GC × GC-TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures.

PubMed

Stefanuto, Pierre-Hugues; Perrault, Katelynn A; Stadler, Sonja; Pesesse, Romain; LeBlanc, Helene N; Forbes, Shari L; Focant, Jean-François

2015-06-01

In forensic thanato-chemistry, the understanding of the process of soft tissue decomposition is still limited. A better understanding of the decomposition process and the characterization of the associated volatile organic compounds (VOC) can help to improve the training of victim recovery (VR) canines, which are used to search for trapped victims in natural disasters or to locate corpses during criminal investigations. The complexity of matrices and the dynamic nature of this process require the use of comprehensive analytical methods for investigation. Moreover, the variability of the environment and between individuals creates additional difficulties in terms of normalization. The resolution of the complex mixture of VOCs emitted by a decaying corpse can be improved using comprehensive two-dimensional gas chromatography (GC × GC), compared to classical single-dimensional gas chromatography (1DGC). This study combines the analytical advantages of GC × GC coupled to time-of-flight mass spectrometry (TOFMS) with the data handling robustness of supervised multivariate statistics to investigate the VOC profile of human remains during early stages of decomposition. Various supervised multivariate approaches are compared to interpret the large data set. Moreover, early decomposition stages of pig carcasses (typically used as human surrogates in field studies) are also monitored to obtain a direct comparison of the two VOC profiles and estimate the robustness of this human decomposition analog model. In this research, we demonstrate that pig and human decomposition processes can be described by the same trends for the major compounds produced during the early stages of soft tissue decomposition.

TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

PubMed Central

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

2017-01-01

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Characterising laser beams with liquid crystal displays

NASA Astrophysics Data System (ADS)

Dudley, Angela; Naidoo, Darryl; Forbes, Andrew

2016-02-01

We show how one can determine the various properties of light, from the modal content of laser beams to decoding the information stored in optical fields carrying orbital angular momentum, by performing a modal decomposition. Although the modal decomposition of light has been known for a long time, applied mostly to pattern recognition, we illustrate how this technique can be implemented with the use of liquid-crystal displays. We show experimentally how liquid crystal displays can be used to infer the intensity, phase, wavefront, Poynting vector, and orbital angular momentum density of unknown optical fields. This measurement technique makes use of a single spatial light modulator (liquid crystal display), a Fourier transforming lens and detector (CCD or photo-diode). Such a diagnostic tool is extremely relevant to the real-time analysis of solid-state and fibre laser systems as well as mode division multiplexing as an emerging technology in optical communication.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Real-time determination of laser beam quality by modal decomposition.

PubMed

Schmidt, Oliver A; Schulze, Christian; Flamm, Daniel; Brüning, Robert; Kaiser, Thomas; Schröter, Siegmund; Duparré, Michael

2011-03-28

We present a real-time method to determine the beam propagation ratio M2 of laser beams. The all-optical measurement of modal amplitudes yields M2 parameters conform to the ISO standard method. The experimental technique is simple and fast, which allows to investigate laser beams under conditions inaccessible to other methods.

Linked independent component analysis for multimodal data fusion.

PubMed

Groves, Adrian R; Beckmann, Christian F; Smith, Steve M; Woolrich, Mark W

2011-02-01

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode). Copyright © 2010 Elsevier Inc. All rights reserved.

A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Rizzi, Egidio

2017-07-01

Output-only structural identification is developed by a refined Frequency Domain Decomposition ( rFDD) approach, towards assessing current modal properties of heavy-damped buildings (in terms of identification challenge), under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type II bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames (with variable structural features) are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 "Far-Field" (22 NS, 22 WE) earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.

Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes

NASA Astrophysics Data System (ADS)

Debnath, M.; Santoni, C.; Leonardi, S.; Iungo, G. V.

2017-03-01

The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator. This article is part of the themed issue 'Wind energy in complex terrains'.

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

PubMed

Vergara, Victor M; Ulloa, Alvaro; Calhoun, Vince D; Boutte, David; Chen, Jiayu; Liu, Jingyu

2014-09-01

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications. Copyright © 2014 Elsevier Inc. All rights reserved.

Multivariate Curve Resolution Applied to Infrared Reflection Measurements of Soil Contaminated with an Organophosphorus Analyte

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

Gallagher, Neal B.; Blake, Thomas A.; Gassman, Paul L.

2006-07-01

Multivariate curve resolution (MCR) is a powerful technique for extracting chemical information from measured spectra on complex mixtures. The difficulty with applying MCR to soil reflectance measurements is that light scattering artifacts can contribute much more variance to the measurements than the analyte(s) of interest. Two methods were integrated into a MCR decomposition to account for light scattering effects. Firstly, an extended mixture model using pure analyte spectra augmented with scattering ‘spectra’ was used for the measured spectra. And secondly, second derivative preprocessed spectra, which have higher selectivity than the unprocessed spectra, were included in a second block as amore » part of the decomposition. The conventional alternating least squares (ALS) algorithm was modified to simultaneously decompose the measured and second derivative spectra in a two-block decomposition. Equality constraints were also included to incorporate information about sampling conditions. The result was an MCR decomposition that provided interpretable spectra from soil reflectance measurements.« less

The Fourier decomposition method for nonlinear and non-stationary time series analysis.

PubMed

Singh, Pushpendra; Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-03-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

The Fourier decomposition method for nonlinear and non-stationary time series analysis

PubMed Central

Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-01-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of ‘Fourier intrinsic band functions’ (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms. PMID:28413352

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

NASA Technical Reports Server (NTRS)

Juang, J. N.; Pappa, R. S.

1985-01-01

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes.

PubMed

Debnath, M; Santoni, C; Leonardi, S; Iungo, G V

2017-04-13

The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator.This article is part of the themed issue 'Wind energy in complex terrains'. © 2017 The Author(s).

The Development of the Obligation System in Mexican Spanish: A Variationist Sociolinguistic Perspective

ERIC Educational Resources Information Center

Honea, Katherine

2012-01-01

Research that examines diachronic change and modality posit that modal verbs follow certain universal paths of development (e.g. Cornillie, 2007; Bybee & Fleischman, 1995; Bybee, Perkins & Pagliuca, 1994). The present study examines the development of Spanish modality in Mexico through the use of multivariate analyses, relative…

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.

PubMed

Gohel, Bakul; Lee, Peter; Jeong, Yong

2016-08-01

Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Structural modal parameter identification using local mean decomposition

NASA Astrophysics Data System (ADS)

Keyhani, Ali; Mohammadi, Saeed

2018-02-01

Modal parameter identification is the first step in structural health monitoring of existing structures. Already, many powerful methods have been proposed for this concept and each method has some benefits and shortcomings. In this study, a new method based on local mean decomposition is proposed for modal identification of civil structures from free or ambient vibration measurements. The ability of the proposed method was investigated using some numerical studies and the results compared with those obtained from the Hilbert-Huang transform (HHT). As a major advantage, the proposed method can extract natural frequencies and damping ratios of all active modes from only one measurement. The accuracy of the identified modes depends on their participation in the measured responses. Nevertheless, the identified natural frequencies have reasonable accuracy in both cases of free and ambient vibration measurements, even in the presence of noise. The instantaneous phase angle and the natural logarithm of instantaneous amplitude curves obtained from the proposed method have more linearity rather than those from the HHT algorithm. Also, the end effect is more restricted for the proposed method.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

PubMed

Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

2016-07-15

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

A modal analysis of lamellar diffraction gratings in conical mountings

NASA Technical Reports Server (NTRS)

Li, Lifeng

1992-01-01

A rigorous modal analysis of lamellar grating, i.e., gratings having rectangular grooves, in conical mountings is presented. It is an extension of the analysis of Botten et al. which considered non-conical mountings. A key step in the extension is a decomposition of the electromagnetic field in the grating region into two orthogonal components. A computer program implementing this extended modal analysis is capable of dealing with plane wave diffraction by dielectric and metallic gratings with deep grooves, at arbitrary angles of incidence, and having arbitrary incident polarizations. Some numerical examples are included.

Primary decomposition of zero-dimensional ideals over finite fields

NASA Astrophysics Data System (ADS)

Gao, Shuhong; Wan, Daqing; Wang, Mingsheng

2009-03-01

A new algorithm is presented for computing primary decomposition of zero-dimensional ideals over finite fields. Like Berlekamp's algorithm for univariate polynomials, the new method is based on the invariant subspace of the Frobenius map acting on the quotient algebra. The dimension of the invariant subspace equals the number of primary components, and a basis of the invariant subspace yields a complete decomposition. Unlike previous approaches for decomposing multivariate polynomial systems, the new method does not need primality testing nor any generic projection, instead it reduces the general decomposition problem directly to root finding of univariate polynomials over the ground field. Also, it is shown how Groebner basis structure can be used to get partial primary decomposition without any root finding.

A time domain frequency-selective multivariate Granger causality approach.

PubMed

Leistritz, Lutz; Witte, Herbert

2016-08-01

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

Decomposition of algebraic sets and applications to weak centers of cubic systems

NASA Astrophysics Data System (ADS)

Chen, Xingwu; Zhang, Weinian

2009-10-01

There are many methods such as Gröbner basis, characteristic set and resultant, in computing an algebraic set of a system of multivariate polynomials. The common difficulties come from the complexity of computation, singularity of the corresponding matrices and some unnecessary factors in successive computation. In this paper, we decompose algebraic sets, stratum by stratum, into a union of constructible sets with Sylvester resultants, so as to simplify the procedure of elimination. Applying this decomposition to systems of multivariate polynomials resulted from period constants of reversible cubic differential systems which possess a quadratic isochronous center, we determine the order of weak centers and discuss the bifurcation of critical periods.

Amodal processing in human prefrontal cortex.

PubMed

Tamber-Rosenau, Benjamin J; Dux, Paul E; Tombu, Michael N; Asplund, Christopher L; Marois, René

2013-07-10

Information enters the cortex via modality-specific sensory regions, whereas actions are produced by modality-specific motor regions. Intervening central stages of information processing map sensation to behavior. Humans perform this central processing in a flexible, abstract manner such that sensory information in any modality can lead to response via any motor system. Cognitive theories account for such flexible behavior by positing amodal central information processing (e.g., "central executive," Baddeley and Hitch, 1974; "supervisory attentional system," Norman and Shallice, 1986; "response selection bottleneck," Pashler, 1994). However, the extent to which brain regions embodying central mechanisms of information processing are amodal remains unclear. Here we apply multivariate pattern analysis to functional magnetic resonance imaging (fMRI) data to compare response selection, a cognitive process widely believed to recruit an amodal central resource across sensory and motor modalities. We show that most frontal and parietal cortical areas known to activate across a wide variety of tasks code modality, casting doubt on the notion that these regions embody a central processor devoid of modality representation. Importantly, regions of anterior insula and dorsolateral prefrontal cortex consistently failed to code modality across four experiments. However, these areas code at least one other task dimension, process (instantiated as response selection vs response execution), ensuring that failure to find coding of modality is not driven by insensitivity of multivariate pattern analysis in these regions. We conclude that abstract encoding of information modality is primarily a property of subregions of the prefrontal cortex.

Vibro-acoustics for Space Station applications

NASA Technical Reports Server (NTRS)

Vaicaitis, R.; Bofilios, D. A.

1986-01-01

An analytical procedure has been developed to study noise generation in a double wall and single wall cylindrical shell due to mechanical point loads. The objective of this study is to develop theoretical procedures for parametetric evaluation of noise generation andd noise transmission for the habitability modules of the proposed Space Station operation. The solutions of the governing acoustic-structural equations are obtained utilizing modal decomposition. The numerical results include modal frequencies, deflection response spectral densities and interior noise sound pressure levels.

Estimating the decomposition of predictive information in multivariate systems

NASA Astrophysics Data System (ADS)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Robust-mode analysis of hydrodynamic flows

NASA Astrophysics Data System (ADS)

Roy, Sukesh; Gord, James R.; Hua, Jia-Chen; Gunaratne, Gemunu H.

2017-04-01

The emergence of techniques to extract high-frequency high-resolution data introduces a new avenue for modal decomposition to assess the underlying dynamics, especially of complex flows. However, this task requires the differentiation of robust, repeatable flow constituents from noise and other irregular features of a flow. Traditional approaches involving low-pass filtering and principle components analysis have shortcomings. The approach outlined here, referred to as robust-mode analysis, is based on Koopman decomposition. Three applications to (a) a counter-rotating cellular flame state, (b) variations in financial markets, and (c) turbulent injector flows are provided.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Simulation of multivariate stationary stochastic processes using dimension-reduction representation methods

NASA Astrophysics Data System (ADS)

Liu, Zhangjun; Liu, Zenghui; Peng, Yongbo

2018-03-01

In view of the Fourier-Stieltjes integral formula of multivariate stationary stochastic processes, a unified formulation accommodating spectral representation method (SRM) and proper orthogonal decomposition (POD) is deduced. By introducing random functions as constraints correlating the orthogonal random variables involved in the unified formulation, the dimension-reduction spectral representation method (DR-SRM) and the dimension-reduction proper orthogonal decomposition (DR-POD) are addressed. The proposed schemes are capable of representing the multivariate stationary stochastic process with a few elementary random variables, bypassing the challenges of high-dimensional random variables inherent in the conventional Monte Carlo methods. In order to accelerate the numerical simulation, the technique of Fast Fourier Transform (FFT) is integrated with the proposed schemes. For illustrative purposes, the simulation of horizontal wind velocity field along the deck of a large-span bridge is proceeded using the proposed methods containing 2 and 3 elementary random variables. Numerical simulation reveals the usefulness of the dimension-reduction representation methods.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

MMPI Modal Profiles in a Juvenile Delinquent Population.

ERIC Educational Resources Information Center

Pickett, Lawrence K., Jr.

1981-01-01

The MMPI results obtained from 245 adolescent males referred to the evaluation unit of a Juvenile Court were submitted to a multivariate classification system. By correlating individual subject profiles with the modal profiles, six membership groups were formed. No relationship was found between group membership and age or race. (Author)

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

NASA Astrophysics Data System (ADS)

Özdemir, Gizem; Demiralp, Metin

2015-12-01

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

System identification of timber masonry walls using shaking table test

NASA Astrophysics Data System (ADS)

Roy, Timir B.; Guerreiro, Luis; Bagchi, Ashutosh

2017-04-01

Dynamic study is important in order to design, repair and rehabilitation of structures. It has played an important role in the behavior characterization of structures; such as: bridges, dams, high rise buildings etc. There had been substantial development in this area over the last few decades, especially in the field of dynamic identification techniques of structural systems. Frequency Domain Decomposition (FDD) and Time Domain Decomposition are most commonly used methods to identify modal parameters; such as: natural frequency, modal damping and mode shape. The focus of the present research is to study the dynamic characteristics of typical timber masonry walls commonly used in Portugal. For that purpose, a multi-storey structural prototype of such wall has been tested on a seismic shake table at the National Laboratory for Civil Engineering, Portugal (LNEC). Signal processing has been performed of the output response, which is collected from the shaking table experiment of the prototype using accelerometers. In the present work signal processing of the output response, based on the input response has been done in two ways: FDD and Stochastic Subspace Identification (SSI). In order to estimate the values of the modal parameters, algorithms for FDD are formulated and parametric functions for the SSI are computed. Finally, estimated values from both the methods are compared to measure the accuracy of both the techniques.

Information Graphic Classification, Decomposition and Alternative Representation

ERIC Educational Resources Information Center

Gao, Jinglun

2012-01-01

This thesis work is mainly focused on two problems related to improving accessibility of information graphics for visually impaired users. The first problem is automated analysis of information graphics for information extraction and the second problem is multi-modal representations for accessibility. Information graphics are graphical…

Multi-variants synthesis of Petri nets for FPGA devices

NASA Astrophysics Data System (ADS)

Bukowiec, Arkadiusz; Doligalski, Michał

2015-09-01

There is presented new method of synthesis of application specific logic controllers for FPGA devices. The specification of control algorithm is made with use of control interpreted Petri net (PT type). It allows specifying parallel processes in easy way. The Petri net is decomposed into state-machine type subnets. In this case, each subnet represents one parallel process. For this purpose there are applied algorithms of coloring of Petri nets. There are presented two approaches of such decomposition: with doublers of macroplaces or with one global wait place. Next, subnets are implemented into two-level logic circuit of the controller. The levels of logic circuit are obtained as a result of its architectural decomposition. The first level combinational circuit is responsible for generation of next places and second level decoder is responsible for generation output symbols. There are worked out two variants of such circuits: with one shared operational memory or with many flexible distributed memories as a decoder. Variants of Petri net decomposition and structures of logic circuits can be combined together without any restrictions. It leads to existence of four variants of multi-variants synthesis.

Vibration fatigue using modal decomposition

NASA Astrophysics Data System (ADS)

Mršnik, Matjaž; Slavič, Janko; Boltežar, Miha

2018-01-01

Vibration-fatigue analysis deals with the material fatigue of flexible structures operating close to natural frequencies. Based on the uniaxial stress response, calculated in the frequency domain, the high-cycle fatigue model using the S-N curve material data and the Palmgren-Miner hypothesis of damage accumulation is applied. The multiaxial criterion is used to obtain the equivalent uniaxial stress response followed by the spectral moment approach to the cycle-amplitude probability density estimation. The vibration-fatigue analysis relates the fatigue analysis in the frequency domain to the structural dynamics. However, once the stress response within a node is obtained, the physical model of the structure dictating that response is discarded and does not propagate through the fatigue-analysis procedure. The structural model can be used to evaluate how specific dynamic properties (e.g., damping, modal shapes) affect the damage intensity. A new approach based on modal decomposition is presented in this research that directly links the fatigue-damage intensity with the dynamic properties of the system. It thus offers a valuable insight into how different modes of vibration contribute to the total damage to the material. A numerical study was performed showing good agreement between results obtained using the newly presented approach with those obtained using the classical method, especially with regards to the distribution of damage intensity and critical point location. The presented approach also offers orders of magnitude faster calculation in comparison with the conventional procedure. Furthermore, it can be applied in a straightforward way to strain experimental modal analysis results, taking advantage of experimentally measured strains.

Linearised dynamics and non-modal instability analysis of an impinging under-expanded supersonic jet

NASA Astrophysics Data System (ADS)

Karami, Shahram; Stegeman, Paul C.; Theofilis, Vassilis; Schmid, Peter J.; Soria, Julio

2018-04-01

Non-modal instability analysis of the shear layer near the nozzle of a supersonic under-expanded impinging jet is studied. The shear layer instability is considered to be one of the main components of the feedback loop in supersonic jets. The feedback loop is observed in instantaneous visualisations of the density field where it is noted that acoustic waves scattered by the nozzle lip internalise as shear layer instabilities. A modal analysis describes the asymptotic limit of the instability disturbances and fails to capture short-time responses. Therefore, a non-modal analysis which allows the quantitative description of the short-time amplification or decay of a disturbance is performed by means of a local far-field pressure pulse. An impulse response analysis is performed which allows a wide range of frequencies to be excited. The temporal and spatial growths of the disturbances in the shear layer near the nozzle are studied by decomposing the response using dynamic mode decomposition and Hilbert transform analysis. The short-time response shows that disturbances with non-dimensionalised temporal frequencies in the range of 1 to 4 have positive growth rates in the shear layer. The Hilbert transform analysis shows that high non-dimensionalised temporal frequencies (>4) are dampened immediately, whereas low non-dimensionalised temporal frequencies (<1) are neutral. Both dynamic mode decomposition and Hilbert transform analysis show that spatial frequencies between 1 and 3 have positive spatial growth rates. Finally, the envelope of the streamwise velocity disturbances reveals the presence of a convective instability.

Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification.

PubMed

Fiave, Prosper Agbesi; Sharma, Saloni; Jastorff, Jan; Nelissen, Koen

2018-05-19

Mirror neurons are generally described as a neural substrate hosting shared representations of actions, by simulating or 'mirroring' the actions of others onto the observer's own motor system. Since single neuron recordings are rarely feasible in humans, it has been argued that cross-modal multi-variate pattern analysis (MVPA) of non-invasive fMRI data is a suitable technique to investigate common coding of observed and executed actions, allowing researchers to infer the presence of mirror neurons in the human brain. In an effort to close the gap between monkey electrophysiology and human fMRI data with respect to the mirror neuron system, here we tested this proposal for the first time in the monkey. Rhesus monkeys either performed reach-and-grasp or reach-and-touch motor acts with their right hand in the dark or observed videos of human actors performing similar motor acts. Unimodal decoding showed that both executed or observed motor acts could be decoded from numerous brain regions. Specific portions of rostral parietal, premotor and motor cortices, previously shown to house mirror neurons, in addition to somatosensory regions, yielded significant asymmetric action-specific cross-modal decoding. These results validate the use of cross-modal multi-variate fMRI analyses to probe the representations of own and others' actions in the primate brain and support the proposed mapping of others' actions onto the observer's own motor cortices. Copyright © 2018 Elsevier Inc. All rights reserved.

Morphological Decomposition and Semantic Integration in Word Processing

ERIC Educational Resources Information Center

Meunier, Fanny; Longtin, Catherine-Marie

2007-01-01

In the present study, we looked at cross-modal priming effects produced by auditory presentation of morphologically complex pseudowords in order to investigate semantic integration during the processing of French morphologically complex items. In Experiment 1, we used as primes pseudowords consisting of a non-interpretable combination of roots and…

The pattern of choosing dialysis modality and related mortality outcomes in Korea: a national population-based study

PubMed Central

Kim, Hyung Jong; Park, Jung Tak; Han, Seung Hyeok; Yoo, Tae-Hyun; Park, Hyeong-Cheon; Kang, Shin-Wook; Kim, Kyoung Hoon; Ryu, Dong-Ryeol; Kim, Hyunwook

2017-01-01

Background/Aims Since comorbidities are major determinants of modality choice, and also interact with dialysis modality on mortality outcomes, we examined the pattern of modality choice according to comorbidities and then evaluated how such choices affected mortality in incident dialysis patients. Methods We analyzed 32,280 incident dialysis patients in Korea. Patterns in initial dialysis choice were assessed by multivariate logistic regression analyses. Multivariate Poisson regression analyses were performed to evaluate the effects of interactions between comorbidities and dialysis modality on mortality and to quantify these interactions using the synergy factor. Results Prior histories of myocardial infarction (p = 0.031), diabetes (p = 0.001), and congestive heart failure (p = 0.003) were independent factors favoring the initiation with peritoneal dialysis (PD), but were associated with increased mortality with PD. In contrast, a history of cerebrovascular disease and 1-year increase in age favored initiation with hemodialysis (HD) and were related to a survival benefit with HD (p < 0.001, both). While favoring initiation with HD, having Medical Aid (p = 0.001) and male gender (p = 0.047) were related to increased mortality with HD. Furthermore, although the severity of comorbidities did not inf luence dialysis modality choice, mortality in incident PD patients was significantly higher compared to that in HD patients as the severity of comorbidities increased (p for trend < 0.001). Conclusions Some comorbidities exerted independent effects on initial choice of dialysis modality, but this choice did not always lead to the best results. Further analyses of the pattern of choosing dialysis modality according to baseline comorbid conditions and related consequent mortality outcomes are needed. PMID:28651309

Evaluating the performance of distributed approaches for modal identification

NASA Astrophysics Data System (ADS)

Krishnan, Sriram S.; Sun, Zhuoxiong; Irfanoglu, Ayhan; Dyke, Shirley J.; Yan, Guirong

2011-04-01

In this paper two modal identification approaches appropriate for use in a distributed computing environment are applied to a full-scale, complex structure. The natural excitation technique (NExT) is used in conjunction with a condensed eigensystem realization algorithm (ERA), and the frequency domain decomposition with peak-picking (FDD-PP) are both applied to sensor data acquired from a 57.5-ft, 10 bay highway sign truss structure. Monte-Carlo simulations are performed on a numerical example to investigate the statistical properties and sensitivity to noise of the two distributed algorithms. Experimental results are provided and discussed.

Modal decomposition of turbulent supersonic cavity

NASA Astrophysics Data System (ADS)

Soni, R. K.; Arya, N.; De, A.

2018-06-01

Self-sustained oscillations in a Mach 3 supersonic cavity with a length-to-depth ratio of three are investigated using wall-modeled large eddy simulation methodology for ReD = 3.39× 105 . The unsteady data obtained through computation are utilized to investigate the spatial and temporal evolution of the flow field, especially the second invariant of the velocity tensor, while the phase-averaged data are analyzed over a feedback cycle to study the spatial structures. This analysis is accompanied by the proper orthogonal decomposition (POD) data, which reveals the presence of discrete vortices along the shear layer. The POD analysis is performed in both the spanwise and streamwise planes to extract the coherence in flow structures. Finally, dynamic mode decomposition is performed on the data sequence to obtain the dynamic information and deeper insight into the self-sustained mechanism.

Thermal decomposition of ammonium hexachloroosmate.

PubMed

Asanova, T I; Kantor, I; Asanov, I P; Korenev, S V; Yusenko, K V

2016-12-07

Structural changes of (NH 4 ) 2 [OsCl 6 ] occurring during thermal decomposition in a reduction atmosphere have been studied in situ using combined energy-dispersive X-ray absorption spectroscopy (ED-XAFS) and powder X-ray diffraction (PXRD). According to PXRD, (NH 4 ) 2 [OsCl 6 ] transforms directly to metallic Os without the formation of any crystalline intermediates but through a plateau where no reactions occur. XANES and EXAFS data by means of Multivariate Curve Resolution (MCR) analysis show that thermal decomposition occurs with the formation of an amorphous intermediate {OsCl 4 } x with a possible polymeric structure. Being revealed for the first time the intermediate was subjected to determine the local atomic structure around osmium. The thermal decomposition of hexachloroosmate is much more complex and occurs within a minimum two-step process, which has never been observed before.

A comparative study on book shelf structure based on different domain modal analysis

NASA Astrophysics Data System (ADS)

Sabamehr, Ardalan; Roy, Timir Baran; Bagchi, Ashutosh

2017-04-01

Structural Health Monitoring (SHM) based on the vibration of structures has been very attractive topic for researchers in different fields such as: civil, aeronautical and mechanical engineering. The aim of this paper is to compare three most common modal identification techniques such as Frequency Domain Decomposition (FDD), Stochastic Subspace Identification (SSI) and Continuous Wavelet Transform (CWT) to find modal properties (such as natural frequency, mode shape and damping ratio) of three story book shelf steel structure which was built in Concordia University Lab. The modified Complex Morlet wavelet have been selected for wavelet in order to use asymptotic signal rather than real one with variable bandwidth and wavelet central frequency. So, CWT is able to detect instantaneous modulus and phase by use of local maxima ridge detection.

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Cooper, J. E.; Wright, J. R.

1987-01-01

A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.

Application of reiteration of Hankel singular value decomposition in quality control

NASA Astrophysics Data System (ADS)

Staniszewski, Michał; Skorupa, Agnieszka; Boguszewicz, Łukasz; Michalczuk, Agnieszka; Wereszczyński, Kamil; Wicher, Magdalena; Konopka, Marek; Sokół, Maria; Polański, Andrzej

2017-07-01

Medical centres are obliged to store past medical records, including the results of quality assurance (QA) tests of the medical equipment, which is especially useful in checking reproducibility of medical devices and procedures. Analysis of multivariate time series is an important part of quality control of NMR data. In this work we proposean anomaly detection tool based on Reiteration of Hankel Singular Value Decomposition method. The presented method was compared with external software and authors obtained comparable results.

Adomian decomposition

NASA Astrophysics Data System (ADS)

Daftardar-Gejji, Varsha; Jafari, Hossein

2005-01-01

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

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Integrand reduction for two-loop scattering amplitudes through multivariate polynomial division

NASA Astrophysics Data System (ADS)

Mastrolia, Pierpaolo; Mirabella, Edoardo; Ossola, Giovanni; Peraro, Tiziano

2013-04-01

We describe the application of a novel approach for the reduction of scattering amplitudes, based on multivariate polynomial division, which we have recently presented. This technique yields the complete integrand decomposition for arbitrary amplitudes, regardless of the number of loops. It allows for the determination of the residue at any multiparticle cut, whose knowledge is a mandatory prerequisite for applying the integrand-reduction procedure. By using the division modulo Gröbner basis, we can derive a simple integrand recurrence relation that generates the multiparticle pole decomposition for integrands of arbitrary multiloop amplitudes. We apply the new reduction algorithm to the two-loop planar and nonplanar diagrams contributing to the five-point scattering amplitudes in N=4 super Yang-Mills and N=8 supergravity in four dimensions, whose numerator functions contain up to rank-two terms in the integration momenta. We determine all polynomial residues parametrizing the cuts of the corresponding topologies and subtopologies. We obtain the integral basis for the decomposition of each diagram from the polynomial form of the residues. Our approach is well suited for a seminumerical implementation, and its general mathematical properties provide an effective algorithm for the generalization of the integrand-reduction method to all orders in perturbation theory.

Kinetics of Thermal Decomposition of Ammonium Perchlorate by TG/DSC-MS-FTIR

NASA Astrophysics Data System (ADS)

Zhu, Yan-Li; Huang, Hao; Ren, Hui; Jiao, Qing-Jie

2014-01-01

The method of thermogravimetry/differential scanning calorimetry-mass spectrometry-Fourier transform infrared (TG/DSC-MS-FTIR) simultaneous analysis has been used to study thermal decomposition of ammonium perchlorate (AP). The processing of nonisothermal data at various heating rates was performed using NETZSCH Thermokinetics. The MS-FTIR spectra showed that N2O and NO2 were the main gaseous products of the thermal decomposition of AP, and there was a competition between the formation reaction of N2O and that of NO2 during the process with an iso-concentration point of N2O and NO2. The dependence of the activation energy calculated by Friedman's iso-conversional method on the degree of conversion indicated that the AP decomposition process can be divided into three stages, which are autocatalytic, low-temperature diffusion and high-temperature, stable-phase reaction. The corresponding kinetic parameters were determined by multivariate nonlinear regression and the mechanism of the AP decomposition process was proposed.

Characterization of an elastic target in a shallow water waveguide by decomposition of the time-reversal operator.

PubMed

Philippe, Franck D; Prada, Claire; de Rosny, Julien; Clorennec, Dominique; Minonzio, Jean-Gabriel; Fink, Mathias

2008-08-01

This paper reports the results of an investigation into extracting of the backscattered frequency signature of a target in a waveguide. Retrieving the target signature is difficult because it is blurred by waveguide reflections and modal interference. It is shown that the decomposition of the time-reversal operator method provides a solution to this problem. Using a modal theory, this paper shows that the first singular value associated with a target is proportional to the backscattering form function. It is linked to the waveguide geometry through a factor that weakly depends on frequency as long as the target is far from the boundaries. Using the same approach, the second singular value is shown to be proportional to the second derivative of the angular form function which is a relevant parameter for target identification. Within this framework the coupling between two targets is considered. Small scale experimental studies are performed in the 3.5 MHz frequency range for 3 mm spheres in a 28 mm deep and 570 mm long waveguide and confirm the theoretical results.

