolution in time, and strong and consistent spatial structures emerge as the dominant mode of variability. In situ anomalies during both extreme phases of ENSO 2009-2011 over the Magdalena-Cauca River basins are linked at the continental scale. The ENSO-driven hydroclimatic effects extend from the diurnal cycle to interannual timescales, as reflected in temperature data from tropical glaciers and the rain-snow boundary in the highest peaks of the Central Andes of Colombia to river levels along the Caribbean lowlands of the Magdalena-Cauca River basin.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns

PubMed Central

Haghnegahdar, A.A.; Kolahi, S.; Khojastepour, L.; Tajeripour, F.

2018-01-01

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages. PMID:29732343

Decomposition techniques

USGS Publications Warehouse

Chao, T.T.; Sanzolone, R.F.

1992-01-01

Sample decomposition is a fundamental and integral step in the procedure of geochemical analysis. It is often the limiting factor to sample throughput, especially with the recent application of the fast and modern multi-element measurement instrumentation. The complexity of geological materials makes it necessary to choose the sample decomposition technique that is compatible with the specific objective of the analysis. When selecting a decomposition technique, consideration should be given to the chemical and mineralogical characteristics of the sample, elements to be determined, precision and accuracy requirements, sample throughput, technical capability of personnel, and time constraints. This paper addresses these concerns and discusses the attributes and limitations of many techniques of sample decomposition along with examples of their application to geochemical analysis. The chemical properties of reagents as to their function as decomposition agents are also reviewed. The section on acid dissolution techniques addresses the various inorganic acids that are used individually or in combination in both open and closed systems. Fluxes used in sample fusion are discussed. The promising microwave-oven technology and the emerging field of automation are also examined. A section on applications highlights the use of decomposition techniques for the determination of Au, platinum group elements (PGEs), Hg, U, hydride-forming elements, rare earth elements (REEs), and multi-elements in geological materials. Partial dissolution techniques used for geochemical exploration which have been treated in detail elsewhere are not discussed here; nor are fire-assaying for noble metals and decomposition techniques for X-ray fluorescence or nuclear methods be discussed. ?? 1992.

Using Microwave Sample Decomposition in Undergraduate Analytical Chemistry

NASA Astrophysics Data System (ADS)

Griff Freeman, R.; McCurdy, David L.

1998-08-01

A shortcoming of many undergraduate classes in analytical chemistry is that students receive little exposure to sample preparation in chemical analysis. This paper reports the progress made in introducing microwave sample decomposition into several quantitative analysis experiments at Truman State University. Two experiments being performed in our current laboratory rotation include closed vessel microwave decomposition applied to the classical gravimetric determination of nickel and the determination of sodium in snack foods by flame atomic emission spectrometry. A third lab, using open-vessel microwave decomposition for the Kjeldahl nitrogen determination is now ready for student trial. Microwave decomposition reduces the time needed to complete these experiments and significantly increases the student awareness of the importance of sample preparation in quantitative chemical analyses, providing greater breadth and realism in the experiments.