Modal Structures in flow past a cylinder

NASA Astrophysics Data System (ADS)

Murshed, Mohammad

2017-11-01

With the advent of data, there have been opportunities to apply formalism to detect patterns or simple relations. For instance, a phenomenon can be defined through a partial differential equation which may not be very useful right away, whereas a formula for the evolution of a primary variable may be interpreted quite easily. Having access to data is not enough to move on since doing advanced linear algebra can put strain on the way computations are being done. A canonical problem in the field of aerodynamics is the transient flow past a cylinder where the viscosity can be adjusted to set the Reynolds number (Re). We observe the effect of the critical Re on the certain modes of behavior in time scale. A 2D-velocity field works as an input to analyze the modal structure of the flow using the Proper Orthogonal Decomposition and Koopman Mode/Dynamic Mode Decomposition. This will enable prediction of the solution further in time (taking into account the dependence on Re) and help us evaluate and discuss the associated error in the mechanism.

Numerical predictions and experiments for optimizing hidden corrosion detection in aircraft structures using Lamb modes.

PubMed

Terrien, N; Royer, D; Lepoutre, F; Déom, A

2007-06-01

To increase the sensitivity of Lamb waves to hidden corrosion in aircraft structures, a preliminary step is to understand the phenomena governing this interaction. A hybrid model combining a finite element approach and a modal decomposition method is used to investigate the interaction of Lamb modes with corrosion pits. The finite element mesh is used to describe the region surrounding the corrosion pits while the modal decomposition method permits to determine the waves reflected and transmitted by the damaged area. Simulations make easier the interpretation of some parts of the measured waveform corresponding to superposition of waves diffracted by the corroded area. Numerical results permit to extract significant information from the transmitted waveform and thus to optimize the signal processing for the detection of corrosion at an early stage. Now, we are able to detect corrosion pits down to 80-mum depth distributed randomly on a square centimeter of an aluminum plate. Moreover, thickness variations present on aircraft structures can be discriminated from a slightly corroded area. Finally, using this experimental setup, aircraft structures have been tested.

A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

PubMed Central

Adali, Tülay; Yu, Qingbao; Calhoun, Vince D.

2011-01-01

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data. PMID:22108139

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

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

NASA Astrophysics Data System (ADS)

Ma, Zhisai; Liu, Li; Zhou, Sida; Naets, Frank; Heylen, Ward; Desmet, Wim

2017-03-01

The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the "frozen-time" assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stability-preserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system's stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

Scattering amplitudes from multivariate polynomial division

NASA Astrophysics Data System (ADS)

Mastrolia, Pierpaolo; Mirabella, Edoardo; Ossola, Giovanni; Peraro, Tiziano

2012-11-01

We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Gröbner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.

Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.

PubMed

Hemakom, Apit; Goverdovsky, Valentin; Looney, David; Mandic, Danilo P

2016-04-13

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain-computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate. © 2016 The Author(s).

Understanding the critical challenges of self-aligned octuple patterning

NASA Astrophysics Data System (ADS)

Yu, Ji; Xiao, Wei; Kang, Weiling; Chen, Yijian

2014-03-01

In this paper, we present a thorough investigation of self-aligned octuple patterning (SAOP) process characteristics, cost structure, integration challenges, and layout decomposition. The statistical characteristics of SAOP CD variations such as multi-modality are analyzed and contributions from various features to CDU and MTT (mean-to-target) budgets are estimated. The gap space is found to have the worst CDU+MTT performance and is used to determine the required overlay accuracy to ensure a satisfactory edge-placement yield of a cut process. Moreover, we propose a 5-mask positive-tone SAOP (pSAOP) process for memory FEOL patterning and a 3-mask negative-tone SAOP (nSAOP) process for logic BEOL patterning. The potential challenges of 2-D SAOP layout decomposition for BEOL applications are identified. Possible decomposition approaches are explored and the functionality of several developed algorithm is verified using 2-D layout examples from Open Cell Library.

Combining optimization methods with response spectra curve-fitting toward improved damping ratio estimation

NASA Astrophysics Data System (ADS)

Brewick, Patrick T.; Smyth, Andrew W.

2016-12-01

The authors have previously shown that many traditional approaches to operational modal analysis (OMA) struggle to properly identify the modal damping ratios for bridges under traffic loading due to the interference caused by the driving frequencies of the traffic loads. This paper presents a novel methodology for modal parameter estimation in OMA that overcomes the problems presented by driving frequencies and significantly improves the damping estimates. This methodology is based on finding the power spectral density (PSD) of a given modal coordinate, and then dividing the modal PSD into separate regions, left- and right-side spectra. The modal coordinates were found using a blind source separation (BSS) algorithm and a curve-fitting technique was developed that uses optimization to find the modal parameters that best fit each side spectra of the PSD. Specifically, a pattern-search optimization method was combined with a clustering analysis algorithm and together they were employed in a series of stages in order to improve the estimates of the modal damping ratios. This method was used to estimate the damping ratios from a simulated bridge model subjected to moving traffic loads. The results of this method were compared to other established OMA methods, such as Frequency Domain Decomposition (FDD) and BSS methods, and they were found to be more accurate and more reliable, even for modes that had their PSDs distorted or altered by driving frequencies.

[EMD Time-Frequency Analysis of Raman Spectrum and NIR].

PubMed

Zhao, Xiao-yu; Fang, Yi-ming; Tan, Feng; Tong, Liang; Zhai, Zhe

2016-02-01

This paper analyzes the Raman spectrum and Near Infrared Spectrum (NIR) with time-frequency method. The empirical mode decomposition spectrum becomes intrinsic mode functions, which the proportion calculation reveals the Raman spectral energy is uniform distributed in each component, while the NIR's low order intrinsic mode functions only undertakes fewer primary spectroscopic effective information. Both the real spectrum and numerical experiments show that the empirical mode decomposition (EMD) regard Raman spectrum as the amplitude-modulated signal, which possessed with high frequency adsorption property; and EMD regards NIR as the frequency-modulated signal, which could be preferably realized high frequency narrow-band demodulation during first-order intrinsic mode functions. The first-order intrinsic mode functions Hilbert transform reveals that during the period of empirical mode decomposes Raman spectrum, modal aliasing happened. Through further analysis of corn leaf's NIR in time-frequency domain, after EMD, the first and second orders components of low energy are cut off, and reconstruct spectral signal by using the remaining intrinsic mode functions, the root-mean-square error is 1.001 1, and the correlation coefficient is 0.981 3, both of these two indexes indicated higher accuracy in re-construction; the decomposition trend term indicates the absorbency is ascending along with the decreasing to wave length in the near-infrared light wave band; and the Hilbert transform of characteristic modal component displays, 657 cm⁻¹ is the specific frequency by the corn leaf stress spectrum, which could be regarded as characteristic frequency for identification.

On the physical significance of the Effective Independence method for sensor placement

NASA Astrophysics Data System (ADS)

Jiang, Yaoguang; Li, Dongsheng; Song, Gangbing

2017-05-01

Optimally deploy sparse sensors for better damage identification and structural health monitoring is always a challenging task. The Effective Independence(EI) is one of the most influential sensor placement method and to be discussed in the paper. Specifically, the effect of the different weighting coefficients on the maximization of the Fisher information matrix(FIM) and the physical significance of the re-orthogonalization of modal shapes through QR decomposition in the EI method are addressed. By analyzing the widely used EI method, we found that the absolute identification space put forward along with the EI method is preferable to ensuring the maximization of the FIM, instead of the original EI coefficient which was post-multiolied by a weighting matrix. That is, deleting the row with the minimum EI coefficient can’t achieve the objective of maximizing the trace of FIM as initially conceived. Furthermore, we observed that in the computation of EI method, the sum of each retained row in the absolute identification space is a constant in each iteration. This potential property can be revealed distinctively by the product of target mode and its transpose, and its form is similar to an alternative formula of the EI method through orthogonal-triangular(QR) decomposition previously proposed by the authors. With it, the physical significance of re-orthogonalization of modal shapes through QR decomposition in the computation of EI method can be obviously manifested from a new perspective. Finally, two simple examples are provided to demonstrate the above two observations.

Bi-orthogonality relations for fluid-filled elastic cylindrical shells: Theory, generalisations and application to construct tailored Green's matrices

NASA Astrophysics Data System (ADS)

Ledet, Lasse S.; Sorokin, Sergey V.

2018-03-01

The paper addresses the classical problem of time-harmonic forced vibrations of a fluid-filled cylindrical shell considered as a multi-modal waveguide carrying infinitely many waves. The forced vibration problem is solved using tailored Green's matrices formulated in terms of eigenfunction expansions. The formulation of Green's matrix is based on special (bi-)orthogonality relations between the eigenfunctions, which are derived here for the fluid-filled shell. Further, the relations are generalised to any multi-modal symmetric waveguide. Using the orthogonality relations the transcendental equation system is converted into algebraic modal equations that can be solved analytically. Upon formulation of Green's matrices the solution space is studied in terms of completeness and convergence (uniformity and rate). Special features and findings exposed only through this modal decomposition method are elaborated and the physical interpretation of the bi-orthogonality relation is discussed in relation to the total energy flow which leads to derivation of simplified equations for the energy flow components.

Nano- and micro-electromechanical switch dynamics

NASA Astrophysics Data System (ADS)

Pulskamp, Jeffrey S.; Proie, Robert M.; Polcawich, Ronald G.

2013-01-01

This paper reports theoretical analysis and experimental results on the dynamics of piezoelectric MEMS mechanical logic relays. The multiple degree of freedom analytical model, based on modal decomposition, utilizes modal parameters obtained from finite element analysis and an analytical model of piezoelectric actuation. The model accounts for exact device geometry, damping, drive waveform variables, and high electric field piezoelectric nonlinearity. The piezoelectrically excited modal force is calculated directly and provides insight into design optimization for switching speed. The model accurately predicts the propagation delay dependence on actuation voltage of mechanically distinct relay designs. The model explains the observed discrepancies in switching speed of these devices relative to single degree of freedom switching speed models and suggests the strong potential for improved switching speed performance in relays designed for mechanical logic and RF circuits through the exploitation of higher order vibrational modes.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Can we infer post mortem interval on the basis of decomposition rate? A case from a Portuguese cemetery.

PubMed

Ferreira, M Teresa; Cunha, Eugénia

2013-03-10

Post mortem interval estimation is crucial in forensic sciences for both positive identification and reconstruction of perimortem events. However, reliable dating of skeletonized remains poses a scientific challenge since human remains decomposition involves a set of complex and highly variable processes. Many of the difficulties in determining post mortem interval and/or the permanence of a body in a specific environment relates with the lack of systematic observations and research in human body decomposition modalities in different environments. In March 2006, in order to solve a problem of misidentification, a team of the South Branch of Portuguese National Institute of Legal Medicine carried out the exhumation of 25 identified individuals buried for almost five years in the same cemetery plot. Even though all individuals shared similar post mortem intervals, they presented different stages of decomposition. In order to analyze the post mortem factors associated with the different stages of decomposition displayed by the 25 exhumed individuals, the stages of decomposition were scored. Information regarding age at death and sex of the individuals were gathered and recorded as well as data in the cause of death and grave and coffin characteristics. Although the observed distinct decay stages may be explained by the burial conditions, namely by the micro taphonomic environments, individual endogenous factors also play an important role on differential decomposition as witnessed by the present case. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

PubMed

Ferreira, Fábio S; Pereira, João M S; Duarte, João V; Castelo-Branco, Miguel

2017-01-01

Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8

PubMed Central

Ferreira, Fábio S.; Pereira, João M.S.; Duarte, João V.; Castelo-Branco, Miguel

2017-01-01

Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Objective: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). Method: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately – using standard univariate VBM - and simultaneously, with multivariate analyses. Results: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. Conclusion: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities. PMID:28761571

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Spectral Diffusion: An Algorithm for Robust Material Decomposition of Spectral CT Data

PubMed Central

Clark, Darin P.; Badea, Cristian T.

2014-01-01

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piece-wise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg/mL), gold (0.9 mg/mL), and gadolinium (2.9 mg/mL) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen. PMID:25296173

Spectral diffusion: an algorithm for robust material decomposition of spectral CT data.

PubMed

Clark, Darin P; Badea, Cristian T

2014-11-07

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piecewise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg mL(-1)), gold (0.9 mg mL(-1)), and gadolinium (2.9 mg mL(-1)) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen.

Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation.

PubMed

He, Jingjing; Zhou, Yibin; Guan, Xuefei; Zhang, Wei; Zhang, Weifang; Liu, Yongming

2016-08-16

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

fMRI-based Multivariate Pattern Analyses Reveal Imagery Modality and Imagery Content Specific Representations in Primary Somatosensory, Motor and Auditory Cortices.

PubMed

de Borst, Aline W; de Gelder, Beatrice

2017-08-01

Previous studies have shown that the early visual cortex contains content-specific representations of stimuli during visual imagery, and that these representational patterns of imagery content have a perceptual basis. To date, there is little evidence for the presence of a similar organization in the auditory and tactile domains. Using fMRI-based multivariate pattern analyses we showed that primary somatosensory, auditory, motor, and visual cortices are discriminative for imagery of touch versus sound. In the somatosensory, motor and visual cortices the imagery modality discriminative patterns were similar to perception modality discriminative patterns, suggesting that top-down modulations in these regions rely on similar neural representations as bottom-up perceptual processes. Moreover, we found evidence for content-specific representations of the stimuli during auditory imagery in the primary somatosensory and primary motor cortices. Both the imagined emotions and the imagined identities of the auditory stimuli could be successfully classified in these regions. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Direct calculation of modal parameters from matrix orthogonal polynomials

NASA Astrophysics Data System (ADS)

El-Kafafy, Mahmoud; Guillaume, Patrick

2011-10-01

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

a 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.

A new method to extract modal parameters using output-only responses

NASA Astrophysics Data System (ADS)

Kim, Byeong Hwa; Stubbs, Norris; Park, Taehyo

2005-04-01

This work proposes a new output-only modal analysis method to extract mode shapes and natural frequencies of a structure. The proposed method is based on an approach with a single-degree-of-freedom in the time domain. For a set of given mode-isolated signals, the un-damped mode shapes are extracted utilizing the singular value decomposition of the output energy correlation matrix with respect to sensor locations. The natural frequencies are extracted from a noise-free signal that is projected on the estimated modal basis. The proposed method is particularly efficient when a high resolution of mode shape is essential. The accuracy of the method is numerically verified using a set of time histories that are simulated using a finite-element method. The feasibility and practicality of the method are verified using experimental data collected at the newly constructed King Storm Water Bridge in California, United States.

Achieving a high mode count in the exact electromagnetic simulation of diffractive optical elements.

PubMed

Junker, André; Brenner, Karl-Heinz

2018-03-01

The application of rigorous optical simulation algorithms, both in the modal as well as in the time domain, is known to be limited to the nano-optical scale due to severe computing time and memory constraints. This is true even for today's high-performance computers. To address this problem, we develop the fast rigorous iterative method (FRIM), an algorithm based on an iterative approach, which, under certain conditions, allows solving also large-size problems approximation free. We achieve this in the case of a modal representation by avoiding the computationally complex eigenmode decomposition. Thereby, the numerical cost is reduced from O(N 3 ) to O(N log N), enabling a simulation of structures like certain diffractive optical elements with a significantly higher mode count than presently possible. Apart from speed, another major advantage of the iterative FRIM over standard modal methods is the possibility to trade runtime against accuracy.

OMA analysis of a launcher under operational conditions with time-varying properties

NASA Astrophysics Data System (ADS)

Eugeni, M.; Coppotelli, G.; Mastroddi, F.; Gaudenzi, P.; Muller, S.; Troclet, B.

2018-05-01

The objective of this paper is the investigation of the capability of operational modal analysis approaches to deal with time-varying system in the low-frequency domain. Specifically, the problem of the identification of the dynamic properties of a launch vehicle, working under actual operative conditions, is studied. Two OMA methods are considered: the frequency-domain decomposition and the Hilbert transform method. It is demonstrated that both OMA approaches allow the time-tracking of modal parameters, namely, natural frequencies, damping ratios, and mode shapes, from the response accelerations only recorded during actual flight tests of a launcher characterized by a large mass variation due to fuel burning typical of the first phase of the flight.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

A modal analysis of flexible aircraft dynamics with handling qualities implications

NASA Technical Reports Server (NTRS)

Schmidt, D. K.

1983-01-01

A multivariable modal analysis technique is presented for evaluating flexible aircraft dynamics, focusing on meaningful vehicle responses to pilot inputs and atmospheric turbulence. Although modal analysis is the tool, vehicle time response is emphasized, and the analysis is performed on the linear, time-domain vehicle model. In evaluating previously obtained experimental pitch tracking data for a family of vehicle dynamic models, it is shown that flexible aeroelastic effects can significantly affect pitch attitude handling qualities. Consideration of the eigenvalues alone, of both rigid-body and aeroelastic modes, does not explain the simulation results. Modal analysis revealed, however, that although the lowest aeroelastic mode frequency was still three times greater than the short-period frequency, the rigid-body attitude response was dominated by this aeroelastic mode. This dominance was defined in terms of the relative magnitudes of the modal residues in selected vehicle responses.

Restricted Acoustic Modal Analysis Applied to Internal Combustor Spectra and Cross-Spectra Measurements

NASA Technical Reports Server (NTRS)

Miles, Jeffrey Hilton

2006-01-01

A treatment of the modal decomposition of the pressure field in a combustor as determined by two Kulite pressure measurements is developed herein. It is applied to a Pratt & Whitney PW4098 engine combustor over a range of operating conditions. For modes other than the plane wave the new part of the treatment is the assumption that there are distinct frequency bands in which the individual modes, including the plane wave mode, overlap such that if circumferential mode m and circumferential mode m-1 are present than circumferential mode m 2 is not. Consequently, in the analysis used herein at frequencies above the first cut-off mode frequency, only pairs of circumferential modes are individually present at each frequency. Consequently, this is a restricted modal analysis. A new result is that the successful use of the same modal span frequencies over a range of operating conditions for this particular engine suggests that the temperature, T, and the velocity, v, of the flow at each operating condition are related by c(sup 2)-v(sup 2) = a constant where c is the speed of sound.

Seeing touch is correlated with content-specific activity in primary somatosensory cortex.

PubMed

Meyer, Kaspar; Kaplan, Jonas T; Essex, Ryan; Damasio, Hanna; Damasio, Antonio

2011-09-01

There is increasing evidence to suggest that primary sensory cortices can become active in the absence of external stimulation in their respective modalities. This occurs, for example, when stimuli processed via one sensory modality imply features characteristic of a different modality; for instance, visual stimuli that imply touch have been observed to activate the primary somatosensory cortex (SI). In the present study, we addressed the question of whether such cross-modal activations are content specific. To this end, we investigated neural activity in the primary somatosensory cortex of subjects who observed human hands engaged in the haptic exploration of different everyday objects. Using multivariate pattern analysis of functional magnetic resonance imaging data, we were able to predict, based exclusively on the activity pattern in SI, which of several objects a subject saw being explored. Along with previous studies that found similar evidence for other modalities, our results suggest that primary sensory cortices represent information relevant for their modality even when this information enters the brain via a different sensory system.

Modal Identification of Tsing MA Bridge by Using Improved Eigensystem Realization Algorithm

NASA Astrophysics Data System (ADS)

QIN, Q.; LI, H. B.; QIAN, L. Z.; LAU, C.-K.

2001-10-01

This paper presents the results of research work on modal identification of Tsing Ma bridge ambient testing data by using an improved eigensystem realization algorithm. The testing was carried out before the bridge was open to traffic and after the completion of surfacing. Without traffic load, ambient excitations were much less intensive, and the bridge responses to such ambient excitation were also less intensive. Consequently, the bridge responses were significantly influenced by the random movement of heavy construction vehicles on the deck. To cut off noises in the testing data and make the ambient signals more stationary, the Chebyshev digital filter was used instead of the digital filter with a Hanning window. Random decrement (RD) functions were built to convert the ambient responses to free vibrations. An improved eigensystem realization algorithm was employed to improve the accuracy and the efficiency of modal identification. It uses cross-correlation functions ofRD functions to form the Hankel matrix instead of RD functions themselves and uses eigenvalue decomposition instead of singular value decomposition. The data for response accelerations were acquired group by group because of limited number of high-quality accelerometers and channels of data loggers available. The modes were identified group by group and then assembled by using response accelerations acquired at reference points to form modes of the complete bridge. Seventy-nine modes of the Tsing Ma bridge were identified, including five complex modes formed in accordance with unevenly distributed damping in the bridge. The identified modes in time domain were then compared with those identified in frequency domain and finite element analytical results.

Operational modal analysis using SVD of power spectral density transmissibility matrices

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Laier, Jose Elias

2014-05-01

This paper proposes the singular value decomposition of power spectrum density transmissibility matrices with different references, (PSDTM-SVD), as an identification method of natural frequencies and mode shapes of a dynamic system subjected to excitations under operational conditions. At the system poles, the rows of the proposed transmissibility matrix converge to the same ratio of amplitudes of vibration modes. As a result, the matrices are linearly dependent on the columns, and their singular values converge to zero. Singular values are used to determine the natural frequencies, and the first left singular vectors are used to estimate mode shapes. A numerical example of the finite element model of a beam subjected to colored noise excitation is analyzed to illustrate the accuracy of the proposed method. Results of the PSDTM-SVD method in the numerical example are compared with obtained using frequency domain decomposition (FDD) and power spectrum density transmissibility (PSDT). It is demonstrated that the proposed method does not depend on the excitation characteristics contrary to the FDD method that assumes white noise excitation, and further reduces the risk to identify extra non-physical poles in comparison to the PSDT method. Furthermore, a case study is performed using data from an operational vibration test of a bridge with a simply supported beam system. The real application of a full-sized bridge has shown that the proposed PSDTM-SVD method is able to identify the operational modal parameter. Operational modal parameters identified by the PSDTM-SVD in the real application agree well those identified by the FDD and PSDT methods.

Modal Analysis Using the Singular Value Decomposition and Rational Fraction Polynomials

DTIC Science & Technology

2017-04-06

information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...results. The programs are designed for experimental datasets with multiple drive and response points and have proven effective even for systems with... designed for experimental datasets with multiple drive and response points and have proven effective even for systems with numerous closely-spaced

A TV-constrained decomposition method for spectral CT

NASA Astrophysics Data System (ADS)

Guo, Xiaoyue; Zhang, Li; Xing, Yuxiang

2017-03-01

Spectral CT is attracting more and more attention in medicine, industrial nondestructive testing and security inspection field. Material decomposition is an important issue to a spectral CT to discriminate materials. Because of the spectrum overlap of energy channels, as well as the correlation of basis functions, it is well acknowledged that decomposition step in spectral CT imaging causes noise amplification and artifacts in component coefficient images. In this work, we propose materials decomposition via an optimization method to improve the quality of decomposed coefficient images. On the basis of general optimization problem, total variance minimization is constrained on coefficient images in our overall objective function with adjustable weights. We solve this constrained optimization problem under the framework of ADMM. Validation on both a numerical dental phantom in simulation and a real phantom of pig leg on a practical CT system using dual-energy imaging is executed. Both numerical and physical experiments give visually obvious better reconstructions than a general direct inverse method. SNR and SSIM are adopted to quantitatively evaluate the image quality of decomposed component coefficients. All results demonstrate that the TV-constrained decomposition method performs well in reducing noise without losing spatial resolution so that improving the image quality. The method can be easily incorporated into different types of spectral imaging modalities, as well as for cases with energy channels more than two.

Output-only modal dynamic identification of frames by a refined FDD algorithm at seismic input and high damping

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Ferrari, Rosalba; Rizzi, Egidio

2016-02-01

The present paper deals with the seismic modal dynamic identification of frame structures by a refined Frequency Domain Decomposition (rFDD) algorithm, autonomously formulated and implemented within MATLAB. First, the output-only identification technique is outlined analytically and then employed to characterize all modal properties. Synthetic response signals generated prior to the dynamic identification are adopted as input channels, in view of assessing a necessary condition for the procedure's efficiency. Initially, the algorithm is verified on canonical input from random excitation. Then, modal identification has been attempted successfully at given seismic input, taken as base excitation, including both strong motion data and single and multiple input ground motions. Rather than different attempts investigating the role of seismic response signals in the Time Domain, this paper considers the identification analysis in the Frequency Domain. Results turn-out very much consistent with the target values, with quite limited errors in the modal estimates, including for the damping ratios, ranging from values in the order of 1% to 10%. Either seismic excitation and high values of damping, resulting critical also in case of well-spaced modes, shall not fulfill traditional FFD assumptions: this shows the consistency of the developed algorithm. Through original strategies and arrangements, the paper shows that a comprehensive rFDD modal dynamic identification of frames at seismic input is feasible, also at concomitant high damping.

Design and implementation of a novel modal space active force control concept for spatial multi-DOF parallel robotic manipulators actuated by electrical actuators.

PubMed

Yang, Chifu; Zhao, Jinsong; Li, Liyi; Agrawal, Sunil K

2018-01-01

Robotic spine brace based on parallel-actuated robotic system is a new device for treatment and sensing of scoliosis, however, the strong dynamic coupling and anisotropy problem of parallel manipulators result in accuracy loss of rehabilitation force control, including big error in direction and value of force. A novel active force control strategy named modal space force control is proposed to solve these problems. Considering the electrical driven system and contact environment, the mathematical model of spatial parallel manipulator is built. The strong dynamic coupling problem in force field is described via experiments as well as the anisotropy problem of work space of parallel manipulators. The effects of dynamic coupling on control design and performances are discussed, and the influences of anisotropy on accuracy are also addressed. With mass/inertia matrix and stiffness matrix of parallel manipulators, a modal matrix can be calculated by using eigenvalue decomposition. Making use of the orthogonality of modal matrix with mass matrix of parallel manipulators, the strong coupled dynamic equations expressed in work space or joint space of parallel manipulator may be transformed into decoupled equations formulated in modal space. According to this property, each force control channel is independent of others in the modal space, thus we proposed modal space force control concept which means the force controller is designed in modal space. A modal space active force control is designed and implemented with only a simple PID controller employed as exampled control method to show the differences, uniqueness, and benefits of modal space force control. Simulation and experimental results show that the proposed modal space force control concept can effectively overcome the effects of the strong dynamic coupling and anisotropy problem in the physical space, and modal space force control is thus a very useful control framework, which is better than the current joint space control and work space control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Modal control theory and application to aircraft lateral handling qualities design

NASA Technical Reports Server (NTRS)

Srinathkumar, S.

1978-01-01

A multivariable synthesis procedure based on eigenvalue/eigenvector assignment is reviewed and is employed to develop a systematic design procedure to meet the lateral handling qualities design objectives of a fighter aircraft over a wide range of flight conditions. The closed loop modal characterization developed provides significant insight into the design process and plays a pivotal role in the synthesis of robust feedback systems. The simplicity of the synthesis algorithm yields an efficient computer aided interactive design tool for flight control system synthesis.

Comparison of Modal to Nodal Approaches for Wavefront Correction,

DTIC Science & Technology

1986-02-01

the influence function of the wavefront corrector. (Implicit here is the assumption that the influence function is the same for every node, which is...To implement a nodal correction, the wavefront to be corrected is -. .. decomposed using a basis which is determined by the nodal (actuator) influence ... function of the wavefront corrector. This decomposition results in a set of coefficients which correspond to the drive signal required at the

Multi-scale fluctuation analysis of precipitation in Beijing by Extreme-point Symmetric Mode Decomposition

NASA Astrophysics Data System (ADS)

Li, Jiqing; Duan, Zhipeng; Huang, Jing

2018-06-01

With the aggravation of the global climate change, the shortage of water resources in China is becoming more and more serious. Using reasonable methods to study changes in precipitation is very important for planning and management of water resources. Based on the time series of precipitation in Beijing from 1951 to 2015, the multi-scale features of precipitation are analyzed by the Extreme-point Symmetric Mode Decomposition (ESMD) method to forecast the precipitation shift. The results show that the precipitation series have periodic changes of 2.6, 4.3, 14 and 21.7 years, and the variance contribution rate of each modal component shows that the inter-annual variation dominates the precipitation in Beijing. It is predicted that precipitation in Beijing will continue to decrease in the near future.

A method to identify the main mode of machine tool under operating conditions

NASA Astrophysics Data System (ADS)

Wang, Daming; Pan, Yabing

2017-04-01

The identification of the modal parameters under experimental conditions is the most common procedure when solving the problem of machine tool structure vibration. However, the influence of each mode on the machine tool vibration in real working conditions remains unknown. In fact, the contributions each mode makes to the machine tool vibration during machining process are different. In this article, an active excitation modal analysis is applied to identify the modal parameters in operational condition, and the Operating Deflection Shapes (ODS) in frequencies of high level vibration that affect the quality of machining in real working conditions are obtained. Then, the ODS is decomposed by the mode shapes which are identified in operational conditions. So, the contributions each mode makes to machine tool vibration during machining process are got by decomposition coefficients. From the previous steps, we can find out the main modes which effect the machine tool more significantly in working conditions. This method was also verified to be effective by experiments.

Harmonic analysis of electrified railway based on improved HHT

NASA Astrophysics Data System (ADS)

Wang, Feng

2018-04-01

In this paper, the causes and harms of the current electric locomotive electrical system harmonics are firstly studied and analyzed. Based on the characteristics of the harmonics in the electrical system, the Hilbert-Huang transform method is introduced. Based on the in-depth analysis of the empirical mode decomposition method and the Hilbert transform method, the reasons and solutions to the endpoint effect and modal aliasing problem in the HHT method are explored. For the endpoint effect of HHT, this paper uses point-symmetric extension method to extend the collected data; In allusion to the modal aliasing problem, this paper uses the high frequency harmonic assistant method to preprocess the signal and gives the empirical formula of high frequency auxiliary harmonic. Finally, combining the suppression of HHT endpoint effect and modal aliasing problem, an improved HHT method is proposed and simulated by matlab. The simulation results show that the improved HHT is effective for the electric locomotive power supply system.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

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.

Sight and sound converge to form modality-invariant representations in temporo-parietal cortex

PubMed Central

Man, Kingson; Kaplan, Jonas T.; Damasio, Antonio; Meyer, Kaspar

2013-01-01

People can identify objects in the environment with remarkable accuracy, irrespective of the sensory modality they use to perceive them. This suggests that information from different sensory channels converges somewhere in the brain to form modality-invariant representations, i.e., representations that reflect an object independently of the modality through which it has been apprehended. In this functional magnetic resonance imaging study of human subjects, we first identified brain areas that responded to both visual and auditory stimuli and then used crossmodal multivariate pattern analysis to evaluate the neural representations in these regions for content-specificity (i.e., do different objects evoke different representations?) and modality-invariance (i.e., do the sight and the sound of the same object evoke a similar representation?). While several areas became activated in response to both auditory and visual stimulation, only the neural patterns recorded in a region around the posterior part of the superior temporal sulcus displayed both content-specificity and modality-invariance. This region thus appears to play an important role in our ability to recognize objects in our surroundings through multiple sensory channels and to process them at a supra-modal (i.e., conceptual) level. PMID:23175818

Permutation Tests of Hierarchical Cluster Analyses of Carrion Communities and Their Potential Use in Forensic Entomology.

PubMed

van der Ham, Joris L

2016-05-19

Forensic entomologists can use carrion communities' ecological succession data to estimate the postmortem interval (PMI). Permutation tests of hierarchical cluster analyses of these data provide a conceptual method to estimate part of the PMI, the post-colonization interval (post-CI). This multivariate approach produces a baseline of statistically distinct clusters that reflect changes in the carrion community composition during the decomposition process. Carrion community samples of unknown post-CIs are compared with these baseline clusters to estimate the post-CI. In this short communication, I use data from previously published studies to demonstrate the conceptual feasibility of this multivariate approach. Analyses of these data produce series of significantly distinct clusters, which represent carrion communities during 1- to 20-day periods of the decomposition process. For 33 carrion community samples, collected over an 11-day period, this approach correctly estimated the post-CI within an average range of 3.1 days. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Restricted Modal Analysis Applied to Internal Annular Combustor Autospectra and Cross-Spectra Measurements

NASA Technical Reports Server (NTRS)

Miles, Jeffrey Hilton

2007-01-01

A treatment of the modal decomposition of the pressure field in a combustor as determined by two pressure time history measurements is developed herein. It is applied to a Pratt and Whitney PW4098 engine combustor over a range of operating conditions. For modes other than the plane wave the assumption is made that there are distinct frequency bands in which the individual modes, including the plane wave mode, overlap such that if circumferential mode m and circumferential mode m-1 are present then circumferential mode m-2 is not. In the analysis used herein at frequencies above the first cutoff mode frequency, only pairs of circumferential modes are individually present at each frequency. Consequently, this is a restricted modal analysis. As part of the analysis one specifies mode cut-on frequencies. This creates a set of frequencies that each mode spans. One finding was the successful use of the same modal span frequencies over a range of operating conditions for this particular engine. This suggests that for this case the cut-on frequencies are in proximity at each operating condition. Consequently, the combustion noise spectrum related to the circumferential modes might not change much with operating condition.

Interactive natural language acquisition in a multi-modal recurrent neural architecture

NASA Astrophysics Data System (ADS)

Heinrich, Stefan; Wermter, Stefan

2018-01-01

For the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural network, where parts have different leakage characteristics and thus operate on multiple timescales for every modality and the association of the higher level nodes of all modalities into cell assemblies. The model is capable of learning language production grounded in both, temporal dynamic somatosensation and vision, and features hierarchical concept abstraction, concept decomposition, multi-modal integration, and self-organisation of latent representations.

Visual and acoustic communication in non-human animals: a comparison.

PubMed

Rosenthal, G G; Ryan, M J

2000-09-01

The visual and auditory systems are two major sensory modalities employed in communication. Although communication in these two sensory modalities can serve analogous functions and evolve in response to similar selection forces, the two systems also operate under different constraints imposed by the environment and the degree to which these sensory modalities are recruited for non-communication functions. Also, the research traditions in each tend to differ, with studies of mechanisms of acoustic communication tending to take a more reductionist tack often concentrating on single signal parameters, and studies of visual communication tending to be more concerned with multivariate signal arrays in natural environments and higher level processing of such signals. Each research tradition would benefit by being more expansive in its approach.

An explicit analytical solution for sound propagation in a three-dimensional penetrable wedge with small apex angle.

PubMed

Petrov, Pavel S; Sturm, Frédéric

2016-03-01

A problem of sound propagation in a shallow-water waveguide with a weakly sloping penetrable bottom is considered. The adiabatic mode parabolic equations are used to approximate the solution of the three-dimensional (3D) Helmholtz equation by modal decomposition of the acoustic pressure field. The mode amplitudes satisfy parabolic equations that admit analytical solutions in the special case of the 3D wedge. Using the analytical formula for modal amplitudes, an explicit and remarkably simple expression for the acoustic pressure in the wedge is obtained. The proposed solution is validated by the comparison with a solution of the 3D penetrable wedge problem obtained using a fully 3D parabolic equation that includes a leading-order cross term correction.

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Is There Disparity in Physician Service Use? A Comparison of Hispanic and White Medicare Beneficiaries

ERIC Educational Resources Information Center

Chen, Li-Mei

2010-01-01

This article investigates general physician service use by a national sample of non-Hispanic white and Hispanic Medicare beneficiaries age 65 and older. Using the health behavior model as the conceptual framework, Oaxaca decomposition multivariate analyses were conducted to examine predictors for contact with a physician and the number of…

Exact nonlinear model reduction for a von Kármán beam: Slow-fast decomposition and spectral submanifolds

NASA Astrophysics Data System (ADS)

Jain, Shobhit; Tiso, Paolo; Haller, George

2018-06-01

We apply two recently formulated mathematical techniques, Slow-Fast Decomposition (SFD) and Spectral Submanifold (SSM) reduction, to a von Kármán beam with geometric nonlinearities and viscoelastic damping. SFD identifies a global slow manifold in the full system which attracts solutions at rates faster than typical rates within the manifold. An SSM, the smoothest nonlinear continuation of a linear modal subspace, is then used to further reduce the beam equations within the slow manifold. This two-stage, mathematically exact procedure results in a drastic reduction of the finite-element beam model to a one-degree-of freedom nonlinear oscillator. We also introduce the technique of spectral quotient analysis, which gives the number of modes relevant for reduction as output rather than input to the reduction process.

Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices

NASA Astrophysics Data System (ADS)

Finn, Conor; Lizier, Joseph

2018-04-01

What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.

Extended FDD-WT method based on correcting the errors due to non-synchronous sensing of sensors

NASA Astrophysics Data System (ADS)

Tarinejad, Reza; Damadipour, Majid

2016-05-01

In this research, a combinational non-parametric method called frequency domain decomposition-wavelet transform (FDD-WT) that was recently presented by the authors, is extended for correction of the errors resulting from asynchronous sensing of sensors, in order to extend the application of the algorithm for different kinds of structures, especially for huge structures. Therefore, the analysis process is based on time-frequency domain decomposition and is performed with emphasis on correcting time delays between sensors. Time delay estimation (TDE) methods are investigated for their efficiency and accuracy for noisy environmental records and the Phase Transform - β (PHAT-β) technique was selected as an appropriate method to modify the operation of traditional FDD-WT in order to achieve the exact results. In this paper, a theoretical example (3DOF system) has been provided in order to indicate the non-synchronous sensing effects of the sensors on the modal parameters; moreover, the Pacoima dam subjected to 13 Jan 2001 earthquake excitation was selected as a case study. The modal parameters of the dam obtained from the extended FDD-WT method were compared with the output of the classical signal processing method, which is referred to as 4-Spectral method, as well as other literatures relating to the dynamic characteristics of Pacoima dam. The results comparison indicates that values are correct and reliable.

Dual-modal cancer detection based on optical pH sensing and Raman spectroscopy

NASA Astrophysics Data System (ADS)

Kim, Soogeun; Lee, Seung Ho; Min, Sun Young; Byun, Kyung Min; Lee, Soo Yeol

2017-10-01

A dual-modal approach using Raman spectroscopy and optical pH sensing was investigated to discriminate between normal and cancerous tissues. Raman spectroscopy has demonstrated the potential for in vivo cancer detection. However, Raman spectroscopy has suffered from strong fluorescence background of biological samples and subtle spectral differences between normal and disease tissues. To overcome those issues, pH sensing is adopted to Raman spectroscopy as a dual-modal approach. Based on the fact that the pH level in cancerous tissues is lower than that in normal tissues due to insufficient vasculature formation, the dual-modal approach combining the chemical information of Raman spectrum and the metabolic information of pH level can improve the specificity of cancer diagnosis. From human breast tissue samples, Raman spectra and pH levels are measured using fiber-optic-based Raman and pH probes, respectively. The pH sensing is based on the dependence of pH level on optical transmission spectrum. Multivariate statistical analysis is performed to evaluate the classification capability of the dual-modal method. The analytical results show that the dual-modal method based on Raman spectroscopy and optical pH sensing can improve the performance of cancer classification.

Three-way parallel independent component analysis for imaging genetics using multi-objective optimization.

PubMed

Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios

2014-01-01

In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

The Multivariate Structure of Communication Avoidance.

ERIC Educational Resources Information Center

Bell, Robert A.

1986-01-01

Clarifies the nature of communication avoidance through a structural analysis grounded in facet theory. Presents evidence for a duplex model of avoidance in which theoretical distinctions among modalities of approach-avoidance and context proved empirically relevant. Discusses implications of these findings for the explication, treatment, and…

Tailored multivariate analysis for modulated enhanced diffraction

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

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

A Longitudinal Study on Human Outdoor Decomposition in Central Texas.

PubMed

Suckling, Joanna K; Spradley, M Katherine; Godde, Kanya

2016-01-01

The development of a methodology that estimates the postmortem interval (PMI) from stages of decomposition is a goal for which forensic practitioners strive. A proposed equation (Megyesi et al. 2005) that utilizes total body score (TBS) and accumulated degree days (ADD) was tested using longitudinal data collected from human remains donated to the Forensic Anthropology Research Facility (FARF) at Texas State University-San Marcos. Exact binomial tests examined the rate of the equation to successfully predict ADD. Statistically significant differences were found between ADD estimated by the equation and the observed value for decomposition stage. Differences remained significant after carnivore scavenged donations were removed from analysis. Low success rates for the equation to predict ADD from TBS and the wide standard errors demonstrate the need to re-evaluate the use of this equation and methodology for PMI estimation in different environments; rather, multivariate methods and equations should be derived that are environmentally specific. © 2015 American Academy of Forensic Sciences.

The interactive effect of the degradation of cotton clothing and decomposition fluid production associated with decaying remains.

PubMed

Ueland, Maiken; Nizio, Katie D; Forbes, Shari L; Stuart, Barbara H

2015-10-01

Textiles are a commonly encountered source of evidence in forensic cases. In the past, most research has been focused on how textiles affect the decomposition process while little attention has been paid to how the decomposition products interact with the textiles. While some studies have shown that the presence of remains will have an effect on the degradation of clothing associated with a decaying body, very little work has been carried out on the specific mechanisms that prevent or delay textile degradation when in contact with decomposing remains. In order to investigate the effect of decomposition fluid on textile degradation, three clothed domestic pig (Sus scrofa domesticus) carcasses were placed on a soil surface, textile specimens were collected over a period of a year and were then analysed using ATR-FTIR spectroscopy and GC-MS. Multivariate statistical analysis was used to analyse the data. Cotton specimens not associated with remains degraded markedly, whereas the samples exposed to decomposition fluids remained relatively intact over the same time frame. An investigation of the decomposition by-products found that the protein-related bands remained stable and unchanged throughout the experiment. Lipid components, on the other hand, demonstrated a significant change; this was confirmed with the use of both ATR-FTIR spectroscopy and GC-MS. Through an advanced statistical approach, information about the decomposition by-products and their characteristics was obtained. There is potential that the lipid profile in a textile specimen could be a valuable tool used in the examination of clothing located at a crime scene. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Flexible multibody simulation of automotive systems with non-modal model reduction techniques

NASA Astrophysics Data System (ADS)

Shiiba, Taichi; Fehr, Jörg; Eberhard, Peter

2012-12-01

The stiffness of the body structure of an automobile has a strong relationship with its noise, vibration, and harshness (NVH) characteristics. In this paper, the effect of the stiffness of the body structure upon ride quality is discussed with flexible multibody dynamics. In flexible multibody simulation, the local elastic deformation of the vehicle has been described traditionally with modal shape functions. Recently, linear model reduction techniques from system dynamics and mathematics came into the focus to find more sophisticated elastic shape functions. In this work, the NVH-relevant states of a racing kart are simulated, whereas the elastic shape functions are calculated with modern model reduction techniques like moment matching by projection on Krylov-subspaces, singular value decomposition-based reduction techniques, and combinations of those. The whole elastic multibody vehicle model consisting of tyres, steering, axle, etc. is considered, and an excitation with a vibration characteristics in a wide frequency range is evaluated in this paper. The accuracy and the calculation performance of those modern model reduction techniques is investigated including a comparison of the modal reduction approach.

Nonlinear Reduced-Order Analysis with Time-Varying Spatial Loading Distributions

NASA Technical Reports Server (NTRS)

Prezekop, Adam

2008-01-01

Oscillating shocks acting in combination with high-intensity acoustic loadings present a challenge to the design of resilient hypersonic flight vehicle structures. This paper addresses some features of this loading condition and certain aspects of a nonlinear reduced-order analysis with emphasis on system identification leading to formation of a robust modal basis. The nonlinear dynamic response of a composite structure subject to the simultaneous action of locally strong oscillating pressure gradients and high-intensity acoustic loadings is considered. The reduced-order analysis used in this work has been previously demonstrated to be both computationally efficient and accurate for time-invariant spatial loading distributions, provided that an appropriate modal basis is used. The challenge of the present study is to identify a suitable basis for loadings with time-varying spatial distributions. Using a proper orthogonal decomposition and modal expansion, it is shown that such a basis can be developed. The basis is made more robust by incrementally expanding it to account for changes in the location, frequency and span of the oscillating pressure gradient.

Automatic vibration mode selection and excitation; combining modal filtering with autoresonance

NASA Astrophysics Data System (ADS)

Davis, Solomon; Bucher, Izhak

2018-02-01

Autoresonance is a well-known nonlinear feedback method used for automatically exciting a system at its natural frequency. Though highly effective in exciting single degree of freedom systems, in its simplest form it lacks a mechanism for choosing the mode of excitation when more than one is present. In this case a single mode will be automatically excited, but this mode cannot be chosen or changed. In this paper a new method for automatically exciting a general second-order system at any desired natural frequency using Autoresonance is proposed. The article begins by deriving a concise expression for the frequency of the limit cycle induced by an Autoresonance feedback loop enclosed on the system. The expression is based on modal decomposition, and provides valuable insight into the behavior of a system controlled in this way. With this expression, a method for selecting and exciting a desired mode naturally follows by combining Autoresonance with Modal Filtering. By taking various linear combinations of the sensor signals, by orthogonality one can "filter out" all the unwanted modes effectively. The desired mode's natural frequency is then automatically reflected in the limit cycle. In experiment the technique has proven extremely robust, even if the amplitude of the desired mode is significantly smaller than the others and the modal filters are greatly inaccurate.

On the effects of multimodal information integration in multitasking.

PubMed

Stock, Ann-Kathrin; Gohil, Krutika; Huster, René J; Beste, Christian

2017-07-07

There have recently been considerable advances in our understanding of the neuronal mechanisms underlying multitasking, but the role of multimodal integration for this faculty has remained rather unclear. We examined this issue by comparing different modality combinations in a multitasking (stop-change) paradigm. In-depth neurophysiological analyses of event-related potentials (ERPs) were conducted to complement the obtained behavioral data. Specifically, we applied signal decomposition using second order blind identification (SOBI) to the multi-subject ERP data and source localization. We found that both general multimodal information integration and modality-specific aspects (potentially related to task difficulty) modulate behavioral performance and associated neurophysiological correlates. Simultaneous multimodal input generally increased early attentional processing of visual stimuli (i.e. P1 and N1 amplitudes) as well as measures of cognitive effort and conflict (i.e. central P3 amplitudes). Yet, tactile-visual input caused larger impairments in multitasking than audio-visual input. General aspects of multimodal information integration modulated the activity in the premotor cortex (BA 6) as well as different visual association areas concerned with the integration of visual information with input from other modalities (BA 19, BA 21, BA 37). On top of this, differences in the specific combination of modalities also affected performance and measures of conflict/effort originating in prefrontal regions (BA 6).

Identification of multi-modal plasma responses to applied magnetic perturbations using the plasma reluctance

DOE PAGES

Logan, Nikolas C.; Paz-Soldan, Carlos; Park, Jong-Kyu; ...

2016-05-03

Using the plasma reluctance, the Ideal Perturbed Equilibrium Code is able to efficiently identify the structure of multi-modal magnetic plasma response measurements and the corresponding impact on plasma performance in the DIII-D tokamak. Recent experiments demonstrated that multiple kink modes of comparable amplitudes can be driven by applied nonaxisymmetric fields with toroidal mode number n = 2. This multi-modal response is in good agreement with ideal magnetohydrodynamic models, but detailed decompositions presented here show that the mode structures are not fully described by either the least stable modes or the resonant plasma response. This paper identifies the measured response fieldsmore » as the first eigenmodes of the plasma reluctance, enabling clear diagnosis of the plasma modes and their impact on performance from external sensors. The reluctance shows, for example, how very stable modes compose a significant portion of the multi-modal plasma response field and that these stable modes drive significant resonant current. Finally, this work is an overview of the first experimental applications using the reluctance to interpret the measured response and relate it to multifaceted physics, aimed towards providing the foundation of understanding needed to optimize nonaxisymmetric fields for independent control of stability and transport.« less

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2017-12-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

NASA Astrophysics Data System (ADS)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.

Techniques to measure complex-plane fields

NASA Astrophysics Data System (ADS)

Dudley, Angela; Majola, Nombuso; Chetty, Naven; Forbes, Andrew

2014-10-01

In this work we construct coherent superpositions of Gaussian and vortex modes which can be described to occupy the complex-plane. We demonstrate how these fields can be experimentally constructed in a digital, controllable manner with a spatial light modulator. Once these fields have been generated we illustrate, with three separate techniques, how the constituent components of these fields can be extracted, namely by measuring the intensity of the field at two adjacent points; performing a modal decomposition and a new digital Stokes measurement.

Data-driven sensor placement from coherent fluid structures

NASA Astrophysics Data System (ADS)

Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

2017-11-01

Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.

pH-Responsive, Self-Sacrificial Nanotheranostic Agent for Potential In Vivo and In Vitro Dual Modal MRI/CT Imaging, Real-Time, and In Situ Monitoring of Cancer Therapy.

PubMed

Yue, Ludan; Wang, Jinlong; Dai, Zhichao; Hu, Zunfu; Chen, Xue; Qi, Yafei; Zheng, Xiuwen; Yu, Dexin

2017-02-15

Multifunctional nanotheranostic agents have been highly commended due to the application to image-guided cancer therapy. Herein, based on the chemically disordered face centered cubic (fcc) FePt nanoparticles (NPs) and graphene oxide (GO), we develop a pH-responsive FePt-based multifunctional theranostic agent for potential in vivo and in vitro dual modal MRI/CT imaging and in situ cancer inhibition. The fcc-FePt will release highly active Fe ions due to the low pH in tumor cells, which would catalyze H 2 O 2 decomposition into reactive oxygen species (ROS) within the cells and further induce cancer cell apoptosis. Conjugated with folic acid (FA), the iron platinum-dimercaptosuccinnic acid/PEGylated graphene oxide-folic acid (FePt-DMSA/GO-PEG-FA) composite nanoassemblies (FePt/GO CNs) could effectively target and show significant toxicity to FA receptor-positive tumor cells, but no obvious toxicity to FA receptor-negative normal cells, which was evaluated by WST-1 assay. The FePt-based multifunctional nanoparticles allow real-time monitoring of Fe release by T 2 -weighted MRI, and the selective contrast enhancement in CT could be estimated in vivo after injection. The results showed that FePt-based NPs displayed excellent biocompatibility and favorable MRI/CT imaging ability in vivo and in vitro. Meanwhile, the decomposition of FePt will dramatically decrease the T 2 -weighted MRI signal and increase the ROS signal, which enables real-time and in situ visualized monitoring of Fe release in tumor cells. In addition, the self-sacrificial decomposition of fcc-FePt will be propitious to the self-clearance of the as-prepared FePt-based nanocomposite in vivo. Therefore, the FePt/GO CNs could serve as a potential multifunctional theranostic nanoplatform of MRI/CT imaging guided cancer diagnosis and therapy in the clinic.

The increased use of computed tomography angiography and magnetic resonance angiography as the sole imaging modalities prior to infrainguinal bypass has had no effect on outcomes.

PubMed

Shue, Bing; Damle, Rachelle N; Flahive, Julie; Kalish, Jeffrey A; Stone, David H; Patel, Virendra I; Schanzer, Andres; Baril, Donald T

2015-08-01

Angiography remains the gold standard imaging modality before infrainguinal bypass. Computed tomography angiography (CTA) and magnetic resonance angiography (MRA) have emerged as noninvasive alternatives for preoperative imaging. We sought to examine contemporary trends in the utilization of CTA and MRA as isolated imaging modalities before infrainguinal bypass and to compare outcomes following infrainguinal bypass in patients who underwent CTA or MRA versus those who underwent conventional arteriography. Patients undergoing infrainguinal bypass within the Vascular Study Group of New England were identified (2003-2012). Patients were stratified by preoperative imaging modality: CTA/MRA alone or conventional angiography. Trends in utilization of these modalities were examined and demographics of these groups were compared. Primary end points included primary patency, secondary patency, and major adverse limb events (MALE) at 1 year as determined by Kaplan-Meier analysis. Multivariable Cox proportional hazards models were constructed to evaluate the effect of imaging modality on primary patency, secondary patency, and MALE after adjusting for confounders. In 3123 infrainguinal bypasses, CTA/MRA alone was used in 462 cases (15%) and angiography was used in 2661 cases (85%). Use of CTA/MRA alone increased over time, with 52 (11%) bypasses performed between 2003 and 2005, 189 (41%) bypasses performed between 2006 and 2009, and 221 (48%) bypasses performed between 2010 and 2012 (P < 0.001). Patients with CTA/MRA alone, compared with patients with angiography, more frequently underwent bypass for claudication (33% vs. 26%, P = 0.001) or acute limb ischemia (13% vs. 5%, P < 0.0001), more frequently had prosthetic conduits (39% vs. 30%, P = 0.001), and less frequently had tibial/pedal targets (32% vs. 40%, P = 0.002). After adjusting for these and other confounders, multivariable analysis demonstrated that the use of CTA/MRA alone was not associated with a significant difference in 1 year primary patency (hazard ratio [HR] 0.95, 95% confidence interval [CI] 0.78-1.16), secondary patency (HR 1.30, 95% CI 0.99-1.72), or MALE (HR 1.08, 95% CI 0.89-1.32). CTA and MRA are being increasingly used as the sole preoperative imaging modality before infrainguinal bypass. This shift in practice patterns appears to have no measurable effect on outcomes at 1 year. Copyright © 2015 Elsevier Inc. All rights reserved.

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG-fMRI recordings.

PubMed

Ji, Hong; Petro, Nathan M; Chen, Badong; Yuan, Zejian; Wang, Jianji; Zheng, Nanning; Keil, Andreas

2018-02-06

Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks: (1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady-state-visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase-reversing Gabor patches. We test the hypothesis that fluctuations in visuo-cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC-based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG-fMRI covariance. Furthermore xMCC converged with other extant methods for EEG-fMRI analysis. © 2018 The Authors Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

Synchronous Motions Across the Instrumental Climate Record

NASA Astrophysics Data System (ADS)

Carl, Peter

The Earth's climate system bears a rich variety of feedback mechanisms that may give rise to complex, evolving modal structures under internal and external control. Various types of synchronization may be identified in the system's motion when looking at representative time series of the instrumental period through the glasses of an advanced technique of sparse data approximation, the Matching Pursuit (MP) approach. To disentangle the emerging network of oscillatory modes to the degree that climate dynamics turns out to be separable, a large dictionary of "Gaussian logons," i.e. frequency modulated (FM) Gabor atoms, is applied. Though the extracted modes make up linear decompositions, this flexible analyzing signal matches highly nonlinear waveforms. Univariate analyses over the period 1870-1997 are presented of a set of customary time series in annual resolution, comprising global and regional climate, central European synoptic systems, German precipitation, and runoff of the Elbe river near Dresden. All the evidence from this first-generation MP-FM study, obtained in subsequent multivariate syntheses, points to dynamically excited regimes of an organized yet complex climate system under permanent change—perhaps a (pre)chaotic one at centennial timescales, suggesting a "chaos control" perspective on global climate dynamics and change. Findings and conclusions include, among others, internal structure of reconstructed insolation, the episodic nature of global warming as reflected in multidecadal temperature modes, their swarm of "interdomain" companions across the whole system that unveils an unknown regime character of interannual climate dynamics, and the apparent onset early in the 1990s of the present thermal stagnation.

Multilevel predictors of colorectal cancer testing modality among publicly and privately insured people turning 50.

PubMed

Wheeler, Stephanie B; Kuo, Tzy-Mey; Meyer, Anne Marie; Martens, Christa E; Hassmiller Lich, Kristen M; Tangka, Florence K L; Richardson, Lisa C; Hall, Ingrid J; Smith, Judith Lee; Mayorga, Maria E; Brown, Paul; Crutchfield, Trisha M; Pignone, Michael P

2017-06-01

Understanding multilevel predictors of colorectal cancer (CRC) screening test modality can help inform screening program design and implementation. We used North Carolina Medicare, Medicaid, and private, commercially available, health plan insurance claims data from 2003 to 2008 to ascertain CRC test modality among people who received CRC screening around their 50th birthday, when guidelines recommend that screening should commence for normal risk individuals. We ascertained receipt of colonoscopy, fecal occult blood test (FOBT) and fecal immunochemical test (FIT) from billing codes. Person-level and county-level contextual variables were included in multilevel random intercepts models to understand predictors of CRC test modality, stratified by insurance type. Of 12,570 publicly-insured persons turning 50 during the study period who received CRC testing, 57% received colonoscopy, whereas 43% received FOBT/FIT, with significant regional variation. In multivariable models, females with public insurance had lower odds of colonoscopy than males (odds ratio [OR] = 0.68; p < 0.05). Of 56,151 privately-insured persons turning 50 years old who received CRC testing, 42% received colonoscopy, whereas 58% received FOBT/FIT, with significant regional variation. In multivariable models, females with private insurance had lower odds of colonoscopy than males (OR = 0.43; p < 0.05). People living 10-15 miles away from endoscopy facilities also had lower odds of colonoscopy than those living within 5 miles (OR = 0.91; p < 0.05). Both colonoscopy and FOBT/FIT are widely used in North Carolina among insured persons newly age-eligible for screening. The high level of FOBT/FIT use among privately insured persons and women suggests that renewed emphasis on FOBT/FIT as a viable screening alternative to colonoscopy may be important.

Dual-modal cancer detection based on optical pH sensing and Raman spectroscopy.

PubMed

Kim, Soogeun; Lee, Seung Ho; Min, Sun Young; Byun, Kyung Min; Lee, Soo Yeol

2017-10-01

A dual-modal approach using Raman spectroscopy and optical pH sensing was investigated to discriminate between normal and cancerous tissues. Raman spectroscopy has demonstrated the potential for in vivo cancer detection. However, Raman spectroscopy has suffered from strong fluorescence background of biological samples and subtle spectral differences between normal and disease tissues. To overcome those issues, pH sensing is adopted to Raman spectroscopy as a dual-modal approach. Based on the fact that the pH level in cancerous tissues is lower than that in normal tissues due to insufficient vasculature formation, the dual-modal approach combining the chemical information of Raman spectrum and the metabolic information of pH level can improve the specificity of cancer diagnosis. From human breast tissue samples, Raman spectra and pH levels are measured using fiber-optic-based Raman and pH probes, respectively. The pH sensing is based on the dependence of pH level on optical transmission spectrum. Multivariate statistical analysis is performed to evaluate the classification capability of the dual-modal method. The analytical results show that the dual-modal method based on Raman spectroscopy and optical pH sensing can improve the performance of cancer classification. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Bibliography on Cold Regions Science and Technology. Volume 41. Part 2

DTIC Science & Technology

1987-12-01

Aletschgletscher [1984, p.9-25, eng, 41-622 Aleksandrov, B.M. Multivariate regression analysis of the process of frozen peat dehydration [1986. p.15-19...freezing of high- way bridge decks [1977. 5p., eng] 41-4604 Britton, K.B. Low temperature effects on sorption. hydrolysis ...snowy season in 1986 at Sapporo [1986. p.17-23. jpn) 41-3503 Ishikawa, S. Experimental decomposition of

Application of curve resolution algorithms in the study of drug photodegradation kinetics -- the example of moclobemide.

PubMed

Skibiński, Robert; Komsta, Łukasz

2012-01-01

The photodegradation of moclobemide was studied in methanolic media. Ultra-HPLC (UHPLC)/MS/MS analysis proved decomposition to 4-chlorobenzamide as a major degradation product and small amounts of Ro 16-3177 (4-chloro-N-[2-[(2-hydroxyethyl)amino] ethyl]benzamide) and 2-[(4-chlorobenzylidene)amino]-N-[2-ethoxyethenyl]ethenamine. The methanolic solution was investigated spectrophotometrically in the UV region, registering the spectra during 30 min of degradation. Using reference spectra and a multivariate chemometric method (multivariate curve resolution-alternating least squares), the spectra were resolved and concentration profiles were obtained. The obtained results were in good agreement with a quantitative approach, with UHPLC-diode array detection as the reference method.

Tailored multivariate analysis for modulated enhanced diffraction

DOE PAGES

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals.

PubMed

Azami, Hamed; Smith, Keith; Escudero, Javier

2016-08-01

Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the relative instability of mvSE for short signals remains a problem. Here, we address this issue by proposing the multivariate fuzzy entropy (mvFE) with a new fuzzy membership function. The results using white Gaussian noise show that the mvFE leads to more reliable and stable results, especially for short signals, in comparison with mvSE. Accordingly, we propose MEMD-enhanced mvFE to quantify the complexity of signals. The characteristics of brain regions influenced by partial epilepsy are investigated by focal and non-focal electroencephalogram (EEG) time series. In this sense, the proposed MEMD-enhanced mvFE and mvSE are employed to discriminate focal EEG signals from non-focal ones. The results demonstrate the MEMD-enhanced mvFE values have a smaller coefficient of variation in comparison with those obtained by the MEMD-enhanced mvSE, even for long signals. The results also show that the MEMD-enhanced mvFE has better performance to quantify focal and non-focal signals compared with multivariate multiscale permutation entropy.

Decomposing Racial/Ethnic Disparities in Influenza Vaccination among the Elderly

PubMed Central

Yoo, Byung-Kwang; Hasebe, Takuya; Szilagyi, Peter G.

2015-01-01

While persistent racial/ethnic disparities in influenza vaccination have been reported among the elderly, characteristics contributing to disparities are poorly understood. This study aimed to assess characteristics associated with racial/ethnic disparities in influenza vaccination using a nonlinear Oaxaca-Blinder decomposition method. We performed cross-sectional multivariable logistic regression analyses for which the dependent variable was self-reported receipt of influenza vaccine during the 2010–2011 season among community dwelling non-Hispanic African-American (AA), non-Hispanic White (W), English-speaking Hispanic (EH) and Spanish-speaking Hispanic (SH) elderly, enrolled in the 2011 Medicare Current Beneficiary Survey (MCBS) (un-weighted/weighted N= 6,095/19.2million). Using the nonlinear Oaxaca-Blinder decomposition method, we assessed the relative contribution of seventeen covariates—including socio-demographic characteristics, health status, insurance, access, preference regarding healthcare, and geographic regions —to disparities in influenza vaccination. Unadjusted racial/ethnic disparities in influenza vaccination were 14.1 percentage points (pp) (W-AA disparity, p<.001), 25.7 pp (W-SH disparity, p<.001) and 0.6 pp (W-EH disparity, p>.8). The Oaxaca-Blinder decomposition method estimated that the unadjusted W-AA and W-SH disparities in vaccination could be reduced by only 45% even if AA and SH groups become equivalent to Whites in all covariates in multivariable regression models. The remaining 55% of disparities were attributed to (a) racial/ethnic differences in the estimated coefficients (e.g., odds ratios) in the regression models and (b) characteristics not included in the regression models. Our analysis found that only about 45% of racial/ethnic disparities in influenza vaccination among the elderly could be reduced by equalizing recognized characteristics among racial/ethnic groups. Future studies are needed to identify additional modifiable characteristics causing disparities in influenza vaccination. PMID:25900133

Surface Enhanced Raman Spectroscopy (SERS) and multivariate analysis as a screening tool for detecting Sudan I dye in culinary spices

NASA Astrophysics Data System (ADS)

Di Anibal, Carolina V.; Marsal, Lluís F.; Callao, M. Pilar; Ruisánchez, Itziar

2012-02-01

Raman spectroscopy combined with multivariate analysis was evaluated as a tool for detecting Sudan I dye in culinary spices. Three Raman modalities were studied: normal Raman, FT-Raman and SERS. The results show that SERS is the most appropriate modality capable of providing a proper Raman signal when a complex matrix is analyzed. To get rid of the spectral noise and background, Savitzky-Golay smoothing with polynomial baseline correction and wavelet transform were applied. Finally, to check whether unadulterated samples can be differentiated from samples adulterated with Sudan I dye, an exploratory analysis such as principal component analysis (PCA) was applied to raw data and data processed with the two mentioned strategies. The results obtained by PCA show that Raman spectra need to be properly treated if useful information is to be obtained and both spectra treatments are appropriate for processing the Raman signal. The proposed methodology shows that SERS combined with appropriate spectra treatment can be used as a practical screening tool to distinguish samples suspicious to be adulterated with Sudan I dye.

Identification and quantification of ciprofloxacin in urine through excitation-emission fluorescence and three-way PARAFAC calibration.

PubMed

Ortiz, M C; Sarabia, L A; Sánchez, M S; Giménez, D

2009-05-29

Due to the second-order advantage, calibration models based on parallel factor analysis (PARAFAC) decomposition of three-way data are becoming important in routine analysis. This work studies the possibility of fitting PARAFAC models with excitation-emission fluorescence data for the determination of ciprofloxacin in human urine. The finally chosen PARAFAC decomposition is built with calibration samples spiked with ciprofloxacin, and with other series of urine samples that were also spiked. One of the series of samples has also another drug because the patient was taking mesalazine. The mesalazine is a fluorescent substance that interferes with the ciprofloxacin. Finally, the procedure is applied to samples of a patient who was being treated with ciprofloxacin. The trueness has been established by the regression "predicted concentration versus added concentration". The recovery factor is 88.3% for ciprofloxacin in urine, and the mean of the absolute value of the relative errors is 4.2% for 46 test samples. The multivariate sensitivity of the fit calibration model is evaluated by a regression between the loadings of PARAFAC linked to ciprofloxacin versus the true concentration in spiked samples. The multivariate capability of discrimination is near 8 microg L(-1) when the probabilities of false non-compliance and false compliance are fixed at 5%.

Multivariate analysis of progressive thermal desorption coupled gas chromatography-mass spectrometry.

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

Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel

Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that varymore » as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief that this multivariate analysis will enable superior differentiation capabilities. In addition, noise and system artifacts challenge the analysis of GC-MS data collected on lower cost equipment, ubiquitous in commercial laboratories. This research has the potential to affect many areas of analytical chemistry including materials analysis, medical testing, and environmental surveillance. It could also provide a method to measure adsorption parameters for chemical interactions on various surfaces by measuring desorption as a function of temperature for mixtures. We have presented results of a novel method for examining offgas products of a common PDMS material. Our method involves utilizing a stepped TD/GC-MS data acquisition scheme that may be almost totally automated, coupled with multivariate analysis schemes. This method of data generation and analysis can be applied to a number of materials aging and thermal degradation studies.« less

The effect of time synchronization of wireless sensors on the modal analysis of structures

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.; Fowler, K.; Sazonov, E.

2008-10-01

Driven by the need to reduce the installation cost and maintenance cost of structural health monitoring (SHM) systems, wireless sensor networks (WSNs) are becoming increasingly popular. Perfect time synchronization amongst the wireless sensors is a key factor enabling the use of low-cost, low-power WSNs for structural health monitoring applications based on output-only modal analysis of structures. In this paper we present a theoretical framework for analysis of the impact created by time delays in the measured system response on the reconstruction of mode shapes using the popular frequency domain decomposition (FDD) technique. This methodology directly estimates the change in mode shape values based on sensor synchronicity. We confirm the proposed theoretical model by experimental validation in modal identification experiments performed on an aluminum beam. The experimental validation was performed using a wireless intelligent sensor and actuator network (WISAN) which allows for close time synchronization between sensors (0.6-10 µs in the tested configuration) and guarantees lossless data delivery under normal conditions. The experimental results closely match theoretical predictions and show that even very small delays in output response impact the mode shapes.

Nonstationary Dynamics Data Analysis with Wavelet-SVD Filtering

NASA Technical Reports Server (NTRS)

Brenner, Marty; Groutage, Dale; Bessette, Denis (Technical Monitor)

2001-01-01

Nonstationary time-frequency analysis is used for identification and classification of aeroelastic and aeroservoelastic dynamics. Time-frequency multiscale wavelet processing generates discrete energy density distributions. The distributions are processed using the singular value decomposition (SVD). Discrete density functions derived from the SVD generate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then compared with a transformed-SVD, or TSVD, which reduces the number of features into more compact energy density concentrations. Finally, from the feature extraction, wavelet-based modal parameter estimation is applied.

Modal processing for acoustic communications in shallow water experiment.

PubMed

Morozov, Andrey K; Preisig, James C; Papp, Joseph

2008-09-01

Acoustical array data from the Shallow Water Acoustics experiment was processed to show the feasibility of broadband mode decomposition as a preprocessing method to reduce the effective channel delay spread and concentrate received signal energy in a small number of independent channels. The data were collected by a vertical array designed at the Woods Hole Oceanographic Institution. Phase-shift Keying (PSK) m-sequence modulated signals with different carrier frequencies were transmitted at a distance 19.2 km from the array. Even during a strong internal waves activity a low bit error rate was achieved.

Intracavity vortex beam generation

NASA Astrophysics Data System (ADS)

Naidoo, Darryl; Aït-Ameur, Kamel; Forbes, Andrew

2011-10-01

In this paper we explore vortex beams and in particular the generation of single LG0l modes and superpositions thereof. Vortex beams carry orbital angular momentum (OAM) and this intrinsic property makes them prevalent in transferring this OAM to matter and to be used in quantum information processing. We explore an extra-cavity and intra-cavity approach in LG0l mode generation respectively. The outputs of a Porro-prism resonator are represented by "petals" and we show that through a full modal decomposition, the "petal" fields are a superposition of two LG0l modes.

A three-dimensional multivariate representation of atmospheric variability

NASA Astrophysics Data System (ADS)

Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

2016-04-01

A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically and along the equator from its main generation regions in the upper troposphere over the Indian and Pacific region. The validation of the 10-day ECMWF forecasts with analyses in the modal space suggests a lack of variability in the tropics in the medium range. Reference: Žagar, N. et al., 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169-1195, doi:10.5194/gmd-8-1169-2015 Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444 The MODES software is available from http://meteo.fmf.uni-lj.si/MODES.

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

Harmonic analysis of electric locomotive and traction power system based on wavelet singular entropy

NASA Astrophysics Data System (ADS)

Dun, Xiaohong

2018-05-01

With the rapid development of high-speed railway and heavy-haul transport, the locomotive and traction power system has become the main harmonic source of China's power grid. In response to this phenomenon, the system's power quality issues need timely monitoring, assessment and governance. Wavelet singular entropy is an organic combination of wavelet transform, singular value decomposition and information entropy theory, which combines the unique advantages of the three in signal processing: the time-frequency local characteristics of wavelet transform, singular value decomposition explores the basic modal characteristics of data, and information entropy quantifies the feature data. Based on the theory of singular value decomposition, the wavelet coefficient matrix after wavelet transform is decomposed into a series of singular values that can reflect the basic characteristics of the original coefficient matrix. Then the statistical properties of information entropy are used to analyze the uncertainty of the singular value set, so as to give a definite measurement of the complexity of the original signal. It can be said that wavelet entropy has a good application prospect in fault detection, classification and protection. The mat lab simulation shows that the use of wavelet singular entropy on the locomotive and traction power system harmonic analysis is effective.

A GRAPH PARTITIONING APPROACH TO PREDICTING PATTERNS IN LATERAL INHIBITION SYSTEMS

PubMed Central

RUFINO FERREIRA, ANA S.; ARCAK, MURAT

2017-01-01

We analyze spatial patterns on networks of cells where adjacent cells inhibit each other through contact signaling. We represent the network as a graph where each vertex represents the dynamics of identical individual cells and where graph edges represent cell-to-cell signaling. To predict steady-state patterns we find equitable partitions of the graph vertices and assign them into disjoint classes. We then use results from monotone systems theory to prove the existence of patterns that are structured in such a way that all the cells in the same class have the same final fate. To study the stability properties of these patterns, we rely on the graph partition to perform a block decomposition of the system. Then, to guarantee stability, we provide a small-gain type criterion that depends on the input-output properties of each cell in the reduced system. Finally, we discuss pattern formation in stochastic models. With the help of a modal decomposition we show that noise can enhance the parameter region where patterning occurs. PMID:29225552

Identification of flow structures in fully developed canonical and wavy channels by means of modal decomposition techniques

NASA Astrophysics Data System (ADS)

Ghebali, Sacha; Garicano-Mena, Jesús; Ferrer, Esteban; Valero, Eusebio

2018-04-01

A Dynamic Mode Decomposition (DMD) of Direct Numerical Simulations (DNS) of fully developed channel flows is undertaken in order to study the main differences in flow features between a plane-channel flow and a passively “controlled” flow wherein the mean friction was reduced relative to the baseline by modifying the geometry in order to generate a streamwise-periodic spanwise pressure gradient, as is the case for an oblique wavy wall. The present analysis reports POD and DMD modes for the plane channel, jointly with the application of a sparsity-promoting method, as well as a reconstruction of the Reynolds shear stress with the dynamic modes. Additionally, a dynamic link between the streamwise velocity fluctuations and the friction on the wall is sought by means of a composite approach both in the plane and wavy cases. One of the DMD modes associated with the wavy-wall friction exhibits a meandering motion which was hardly identifiable on the instantaneous friction fluctuations.

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities.

PubMed

Frelat, Romain; Lindegren, Martin; Denker, Tim Spaanheden; Floeter, Jens; Fock, Heino O; Sguotti, Camilla; Stäbler, Moritz; Otto, Saskia A; Möllmann, Christian

2017-01-01

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.

Common Sense in Choice: The Effect of Sensory Modality on Neural Value Representations.

PubMed

Shuster, Anastasia; Levy, Dino J

2018-01-01

Although it is well established that the ventromedial prefrontal cortex (vmPFC) represents value using a common currency across categories of rewards, it is unknown whether the vmPFC represents value irrespective of the sensory modality in which alternatives are presented. In the current study, male and female human subjects completed a decision-making task while their neural activity was recorded using functional magnetic resonance imaging. On each trial, subjects chose between a safe alternative and a lottery, which was presented visually or aurally. A univariate conjunction analysis revealed that the anterior portion of the vmPFC tracks subjective value (SV) irrespective of the sensory modality. Using a novel cross-modality multivariate classifier, we were able to decode auditory value based on visual trials and vice versa. In addition, we found that the visual and auditory sensory cortices, which were identified using functional localizers, are also sensitive to the value of stimuli, albeit in a modality-specific manner. Whereas both primary and higher-order auditory cortices represented auditory SV (aSV), only a higher-order visual area represented visual SV (vSV). These findings expand our understanding of the common currency network of the brain and shed a new light on the interplay between sensory and value information processing.

Common Sense in Choice: The Effect of Sensory Modality on Neural Value Representations

PubMed Central

2018-01-01

Abstract Although it is well established that the ventromedial prefrontal cortex (vmPFC) represents value using a common currency across categories of rewards, it is unknown whether the vmPFC represents value irrespective of the sensory modality in which alternatives are presented. In the current study, male and female human subjects completed a decision-making task while their neural activity was recorded using functional magnetic resonance imaging. On each trial, subjects chose between a safe alternative and a lottery, which was presented visually or aurally. A univariate conjunction analysis revealed that the anterior portion of the vmPFC tracks subjective value (SV) irrespective of the sensory modality. Using a novel cross-modality multivariate classifier, we were able to decode auditory value based on visual trials and vice versa. In addition, we found that the visual and auditory sensory cortices, which were identified using functional localizers, are also sensitive to the value of stimuli, albeit in a modality-specific manner. Whereas both primary and higher-order auditory cortices represented auditory SV (aSV), only a higher-order visual area represented visual SV (vSV). These findings expand our understanding of the common currency network of the brain and shed a new light on the interplay between sensory and value information processing. PMID:29619408

Outcome of transarterial chemoembolization-based multi-modal treatment in patients with unresectable hepatocellular carcinoma.

PubMed

Song, Do Seon; Nam, Soon Woo; Bae, Si Hyun; Kim, Jin Dong; Jang, Jeong Won; Song, Myeong Jun; Lee, Sung Won; Kim, Hee Yeon; Lee, Young Joon; Chun, Ho Jong; You, Young Kyoung; Choi, Jong Young; Yoon, Seung Kew

2015-02-28

To investigate the efficacy and safety of transarterial chemoembolization (TACE)-based multimodal treatment in patients with large hepatocellular carcinoma (HCC). A total of 146 consecutive patients were included in the analysis, and their medical records and radiological data were reviewed retrospectively. In total, 119 patients received TACE-based multi-modal treatments, and the remaining 27 received conservative management. Overall survival (P<0.001) and objective tumor response (P=0.003) were significantly better in the treatment group than in the conservative group. After subgroup analysis, survival benefits were observed not only in the multi-modal treatment group compared with the TACE-only group (P=0.002) but also in the surgical treatment group compared with the loco-regional treatment-only group (P<0.001). Multivariate analysis identified tumor stage (P<0.001) and tumor type (P=0.009) as two independent pre-treatment factors for survival. After adjusting for significant pre-treatment prognostic factors, objective response (P<0.001), surgical treatment (P=0.009), and multi-modal treatment (P=0.002) were identified as independent post-treatment prognostic factors. TACE-based multi-modal treatments were safe and more beneficial than conservative management. Salvage surgery after successful downstaging resulted in long-term survival in patients with large, unresectable HCC.

Outcome of transarterial chemoembolization-based multi-modal treatment in patients with unresectable hepatocellular carcinoma

PubMed Central

Song, Do Seon; Nam, Soon Woo; Bae, Si Hyun; Kim, Jin Dong; Jang, Jeong Won; Song, Myeong Jun; Lee, Sung Won; Kim, Hee Yeon; Lee, Young Joon; Chun, Ho Jong; You, Young Kyoung; Choi, Jong Young; Yoon, Seung Kew

2015-01-01

AIM: To investigate the efficacy and safety of transarterial chemoembolization (TACE)-based multimodal treatment in patients with large hepatocellular carcinoma (HCC). METHODS: A total of 146 consecutive patients were included in the analysis, and their medical records and radiological data were reviewed retrospectively. RESULTS: In total, 119 patients received TACE-based multi-modal treatments, and the remaining 27 received conservative management. Overall survival (P < 0.001) and objective tumor response (P = 0.003) were significantly better in the treatment group than in the conservative group. After subgroup analysis, survival benefits were observed not only in the multi-modal treatment group compared with the TACE-only group (P = 0.002) but also in the surgical treatment group compared with the loco-regional treatment-only group (P < 0.001). Multivariate analysis identified tumor stage (P < 0.001) and tumor type (P = 0.009) as two independent pre-treatment factors for survival. After adjusting for significant pre-treatment prognostic factors, objective response (P < 0.001), surgical treatment (P = 0.009), and multi-modal treatment (P = 0.002) were identified as independent post-treatment prognostic factors. CONCLUSION: TACE-based multi-modal treatments were safe and more beneficial than conservative management. Salvage surgery after successful downstaging resulted in long-term survival in patients with large, unresectable HCC. PMID:25741147

Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment.

PubMed

Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

2018-06-05

Multiple neuroimaging modalities have been developed providing various aspects of information on the human brain. Used together and properly, these complementary multimodal neuroimaging data integrate multisource information which can facilitate a diagnosis and improve the diagnostic accuracy. In this study, 3 types of brain imaging data (sMRI, FDG-PET, and florbetapir-PET) were fused in the hope to improve diagnostic accuracy, and multivariate methods (logistic regression) were applied to these trimodal neuroimaging indices. Then, the receiver-operating characteristic (ROC) method was used to analyze the outcomes of the logistic classifier, with either each index, multiples from each modality, or all indices from all 3 modalities, to investigate their differential abilities to identify the disease. With increasing numbers of indices within each modality and across modalities, the accuracy of identifying Alzheimer disease (AD) increases to varying degrees. For example, the area under the ROC curve is above 0.98 when all the indices from the 3 imaging data types are combined. Using a combination of different indices, the results confirmed the initial hypothesis that different biomarkers were potentially complementary, and thus the conjoint analysis of multiple information from multiple sources would improve the capability to identify diseases such as AD and mild cognitive impairment. © 2018 S. Karger AG, Basel.

Validation of a new modal performance measure for flexible controllers design

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

Simo, J.B.; Tahan, S.A.; Kamwa, I.

1996-05-01

A new modal performance measure for power system stabilizer (PSS) optimization is proposed in this paper. The new method is based on modifying the square envelopes of oscillating modes, in order to take into account their damping ratios while minimizing the performance index. This criteria is applied to flexible controllers optimal design, on a multi-input-multi-output (MIMO) reduced-order model of a prototype power system. The multivariable model includes four generators, each having one input and one output. Linear time-response simulation and transient stability analysis with a nonlinear package confirm the superiority of the proposed criteria and illustrate its effectiveness in decentralizedmore » control.« less

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

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

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

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.

Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-07-01

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

PubMed Central

Amezquita-Sanchez, Juan P.; Romero-Troncoso, Rene J.; Osornio-Rios, Roque A.; Garcia-Perez, Arturo

2014-01-01

This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals. PMID:24683346

EEMD-MUSIC-based analysis for natural frequencies identification of structures using artificial and natural excitations.

PubMed

Camarena-Martinez, David; Amezquita-Sanchez, Juan P; Valtierra-Rodriguez, Martin; Romero-Troncoso, Rene J; Osornio-Rios, Roque A; Garcia-Perez, Arturo

2014-01-01

This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.

Explaining the power-law distribution of human mobility through transportation modality decomposition.

PubMed

Zhao, Kai; Musolesi, Mirco; Hui, Pan; Rao, Weixiong; Tarkoma, Sasu

2015-03-16

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns.

Explaining the power-law distribution of human mobility through transportation modality decomposition

NASA Astrophysics Data System (ADS)

Zhao, Kai; Musolesi, Mirco; Hui, Pan; Rao, Weixiong; Tarkoma, Sasu

2015-03-01

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns.

Explaining the power-law distribution of human mobility through transportation modality decomposition

PubMed Central

Zhao, Kai; Musolesi, Mirco; Hui, Pan; Rao, Weixiong; Tarkoma, Sasu

2015-01-01

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns. PMID:25779306

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.

PubMed

Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul J

2016-08-01

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

Development of an Experimental Rig for Investigation of Higher Order Modes in Ducts

NASA Technical Reports Server (NTRS)

Gerhold, Carl H.; Cabell, Randolph H.; Brown, Martha C.

2006-01-01

Continued progress to reduce fan noise emission from high bypass ratio engine ducts in aircraft increasingly relies on accurate description of the sound propagation in the duct. A project has been undertaken at NASA Langley Research Center to investigate the propagation of higher order modes in ducts with flow. This is a two-pronged approach, including development of analytic models (the subject of a separate paper) and installation of a laboratory-quality test rig. The purposes of the rig are to validate the analytical models and to evaluate novel duct acoustic liner concepts, both passive and active. The dimensions of the experimental rig test section scale to between 25% and 50% of the aft bypass ducts of most modern engines. The duct is of rectangular cross section so as to provide flexibility to design and fabricate test duct liner samples. The test section can accommodate flow paths that are straight through or offset from inlet to discharge, the latter design allowing investigation of the effect of curvature on sound propagation and duct liner performance. The maximum air flow rate through the duct is Mach 0.3. Sound in the duct is generated by an array of 16 high-intensity acoustic drivers. The signals to the loudspeaker array are generated by a multi-input/multi-output feedforward control system that has been developed for this project. The sound is sampled by arrays of flush-mounted microphones and a modal decomposition is performed at the frequency of sound generation. The data acquisition system consists of two arrays of flush-mounted microphones, one upstream of the test section and one downstream. The data are used to determine parameters such as the overall insertion loss of the test section treatment as well as the effect of the treatment on a modal basis such as mode scattering. The methodology used for modal decomposition is described, as is a description of the mode generation control system. Data are presented which demonstrate the performance of the controller to generate the desired mode while suppressing all other cut on modes in the duct.

Feature-based fusion of medical imaging data.

PubMed

Calhoun, Vince D; Adali, Tülay

2009-09-01

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.

Data-driven Analysis and Prediction of Arctic Sea Ice

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.; Chekroun, M.; Ghil, M.; Yuan, X.; Ting, M.

2015-12-01

We present results of data-driven predictive analyses of sea ice over the main Arctic regions. Our approach relies on the Multilayer Stochastic Modeling (MSM) framework of Kondrashov, Chekroun and Ghil [Physica D, 2015] and it leads to prognostic models of sea ice concentration (SIC) anomalies on seasonal time scales.This approach is applied to monthly time series of leading principal components from the multivariate Empirical Orthogonal Function decomposition of SIC and selected climate variables over the Arctic. We evaluate the predictive skill of MSM models by performing retrospective forecasts with "no-look ahead" forup to 6-months ahead. It will be shown in particular that the memory effects included in our non-Markovian linear MSM models improve predictions of large-amplitude SIC anomalies in certain Arctic regions. Furtherimprovements allowed by the MSM framework will adopt a nonlinear formulation, as well as alternative data-adaptive decompositions.

Investigating the Important Correlates of Maternal Education and Childhood Malaria Infections

PubMed Central

Njau, Joseph D.; Stephenson, Rob; Menon, Manoj P.; Kachur, S. Patrick; McFarland, Deborah A.

2014-01-01

The relationship between maternal education and child health has intrigued researchers for decades. This study explored the interaction between maternal education and childhood malaria infection. Cross-sectional survey data from three African countries were used. Descriptive analysis and multivariate logistic regression models were completed in line with identified correlates. Marginal effects and Oaxaca decomposition analysis on maternal education and childhood malaria infection were also estimated. Children with mothers whose education level was beyond primary school were 4.7% less likely to be malaria-positive (P < 0.001). The Oaxaca decomposition analysis exhibited an 8% gap in childhood malaria infection for educated and uneducated mothers. Over 60% of the gap was explained by differences in household wealth (26%), household place of domicile (21%), malaria transmission intensities (14%), and media exposure (12%). All other correlates accounted for only 27%. The full adjusted model showed a robust and significant relationship between maternal education and childhood malaria infection. PMID:25002302

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

PubMed Central

Lindegren, Martin; Denker, Tim Spaanheden; Floeter, Jens; Fock, Heino O.; Sguotti, Camilla; Stäbler, Moritz; Otto, Saskia A.; Möllmann, Christian

2017-01-01

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs. PMID:29136658

Convergent and invariant object representations for sight, sound, and touch.

PubMed

Man, Kingson; Damasio, Antonio; Meyer, Kaspar; Kaplan, Jonas T

2015-09-01

We continuously perceive objects in the world through multiple sensory channels. In this study, we investigated the convergence of information from different sensory streams within the cerebral cortex. We presented volunteers with three common objects via three different modalities-sight, sound, and touch-and used multivariate pattern analysis of functional magnetic resonance imaging data to map the cortical regions containing information about the identity of the objects. We could reliably predict which of the three stimuli a subject had seen, heard, or touched from the pattern of neural activity in the corresponding early sensory cortices. Intramodal classification was also successful in large portions of the cerebral cortex beyond the primary areas, with multiple regions showing convergence of information from two or all three modalities. Using crossmodal classification, we also searched for brain regions that would represent objects in a similar fashion across different modalities of presentation. We trained a classifier to distinguish objects presented in one modality and then tested it on the same objects presented in a different modality. We detected audiovisual invariance in the right temporo-occipital junction, audiotactile invariance in the left postcentral gyrus and parietal operculum, and visuotactile invariance in the right postcentral and supramarginal gyri. Our maps of multisensory convergence and crossmodal generalization reveal the underlying organization of the association cortices, and may be related to the neural basis for mental concepts. © 2015 Wiley Periodicals, Inc.

Selective Attention in Fast and Slow Learners During Discrimination Learning in the Haptic Modality. Information and Research Report.

ERIC Educational Resources Information Center

Hunt, Dennis; And Others

Sixty-four 8-year-old children were divided into fast and slow learner groups and trained on a tactile simultaneous discrimination task. Selective attention was measured in terms of percentage contact time per trial to the relevant dimension. Inter- and intracouplings per trial were also recorded. A multivariate analysis was carried out to examine…

Application of the nonlinear Blinder-Oaxaca decomposition to study racial/ethnic disparities in antiobesity medication use in the United States.

PubMed

Mehta, Hemalkumar B; Rajan, Suja S; Aparasu, Rajender R; Johnson, Michael L

2013-01-01

The nonlinear Blinder-Oaxaca (BO) decomposition method is gaining popularity in health services research because of its ability to explain disparity issues. The present study demonstrates the use of this method for categorical variables by addressing antiobesity medication use disparity. To examine racial/ethnic disparity in antiobesity medication use and to quantify the observed factor contribution behind the disparity using the nonlinear BO decomposition. Medical Expenditure Panel Survey data, 2002-2007, were used in this retrospective cross-sectional study. Adults with body mass index (BMI) >30, or BMI ≥27 and comorbidities such as hypertension, cardiovascular diseases, diabetes, or hyperlipidemia were included in the cohort (N=65,886,625). Multivariable logistic regression was performed to examine racial/ethnic disparity in antiobesity medication use controlling for predisposing, enabling, and need factors. The nonlinear BO decomposition was used to identify the contribution of each predisposing, enabling, and need factors in explaining the racial/ethnic disparity and to estimate the residual unexplained disparity. Non-Hispanic Blacks were 46% (odds ratio [OR]: 0.54; 95% confidence interval [CI]: 0.35-0.83) less likely to use antiobesity drugs compared with non-Hispanic Whites, whereas no difference was observed between Hispanics and non-Hispanic Whites. A 0.22 percentage point of disparity existed between non-Hispanic Whites and Blacks. The nonlinear BO decomposition estimated a decomposition coefficient of -0.0013 indicating that the observed disparity would have been 58% higher (-0.0013/0.0022) if non-Hispanic Blacks had similar observed characteristics as non-Hispanic Whites. Age, gender, marital status, region, and BMI were significant factors in the decomposition model; only marital status explained the racial/ethnic disparity among all observed characteristics. The study revealed that differences in the predisposing, enabling, and need characteristics (except marital status) did not successfully explain the racial/ethnic disparity in antiobesity medication use. Further studies examining racial/ethnic differences in individual beliefs, behavioral patterns, and provider prescription patterns are vital to understand these disparities. Copyright © 2013 Elsevier Inc. All rights reserved.

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models.

PubMed

Nazem-Zadeh, Mohammad-Reza; Elisevich, Kost V; Schwalb, Jason M; Bagher-Ebadian, Hassan; Mahmoudi, Fariborz; Soltanian-Zadeh, Hamid

2014-12-15

Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Multi-material decomposition of spectral CT images

NASA Astrophysics Data System (ADS)

Mendonça, Paulo R. S.; Bhotika, Rahul; Maddah, Mahnaz; Thomsen, Brian; Dutta, Sandeep; Licato, Paul E.; Joshi, Mukta C.

2010-04-01

Spectral Computed Tomography (Spectral CT), and in particular fast kVp switching dual-energy computed tomography, is an imaging modality that extends the capabilities of conventional computed tomography (CT). Spectral CT enables the estimation of the full linear attenuation curve of the imaged subject at each voxel in the CT volume, instead of a scalar image in Hounsfield units. Because the space of linear attenuation curves in the energy ranges of medical applications can be accurately described through a two-dimensional manifold, this decomposition procedure would be, in principle, limited to two materials. This paper describes an algorithm that overcomes this limitation, allowing for the estimation of N-tuples of material-decomposed images. The algorithm works by assuming that the mixing of substances and tissue types in the human body has the physicochemical properties of an ideal solution, which yields a model for the density of the imaged material mix. Under this model the mass attenuation curve of each voxel in the image can be estimated, immediately resulting in a material-decomposed image triplet. Decomposition into an arbitrary number of pre-selected materials can be achieved by automatically selecting adequate triplets from an application-specific material library. The decomposition is expressed in terms of the volume fractions of each constituent material in the mix; this provides for a straightforward, physically meaningful interpretation of the data. One important application of this technique is in the digital removal of contrast agent from a dual-energy exam, producing a virtual nonenhanced image, as well as in the quantification of the concentration of contrast observed in a targeted region, thus providing an accurate measure of tissue perfusion.

Modal identification of structures by a novel approach based on FDD-wavelet method

NASA Astrophysics Data System (ADS)

Tarinejad, Reza; Damadipour, Majid

2014-02-01

An important application of system identification in structural dynamics is the determination of natural frequencies, mode shapes and damping ratios during operation which can then be used for calibrating numerical models. In this paper, the combination of two advanced methods of Operational Modal Analysis (OMA) called Frequency Domain Decomposition (FDD) and Continuous Wavelet Transform (CWT) based on novel cyclic averaging of correlation functions (CACF) technique are used for identification of dynamic properties. By using this technique, the autocorrelation of averaged correlation functions is used instead of original signals. Integration of FDD and CWT methods is used to overcome their deficiency and take advantage of the unique capabilities of these methods. The FDD method is able to accurately estimate the natural frequencies and mode shapes of structures in the frequency domain. On the other hand, the CWT method is in the time-frequency domain for decomposition of a signal at different frequencies and determines the damping coefficients. In this paper, a new formulation applied to the wavelet transform of the averaged correlation function of an ambient response is proposed. This application causes to accurate estimation of damping ratios from weak (noise) or strong (earthquake) vibrations and long or short duration record. For this purpose, the modified Morlet wavelet having two free parameters is used. The optimum values of these two parameters are obtained by employing a technique which minimizes the entropy of the wavelet coefficients matrix. The capabilities of the novel FDD-Wavelet method in the system identification of various dynamic systems with regular or irregular distribution of mass and stiffness are illustrated. This combined approach is superior to classic methods and yields results that agree well with the exact solutions of the numerical models.

Evaluation of the Spies™ modalities image quality

PubMed Central

Emiliani, Esteban; Talso, Michele; Baghdadi, Mohammed; Barreiro, Aarón; Orosa, Andrea; Serviàn, Pol; Gavrilov, Pavel; Proietti, Silvia; Traxer, Olivier

2017-01-01

Introduction The Spies™ system (Karl-Storz®) was introduced into digital ureteroscopy to improve endoscopic vision. To date, there is no data to either indicate which of the Spies modalities is better for improving diagnosis and treatment procedures, nor to compare the modalities in terms of image quality. The aim of this study was to evaluate and compare the image quality of five Spies™ modalities (SM) to the standard white light in an in-vitro model. Materials and Methods Two standardized grids and 3 stones of different composition were recorded in white light and the 5SM (Clara, Chroma, Clara+Chroma), Spectra A and B) using 4 standardized aqueous scenarios. Twelve templates were done in order to simultaneously compare the same objective in the different modalities. Six urologists, five medical students, five urology residents, and five persons not involved with urology evaluated each video on a scale of 1 (very bad) to 5 (very good). Results Comparing white light to SM, subjects scored better the quality of Clara and Clara+Chroma than white light (p=0.0139 and p<0.05) and scored worse Spectra A and B (p=0.0005 and p=0.0023)). When comparing Clara to the other SM, it was ranked equivalent to Clara+Chroma (p=0.67) and obtained a higher rank than Chroma, Spectra A and B (p<0.05, p=0.0001 and p=0.0001). In the multivariate analysis mean scores were higher among urologists. Conclusion In all analyzed scenarios, the subjects ranked Clara and Clara+Chroma as the modalities with better image quality compared to white light. PMID:28338307

Evaluation of the Spies TM modalities image quality.

PubMed

Emiliani, Esteban; Talso, Michele; Baghdadi, Mohammed; Barreiro, Aaron; Orosa, Andrea; Serviàn, Pol; Gavrilov, Pavel; Proietti, Silvia; Traxer, Olivier

2017-01-01

The Spies™ system (Karl-Storz®) was introduced into digital ureteroscopy to improve endoscopic vision. To date, there is no data to either indicate which of the Spies modalities is better for improving diagnosis and treatment procedures, nor to compare the modalities in terms of image quality. The aim of this study was to evaluate and compare the image quality of five Spies™ modalities (SM) to the standard white light in an in-vitro model. Two standardized grids and 3 stones of different composition were recorded in white light and the 5SM (Clara, Chroma, Clara+Chroma), Spectra A and B) using 4 standardized aqueous scenarios. Twelve templates were done in order to simultaneously compare the same objective in the different modalities. Six urologists, five medical students, five urology residents, and five persons not involved with urology evaluated each video on a scale of 1 (very bad) to 5 (very good). Comparing white light to SM, subjects scored better the quality of Clara and Clara+Chroma than white light (p=0.0139 and p<0.05) and scored worse Spectra A and B (p=0.0005 and p=0.0023). When comparing Clara to the other SM, it was ranked equivalent to Clara+Chroma (p=0.67) and obtained a higher rank than Chroma, Spectra A and B (p<0.05, p=0.0001 and p=0.0001). In the multivariate analysis mean scores were higher among urologists. In all analyzed scenarios, the subjects ranked Clara and Clara+Chroma as the modalities with better image quality compared to white light. Copyright® by the International Brazilian Journal of Urology.

Assessment of swirl spray interaction in lab scale combustor using time-resolved measurements

NASA Astrophysics Data System (ADS)

Rajamanickam, Kuppuraj; Jain, Manish; Basu, Saptarshi

2017-11-01

Liquid fuel injection in highly turbulent swirling flows becomes common practice in gas turbine combustors to improve the flame stabilization. It is well known that the vortex bubble breakdown (VBB) phenomenon in strong swirling jets exhibits complicated flow structures in the spatial domain. In this study, the interaction of hollow cone liquid sheet with such coaxial swirling flow field has been studied experimentally using time-resolved measurements. In particular, much attention is focused towards the near field breakup mechanism (i.e. primary atomization) of liquid sheet. The detailed swirling gas flow field characterization is carried out using time-resolved PIV ( 3.5 kHz). Furthermore, the complicated breakup mechanisms and interaction of the liquid sheet are imaged with the help of high-speed shadow imaging system. Subsequently, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) is implemented over the instantaneous data sets to retrieve the modal information associated with the interaction dynamics. This helps to delineate more quantitative nature of interaction process between the liquid sheet and swirling gas phase flow field.

Cross-Modal Multivariate Pattern Analysis

PubMed Central

Meyer, Kaspar; Kaplan, Jonas T.

2011-01-01

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

Two-dimensional NMR spectroscopy links structural moieties of soil organic matter to the temperature sensitivity of its decomposition

NASA Astrophysics Data System (ADS)

Soucemarianadin, Laure; Erhagen, Björn; Öquist, Mats; Nilsson, Mats; Schleucher, Jürgen

2015-04-01

Soil organic matter (SOM) represents a huge carbon pool, specifically in boreal ecosystems. Warming-induced release of large amounts of CO2 from the soil carbon pool might become a significant exacerbating feedback to global warming, if decomposition rates of boreal soils were more sensitive to increased temperatures. Despite a large number of studies dedicated to the topic, it has proven difficult to elucidate how the organo-chemical composition of SOM influences its decomposition, or its quality as a substrate for microbial metabolism. A great part of this challenge results from our inability to achieve a detailed characterization of the complex composition of SOM on the level of molecular structural moieties. 13C nuclear magnetic resonance (NMR) spectroscopy is a common tool to characterize SOM. However, SOM is a very complex mixture and the chemical shift regions distinguished in the 13C NMR spectra often represent many different molecular fragments. For example, in the carbohydrates region, signals of all monosaccharides present in many different polymers overlap. This overlap thwarts attempts to identify molecular moieties, resulting in insufficient information to characterize SOM composition. We applied two-dimensional (2D) NMR to characterize SOM with highly increased resolution. We directly dissolved finely ground litters and forest floors'fibric and humic horizons'of both coniferous and deciduous boreal forests in dimethyl sulfoxide and analyzed the resulting solution with a 2D 1H-13C NMR experiment. In the 2D planes of these spectra, signals of CH groups can be resolved based on their 13C and 1H chemical shifts, hence the resolving power and information content of these NMR spectra is hugely increased. The 2D spectra indeed resolved overlaps observed in 1D 13C spectra, so that hundreds of distinct CH groups could be observed and many molecular fragments could be identified. For instance, in the aromatics region, signals from individual lignin units could be recognized. It was hence possible to follow the fate of specific structural moieties in soils. We observed differences between litter and soil samples, and were able to relate them to the decomposition of identifiable moieties. Using multivariate data analysis, we aimed at linking the detailed chemical fingerprints of SOM to turnover rates in a soil incubation experiment. With the multivariate models, we were able to relate signal patterns in the 2D spectra and intensities of identifiable molecular moieties to variability in the temperature response of organic matter decomposition, as assessed by Q10. In conclusion, the characterization of SOM composition at the molecular level by solution-state 2D NMR spectroscopy is highly promising; it offers unprecedented possibilities to link SOM molecular composition to ecosystem processes, and their responses to environmental changes.

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

PubMed Central

Xiao, Li; Wei, Hui; Himmel, Michael E.; Jameel, Hasan; Kelley, Stephen S.

2014-01-01

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass. PMID:25147552

New applications of a model of electromechanical impedance for SHM

NASA Astrophysics Data System (ADS)

Pavelko, Vitalijs

2014-03-01

The paper focuses on the further development of the model of the electromechanical impedance (EMI) of the piezoceramics transducer (PZT) and its application for aircraft structural health monitoring (SHM). There was obtained an expression of the electromechanical impedance common to any dimension of models (1D, 2D, 3D), and directly independent from imposed constraints. Determination of the dynamic response of the system "host structure - PZT", which is crucial for the practical application supposes the use of modal analysis. This allows to get a general tool to determine EMI regardless of the specific features of a particular application. Earlier there was considered the technology of separate determination of the dynamic response for the PZT and the structural element". Here another version that involves the joint modal analysis of the entire system "host structure - PZT" is presented. As a result, the dynamic response is obtained in the form of modal decomposition of transducer mechanical strains. The use of models for the free and constrained transducer, analysis of the impact of the adhesive layer to the EMI is demonstrated. In all cases there was analyzed the influence of the dimension of the model (2D and 3D). The validity of the model is confirmed by experimental studies. Correlation between the fatigue crack length in a thin-walled Al plate and EMI of embedded PZT was simulated and compared with test result.

Acoustic wave propagation and intensity fluctuations in shallow water 2006 experiment

NASA Astrophysics Data System (ADS)

Luo, Jing

Fluctuations of low frequency sound propagation in the presence of nonlinear internal waves during the Shallow Water 2006 experiment are analyzed. Acoustic waves and environmental data including on-board ship radar images were collected simultaneously before, during, and after a strong internal solitary wave packet passed through a source-receiver acoustic track. Analysis of the acoustic wave signals shows temporal intensity fluctuations. These fluctuations are affected by the passing internal wave and agrees well with the theory of the horizontal refraction of acoustic wave propagation in shallow water. The intensity focusing and defocusing that occurs in a fixed source-receiver configuration while internal wave packet approaches and passes the acoustic track is addressed in this thesis. Acoustic ray-mode theory is used to explain the modal evolution of broadband acoustic waves propagating in a shallow water waveguide in the presence of internal waves. Acoustic modal behavior is obtained from the data through modal decomposition algorithms applied to data collected by a vertical line array of hydrophones. Strong interference patterns are observed in the acoustic data, whose main cause is identified as the horizontal refraction referred to as the horizontal Lloyd mirror effect. To analyze this interference pattern, combined Parabolic Equation model and Vertical-mode horizontal-ray model are utilized. A semi-analytic formula for estimating the horizontal Lloyd mirror effect is developed.

Reconfigurable wireless monitoring systems for bridges: validation on the Yeondae Bridge

NASA Astrophysics Data System (ADS)

Kim, Junhee; Lynch, Jerome P.; Zonta, Daniele; Lee, Jong-Jae; Yun, Chung-Bang

2009-03-01

The installation of a structural monitoring system on a medium- to large-span bridge can be a challenging undertaking due to high system costs and time consuming installations. However, these historical challenges can be eliminated by using wireless sensors as the primary building block of a structural monitoring system. Wireless sensors are low-cost data acquisition nodes that utilize wireless communication to transfer data from the sensor to the data repository. Another advantageous characteristic of wireless sensors is their ability to be easily removed and reinstalled in another sensor location on the same structure; this installation modularity is highlighted in this study. Wireless sensor nodes designed for structural monitoring applications are installed on the 180 m long Yeondae Bridge (Korea) to measure the dynamic response of the bridge to controlled truck loading. To attain a high nodal density with a small number (20) of wireless sensors, the wireless sensor network is installed three times with each installation concentrating sensors in one portion of the bridge. Using forced and free vibration response data from the three installations, the modal properties of the bridge are accurately identified. Intentional nodal overlapping of the three different sensor installations allows mode shapes from each installation to be stitched together into global mode shapes. Specifically, modal properties of the Yeondae Bridge are derived off-line using frequency domain decomposition (FDD) modal analysis methods.

Thermoresponsive core-shell magnetic nanoparticles for combined modalities of cancer therapy.

PubMed

Purushotham, S; Chang, P E J; Rumpel, H; Kee, I H C; Ng, R T H; Chow, P K H; Tan, C K; Ramanujan, R V

2009-07-29

Thermoresponsive polymer-coated magnetic nanoparticles loaded with anti-cancer drugs are of considerable interest for novel multi-modal cancer therapies. Such nanoparticles can be used for magnetic drug targeting followed by simultaneous hyperthermia and drug release. Gamma-Fe(2)O(3) iron oxide magnetic nanoparticles (MNP) with average sizes of 14, 19 and 43 nm were synthesized by high temperature decomposition. Composite magnetic nanoparticles (CNP) of 43 nm MNP coated with the thermoresponsive polymer poly-n-isopropylacrylamide (PNIPAM) were prepared by dispersion polymerization of n-isopropylacrylamide monomer in the presence of the MNP. In vitro drug release of doxorubicin-(dox) loaded dehydrated CNP at temperatures below and above the lower critical solution temperature of PNIPAM (34 degrees C) revealed a weak dependence of drug release on swelling behavior. The particles displayed Fickian diffusion release kinetics; the maximum dox release at 42 degrees C after 101 h was 41%. In vitro simultaneous hyperthermia and drug release of therapeutically relevant quantities of dox was achieved, 14.7% of loaded dox was released in 47 min at hyperthermia temperatures. In vivo magnetic targeting of dox-loaded CNP to hepatocellular carcinoma (HCC) in a buffalo rat model was studied by magnetic resonance imaging (MRI) and histology. In summary, the good in vitro and in vivo performance of the doxorubicin-loaded thermoresponsive polymer-coated magnetic nanoparticles suggests considerable promise for applications in multi-modal treatment of cancer.

Differential diagnosis between benign and malignant soft tissue tumors utilizing ultrasound parameters.

PubMed

Morii, Takeshi; Kishino, Tomonori; Shimamori, Naoko; Motohashi, Mitsue; Ohnishi, Hiroaki; Honya, Keita; Aoyagi, Takayuki; Tajima, Takashi; Ichimura, Shoichi

2018-01-01

Preoperative discrimination between benign and malignant soft tissue tumors is critical for the prevention of excess application of magnetic resonance imaging and biopsy as well as unplanned resection. Although ultrasound, including power Doppler imaging, is an easy, noninvasive, and cost-effective modality for screening soft tissue tumors, few studies have investigated reliable discrimination between benign and malignant soft tissue tumors. To establish a modality for discrimination between benign and malignant soft tissue tumors using ultrasound, we extracted the significant risk factors for malignancy based on ultrasound information from 40 malignant and 56 benign pathologically diagnosed soft tissue tumors and established a scoring system based on these risk factors. The maximum size, tumor margin, and vascularity evaluated using ultrasound were extracted as significant risk factors. Using the odds ratio from a multivariate regression model, a scoring system was established. Receiver operating characteristic analyses revealed a high area under the curve value (0.85), confirming the accuracy of the scoring system. Ultrasound is a useful modality for establishing the differential diagnosis between benign and malignant soft tissue tumors.

Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks.

PubMed

Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Galka, Andreas; Granert, Oliver; Wolff, Stephan; Deuschl, Guenther; Raethjen, Jan; Heute, Ulrich; Muthuraman, Muthuraman

2013-01-01

Brain activity can be measured using different modalities. Since most of the modalities tend to complement each other, it seems promising to measure them simultaneously. In to be presented research, the data recorded from Functional Magnetic Resonance Imaging (fMRI) and Near Infrared Spectroscopy (NIRS), simultaneously, are subjected to causality analysis using time-resolved partial directed coherence (tPDC). Time-resolved partial directed coherence uses the principle of state space modelling to estimate Multivariate Autoregressive (MVAR) coefficients. This method is useful to visualize both frequency and time dynamics of causality between the time series. Afterwards, causality results from different modalities are compared by estimating the Spearman correlation. In to be presented study, we used directionality vectors to analyze correlation, rather than actual signal vectors. Results show that causality analysis of the fMRI correlates more closely to causality results of oxy-NIRS as compared to deoxy-NIRS in case of a finger sequencing task. However, in case of simple finger tapping, no clear difference between oxy-fMRI and deoxy-fMRI correlation is identified.

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

NASA Astrophysics Data System (ADS)

Chekroun, Mickaël D.; Kondrashov, Dmitri

2017-09-01

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey, furthermore, a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled—provided the decay of temporal correlations is sufficiently well-resolved—within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.

Wavefront reconstruction using computer-generated holograms

NASA Astrophysics Data System (ADS)

Schulze, Christian; Flamm, Daniel; Schmidt, Oliver A.; Duparré, Michael

2012-02-01

We propose a new method to determine the wavefront of a laser beam, based on modal decomposition using computer-generated holograms (CGHs). Thereby the beam under test illuminates the CGH with a specific, inscribed transmission function that enables the measurement of modal amplitudes and phases by evaluating the first diffraction order of the hologram. Since we use an angular multiplexing technique, our method is innately capable of real-time measurements of amplitude and phase, yielding the complete information about the optical field. A measurement of the Stokes parameters, respectively of the polarization state, provides the possibility to calculate the Poynting vector. Two wavefront reconstruction possibilities are outlined: reconstruction from the phase for scalar beams and reconstruction from the Poynting vector for inhomogeneously polarized beams. To quantify single aberrations, the reconstructed wavefront is decomposed into Zernike polynomials. Our technique is applied to beams emerging from different kinds of multimode optical fibers, such as step-index, photonic crystal and multicore fibers, whereas in this work results are exemplarily shown for a step-index fiber and compared to a Shack-Hartmann measurement that serves as a reference.

Barriers and challenges in integration of anthroposophic medicine in supportive breast cancer care.

PubMed

Ben-Arye, Eran; Schiff, Elad; Levy, Moti; Raz, Orit Gressel; Barak, Yael; Bar-Sela, Gil

2013-01-01

In the last decade, more and more oncology centers are challenged with complementary medicine (CM) integration within supportive breast cancer care. Quality of life (QOL) improvement and attenuation of oncology treatment side effects are the core objectives of integrative CM programs in cancer care. Yet, limited research is available on the use of specific CM modalities in an integrative setting and on cancer patients' compliance with CM consultation. Studies are especially warranted to view the clinical application of researched CM modalities, such as anthroposophic medicine (AM), a unique CM modality oriented to cancer supportive care. Our objective was to characterize consultation patterns provided by physicians trained in CM following oncology health-care practitioners' referral of patients receiving chemotherapy. We aimed to identify characteristics of patients who consulted with AM and to explore patients' compliance to AM treatment. Of the 341 patients consulted with integrative physicians, 138 were diagnosed with breast cancer. Following integrative physician consultation, 56 patients were advised about AM treatment and 285 about other CM modalities. Logistic multivariate regression model found that, compared with patients receiving non-anthroposophic CM, the AM group had significantly greater rates of previous CM use [EXP(B) = 3.25, 95% C.I. 1.64-6.29, p = 0.001] and higher rates of cancer recurrence at baseline (p = 0.038). Most AM users (71.4%) used a single AM modality, such as mistletoe (viscum album) injections, oral AM supplements, or music therapy. Compliance with AM modalities following physician recommendation ranged from 44% to 71% of patients. We conclude that AM treatment provided within the integrative oncology setting is feasible based on compliance assessment. Other studies are warranted to explore the effectiveness of AM in improving patients' QOL during chemotherapy.

Modal identification of spindle-tool unit in high-speed machining

NASA Astrophysics Data System (ADS)

Gagnol, Vincent; Le, Thien-Phu; Ray, Pascal

2011-10-01

The accurate knowledge of high-speed motorised spindle dynamic behaviour during machining is important in order to ensure the reliability of machine tools in service and the quality of machined parts. More specifically, the prediction of stable cutting regions, which is a critical requirement for high-speed milling operations, requires the accurate estimation of tool/holder/spindle set dynamic modal parameters. These estimations are generally obtained through Frequency Response Function (FRF) measurements of the non-rotating spindle. However, significant changes in modal parameters are expected to occur during operation, due to high-speed spindle rotation. The spindle's modal variations are highlighted through an integrated finite element model of the dynamic high-speed spindle-bearing system, taking into account rotor dynamics effects. The dependency of dynamic behaviour on speed range is then investigated and determined with accuracy. The objective of the proposed paper is to validate these numerical results through an experiment-based approach. Hence, an experimental setup is elaborated to measure rotating tool vibration during the machining operation in order to determine the spindle's modal frequency variation with respect to spindle speed in an industrial environment. The identification of natural frequencies of the spindle under rotating conditions is challenging, due to the low number of sensors and the presence of many harmonics in the measured signals. In order to overcome these issues and to extract the characteristics of the system, the spindle modes are determined through a 3-step procedure. First, spindle modes are highlighted using the Frequency Domain Decomposition (FDD) technique, with a new formulation at the considered rotating speed. These extracted modes are then analysed through the value of their respective damping ratios in order to separate the harmonics component from structural spindle natural frequencies. Finally, the stochastic properties of the modes are also investigated by considering the probability density of the retained modes. Results show a good correlation between numerical and experiment-based identified frequencies. The identified spindle-tool modal properties during machining allow the numerical model to be considered as representative of the real dynamic properties of the system.

Spectral simplicity of apparent complexity. II. Exact complexities and complexity spectra

NASA Astrophysics Data System (ADS)

Riechers, Paul M.; Crutchfield, James P.

2018-03-01

The meromorphic functional calculus developed in Part I overcomes the nondiagonalizability of linear operators that arises often in the temporal evolution of complex systems and is generic to the metadynamics of predicting their behavior. Using the resulting spectral decomposition, we derive closed-form expressions for correlation functions, finite-length Shannon entropy-rate approximates, asymptotic entropy rate, excess entropy, transient information, transient and asymptotic state uncertainties, and synchronization information of stochastic processes generated by finite-state hidden Markov models. This introduces analytical tractability to investigating information processing in discrete-event stochastic processes, symbolic dynamics, and chaotic dynamical systems. Comparisons reveal mathematical similarities between complexity measures originally thought to capture distinct informational and computational properties. We also introduce a new kind of spectral analysis via coronal spectrograms and the frequency-dependent spectra of past-future mutual information. We analyze a number of examples to illustrate the methods, emphasizing processes with multivariate dependencies beyond pairwise correlation. This includes spectral decomposition calculations for one representative example in full detail.

Formalized classification of moss litters in swampy spruce forests of intermontane depressions of Kuznetsk Alatau

NASA Astrophysics Data System (ADS)

Efremova, T. T.; Avrova, A. F.; Efremov, S. P.

2016-09-01

The approaches of multivariate statistics have been used for the numerical classification of morphogenetic types of moss litters in swampy spruce forests according to their physicochemical properties (the ash content, decomposition degree, bulk density, pH, mass, and thickness). Three clusters of moss litters— peat, peaty, and high-ash peaty—have been specified. The functions of classification for identification of new objects have been calculated and evaluated. The degree of decomposition and the ash content are the main classification parameters of litters, though all other characteristics are also statistically significant. The final prediction accuracy of the assignment of a litter to a particular cluster is 86%. Two leading factors participating in the clustering of litters have been determined. The first factor—the degree of transformation of plant remains (quality)—specifies 49% of the total variance, and the second factor—the accumulation rate (quantity)— specifies 26% of the total variance. The morphogenetic structure and physicochemical properties of the clusters of moss litters are characterized.

Age-associated patterns in gray matter volume, cerebral perfusion and BOLD oscillations in children and adolescents.

PubMed

Bray, Signe

2017-05-01

Healthy brain development involves changes in brain structure and function that are believed to support cognitive maturation. However, understanding how structural changes such as grey matter thinning relate to functional changes is challenging. To gain insight into structure-function relationships in development, the present study took a data driven approach to define age-related patterns of variation in gray matter volume (GMV), cerebral blood flow (CBF) and blood-oxygen level dependent (BOLD) signal variation (fractional amplitude of low-frequency fluctuations; fALFF) in 59 healthy children aged 7-18 years, and examined relationships between modalities. Principal components analysis (PCA) was applied to each modality in parallel, and participant scores for the top components were assessed for age associations. We found that decompositions of CBF, GMV and fALFF all included components for which scores were significantly associated with age. The dominant patterns in GMV and CBF showed significant (GMV) or trend level (CBF) associations with age and a strong spatial overlap, driven by increased signal intensity in default mode network (DMN) regions. GMV, CBF and fALFF additionally showed components accounting for 3-5% of variability with significant age associations. However, these patterns were relatively spatially independent, with small-to-moderate overlap between modalities. Independence of age effects was further demonstrated by correlating individual subject maps between modalities: CBF was significantly less correlated with GMV and fALFF in older children relative to younger. These spatially independent effects of age suggest that the parallel decline observed in global GMV and CBF may not reflect spatially synchronized processes. Hum Brain Mapp 38:2398-2407, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Using experimental modal analysis to assess the behaviour of timber elements

NASA Astrophysics Data System (ADS)

Kouroussis, Georges; Fekih, Lassaad Ben; Descamps, Thierry

2018-03-01

Timber frameworks are one of the most important and widespread types of structures. Their configurations and joints are usually complex and require a high level of craftsmanship to assemble. In the field of restoration, a good understanding of the structural behaviour is necessary and is often based on assessment techniques dedicated to wood characterisation. This paper presents the use of experimental modal analysis for finite element updating. To do this, several timber beams in a free supported condition were analysed in order to extract their bending natural characteristics (frequency, damping and mode shapes). Corresponding ABAQUS finite element models were derived which included the effects of local defects (holes, cracks and wood nodes), moisture and structural decay. To achieve the modal updating, additional simulations were performed in order to study the sensitivity of the mechanical parameters. With the intent to estimate their mechanical properties, a procedure of modal updating was carried out in MatLab with a Python script. This was created to extract the modal information from the ABAQUS modal analysis results to be compared with the experimental results. The updating was based on a minimum of unconstrained multivariable function using a derivative-free method. The objective function was selected from the conventional comparison tools (absolute or relative frequency difference, and/or modal assurance criterion). This testing technique was used to determine the dynamic mechanical properties of timber beams, such as the anisotropic Young's Moduli and damping ratio. To verify the modulus, a series of static 4-point bending tests and STS04 classifications were conducted. The results also revealed that local defects have a negligible influence on natural frequencies. The results demonstrate that this assessment tool offers an effective method to obtain the mechanical properties of timber elements, especially when on-site and non-destructive techniques are needed, for example when retrofitting an existing structure.

Explicit reference governor for linear systems

NASA Astrophysics Data System (ADS)

Garone, Emanuele; Nicotra, Marco; Ntogramatzidis, Lorenzo

2018-06-01

The explicit reference governor is a constrained control scheme that was originally introduced for generic nonlinear systems. This paper presents two explicit reference governor strategies that are specifically tailored for the constrained control of linear time-invariant systems subject to linear constraints. Both strategies are based on the idea of maintaining the system states within an invariant set which is entirely contained in the constraints. This invariant set can be constructed by exploiting either the Lyapunov inequality or modal decomposition. To improve the performance, we show that the two strategies can be combined by choosing at each time instant the least restrictive set. Numerical simulations illustrate that the proposed scheme achieves performances that are comparable to optimisation-based reference governors.

Control of complex dynamics and chaos in distributed parameter systems

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

Chakravarti, S.; Marek, M.; Ray, W.H.

This paper discusses a methodology for controlling complex dynamics and chaos in distributed parameter systems. The reaction-diffusion system with Brusselator kinetics, where the torus-doubling or quasi-periodic (two characteristic incommensurate frequencies) route to chaos exists in a defined range of parameter values, is used as an example. Poincare maps are used for characterization of quasi-periodic and chaotic attractors. The dominant modes or topos, which are inherent properties of the system, are identified by means of the Singular Value Decomposition. Tested modal feedback control schemas based on identified dominant spatial modes confirm the possibility of stabilization of simple quasi-periodic trajectories in themore » complex quasi-periodic or chaotic spatiotemporal patterns.« less

Effect of noise in principal component analysis with an application to ozone pollution

NASA Astrophysics Data System (ADS)

Tsakiri, Katerina G.

This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter

PubMed Central

Kessler, Daniel; Angstadt, Michael; Welsh, Robert C.

2014-01-01

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations. PMID:25505309

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

PubMed

Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

2017-08-01

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

Implementation of Multivariable Logic Functions in Parallel by Electrically Addressing a Molecule of Three Dopants in Silicon.

PubMed

Fresch, Barbara; Bocquel, Juanita; Hiluf, Dawit; Rogge, Sven; Levine, Raphael D; Remacle, Françoise

2017-07-05

To realize low-power, compact logic circuits, one can explore parallel operation on single nanoscale devices. An added incentive is to use multivalued (as distinct from Boolean) logic. Here, we theoretically demonstrate that the computation of all the possible outputs of a multivariate, multivalued logic function can be implemented in parallel by electrical addressing of a molecule made up of three interacting dopant atoms embedded in Si. The electronic states of the dopant molecule are addressed by pulsing a gate voltage. By simulating the time evolution of the non stationary electronic density built by the gate voltage, we show that one can implement a molecular decision tree that provides in parallel all the outputs for all the inputs of the multivariate, multivalued logic function. The outputs are encoded in the populations and in the bond orders of the dopant molecule, which can be measured using an STM tip. We show that the implementation of the molecular logic tree is equivalent to a spectral function decomposition. The function that is evaluated can be field-programmed by changing the time profile of the pulsed gate voltage. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wave-filter-based approach for generation of a quiet space in a rectangular cavity

NASA Astrophysics Data System (ADS)

Iwamoto, Hiroyuki; Tanaka, Nobuo; Sanada, Akira

2018-02-01

This paper is concerned with the generation of a quiet space in a rectangular cavity using active wave control methodology. It is the purpose of this paper to present the wave filtering method for a rectangular cavity using multiple microphones and its application to an adaptive feedforward control system. Firstly, the transfer matrix method is introduced for describing the wave dynamics of the sound field, and then feedforward control laws for eliminating transmitted waves is derived. Furthermore, some numerical simulations are conducted that show the best possible result of active wave control. This is followed by the derivation of the wave filtering equations that indicates the structure of the wave filter. It is clarified that the wave filter consists of three portions; modal group filter, rearrangement filter and wave decomposition filter. Next, from a numerical point of view, the accuracy of the wave decomposition filter which is expressed as a function of frequency is investigated using condition numbers. Finally, an experiment on the adaptive feedforward control system using the wave filter is carried out, demonstrating that a quiet space is generated in the target space by the proposed method.

Acoustics flow analysis in circular duct using sound intensity and dynamic mode decomposition

NASA Astrophysics Data System (ADS)

Weyna, S.

2014-08-01

Sound intensity generation in hard-walled duct with acoustic flow (no mean-flow) is treated experimentally and shown graphically. In paper, numerous methods of visualization illustrating the vortex flow (2D, 3D) can graphically explain diffraction and scattering phenomena occurring inside the duct and around open end area. Sound intensity investigation in annular duct gives a physical picture of sound waves in any duct mode. In the paper, modal energy analysis are discussed with particular reference to acoustics acoustic orthogonal decomposition (AOD). The image of sound intensity fields before and above "cut-off" frequency region are found to compare acoustic modes which might resonate in duct. The experimental results show also the effects of axial and swirling flow. However acoustic field is extremely complicated, because pressures in non-propagating (cut-off) modes cooperate with the particle velocities in propagating modes, and vice versa. Measurement in cylindrical duct demonstrates also the cut-off phenomenon and the effect of reflection from open end. The aim of experimental study was to obtain information on low Mach number flows in ducts in order to improve physical understanding and validate theoretical CFD and CAA models that still may be improved.

Frequency Domain Decomposition performed on the strain data obtained from the aluminium model of an offshore support structure

NASA Astrophysics Data System (ADS)

Mieloszyk, M.; Opoka, S.; Ostachowicz, W.

2015-07-01

This paper presents an application of Fibre Bragg Grating (FBG) sensors for Structural Health Monitoring (SHM) of offshore wind energy support structure model. The analysed structure is a tripod equipped with 16 FBG sensors. From a wide variety of Operational Modal Analysis (OMA) methods Frequency Domain Decomposition (FDD) technique is used in this paper under assumption that the input loading is similar to a white noise excitation. The FDD method can be applied using different sets of sensors, i.e. the one which contains all FBG sensors and the other set of sensors localised only on a particular tripod's leg. The cases considered during investigation were as follows: damaged and undamaged scenarios, different support conditions. The damage was simulated as an dismantled flange on an upper brace in one of the tripod legs. First the model was fixed to an antishaker table and investigated in the air under impulse excitations. Next the tripod was submerged into water basin in order to check the quality of the measurement set-up in different environmental condition. In this case the model was excited by regular waves.

Agile Multi-Scale Decompositions for Automatic Image Registration

NASA Technical Reports Server (NTRS)

Murphy, James M.; Leija, Omar Navarro; Le Moigne, Jacqueline

2016-01-01

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed.

Application of Dynamic Mode Decomposition: Temporal Evolution of Flow Structures in an Aneurysm

NASA Astrophysics Data System (ADS)

Conlin, William; Yu, Paulo; Durgesh, Vibhav

2017-11-01

An aneurysm is an enlargement of a weakened arterial wall that can be fatal or debilitating on rupture. Aneurysm hemodynamics is integral to developing an understanding of aneurysm formation, growth, and rupture. The flow in an aneurysm exhibits complex fluid dynamics behavior due to an inherent unsteady inflow condition and its interactions with large-scale flow structures present in the aneurysm. The objective of this study is to identify the large-scale structures in the aneurysm, study temporal behavior, and quantify their interaction with the inflow condition. For this purpose, detailed Particle Image Velocimetry (PIV) measurements were performed at the center plane of an idealized aneurysm model for a range of inflow conditions. Inflow conditions were precisely controlled using a ViVitro SuperPump system. Dynamic Modal Decomposition (DMD) of the velocity field was used to identify coherent structures and their temporal behavior. DMD was successful in capturing the large-scale flow structures and their temporal behavior. A low dimensional approximation to the flow field was obtained with the most relevant dynamic modes and was used to obtain temporal information about the coherent structures and their interaction with the inflow, formation, evolution, and growth.

Hidden discriminative features extraction for supervised high-order time series modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2016-11-01

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination

NASA Astrophysics Data System (ADS)

Rocha, José Celso; Passalia, Felipe José; Matos, Felipe Delestro; Takahashi, Maria Beatriz; Maserati, Marc Peter, Jr.; Alves, Mayra Fernanda; de Almeida, Tamie Guibu; Cardoso, Bruna Lopes; Basso, Andrea Cristina; Nogueira, Marcelo Fábio Gouveia

2017-12-01

There is currently no objective, real-time and non-invasive method for evaluating the quality of mammalian embryos. In this study, we processed images of in vitro produced bovine blastocysts to obtain a deeper comprehension of the embryonic morphological aspects that are related to the standard evaluation of blastocysts. Information was extracted from 482 digital images of blastocysts. The resulting imaging data were individually evaluated by three experienced embryologists who graded their quality. To avoid evaluation bias, each image was related to the modal value of the evaluations. Automated image processing produced 36 quantitative variables for each image. The images, the modal and individual quality grades, and the variables extracted could potentially be used in the development of artificial intelligence techniques (e.g., evolutionary algorithms and artificial neural networks), multivariate modelling and the study of defined structures of the whole blastocyst.

Statistical Model of Dynamic Markers of the Alzheimer's Pathological Cascade.

PubMed

Balsis, Steve; Geraci, Lisa; Benge, Jared; Lowe, Deborah A; Choudhury, Tabina K; Tirso, Robert; Doody, Rachelle S

2018-05-05

Alzheimer's disease (AD) is a progressive disease reflected in markers across assessment modalities, including neuroimaging, cognitive testing, and evaluation of adaptive function. Identifying a single continuum of decline across assessment modalities in a single sample is statistically challenging because of the multivariate nature of the data. To address this challenge, we implemented advanced statistical analyses designed specifically to model complex data across a single continuum. We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1,056), focusing on indicators from the assessments of magnetic resonance imaging (MRI) volume, fluorodeoxyglucose positron emission tomography (FDG-PET) metabolic activity, cognitive performance, and adaptive function. Item response theory was used to identify the continuum of decline. Then, through a process of statistical scaling, indicators across all modalities were linked to that continuum and analyzed. Findings revealed that measures of MRI volume, FDG-PET metabolic activity, and adaptive function added measurement precision beyond that provided by cognitive measures, particularly in the relatively mild range of disease severity. More specifically, MRI volume, and FDG-PET metabolic activity become compromised in the very mild range of severity, followed by cognitive performance and finally adaptive function. Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.

From Perception to Metacognition: Auditory and Olfactory Functions in Early Blind, Late Blind, and Sighted Individuals

PubMed Central

Cornell Kärnekull, Stina; Arshamian, Artin; Nilsson, Mats E.; Larsson, Maria

2016-01-01

Although evidence is mixed, studies have shown that blind individuals perform better than sighted at specific auditory, tactile, and chemosensory tasks. However, few studies have assessed blind and sighted individuals across different sensory modalities in the same study. We tested early blind (n = 15), late blind (n = 15), and sighted (n = 30) participants with analogous olfactory and auditory tests in absolute threshold, discrimination, identification, episodic recognition, and metacognitive ability. Although the multivariate analysis of variance (MANOVA) showed no overall effect of blindness and no interaction with modality, follow-up between-group contrasts indicated a blind-over-sighted advantage in auditory episodic recognition, that was most pronounced in early blind individuals. In contrast to the auditory modality, there was no empirical support for compensatory effects in any of the olfactory tasks. There was no conclusive evidence for group differences in metacognitive ability to predict episodic recognition performance. Taken together, the results showed no evidence of an overall superior performance in blind relative sighted individuals across olfactory and auditory functions, although early blind individuals exceled in episodic auditory recognition memory. This observation may be related to an experience-induced increase in auditory attentional capacity. PMID:27729884

Damage methodology approach on a composite panel based on a combination of Fringe Projection and 2D Digital Image Correlation

NASA Astrophysics Data System (ADS)

Felipe-Sesé, Luis; Díaz, Francisco A.

2018-02-01

The recent improvement in accessibility to high speed digital cameras has enabled three dimensional (3D) vibration measurements employing full-field optical techniques. Moreover, there is a need to develop a cost-effective and non-destructive testing method to quantify the severity of damages arising from impacts and thus, enhance the service life. This effect is more interesting in composite structures since possible internal damage has low external manifestation. Those possible damages have been previously studied experimentally by using vibration testing. Namely, those analyses were focused on variations in the modal frequencies or, more recently, mode shapes variations employing punctual accelerometers or vibrometers. In this paper it is presented an alternative method to investigate the severity of damage on a composite structure and how the damage affects to its integrity through the analysis of the full field modal behaviour. In this case, instead of punctual measurements, displacement maps are analysed by employing a combination of FP + 2D-DIC during vibration experiments in an industrial component. In addition, to analyse possible mode shape changes, differences between damaged and undamaged specimens are studied by employing a recent methodology based on Adaptive Image Decomposition (AGMD) procedure. It will be demonstrated that AGMD Image decomposition procedure, which decompose the displacement field into shape descriptors, is capable to detect and quantify the differences between mode shapes. As an application example, the proposed approach has been evaluated on two large industrial components (car bonnets) made of short-fibre reinforced composite. Specifically, the evolution of normalized AGMD shape descriptors has been evaluated for three different components with different damage levels. Results demonstrate the potential of the presented approach making it possible to measure the severity of a structural damage by evaluating the mode shape based in the analysis of its shape descriptors.

Challenging the dogma of colorectal peritoneal metastases as an untreatable condition: Results of a population-based study.

PubMed

Razenberg, Lieke G E M; Lemmens, Valery E P P; Verwaal, Victor J; Punt, Cornelis J A; Tanis, Pieter J; Creemers, Geert-Jan; de Hingh, Ignace H J T

2016-09-01

To determine the impact of the implementation of novel systemic regimens and locoregional treatment modalities on survival at population level in colorectal cancer (CRC) patients presenting with peritoneal metastases (PMs). All consecutive CRC patients with synchronous PM (<3 months) between 1995 and 2014 were extracted from the Eindhoven area of the Netherlands Cancer Registry. Trends in treatment and overall survival were assessed in four time periods. Multivariable regression analysis was used to analyse the impact of systemic and locoregional treatment modalities on survival. A total of 37,036 patients were diagnosed with primary CRC between 1995 and 2014. Synchronous PM was diagnosed in 1,661 patients, of whom 55% had also metastases at other sites (n = 917) and 77% received anticancer therapy (n = 1,273). Treatment with systemic therapy increased from 23% in 1995-1999 to 56% in 2010-2014 (p < 0.0001). Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) was applied since 2005 and increased from 10% in 2005-2009 to 23% in 2010-2014. Surgery for lymphatic or haematogenous metastases increased from 2% to 10% in these periods. Median overall survival of the complete cohort improved from 6.0 months in 1995-2000 to 12.5 months in 2010-2014 (p < 0.0001), with a doubling of survival for both PM alone and PM with other involved sites. The influence of year of diagnosis on survival (hazard ratio, 2010-2014 versus 1995-1999; 0.5, 95% confidence interval: 0.43-0.62; p < 0.0001) disappeared after including systemic therapy and locoregional treatment modalities in subsequent multivariable models. CRC patients presenting with PM are increasingly offered a multidisciplinary treatment approach, resulting in an increased overall survival for the entire cohort. Copyright © 2016 Elsevier Ltd. All rights reserved.

Heave and Flow: Understanding the role of resonance and shape evolution for heaving flexible panels

NASA Astrophysics Data System (ADS)

Hoover, Alexander; Cortez, Ricardo; Tytell, Eric; Fauci, Lisa

2017-11-01

Many animals that swim or fly use their body to accelerate the fluid around them, transferring momentum from their bodies to the surrounding fluid. The emergent kinematics from this transfer are a result of the coupling between the fluid and the material properties of the body. Here we present a computational study of a 3-dimensional flexible panel that is heaved at its leading edge in an incompressible, viscous fluid. These high-fidelity numerical simulations enable us to examine the role of resonance, fluid forces, and panel deformations have on swimming performance. Varying both the passive material properties and the heaving frequency of the panel, we find peaks in trailing edge amplitude and forward swimming speed are determined by a dimensionless quantity, the effective flexibility. Modal decompositions of panel deflections reveal that the strength of each mode is related to the effective flexibility and peaks in the swimming speed and trailing edge amplitude correspond to peaks in the contributions of different modes. Panels of different material properties but with similar effective flexibilities have modal contributions that evolve similarly over the phase of the heaving cycle and agreement in dominant vortex structures generated by the panel. NSF RTG 1043626.

Etude vibroacoustique d'un systeme coque-plancher-cavite avec application a un fuselage simplifie

NASA Astrophysics Data System (ADS)

Missaoui, Jemai

L'objectif de ce travail est de developper des modeles semi-analytiques pour etudier le comportement structural, acoustique et vibro-acoustique d'un systeme coque-plancher-cavite. La connection entre la coque et le plancher est assuree en utilisant le concept de rigidite artificielle. Ce concept de modelisation flexible facilite le choix des fonctions de decomposition du mouvement de chaque sous-structure. Les resultats issus de cette etude vont permettre la comprehension des phenomenes physiques de base rencontres dans une structure d'avion. Une approche integro-modale est developpee pour calculer les caracteristiques modales acoustiques. Elle utilise une discretisation de la cavite irreguliere en sous-cavites acoustiques dont les bases de developpement sont connues a priori. Cette approche, a caractere physique, presente l'avantage d'etre efficace et precise. La validite de celle-ci a ete demontree en utilisant des resultats disponibles dans la litterature. Un modele vibro-acoustique est developpe dans un but d'analyser et de comprendre les effets structuraux et acoustiques du plancher dans la configuration. La validite des resultats, en termes de resonance et de fonction de transfert, est verifiee a l'aide des mesures experimentales realisees au laboratoire.

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.

An "education for life" requirement to promote lifelong learning in an internal medicine residency program.

PubMed

Panda, Mukta; Desbiens, Norman A

2010-12-01

Lifelong learning is an integral component of practice-based learning and improvement. Physicians need to be lifelong learners to provide timely, efficient, and state-of-the-art patient care in an environment where knowledge, technology, and social requirements are rapidly changing. To assess graduates' self-reported perception of the usefulness of a residency program requirement to submit a narrative report describing their planned educational modalities for their future continued medical learning ("Education for Life" requirement), and to compare the modalities residents intended to use with their reported educational activities. Data was compiled from the Education for Life reports submitted by internal medicine residents at the University of Tennessee College of Medicine Chattanooga from 1998 to 2000, and from a survey sent to the same 27 graduates 2 to 4 years later from 2000 to 2004. Twenty-four surveys (89%) were returned. Of the responding graduates, 58% (14/24) found the Education for Life requirement useful for their future continued medical learning. Graduates intended to keep up with a mean of 3.4 educational modalities, and they reported keeping up with 4.2. In a multivariable analysis, the number of modalities graduates used was significantly associated with the number they had planned to use before graduation (P  =  .04) but not with their career choice of subspecialization. The majority of residents found the Education for Life requirement useful for their future continued medical learning. Graduates, regardless of specialty, reported using more modalities for continuing their medical education than they thought they would as residents. Considering lifelong learning early in training and then requiring residents to identify ways to practice lifelong learning as a requirement for graduation may be dispositive.

Relationship of Various Open Quotients With Acoustic Property, Phonation Types, Fundamental Frequency, and Intensity.

PubMed

Yokonishi, Hisayuki; Imagawa, Hiroshi; Sakakibara, Ken-Ichi; Yamauchi, Akihito; Nito, Takaharu; Yamasoba, Tatsuya; Tayama, Niro

2016-03-01

In the present study, we examined the relationship between various open quotients (Oqs) and phonation types, fundamental frequency (F0), and intensity by multivariate linear regression analysis (MVA) to determine which Oq best reflects vocal fold vibratory characteristics. Using high-speed digital imaging (HSDI), a sustained vowel /e/ at different phonation types, F0s, and intensities was recorded from six vocally healthy male volunteers: the types of phonation included modal, falsetto, modal breathy, and modal pressed phonations; and each phonation was performed at different F0s and intensities. Electroglottography (EGG) and sound signals were simultaneously recorded with HSDI. From the obtained data, 10 conventional Oqs (four Oqs from the glottal area function, four kymographic Oqs, and two EGG-derived Oqs) and two newly introduced Oqs (Oq(edge)+ and Oq(edge)) were evaluated. And, relationships between various Oqs and phonation types, F0, and intensity were evaluated by MVA. Among the various Oqs, Oq(edge)+ and Oq(edge) revealed the strongest correlations with an acoustic property and could best describe changes in phonation types: Oq(edge) was found to be better than Oq(edge)¯. Oq(MLK), the average of five Oqs from five-line multiline kymography was a very good alternative to Oq(edge)¯. EGG-derived Oqs were able to differentiate between modal phonation and falsetto phonation, but it was necessary to consider the change of F0 simultaneously. MVA showed the changes in Oq values between modal and other phonation types, the degree of involvement of intensity, and no relationship between F0 and Oqs. Among Oqs evaluated in this study, Oq(edge)+ and Oq(edge) were considered to best reflect the vocal fold vibratory characteristics. Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

An “Education for Life” Requirement to Promote Lifelong Learning in an Internal Medicine Residency Program

PubMed Central

Panda, Mukta; Desbiens, Norman A.

2010-01-01

Background Lifelong learning is an integral component of practice-based learning and improvement. Physicians need to be lifelong learners to provide timely, efficient, and state-of-the-art patient care in an environment where knowledge, technology, and social requirements are rapidly changing. Objectives To assess graduates' self-reported perception of the usefulness of a residency program requirement to submit a narrative report describing their planned educational modalities for their future continued medical learning (“Education for Life” requirement), and to compare the modalities residents intended to use with their reported educational activities. Materials and Methods Data was compiled from the Education for Life reports submitted by internal medicine residents at the University of Tennessee College of Medicine Chattanooga from 1998 to 2000, and from a survey sent to the same 27 graduates 2 to 4 years later from 2000 to 2004. Results Twenty-four surveys (89%) were returned. Of the responding graduates, 58% (14/24) found the Education for Life requirement useful for their future continued medical learning. Graduates intended to keep up with a mean of 3.4 educational modalities, and they reported keeping up with 4.2. In a multivariable analysis, the number of modalities graduates used was significantly associated with the number they had planned to use before graduation (P  =  .04) but not with their career choice of subspecialization. Conclusion The majority of residents found the Education for Life requirement useful for their future continued medical learning. Graduates, regardless of specialty, reported using more modalities for continuing their medical education than they thought they would as residents. Considering lifelong learning early in training and then requiring residents to identify ways to practice lifelong learning as a requirement for graduation may be dispositive. PMID:22132278

Ground Vibration Test Planning and Pre-Test Analysis for the X-33 Vehicle

NASA Technical Reports Server (NTRS)

Bedrossian, Herand; Tinker, Michael L.; Hidalgo, Homero

2000-01-01

This paper describes the results of the modal test planning and the pre-test analysis for the X-33 vehicle. The pre-test analysis included the selection of the target modes, selection of the sensor and shaker locations and the development of an accurate Test Analysis Model (TAM). For target mode selection, four techniques were considered, one based on the Modal Cost technique, one based on Balanced Singular Value technique, a technique known as the Root Sum Squared (RSS) method, and a Modal Kinetic Energy (MKE) approach. For selecting sensor locations, four techniques were also considered; one based on the Weighted Average Kinetic Energy (WAKE), one based on Guyan Reduction (GR), one emphasizing engineering judgment, and one based on an optimum sensor selection technique using Genetic Algorithm (GA) search technique combined with a criteria based on Hankel Singular Values (HSV's). For selecting shaker locations, four techniques were also considered; one based on the Weighted Average Driving Point Residue (WADPR), one based on engineering judgment and accessibility considerations, a frequency response method, and an optimum shaker location selection based on a GA search technique combined with a criteria based on HSV's. To evaluate the effectiveness of the proposed sensor and shaker locations for exciting the target modes, extensive numerical simulations were performed. Multivariate Mode Indicator Function (MMIF) was used to evaluate the effectiveness of each sensor & shaker set with respect to modal parameter identification. Several TAM reduction techniques were considered including, Guyan, IRS, Modal, and Hybrid. Based on a pre-test cross-orthogonality checks using various reduction techniques, a Hybrid TAM reduction technique was selected and was used for all three vehicle fuel level configurations.

The effect of vortex merging and non-merging on the transfer of modal turbulent kinetic energy content

NASA Astrophysics Data System (ADS)

Ground, Cody; Vergine, Fabrizio; Maddalena, Luca

2016-08-01

A defining feature of the turbulent free shear layer is that its growth is hindered by compressibility effects, thus limiting its potential to sufficiently mix the injected fuel and surrounding airstream at the supersonic Mach numbers intrinsic to the combustor of air-breathing hypersonic vehicles. The introduction of streamwise vorticity is often proposed in an attempt to counteract these undesired effects. This fact makes the strategy of introducing multiple streamwise vortices and imposing upon them certain modes of mutual interaction in order to potentially enhance mixing an intriguing concept. However, many underlying fundamental characteristics of the flowfields in the presence such interactions are not yet well understood; therefore, the fundamental physics of these flowfields should be independently investigated before the explicit mixing performance is characterized. In this work, experimental measurements are taken with the stereoscopic particle image velocimetry technique on two specifically targeted modes of vortex interaction—the merging and non-merging of two corotating vortices. The fluctuating velocity fields are analyzed utilizing the proper orthogonal decomposition (POD) in order to identify the content, organization, and distribution of the modal turbulent kinetic energy content of the fluctuating velocity eigenmodes. The effects of the two modes of vortex interaction are revealed by the POD analysis which shows distinct differences in the modal features of the two cases. When comparing the low-order eigenmodes of the two cases, the size of the structures contained within the first ten modes is seen to increase as the flow progresses downstream for the merging case, whereas the opposite is true for the non-merging case. Additionally, the relative modal energy contribution of the first ten eigenmodes increases as the vortices evolve downstream for the merging case, whereas in the non-merging case the relative modal energy contribution decreases. The POD results show that the vortex merging process reorients and redistributes the relative turbulent kinetic energy content toward the larger-scale structures within the low-order POD eigenmodes. This result suggests that by specifically designing the vortex generation system to impose preselected modes of vortex interaction upon the flow it is possible to exert some form of control over the downstream evolution and distribution of the global and modal turbulent kinetic energy content.

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

PubMed

Allefeld, Carsten; Bialonski, Stephan

2007-12-01

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

Pusher propeller noise directivity and trends

NASA Technical Reports Server (NTRS)

Block, P. J. W.

1986-01-01

The effects of pylon wake interaction on far-field propeller noise are studied using a model scale SR-2 propeller in a low-speed anechoic wind tunnel. The variation in the pusher noise penalty with axial angle theta and circumferential angle phi is compared to that of the tractor noise penalty; and the former exhibits minima occurring in the propeller plane and maxima occurring toward the propeller axis. The magnitude of the pusher installation noise penalty decreased with in increase in shaft horsepower and tip Mach number. Directivity comparisons revealed that both a noise reduction and a directivity pattern change resulted when the pylon was moved farther from the propeller. Noise emerging from the wake interaction was distinguished from that of the propeller by means of a modal decomposition.

Quantum cascade laser combs: effects of modulation and dispersion.

PubMed

Villares, Gustavo; Faist, Jérôme

2015-01-26

Frequency comb formation in quantum cascade lasers is studied theoretically using a Maxwell-Bloch formalism based on a modal decomposition, where dispersion is considered. In the mid-infrared, comb formation persists in the presence of weak cavity dispersion (500 fs² mm^-1) but disappears when much larger values are used (30'000 fs² mm^-1). Active modulation at the round-trip frequency is found to induce mode-locking in THz devices, where the upper state lifetime is in the tens of picoseconds. Our results show that mode-locking based on four-wave mixing in broadband gain, low dispersion cavities is the most promising way of achieving broadband quantum cascade laser frequency combs.

A novel method of identifying motor primitives using wavelet decomposition*

PubMed Central

Popov, Anton; Olesh, Erienne V.; Yakovenko, Sergiy; Gritsenko, Valeriya

2018-01-01

This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

Wavefront sensing with all-digital Stokes measurements

NASA Astrophysics Data System (ADS)

Dudley, Angela; Milione, Giovanni; Alfano, Robert R.; Forbes, Andrew

2014-09-01

A long-standing question in optics has been to efficiently measure the phase (or wavefront) of an optical field. This has led to numerous publications and commercial devices such as phase shift interferometry, wavefront reconstruction via modal decomposition and Shack-Hartmann wavefront sensors. In this work we develop a new technique to extract the phase which in contrast to previously mentioned methods is based on polarization (or Stokes) measurements. We outline a simple, all-digital approach using only a spatial light modulator and a polarization grating to exploit the amplitude and phase relationship between the orthogonal states of polarization to determine the phase of an optical field. We implement this technique to reconstruct the phase of static and propagating optical vortices.

Ambient Vibration Testing for Story Stiffness Estimation of a Heritage Timber Building

PubMed Central

Min, Kyung-Won; Kim, Junhee; Park, Sung-Ah; Park, Chan-Soo

2013-01-01

This paper investigates dynamic characteristics of a historic wooden structure by ambient vibration testing, presenting a novel estimation methodology of story stiffness for the purpose of vibration-based structural health monitoring. As for the ambient vibration testing, measured structural responses are analyzed by two output-only system identification methods (i.e., frequency domain decomposition and stochastic subspace identification) to estimate modal parameters. The proposed methodology of story stiffness is estimation based on an eigenvalue problem derived from a vibratory rigid body model. Using the identified natural frequencies, the eigenvalue problem is efficiently solved and uniquely yields story stiffness. It is noteworthy that application of the proposed methodology is not necessarily confined to the wooden structure exampled in the paper. PMID:24227999

Embolotherapy for Neuroendocrine Tumor Liver Metastases: Prognostic Factors for Hepatic Progression-Free Survival and Overall Survival

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

Chen, James X.; Rose, Steven; White, Sarah B.

PurposeThe purpose of the study was to evaluate prognostic factors for survival outcomes following embolotherapy for neuroendocrine tumor (NET) liver metastases.Materials and MethodsThis was a multicenter retrospective study of 155 patients (60 years mean age, 57 % male) with NET liver metastases from pancreas (n = 71), gut (n = 68), lung (n = 8), or other/unknown (n = 8) primary sites treated with conventional transarterial chemoembolization (TACE, n = 50), transarterial radioembolization (TARE, n = 64), or transarterial embolization (TAE, n = 41) between 2004 and 2015. Patient-, tumor-, and treatment-related factors were evaluated for prognostic effect on hepatic progression-free survival (HPFS) and overall survival (OS) using unadjusted and propensity score-weighted univariate and multivariate Coxmore » proportional hazards models.ResultsMedian HPFS and OS were 18.5 and 125.1 months for G1 (n = 75), 12.2 and 33.9 months for G2 (n = 60), and 4.9 and 9.3 months for G3 tumors (n = 20), respectively (p < 0.05). Tumor burden >50 % hepatic volume demonstrated 5.5- and 26.8-month shorter median HPFS and OS, respectively, versus burden ≤50 % (p < 0.05). There were no significant differences in HPFS or OS between gut or pancreas primaries. In multivariate HPFS analysis, there were no significant differences among embolotherapy modalities. In multivariate OS analysis, TARE had a higher hazard ratio than TACE (unadjusted Cox model: HR 2.1, p = 0.02; propensity score adjusted model: HR 1.8, p = 0.11), while TAE did not differ significantly from TACE.ConclusionHigher tumor grade and tumor burden prognosticated shorter HPFS and OS. TARE had a higher hazard ratio for OS than TACE. There were no significant differences in HPFS among embolotherapy modalities.« less

Combined high-field intraoperative magnetic resonance imaging and endoscopy increase extent of resection and progression-free survival for pituitary adenomas

PubMed Central

Sylvester, Peter T.; Evans, John A.; Zipfel, Gregory J.; Chole, Richard A.; Uppaluri, Ravindra; Haughey, Bruce H.; Getz, Anne E.; Silverstein, Julie; Rich, Keith M.; Kim, Albert H.; Dacey, Ralph G.

2014-01-01

Purpose The clinical benefit of combined intraoperative magnetic resonance imaging (iMRI) and endoscopy for transsphenoidal pituitary adenoma resection has not been completely characterized. This study assessed the impact of microscopy, endoscopy, and/or iMRI on progression-free survival, extent of resection status (gross-, near-, and subtotal resection), and operative complications. Methods Retrospective analyses were performed on 446 transsphenoidal pituitary adenoma surgeries at a single institution between 1998 and 2012. Multivariate analyses were used to control for baseline characteristics, differences during extent of resection status, and progression-free survival analysis. Results Additional surgery was performed after iMRI in 56/156 cases (35.9 %), which led to increased extent of resection status in 15/156 cases (9.6 %). Multivariate ordinal logistic regression revealed no increase in extent of resection status following iMRI or endoscopy alone; however, combining these modalities increased extent of resection status (odds ratio 2.05, 95 % CI 1.21–3.46) compared to conventional transsphenoidal microsurgery. Multivariate Cox regression revealed that reduced extent of resection status shortened progression-free survival for near- versus gross-total resection [hazard ratio (HR) 2.87, 95 % CI 1.24–6.65] and sub- versus near-total resection (HR 2.10; 95 % CI 1.00–4.40). Complication comparisons between microscopy, endoscopy, and iMRI revealed increased perioperative deaths for endoscopy versus microscopy (4/209 and 0/237, respectively), but this difference was non-significant considering multiple post hoc comparisons (Fisher exact, p = 0.24). Conclusions Combined use of endoscopy and iMRI increased pituitary adenoma extent of resection status compared to conventional transsphenoidal microsurgery, and increased extent of resection status was associated with longer progression-free survival. Treatment modality combination did not significantly impact complication rate. PMID:24599833

From sensation to perception: Using multivariate classification of visual illusions to identify neural correlates of conscious awareness in space and time.

PubMed

Hogendoorn, Hinze

2015-01-01

An important goal of cognitive neuroscience is understanding the neural underpinnings of conscious awareness. Although the low-level processing of sensory input is well understood in most modalities, it remains a challenge to understand how the brain translates such input into conscious awareness. Here, I argue that the application of multivariate pattern classification techniques to neuroimaging data acquired while observers experience perceptual illusions provides a unique way to dissociate sensory mechanisms from mechanisms underlying conscious awareness. Using this approach, it is possible to directly compare patterns of neural activity that correspond to the contents of awareness, independent from changes in sensory input, and to track these neural representations over time at high temporal resolution. I highlight five recent studies using this approach, and provide practical considerations and limitations for future implementations.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

NASA Astrophysics Data System (ADS)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects.

PubMed

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

PubMed Central

2013-01-01

Background Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG. Methods We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation. Results Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications. Conclusions Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG. PMID:24059247

Nonlinear Decoupling Control With ANFIS-Based Unmodeled Dynamics Compensation for a Class of Complex Industrial Processes.

PubMed

Zhang, Yajun; Chai, Tianyou; Wang, Hong; Wang, Dianhui; Chen, Xinkai

2018-06-01

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements.

PubMed

Xiong, Chunbao; Lu, Huali; Zhu, Jinsong

2017-02-23

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

PubMed Central

Xiong, Chunbao; Lu, Huali; Zhu, Jinsong

2017-01-01

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation. PMID:28241472

Analysis of microvascular perfusion with multi-dimensional complete ensemble empirical mode decomposition with adaptive noise algorithm: Processing of laser speckle contrast images recorded in healthy subjects, at rest and during acetylcholine stimulation.

PubMed

Humeau-Heurtier, Anne; Marche, Pauline; Dubois, Severine; Mahe, Guillaume

2015-01-01

Laser speckle contrast imaging (LSCI) is a full-field imaging modality to monitor microvascular blood flow. It is able to give images with high temporal and spatial resolutions. However, when the skin is studied, the interpretation of the bidimensional data may be difficult. This is why an averaging of the perfusion values in regions of interest is often performed and the result is followed in time, reducing the data to monodimensional time series. In order to avoid such a procedure (that leads to a loss of the spatial resolution), we propose to extract patterns from LSCI data and to compare these patterns for two physiological states in healthy subjects: at rest and at the peak of acetylcholine-induced perfusion peak. For this purpose, the recent multi-dimensional complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) algorithm is applied to LSCI data. The results show that the intrinsic mode functions and residue given by MCEEMDAN show different patterns for the two physiological states. The images, as bidimensional data, can therefore be processed to reveal microvascular perfusion patterns, hidden in the images themselves. This work is therefore a feasibility study before analyzing data in patients with microvascular dysfunctions.

Computational Models of the Representation of Bangla Compound Words in the Mental Lexicon.

PubMed

Dasgupta, Tirthankar; Sinha, Manjira; Basu, Anupam

2016-08-01

In this paper we aim to model the organization and processing of Bangla compound words in the mental lexicon. Our objective is to determine whether the mental lexicon access a Bangla compound word as a whole or decomposes the whole word into its constituent morphemes and then recognize them accordingly. To address this issue, we adopted two different strategies. First, we conduct a cross-modal priming experiment over a number of native speakers. Analysis of reaction time (RT) and error rates indicates that in general, Bangla compound words are accessed via partial decomposition process. That is some word follows full-listing mode of representation and some words follow the decomposition route of representation. Next, based on the collected RT data we have developed a computational model that can explain the processing phenomena of the access and representation of Bangla compound words. In order to achieve this, we first explored the individual roles of head word position, morphological complexity, orthographic transparency and semantic compositionality between the constituents and the whole compound word. Accordingly, we have developed a complexity based model by combining these features together. To a large extent we have successfully explained the possible processing phenomena of most of the Bangla compound words. Our proposed model shows an accuracy of around 83 %.

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity.

PubMed

Lizier, Joseph T; Heinzle, Jakob; Horstmann, Annette; Haynes, John-Dylan; Prokopenko, Mikhail

2011-02-01

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.

Pharmacologic attenuation of cross-modal sensory augmentation within the chronic pain insula

PubMed Central

Harte, Steven E.; Ichesco, Eric; Hampson, Johnson P.; Peltier, Scott J.; Schmidt-Wilcke, Tobias; Clauw, Daniel J.; Harris, Richard E.

2016-01-01

Abstract Pain can be elicited through all mammalian sensory pathways yet cross-modal sensory integration, and its relationship to clinical pain, is largely unexplored. Centralized chronic pain conditions such as fibromyalgia are often associated with symptoms of multisensory hypersensitivity. In this study, female patients with fibromyalgia demonstrated cross-modal hypersensitivity to visual and pressure stimuli compared with age- and sex-matched healthy controls. Functional magnetic resonance imaging revealed that insular activity evoked by an aversive level of visual stimulation was associated with the intensity of fibromyalgia pain. Moreover, attenuation of this insular activity by the analgesic pregabalin was accompanied by concomitant reductions in clinical pain. A multivariate classification method using support vector machines (SVM) applied to visual-evoked brain activity distinguished patients with fibromyalgia from healthy controls with 82% accuracy. A separate SVM classification of treatment effects on visual-evoked activity reliably identified when patients were administered pregabalin as compared with placebo. Both SVM analyses identified significant weights within the insular cortex during aversive visual stimulation. These data suggest that abnormal integration of multisensory and pain pathways within the insula may represent a pathophysiological mechanism in some chronic pain conditions and that insular response to aversive visual stimulation may have utility as a marker for analgesic drug development. PMID:27101425

Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

Deconvolution of reacting-flow dynamics using proper orthogonal and dynamic mode decompositions

NASA Astrophysics Data System (ADS)

Roy, Sukesh; Hua, Jia-Chen; Barnhill, Will; Gunaratne, Gemunu H.; Gord, James R.

2015-01-01

Analytical and computational studies of reacting flows are extremely challenging due in part to nonlinearities of the underlying system of equations and long-range coupling mediated by heat and pressure fluctuations. However, many dynamical features of the flow can be inferred through low-order models if the flow constituents (e.g., eddies or vortices) and their symmetries, as well as the interactions among constituents, are established. Modal decompositions of high-frequency, high-resolution imaging, such as measurements of species-concentration fields through planar laser-induced florescence and of velocity fields through particle-image velocimetry, are the first step in the process. A methodology is introduced for deducing the flow constituents and their dynamics following modal decomposition. Proper orthogonal (POD) and dynamic mode (DMD) decompositions of two classes of problems are performed and their strengths compared. The first problem involves a cellular state generated in a flat circular flame front through symmetry breaking. The state contains two rings of cells that rotate clockwise at different rates. Both POD and DMD can be used to deconvolve the state into the two rings. In POD the contribution of each mode to the flow is quantified using the energy. Each DMD mode can be associated with an energy as well as a unique complex growth rate. Dynamic modes with the same spatial symmetry but different growth rates are found to be combined into a single POD mode. Thus, a flow can be approximated by a smaller number of POD modes. On the other hand, DMD provides a more detailed resolution of the dynamics. Two classes of reacting flows behind symmetric bluff bodies are also analyzed. In the first, symmetric pairs of vortices are released periodically from the two ends of the bluff body. The second flow contains von Karman vortices also, with a vortex being shed from one end of the bluff body followed by a second shedding from the opposite end. The way in which DMD can be used to deconvolve the second flow into symmetric and von Karman vortices is demonstrated. The analyses performed illustrate two distinct advantages of DMD: (1) Unlike proper orthogonal modes, each dynamic mode is associated with a unique complex growth rate. By comparing DMD spectra from multiple nominally identical experiments, it is possible to identify "reproducible" modes in a flow. We also find that although most high-energy modes are reproducible, some are not common between experimental realizations; in the examples considered, energy fails to differentiate between reproducible and nonreproducible modes. Consequently, it may not be possible to differentiate reproducible and nonreproducible modes in POD. (2) Time-dependent coefficients of dynamic modes are complex. Even in noisy experimental data, the dynamics of the phase of these coefficients (but not their magnitude) are highly regular. The phase represents the angular position of a rotating ring of cells and quantifies the downstream displacement of vortices in reacting flows. Thus, it is suggested that the dynamical characterizations of complex flows are best made through the phase dynamics of reproducible DMD modes.

Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

NASA Astrophysics Data System (ADS)

Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

2016-02-01

Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.

Design and validation of MEDRYS, a Mediterranean Sea reanalysis over the period 1992-2013

NASA Astrophysics Data System (ADS)

Hamon, Mathieu; Beuvier, Jonathan; Somot, Samuel; Lellouche, Jean-Michel; Greiner, Eric; Jordà, Gabriel; Bouin, Marie-Noëlle; Arsouze, Thomas; Béranger, Karine; Sevault, Florence; Dubois, Clotilde; Drevillon, Marie; Drillet, Yann

2016-04-01

The French research community in the Mediterranean Sea modeling and the French operational ocean forecasting center Mercator Océan have gathered their skill and expertise in physical oceanography, ocean modeling, atmospheric forcings and data assimilation to carry out a MEDiterranean sea ReanalYsiS (MEDRYS) at high resolution for the period 1992-2013. The ocean model used is NEMOMED12, a Mediterranean configuration of NEMO with a 1/12° ( ˜ 7 km) horizontal resolution and 75 vertical z levels with partial steps. At the surface, it is forced by a new atmospheric-forcing data set (ALDERA), coming from a dynamical downscaling of the ERA-Interim atmospheric reanalysis by the regional climate model ALADIN-Climate with a 12 km horizontal and 3 h temporal resolutions. This configuration is used to carry a 34-year hindcast simulation over the period 1979-2013 (NM12-FREE), which is the initial state of the reanalysis in October 1992. MEDRYS uses the existing Mercator Océan data assimilation system SAM2 that is based on a reduced-order Kalman filter with a three-dimensional (3-D) multivariate modal decomposition of the forecast error. Altimeter data, satellite sea surface temperature (SST) and temperature and salinity vertical profiles are jointly assimilated. This paper describes the configuration we used to perform MEDRYS. We then validate the skills of the data assimilation system. It is shown that the data assimilation restores a good average temperature and salinity at intermediate layers compared to the hindcast. No particular biases are identified in the bottom layers. However, the reanalysis shows slight positive biases of 0.02 psu and 0.15 °C above 150 m depth. In the validation stage, it is also shown that the assimilation allows one to better reproduce water, heat and salt transports through the Strait of Gibraltar. Finally, the ability of the reanalysis to represent the sea surface high-frequency variability is shown.

A global perspective of the limits of prediction skill based on the ECMWF ensemble

NASA Astrophysics Data System (ADS)

Zagar, Nedjeljka

2016-04-01

In this talk presents a new model of the global forecast error growth applied to the forecast errors simulated by the ensemble prediction system (ENS) of the ECMWF. The proxy for forecast errors is the total spread of the ECMWF operational ensemble forecasts obtained by the decomposition of the wind and geopotential fields in the normal-mode functions. In this way, the ensemble spread can be quantified separately for the balanced and inertio-gravity (IG) modes for every forecast range. Ensemble reliability is defined for the balanced and IG modes comparing the ensemble spread with the control analysis in each scale. The results show that initial uncertainties in the ECMWF ENS are largest in the tropical large-scale modes and their spatial distribution is similar to the distribution of the short-range forecast errors. Initially the ensemble spread grows most in the smallest scales and in the synoptic range of the IG modes but the overall growth is dominated by the increase of spread in balanced modes in synoptic and planetary scales in the midlatitudes. During the forecasts, the distribution of spread in the balanced and IG modes grows towards the climatological spread distribution characteristic of the analyses. The ENS system is found to be somewhat under-dispersive which is associated with the lack of tropical variability, primarily the Kelvin waves. The new model of the forecast error growth has three fitting parameters to parameterize the initial fast growth and a more slow exponential error growth later on. The asymptotic values of forecast errors are independent of the exponential growth rate. It is found that the asymptotic values of the errors due to unbalanced dynamics are around 10 days while the balanced and total errors saturate in 3 to 4 weeks. Reference: Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444.

Solitary plasmacytoma: population-based analysis of survival trends and effect of various treatment modalities in the USA.

PubMed

Thumallapally, Nishitha; Meshref, Ahmed; Mousa, Mohammed; Terjanian, Terenig

2017-01-05

Solitary plasmacytoma (SP) is a localized neoplastic plasma cell disorder with an annual incidence of less than 450 cases. Given the rarity of this disorder, it is difficult to conduct large-scale population studies. Consequently, very limited information on the disorder is available, making it difficult to estimate the incidence and survival rates. Furthermore, limited information is available on the efficacy of various treatment modalities in relation to primary tumor sites. The data for this retrospective study were drawn from the Surveillance, Epidemiology and End Results (SEER) database, which comprises 18 registries; patient demographics, treatment modalities and survival rates were obtained for those diagnosed with SP from 1998 to 2007. Various prognostic factors were analyzed via Kaplan-Meier analysis and log-rank test, with 5-year relative survival rate defined as the primary outcome of interest. Cox regression analysis was employed in the multivariate analysis. The SEER search from 1998 to 2007 yielded records for 1691 SP patients. The median age at diagnosis was 63 years. The patient cohort was 62.4% male, 37.6% female, 80% Caucasian, 14.6% African American and 5.4% other races. Additionally, 57.8% had osseous plasmacytoma, and 31.9% had extraosseous involvement. Unspecified plasmacytoma was noted in 10.2% of patients. The most common treatment modalities were radiotherapy (RT) (48.8%), followed by combination surgery with RT (21.2%) and surgery alone (11.6%). Univariate analysis of prognostic factors revealed that the survival outcomes were better for younger male patients who received RT with surgery (p < 0.05). Additionally, patients who received neoadjuvant RT had increased survival rates compared to those receiving adjuvant RT (86% vs 73%, p < 0.05). Furthermore, the analyses revealed that 5-year survival rates for patients with axial plasmacytoma were superior when RT was combined with surgery (p < 0.05). In the multivariate analysis, age <60 years and treatment with either RT or surgery showed superior survival rates. Progression to multiple myeloma (MM) was noted in 551 patients. Age >60 years was associated with a lower 5-year survival in patients who progressed to MM compared to those who were diagnosed initially with MM (15.1 vs 16.6%). Finally, those who received RT and progressed to MM still had a higher chance of survival than those who were diagnosed with MM initially and treated with RT/surgery (21.8% vs 15.9%, p < 0.05). A review of the pertinent literature indicates that we provided the most comprehensive population-based analysis of SP to date. Moreover, our study contributes to the establishment of the optimal SP treatment modality, as RT is the favored option in frontline settings. Consensus is currently lacking regarding the benefits of combined treatment including surgery. Thus, the findings reported here elucidate the role of primary treatment modalities while also demonstrating the quantifiable benefits of combining RT with surgery in relation to different primary tumor sites. While our results are promising, they should be confirmed through further large-scale randomized studies.

Common and differential electrophysiological mechanisms underlying semantic object memory retrieval probed by features presented in different stimulus types.

PubMed

Chiang, Hsueh-Sheng; Eroh, Justin; Spence, Jeffrey S; Motes, Michael A; Maguire, Mandy J; Krawczyk, Daniel C; Brier, Matthew R; Hart, John; Kraut, Michael A

2016-08-01

How the brain combines the neural representations of features that comprise an object in order to activate a coherent object memory is poorly understood, especially when the features are presented in different modalities (visual vs. auditory) and domains (verbal vs. nonverbal). We examined this question using three versions of a modified Semantic Object Retrieval Test, where object memory was probed by a feature presented as a written word, a spoken word, or a picture, followed by a second feature always presented as a visual word. Participants indicated whether each feature pair elicited retrieval of the memory of a particular object. Sixteen subjects completed one of the three versions (N=48 in total) while their EEG were recorded simultaneously. We analyzed EEG data in four separate frequency bands (delta: 1-4Hz, theta: 4-7Hz; alpha: 8-12Hz; beta: 13-19Hz) using a multivariate data-driven approach. We found that alpha power time-locked to response was modulated by both cross-modality (visual vs. auditory) and cross-domain (verbal vs. nonverbal) probing of semantic object memory. In addition, retrieval trials showed greater changes in all frequency bands compared to non-retrieval trials across all stimulus types in both response-locked and stimulus-locked analyses, suggesting dissociable neural subcomponents involved in binding object features to retrieve a memory. We conclude that these findings support both modality/domain-dependent and modality/domain-independent mechanisms during semantic object memory retrieval. Copyright © 2016 Elsevier B.V. All rights reserved.

Control of electromagnetic edge effects in electrically-small rectangular plasma reactors

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

Trampel, Christopher P.; Stieler, Daniel S.; PowerFilm, Inc., 2337 230th Street, Ames, Iowa 50014

Electromagnetic fields supported by rectangular reactors for plasma enhanced chemical vapor deposition are studied theoretically. Expressions for the fields in an electrically-small rectangular reactor with plasma in the chamber are derived. Modal field decompositions are employed under the homogeneous plasma slab approximation. The amplitude of each mode is determined analytically. It is shown that the field can be represented by the standing wave, evanescent waves tied to the edges, and an evanescent wave tied to the corners of the reactor. The impact of boundary conditions at the plasma edge on nonuniformity is quantified. Uniformity may be improved by placing amore » lossy magnetic layer on the reactor sidewalls. It is demonstrated that nonuniformity is a decreasing function of layer thickness.« less

On simulating flow with multiple time scales using a method of averages

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

Margolin, L.G.

1997-12-31

The author presents a new computational method based on averaging to efficiently simulate certain systems with multiple time scales. He first develops the method in a simple one-dimensional setting and employs linear stability analysis to demonstrate numerical stability. He then extends the method to multidimensional fluid flow. His method of averages does not depend on explicit splitting of the equations nor on modal decomposition. Rather he combines low order and high order algorithms in a generalized predictor-corrector framework. He illustrates the methodology in the context of a shallow fluid approximation to an ocean basin circulation. He finds that his newmore » method reproduces the accuracy of a fully explicit second-order accurate scheme, while costing less than a first-order accurate scheme.« less

Dynamic stall reattachment revisited

NASA Astrophysics Data System (ADS)

Mulleners, Karen

2017-11-01

Dynamic stall on pitching airfoils is an important practical problem that affects for example rotary wing aircraft and wind turbines. It also comprises a number of interesting fundamental fluid dynamical phenomena such as unsteady flow separation, vortex formation and shedding, unsteady flow reattachment, and dynamic hysteresis. Following up on past efforts focussing on the separation development, we now revisited the flow reattachment or stall recovery process. Experimental time-resolved velocity field and surface pressure data for a two-dimensional sinusoidally pitching airfoil with various reduced frequencies was analysed using different Eulerian, Lagrangian, and modal decomposition methods. This complementary analysis resulted in the identification of the chain of events that play a role in the flow reattachment process, a detailed description of that role, and characterisation of the individual events by the governing time-scales and flow features.

A modal approach to piano soundboard vibroacoustic behavior.

PubMed

Trévisan, Benjamin; Ege, Kerem; Laulagnet, Bernard

2017-02-01

This paper presents an analytical method for modeling the vibro-acoustic behavior of ribbed non-rectangular orthotropic clamped plates. To do this, the non-rectangular plate is embedded in an extended rectangular simply supported plate on which a spring distribution is added, blocking the extended part of the surface, and allowing the description of any inner surface shapes. The acoustical radiation of the embedded plate is ensured using the radiation impedances of the extended rectangular simply supported plate. This method is applied to an upright piano soundboard: a non-rectangular orthotropic plate ribbed in both directions by several straight stiffeners. A modal decomposition is adopted on the basis of the rectangular extended simply supported plate modes, making it possible to calculate the modes of a piano soundboard in the frequency range [0;3000] Hz, showing the different associated mode families. Likewise, the acoustical radiation is calculated using the radiation impedances of a simply supported baffled plate, demonstrating the influence of the string coupling point positions on the acoustic radiated power. The paper ends with the introduction of indicators taking into account spatial and spectral variations of the excitation depending on the notes, which add to the accuracy of the study from the musical standpoint. A parametrical study, which includes several variations of soundboard design, highlights the complexity of rendering high-pitched notes homogeneous.

Effects of Bifurcations on Aft-Fan Engine Nacelle Noise

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Farassat, Fereidoun; Pope, D. Stuart; Vatsa, Veer N.

2004-01-01

Aft-fan engine nacelle noise is a significant factor in the increasingly important issue of aircraft community noise. The ability to predict such noise within complex duct geometries is a valuable tool in studying possible noise attenuation methods. A recent example of code development for such predictions is the ducted fan noise propagation and radiation code CDUCT-LaRC. This work focuses on predicting the effects of geometry changes (i.e. bifurcations, pylons) on aft fan noise propagation. Beginning with simplified geometries, calculations show that bifurcations lead to scattering of acoustic energy into higher order modes. In addition, when circumferential mode number and the number of bifurcations are properly commensurate, bifurcations increase the relative importance of the plane wave mode near the exhaust plane of the bypass duct. This is particularly evident when the bypass duct surfaces include acoustic treatment. Calculations involving more complex geometries further illustrate that bifurcations and pylons clearly affect modal content, in both propagation and radiation calculations. Additionally, results show that consideration of acoustic radiation results may provide further insight into acoustic treatment effectiveness for situations in which modal decomposition may not be straightforward. The ability of CDUCT-LaRC to handle complex (non-axisymmetric) multi-block geometries, as well as axially and circumferentially segmented liners, allows investigation into the effects of geometric elements (bifurcations, pylons).

Variability in anesthetic considerations for arteriovenous fistula creation.

PubMed

Siracuse, Jeffrey J; Gill, Heather L; Parrack, Inkyong; Huang, Zhen S; Schneider, Darren B; Connolly, Peter H; Meltzer, Andrew J

2014-01-01

Anesthetic options for arteriovenous fistula (AVF) creation include regional anesthesia (RA), general anesthesia (GA) and local anesthetic for select cases. In addition to the benefits of avoiding GA in high-risk patients, recent studies suggest that RA may increase perioperative venous dilation and improve maturation. Our objective was to assess perioperative outcomes of AVF creation with respect to anesthetic modality and identify patient-level factors associated with variation in contemporary anesthetic selection. National Surgical Quality Improvement Project (NSQIP) data (2007-2010) were accessed to identify patients undergoing AVF creation. Univariate analysis and multivariate logistic regression were performed to assess the relationships among patient characteristics, anesthesia modality and outcome. Of 1,540 patients undergoing new upper extremity AVF creation, 52% were male and 81% were younger than 75 years. Anesthesia distribution was GA in 85.2%, local/monitored anesthetic care (MAC) in 2.9% and RA in 11.9% of cases. By multivariate analysis, independent predictors of RA were dyspnea at rest (hazard ratio [HR] 2.3, 95% confidence interval [CI] 1.1-4.9), age >75 (HR 1.6, 95% CI 1.1-2.3) and teaching hospital status as indicated by housestaff involvement (HR 3.7, 95% CI 2.5-5.5). RA was associated with higher total operative time, duration of anesthesia, length of time in operating room and duration of anesthesia start until surgery start (p<0.01). There were no differences between perioperative complications or mortality among anesthetic modalities, although all deaths occurred in the GA group. Despite recent reports highlighting potential benefits of RA for AVF creation, GA was surprisingly used in the vast majority of cases in the United States. The only comorbidities associated with preferential RA use were advanced age and dyspnea at rest. Practice environment may influence anesthetic selection for these cases, as a nonteaching environment was associated with GA use. The trend seen here toward higher mortality in GA and the potential perioperative benefits of RA for the access should encourage more widespread use of RA in practice for this high-risk patient population.

Healthcare Expenditures Associated with Depression Among Individuals with Osteoarthritis: Post-Regression Linear Decomposition Approach.

PubMed

Agarwal, Parul; Sambamoorthi, Usha

2015-12-01

Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p < 0.001) compared to those without depression. Post-regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.

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.

Surgical management of breast cancer in China: A 15-year single-center retrospective study of 18,502 patients.

PubMed

Huang, Nai-Si; Liu, Meng-Ying; Chen, Jia-Jian; Yang, Ben-Long; Xue, Jing-Yan; Quan, Chen-Lian; Mo, Miao; Liu, Guang-Yu; Shen, Zhen-Zhou; Shao, Zhi-Min; Wu, Jiong

2016-11-01

The aim of the study was to review the surgical trends in breast cancer treatment in China over the past 15 years and to explore the possible factors related to the choice of surgical modality.The medical records of 18,502 patients with unilateral early stage breast cancer who underwent surgery from January 1999 to December 2013 at our institute were retrospectively reviewed. The utilization of different surgical modalities and the associated clinicopathological factors were analyzed. Furthermore, the prognostic role of surgical modality was also evaluated.The median patient age was 50.0 years. According to the pTNM staging system, 12.5% of the patients were classified as stage 0; 30.2% as stage I; 40.0% as stage II; and 17.3% as stage III. In total, 9.3% of the patients could not be staged. Overall, 67.1% of the breast cancer cases were estrogen receptor (ER) positive. The pattern of breast cancer surgery has changed tremendously over the past 15 years (P < 0.001). The pattern of mastectomy has shifted from radical mastectomy to modified radical mastectomy and simple mastectomy + sentinel lymph node biopsy. A total of 81.7% of the patients underwent mastectomy without immediate reconstruction, 15.2% underwent breast-conserving surgery (BCS), and 3.7% received immediate breast reconstruction after mastectomy. Age, TNM staging, and pathological characteristics greatly affected the choice of surgical modality. The 5-year recurrence-free survival (RFS) rates for the mastectomy, BCS, and reconstruction groups were 87.6%, 93.2%, and 91.7%, respectively (P < 0.001); the RFS rate was likely affected by distant recurrence instead of loco-regional recurrence. We also identified improved RFS over time, stratified by surgical modality and tumor stage. Multivariate Cox-regression analysis revealed that time of treatment, tumor stage, tumor grade, LVI status, and ER status were independent prognostic factors for RFS in our cohort, whereas surgical modality was not.Mastectomy remains the most prevalent surgical modality used to manage early stage breast cancer in China, although the utilization of BCS has increased in the past decade. However, surgical management was not a prognostic factor for RFS. The selection of appropriate patients depended on the assessment of multiple clinicopathological factors, which is essential for making surgical decisions.

Measuring center of pressure signals to quantify human balance using multivariate multiscale entropy by designing a force platform.

PubMed

Huang, Cheng-Wei; Sue, Pei-Der; Abbod, Maysam F; Jiang, Bernard C; Shieh, Jiann-Shing

2013-08-08

To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance.

Quantum attack-resistent certificateless multi-receiver signcryption scheme.

PubMed

Li, Huixian; Chen, Xubao; Pang, Liaojun; Shi, Weisong

2013-01-01

The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC), which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC) based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ) problem and its unforgeability under the Isomorphism of Polynomials (IP) assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards.

Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams.

PubMed

Hemakom, Apit; Powezka, Katarzyna; Goverdovsky, Valentin; Jaffer, Usman; Mandic, Danilo P

2017-12-01

A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).

Risk prediction for myocardial infarction via generalized functional regression models.

PubMed

Ieva, Francesca; Paganoni, Anna M

2016-08-01

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques. © The Author(s) 2013.

Measuring Center of Pressure Signals to Quantify Human Balance Using Multivariate Multiscale Entropy by Designing a Force Platform

PubMed Central

Huang, Cheng-Wei; Sue, Pei-Der; Abbod, Maysam F.; Jiang, Bernard C.; Shieh, Jiann-Shing

2013-01-01

To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance. PMID:23966184

Two-dimensional NMR spectroscopy strongly enhances soil organic matter composition analysis

NASA Astrophysics Data System (ADS)

Soucemarianadin, Laure; Erhagen, Björn; Öquist, Mats; Nilsson, Mats; Hedenström, Mattias; Schleucher, Jürgen

2016-04-01

Soil organic matter (SOM) is the largest terrestrial carbon pool and strongly affects soil properties. With climate change, understanding SOM processes and turnover and how they could be affected by increasing temperatures becomes critical. This is particularly key for organic soils as they represent a huge carbon pool in very sensitive ecosystems, like boreal ecosystems and peatlands. Nevertheless, characterization of SOM molecular composition, which is essential to elucidate soil carbon processes, is not easily achieved, and further advancements in that area are greatly needed. Solid-state one-dimensional (1D) 13C nuclear magnetic resonance (NMR) spectroscopy is often used to characterize its molecular composition, but only provides data on a few major functional groups, which regroup many different molecular fragments. For instance, in the carbohydrates region, signals of all monosaccharides present in many different polymers overlap. This overlap thwarts attempts to identify molecular moieties, resulting in insufficient information to characterize SOM composition. Here we show that two-dimensional (2D) liquid-state 1H-13C NMR spectra provided much richer data on the composition of boreal plant litter and organic surface soil. The 2D spectra indeed resolved overlaps observed in 1D 13C spectra and displayed signals from hundreds of identifiable molecular groups. For example, in the aromatics region, signals from individual lignin units could be recognized. It was hence possible to follow the fate of specific structural moieties in soils. We observed differences between litter and soil samples, and were able to relate them to the decomposition of identifiable moieties. Sample preparation and data acquisition were both simple and fast. Further, using multivariate data analysis, we aimed at linking the detailed chemical fingerprints of SOM to turnover rates in a soil incubation experiment. With the multivariate models, we were able to identify specific molecular moieties correlated to variability in the temperature response of organic matter decomposition, as assessed by Q10. Thus, 2D NMR methods, and their combination with multivariate analysis, can greatly improve analysis of litter and SOM composition, thereby facilitating elucidation of their roles in biogeochemical and ecological processes that are so critical to foresee associated feedback mechanisms on SOM turnover as a result of global environmental change.

Problem decomposition by mutual information and force-based clustering

NASA Astrophysics Data System (ADS)

Otero, Richard Edward

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter- dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

Effect of altered sensory conditions on multivariate descriptors of human postural sway

NASA Technical Reports Server (NTRS)

Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1998-01-01

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE PAGES

Fierce, Laura; McGraw, Robert L.

2017-07-26

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

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

Fierce, Laura; McGraw, Robert L.

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Hierarchical multivariate covariance analysis of metabolic connectivity.

PubMed

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.

PubMed

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

Wavelet-based statistical classification of skin images acquired with reflectance confocal microscopy

PubMed Central

Halimi, Abdelghafour; Batatia, Hadj; Le Digabel, Jimmy; Josse, Gwendal; Tourneret, Jean Yves

2017-01-01

Detecting skin lentigo in reflectance confocal microscopy images is an important and challenging problem. This imaging modality has not yet been widely investigated for this problem and there are a few automatic processing techniques. They are mostly based on machine learning approaches and rely on numerous classical image features that lead to high computational costs given the very large resolution of these images. This paper presents a detection method with very low computational complexity that is able to identify the skin depth at which the lentigo can be detected. The proposed method performs multiresolution decomposition of the image obtained at each skin depth. The distribution of image pixels at a given depth can be approximated accurately by a generalized Gaussian distribution whose parameters depend on the decomposition scale, resulting in a very-low-dimension parameter space. SVM classifiers are then investigated to classify the scale parameter of this distribution allowing real-time detection of lentigo. The method is applied to 45 healthy and lentigo patients from a clinical study, where sensitivity of 81.4% and specificity of 83.3% are achieved. Our results show that lentigo is identifiable at depths between 50μm and 60μm, corresponding to the average location of the the dermoepidermal junction. This result is in agreement with the clinical practices that characterize the lentigo by assessing the disorganization of the dermoepidermal junction. PMID:29296480

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.

Aeroelastic System Development Using Proper Orthogonal Decomposition and Volterra Theory

NASA Technical Reports Server (NTRS)

Lucia, David J.; Beran, Philip S.; Silva, Walter A.

2003-01-01

This research combines Volterra theory and proper orthogonal decomposition (POD) into a hybrid methodology for reduced-order modeling of aeroelastic systems. The out-come of the method is a set of linear ordinary differential equations (ODEs) describing the modal amplitudes associated with both the structural modes and the POD basis functions for the uid. For this research, the structural modes are sine waves of varying frequency, and the Volterra-POD approach is applied to the fluid dynamics equations. The structural modes are treated as forcing terms which are impulsed as part of the uid model realization. Using this approach, structural and uid operators are coupled into a single aeroelastic operator. This coupling converts a free boundary uid problem into an initial value problem, while preserving the parameter (or parameters) of interest for sensitivity analysis. The approach is applied to an elastic panel in supersonic cross ow. The hybrid Volterra-POD approach provides a low-order uid model in state-space form. The linear uid model is tightly coupled with a nonlinear panel model using an implicit integration scheme. The resulting aeroelastic model provides correct limit-cycle oscillation prediction over a wide range of panel dynamic pressure values. Time integration of the reduced-order aeroelastic model is four orders of magnitude faster than the high-order solution procedure developed for this research using traditional uid and structural solvers.

Investigation of laser Doppler anemometry in developing a velocity-based measurement technique

NASA Astrophysics Data System (ADS)

Jung, Ki Won

2009-12-01

Acoustic properties, such as the characteristic impedance and the complex propagation constant, of porous materials have been traditionally characterized based on pressure-based measurement techniques using microphones. Although the microphone techniques have evolved since their introduction, the most general form of the microphone technique employs two microphones in characterizing the acoustic field for one continuous medium. The shortcomings of determining the acoustic field based on only two microphones can be overcome by using numerous microphones. However, the use of a number of microphones requires a careful and intricate calibration procedure. This dissertation uses laser Doppler anemometry (LDA) to establish a new measurement technique which can resolve issues that microphone techniques have: First, it is based on a single sensor, thus the calibration is unnecessary when only overall ratio of the acoustic field is required for the characterization of a system. This includes the measurements of the characteristic impedance and the complex propagation constant of a system. Second, it can handle multiple positional measurements without calibrating the signal at each position. Third, it can measure three dimensional components of velocity even in a system with a complex geometry. Fourth, it has a flexible adaptability which is not restricted to a certain type of apparatus only if the apparatus is transparent. LDA is known to possess several disadvantages, such as the requirement of a transparent apparatus, high cost, and necessity of seeding particles. The technique based on LDA combined with a curvefitting algorithm is validated through measurements on three systems. First, the complex propagation constant of the air is measured in a rigidly terminated cylindrical pipe which has very low dissipation. Second, the radiation impedance of an open-ended pipe is measured. These two parameters can be characterized by the ratio of acoustic field measured at multiple locations. Third, the power dissipated in a variable RLC load is measured. The three experiments validate the LDA technique proposed. The utility of the LDA method is then extended to the measurement of the complex propagation constant of the air inside a 100 ppi reticulated vitreous carbon (RVC) sample. Compared to measurements in the available studies, the measurement with the 100 ppi RVC sample supports the LDA technique in that it can achieve a low uncertainty in the determined quantity. This dissertation concludes with using the LDA technique for modal decomposition of the plane wave mode and the (1,1) mode that are driven simultaneously. This modal decomposition suggests that the LDA technique surpasses microphone-based techniques, because they are unable to determine the acoustic field based on an acoustic model with unconfined propagation constants for each modal component.

Association of Modality with Mortality among Canadian Aboriginals

PubMed Central

Hemmelgarn, Brenda; Rigatto, Claudio; Komenda, Paul; Yeates, Karen; Promislow, Steven; Mojica, Julie; Tangri, Navdeep

2012-01-01

Summary Background and objectives Previous studies have shown that Aboriginals and Caucasians experience similar outcome on dialysis in Canada. Using the Canadian Organ Replacement Registry, this study examined whether dialysis modality (peritoneal or hemodialysis) impacted mortality in Aboriginal patients. Design, setting, participants, & measurements This study identified 31,576 adult patients (hemodialysis: Aboriginal=1839, Caucasian=21,430; peritoneal dialysis: Aboriginal=554, Caucasian=6769) who initiated dialysis between January of 2000 and December of 2009. Aboriginal status was identified by self-report. Dialysis modality was determined 90 days after dialysis initiation. Multivariate Cox proportional hazards and competing risk models were constructed to determine the association between race and mortality by dialysis modality. Results During the study period, 939 (51.1%) Aboriginals and 12,798 (53.3%) Caucasians initiating hemodialysis died, whereas 166 (30.0%) and 2037 (30.1%), respectively, initiating peritoneal dialysis died. Compared with Caucasians, Aboriginals on hemodialysis had a comparable risk of mortality (adjusted hazards ratio=1.04, 95% confidence interval=0.96–1.11, P=0.37). However, on peritoneal dialysis, Aboriginals experienced a higher risk of mortality (adjusted hazards ratio=1.36, 95% confidence interval=1.13–1.62, P=0.001) and technique failure (adjusted hazards ratio=1.29, 95% confidence interval=1.03–1.60, P=0.03) than Caucasians. The risk of technique failure varied by patient age, with younger Aboriginals (<50 years old) more likely to develop technique failure than Caucasians (adjusted hazards ratio=1.76, 95% confidence interval=1.23–2.52, P=0.002). Conclusions Aboriginals on peritoneal dialysis experience higher mortality and technique failure relative to Caucasians. Reasons for this race disparity in peritoneal dialysis outcomes are unclear. PMID:22997343

Digital divide: variation in internet and cellular phone use among women attending an urban sexually transmitted infections clinic.

PubMed

Samal, Lipika; Hutton, Heidi E; Erbelding, Emily J; Brandon, Elizabeth S; Finkelstein, Joseph; Chander, Geetanjali

2010-01-01

We sought to describe: (1) the prevalence of internet, cellular phone, and text message use among women attending an urban sexually transmitted infections (STI) clinic, (2) the acceptability of health advice by each mode of information and communication technology (ICT), and (3) demographic characteristics associated with ICT use. This study is a cross-sectional survey of 200 English-speaking women presenting to a Baltimore City STI clinic with STI complaints. Participants completed a self-administered survey querying ICT use and demographic characteristics. Three separate questions asked about interest in receiving health advice delivered by the three modalities: internet, cellular phone, and text message. We performed logistic regression to examine how demographic factors (age, race, and education) are associated with likelihood of using each modality. The median age of respondents was 27 years; 87% were African American, and 71% had a high school diploma. The rate of any internet use was 80%; 31% reported daily use; 16% reported weekly use; and 32% reported less frequent use. Almost all respondents (93%) reported cellular phone use, and 79% used text messaging. Acceptability of health advice by each of the three modalities was about 60%. In multivariate analysis, higher education and younger age were associated with internet use, text messaging, and cellular phone use. Overall rate of internet use was high, but there was an educational disparity in internet use. Cellular phone use was almost universal in this sample. All three modalities were equally acceptable forms of health communication. Describing baseline ICT access and the acceptability of health advice via ICT, as we have done, is one step toward determining the feasibility of ICT-delivered health interventions in urban populations.

Multi-institutional analysis of radiation modality use and postoperative outcomes of neoadjuvant chemoradiation for esophageal cancer.

PubMed

Lin, Steven H; Merrell, Kenneth W; Shen, Jincheng; Verma, Vivek; Correa, Arlene M; Wang, Lu; Thall, Peter F; Bhooshan, Neha; James, Sarah E; Haddock, Michael G; Suntharalingam, Mohan; Mehta, Minesh P; Liao, Zhongxing; Cox, James D; Komaki, Ritsuko; Mehran, Reza J; Chuong, Michael D; Hallemeier, Christopher L

2017-06-01

Relative radiation dose exposure to vital organs in the thorax could influence clinical outcomes in esophageal cancer (EC). We assessed whether the type of radiation therapy (RT) modality used was associated with postoperative outcomes after neoadjuvant chemoradiation (nCRT). Contemporary data from 580 EC patients treated with nCRT at 3 academic institutions from 2007 to 2013 were reviewed. 3D conformal RT (3D), intensity modulated RT (IMRT) and proton beam therapy (PBT) were used for 214 (37%), 255 (44%), and 111 (19%) patients, respectively. Postoperative outcomes included pulmonary, GI, cardiac, wound healing complications, length of in-hospital stay (LOS), and 90-day postoperative mortality. Cox model fits, and log-rank tests both with and without Inverse Probability of treatment Weighting (IPW) were used to correct for bias due to non-randomization. RT modality was significantly associated with the incidence of pulmonary, cardiac and wound complications, which also bore out on multivariate analysis. Mean LOS was also significantly associated with treatment modality (13.2days for 3D (95%CI 11.7-14.7), 11.6days for IMRT (95%CI 10.9-12.7), and 9.3days for PBT (95%CI 8.2-10.3) (p<0.0001)). The 90day postoperative mortality rates were 4.2%, 4.3%, and 0.9%, respectively, for 3D, IMRT and PBT (p=0.264). Advanced RT technologies (IMRT and PBT) were associated with significantly reduced rate of postoperative complications and LOS compared to 3D, with PBT displaying the greatest benefit in a number of clinical endpoints. Ongoing prospective randomized trial will be needed to validate these results. Copyright © 2017 Elsevier B.V. All rights reserved.

Predictors of Postoperative Complications After Trimodality Therapy for Esophageal Cancer

PubMed Central

Wang, Jingya; Wei, Caimiao; Tucker, Susan L.; Myles, Bevan; Palmer, Matthew; Hofstetter, Wayne L.; Swisher, Stephen G.; Ajani, Jaffer A.; Cox, James D.; Komaki, Ritsuko; Liao, Zhongxing; Lin, Steven H.

2013-01-01

Purpose While trimodality therapy for esophageal cancer has improved patient outcomes, surgical complication rates remain high. The goal of this study was to identify modifiable factors associated with postoperative complications after neoadjuvant chemoradiation. Methods and Materials From 1998 to 2011, 444 patients were treated at our institution with surgical resection after chemoradiation. Postoperative (pulmonary, gastrointestinal [GI], cardiac, wound healing) complications were recorded up to 30 days postoperatively. Kruskal-Wallis tests and χ2 or Fisher exact tests were used to assess associations between continuous and categorical variables. Multivariate logistic regression tested the association between perioperative complications and patient or treatment factors that were significant on univariate analysis. Results The most frequent postoperative complications after trimodality therapy were pulmonary (25%) and GI (23%). Lung capacity and the type of radiation modality used were independent predictors of pulmonary and GI complications. After adjusting for confounding factors, pulmonary and GI complications were increased in patients treated with 3-dimensional conformal radiation therapy (3D-CRT) versus intensity modulated radiation therapy (IMRT; odds ratio [OR], 2.018; 95% confidence interval [CI], 1.104–3.688; OR, 1.704; 95% CI, 1.03–2.82, respectively) and for patients treated with 3D-CRT versus proton beam therapy (PBT; OR, 3.154; 95% CI, 1.365–7.289; OR, 1.55; 95% CI, 0.78–3.08, respectively). Mean lung radiation dose (MLD) was strongly associated with pulmonary complications, and the differences in toxicities seen for the radiation modalities could be fully accounted for by the MLD delivered by each of the modalities. Conclusions The radiation modality used can be a strong mitigating factor of postoperative complications after neoadjuvant chemoradiation. PMID:23845841

Predictors of Postoperative Complications After Trimodality Therapy for Esophageal Cancer

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

Wang, Jingya; Wei, Caimiao; Tucker, Susan L.

2013-08-01

Purpose: While trimodality therapy for esophageal cancer has improved patient outcomes, surgical complication rates remain high. The goal of this study was to identify modifiable factors associated with postoperative complications after neoadjuvant chemoradiation. Methods and Materials: From 1998 to 2011, 444 patients were treated at our institution with surgical resection after chemoradiation. Postoperative (pulmonary, gastrointestinal [GI], cardiac, wound healing) complications were recorded up to 30 days postoperatively. Kruskal-Wallis tests and χ{sup 2} or Fisher exact tests were used to assess associations between continuous and categorical variables. Multivariate logistic regression tested the association between perioperative complications and patient or treatment factorsmore » that were significant on univariate analysis. Results: The most frequent postoperative complications after trimodality therapy were pulmonary (25%) and GI (23%). Lung capacity and the type of radiation modality used were independent predictors of pulmonary and GI complications. After adjusting for confounding factors, pulmonary and GI complications were increased in patients treated with 3-dimensional conformal radiation therapy (3D-CRT) versus intensity modulated radiation therapy (IMRT; odds ratio [OR], 2.018; 95% confidence interval [CI], 1.104-3.688; OR, 1.704; 95% CI, 1.03-2.82, respectively) and for patients treated with 3D-CRT versus proton beam therapy (PBT; OR, 3.154; 95% CI, 1.365-7.289; OR, 1.55; 95% CI, 0.78-3.08, respectively). Mean lung radiation dose (MLD) was strongly associated with pulmonary complications, and the differences in toxicities seen for the radiation modalities could be fully accounted for by the MLD delivered by each of the modalities. Conclusions: The radiation modality used can be a strong mitigating factor of postoperative complications after neoadjuvant chemoradiation.« less

A comparative study of serial and parallel aeroelastic computations of wings

NASA Technical Reports Server (NTRS)

Byun, Chansup; Guruswamy, Guru P.

1994-01-01

A procedure for computing the aeroelasticity of wings on parallel multiple-instruction, multiple-data (MIMD) computers is presented. In this procedure, fluids are modeled using Euler equations, and structures are modeled using modal or finite element equations. The procedure is designed in such a way that each discipline can be developed and maintained independently by using a domain decomposition approach. In the present parallel procedure, each computational domain is scalable. A parallel integration scheme is used to compute aeroelastic responses by solving fluid and structural equations concurrently. The computational efficiency issues of parallel integration of both fluid and structural equations are investigated in detail. This approach, which reduces the total computational time by a factor of almost 2, is demonstrated for a typical aeroelastic wing by using various numbers of processors on the Intel iPSC/860.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data

PubMed Central

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-01-01

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. PMID:26921716

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Do Immigrants Underutilize Optometry Services?

PubMed

Wilson, Fernando A; Wang, Yang; Stimpson, Jim P

2015-11-01

To characterize utilization of office-based optometry services by immigration status using a nationally representative database. The 2007 to 2011 Medical Expenditure Panel Survey is used to examine adults aged 18 years and older. Respondents were classified as US natives, naturalized citizens, and noncitizens. Multivariate logistic regression analysis examined the relationship of having visited an office-based optometrist within the past 12 months by immigrant status, adjusting for age, sex, education, race/ethnicity, marital status, self-reported vision difficulty, use of corrective lenses, poverty status, insurance, language barrier and usual source of care. Oaxaca-Blinder decomposition identified factors that perpetuate or ameliorate disparities in utilization across immigrant groups. The proportion of US natives who had visited an optometrist within the past year was 7.2%, almost three times higher than that for noncitizens (2.5%). Among respondents who reported vision difficulties, only 47.9% of noncitizens used corrective lenses compared with 71.0% of naturalized citizens and 71.6% of US natives. Adjusting for confounding factors, multivariate logistic regression showed that naturalized citizens and noncitizen residents had significantly lower odds than US natives of receiving optometry services (naturalized citizen adjusted odds ratio, 0.77; 95% confidence interval, 0.66 to 0.89; noncitizen adjusted odds ratio, 0.44; 95% confidence interval, 0.36 to 0.53). Decomposition analysis suggested that 17% of the disparity in utilization between noncitizens and US natives resulted from barriers to care such as language barriers, poverty, lack of insurance, and not having a usual source of health care. Prior literature suggests that immigrants have significantly poorer clinical vision outcomes than US natives. Our findings suggest that this disparity in clinical vision outcomes may result from underutilization of optometry services by immigrants compared with US natives. Immigrant patients may need targeted interventions that reduce barriers to care and change their perceptions so that regular eye care services are viewed as necessary and preventative.

Impact of treatment and insurance on socioeconomic disparities in survival after adolescent and young adult Hodgkin lymphoma: A population-based study

PubMed Central

Keegan, Theresa H.M.; DeRouen, Mindy C.; Parsons, Helen M.; Clarke, Christina A.; Goldberg, Debbie; Flowers, Christopher R.; Glaser, Sally L.

2015-01-01

Background Previous studies documented racial/ethnic and socioeconomic disparities in survival after Hodgkin lymphoma (HL) among adolescents and young adults (AYAs), but did not consider the influence of combined-modality treatment and health insurance. Methods Data for 9,353 AYA patients aged 15–39 when diagnosed with HL during 1988–2011 were obtained from the California Cancer Registry. Using multivariate Cox proportional hazards regression, we examined the impact of socio-demographic characteristics (race/ethnicity, neighborhood socioeconomic status (SES), and health insurance), initial combined-modality treatment, and subsequent cancers on survival. Results Over the 24-year study period, we observed improvements in HL-specific survival by diagnostic period and differences in survival by race/ethnicity, neighborhood SES and health insurance for a subset of more recently diagnosed patients (2001–2011). In multivariable analyses, HL-specific survival was worse for Blacks than Whites with early-stage (Hazard Ratio (HR): 1.68; 95% Confidence Interval (CI): 1.14, 2.49) and late-stage disease (HR: 1.68; 95% CI: 1.17, 2.41) and for Hispanics than Whites with late-stage disease (HR: 1.58; 95% CI: 1.22, 2.04). AYAs diagnosed with early-stage disease experienced worse survival if they also resided in lower SES neighborhoods (HR: 2.06; 95% CI: 1.59, 2.68). Furthermore, more recently diagnosed AYAs with public health insurance or who were uninsured experienced worse HL-specific survival (HR: 2.08; 95% CI: 1.52, 2.84). Conclusion Our findings identify several subgroups of HL patients at higher risk for HL mortality. Impact Identifying and reducing barriers to recommended treatment and surveillance in these AYAs at much higher risk of mortality is essential to ameliorating these survival disparities. PMID:26826029

Mapping the Critical Gestational Age at Birth that Alters Brain Development in Preterm-born Infants using Multi-Modal MRI

PubMed Central

Wu, Dan; Chang, Linda; Akazawa, Kentaro; Oishi, Kumiko; Skranes, Jon; Ernst, Thomas; Oishi, Kenichi

2017-01-01

Preterm birth adversely affects postnatal brain development. In order to investigate the critical gestational age at birth (GAB) that alters the developmental trajectory of gray and white matter structures in the brain, we investigated diffusion tensor and quantitative T2 mapping data in 43 term-born and 43 preterm-born infants. A novel multivariate linear model—the change point model, was applied to detect change points in fractional anisotropy, mean diffusivity, and T2 relaxation time. Change points captured the “critical” GAB value associated with a change in the linear relation between GAB and MRI measures. The analysis was performed in 126 regions across the whole brain using an atlas-based image quantification approach to investigate the spatial pattern of the critical GAB. Our results demonstrate that the critical GABs are region- and modality-specific, generally following a central-to-peripheral and bottom-to-top order of structural development. This study may offer unique insights into the postnatal neurological development associated with differential degrees of preterm birth. PMID:28111189

Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI.

PubMed

Wu, Dan; Chang, Linda; Akazawa, Kentaro; Oishi, Kumiko; Skranes, Jon; Ernst, Thomas; Oishi, Kenichi

2017-04-01

Preterm birth adversely affects postnatal brain development. In order to investigate the critical gestational age at birth (GAB) that alters the developmental trajectory of gray and white matter structures in the brain, we investigated diffusion tensor and quantitative T2 mapping data in 43 term-born and 43 preterm-born infants. A novel multivariate linear model-the change point model, was applied to detect change points in fractional anisotropy, mean diffusivity, and T2 relaxation time. Change points captured the "critical" GAB value associated with a change in the linear relation between GAB and MRI measures. The analysis was performed in 126 regions across the whole brain using an atlas-based image quantification approach to investigate the spatial pattern of the critical GAB. Our results demonstrate that the critical GABs are region- and modality-specific, generally following a central-to-peripheral and bottom-to-top order of structural development. This study may offer unique insights into the postnatal neurological development associated with differential degrees of preterm birth. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A trial of team-based versus small-group learning for second-year medical students: does the size of the small group make a difference?

PubMed

Willett, Laura Rees; Rosevear, G Craig; Kim, Sarang

2011-01-01

Team-based learning is a large-group instructional modality intended to provide active learning with modest faculty resources. The goal is to determine if team-based learning could be substituted for small-group learning in case sessions without compromising test performance or satisfaction. One hundred and sixty-seven students were assigned to team-based or small-group learning for 6 case discussion sessions. Examination scores and student satisfaction were compared. Instruction modality had no meaningful effect on examination score, 81.7% team based versus 79.7% small-group, p=.56 after multivariate adjustment. Student satisfaction was lower with team-based learning, 2.45 versus 3.74 on a 5-point scale, p<.001. Survey responses suggested that the very small size (8-10 students) of our small groups influenced the preference for small-group learning. Team-based learning does not adversely affect examination performance. However, student satisfaction may be inferior, especially if compared to instruction in very small groups of 10 or fewer students.

Relationship Between Manometric and Videofluoroscopic Measures of Swallow Function in Healthy Adults and Patients Treated for Head and Neck Cancer with Various Modalities

PubMed Central

Rademaker, Alfred W.; Lazarus, Cathy; Boeckxstaens, Guy; Kahrilas, Peter J.; Logemann, Jerilyn A.

2010-01-01

Pharyngeal manometry complements the modified barium swallow with videofluoroscopy (VFS) in diagnosing pressure-related causes of dysphagia. When manometric analysis is not feasible, it would be ideal if pressure information about the swallow could be inferred accurately from the VFS evaluation. Swallowing function was examined using VFS and concurrent manometry in 18 subjects (11 head and neck patients treated with various modalities and 7 healthy adults). Nonparametric univariate and multivariate analyses revealed significant relationships between manometric and fluoroscopic variables. Increases in pressure wave amplitude were significantly correlated with increased duration of tongue base to pharyngeal wall contact, reduced bolus transit times, and oropharyngeal residue. Pharyngeal residue was the most important VFS variable in reflecting pharyngeal pressure measurements. Certain VFS measures were significantly correlated with measures of pressure assessed with manometry. Further research is needed before observations and measures from VFS alone may be deemed sufficient for determining pressure-generation difficulties during the swallow in patients who are unable or unwilling to submit to manometric testing. PMID:18956228

Social determinants of self-reported health in women and men: understanding the role of gender in population health.

PubMed

Hosseinpoor, Ahmad Reza; Stewart Williams, Jennifer; Amin, Avni; Araujo de Carvalho, Islene; Beard, John; Boerma, Ties; Kowal, Paul; Naidoo, Nirmala; Chatterji, Somnath

2012-01-01

Women and men share similar health challenges yet women report poorer health. The study investigates the social determinants of self-reported health in women and men, and male-female differences in health. Data on 103154 men and 125728 women were analysed from 57 countries in the World Health Survey 2002-2004. Item Response Theory was used to construct a composite measure of health. Associations between health and determinants were assessed using multivariate linear regression. Blinder-Oaxaca decomposition partitioned the inequality in health between women and men into an "explained" component that arises because men and women differ in social and economic characteristics, and an "unexplained" component due to the differential effects of these characteristics. Decomposition was repeated for 18 countries in the World Health Organization (WHO) African region and 19 countries in the WHO European region. Women's health was significantly lower than men's. Health was associated with education, household economic status, employment, and marital status after controlling for age. In the pooled analysis decomposition showed that 30% of the inequality was "explained", of which almost 75% came from employment, education, marital status. The differential effects of being in paid employment increased the inequality. When countries in Africa and Europe were compared, the "explained" component (31% and 39% respectively) was largely attributed to the social determinants in the African countries and to women's longevity in the European countries. Being in paid employment had a greater positive effect on the health of males in both regions. Ways in which age and the social determinants contribute to the poorer health status of women compared with men varies between groups of countries. This study highlights the need for action to address social structures, institutional discrimination and harmful gender norms and roles that differently influence health with ageing.

Stability and modal analysis of shock/boundary layer interactions

NASA Astrophysics Data System (ADS)

Nichols, Joseph W.; Larsson, Johan; Bernardini, Matteo; Pirozzoli, Sergio

2017-02-01

The dynamics of oblique shock wave/turbulent boundary layer interactions is analyzed by mining a large-eddy simulation (LES) database for various strengths of the incoming shock. The flow dynamics is first analyzed by means of dynamic mode decomposition (DMD), which highlights the simultaneous occurrence of two types of flow modes, namely a low-frequency type associated with breathing motion of the separation bubble, accompanied by flapping motion of the reflected shock, and a high-frequency type associated with the propagation of instability waves past the interaction zone. Global linear stability analysis performed on the mean LES flow fields yields a single unstable zero-frequency mode, plus a variety of marginally stable low-frequency modes whose stability margin decreases with the strength of the interaction. The least stable linear modes are grouped into two classes, one of which bears striking resemblance to the breathing mode recovered from DMD and another class associated with revolving motion within the separation bubble. The results of the modal and linear stability analysis support the notion that low-frequency dynamics is intrinsic to the interaction zone, but some continuous forcing from the upstream boundary layer may be required to keep the system near a limit cycle. This can be modeled as a weakly damped oscillator with forcing, as in the early empirical model by Plotkin (AIAA J 13:1036-1040, 1975).

An inventory of bispectrum estimators for redshift space distortions

NASA Astrophysics Data System (ADS)

Regan, Donough

2017-12-01

In order to best improve constraints on cosmological parameters and on models of modified gravity using current and future galaxy surveys it is necessary maximally exploit the available data. As redshift-space distortions mean statistical translation invariance is broken for galaxy observations, this will require measurement of the monopole, quadrupole and hexadecapole of not just the galaxy power spectrum, but also the galaxy bispectrum. A recent (2015) paper by Scoccimarro demonstrated how the standard bispectrum estimator may be expressed in terms of Fast Fourier Transforms (FFTs) to afford an extremely efficient algorithm, allowing the bispectrum multipoles on all scales and triangle shapes to be measured in comparable time to those of the power spectrum. In this paper we present a suite of alternative proxies to measure the three-point correlation multipoles. In particular, we describe a modal (or plane wave) decomposition to capture the information in each multipole in a series of basis coefficients, and also describe three compressed estimators formed using the skew-spectrum, the line correlation function and the integrated bispectrum, respectively. As well as each of the estimators offering a different measurement channel, and thereby a robustness check, it is expected that some (especially the modal estimator) will offer a vast data compression, and so a much reduced covariance matrix. This compression may be vital to reduce the computational load involved in extracting the available three-point information.

Learning multimodal dictionaries.

PubMed

Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

2007-09-01

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

Relaxations to Sparse Optimization Problems and Applications

NASA Astrophysics Data System (ADS)

Skau, Erik West

Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we apply a maximum entropy model, guided by the social media data, to estimate the flooded regions during a 2013 flood in Boulder, CO and show that the results are comparable to those obtained using expert information.

Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning.

PubMed

Gou, Shuiping; Wang, Yueyue; Wu, Jiaolong; Lee, Percy; Sheng, Ke

2015-04-01

Lung dynamic MRI (dMRI) has emerged to be an appealing tool to quantify lung motion for both planning and treatment guidance purposes. However, this modality can result in blurry images due to intrinsically low signal-to-noise ratio in the lung and spatial/temporal interpolation. The image blurring could adversely affect the image processing that depends on the availability of fine landmarks. The purpose of this study is to reduce dMRI blurring using image postprocessing. To enhance the image quality and exploit the spatiotemporal continuity of dMRI sequences, a low-rank decomposition and dictionary learning (LDDL) method was employed to deblur lung dMRI and enhance the conspicuity of lung blood vessels. Fifty frames of continuous 2D coronal dMRI frames using a steady state free precession sequence were obtained from five subjects including two healthy volunteer and three lung cancer patients. In LDDL, the lung dMRI was decomposed into sparse and low-rank components. Dictionary learning was employed to estimate the blurring kernel based on the whole image, low-rank or sparse component of the first image in the lung MRI sequence. Deblurring was performed on the whole image sequences using deconvolution based on the estimated blur kernel. The deblurring results were quantified using an automated blood vessel extraction method based on the classification of Hessian matrix filtered images. Accuracy of automated extraction was calculated using manual segmentation of the blood vessels as the ground truth. In the pilot study, LDDL based on the blurring kernel estimated from the sparse component led to performance superior to the other ways of kernel estimation. LDDL consistently improved image contrast and fine feature conspicuity of the original MRI without introducing artifacts. The accuracy of automated blood vessel extraction was on average increased by 16% using manual segmentation as the ground truth. Image blurring in dMRI images can be effectively reduced using a low-rank decomposition and dictionary learning method using kernels estimated by the sparse component.

Quantum Attack-Resistent Certificateless Multi-Receiver Signcryption Scheme

PubMed Central

Li, Huixian; Chen, Xubao; Pang, Liaojun; Shi, Weisong

2013-01-01

The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC), which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC) based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ) problem and its unforgeability under the Isomorphism of Polynomials (IP) assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards. PMID:23967037

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Rojas, Cristian R.; Drake, James R.; Hjalmarsson, Håkan

2011-08-01

The usage of computationally feasible overparametrized and nonregularized system identification signal processing methods is assessed for automated determination of the full reversed-field pinch external plasma response spectrum for the experiment EXTRAP T2R. No assumptions on the geometry of eigenmodes are imposed. The attempted approach consists of high-order autoregressive exogenous estimation followed by Markov block coefficient construction and Hankel matrix singular value decomposition. It is seen that the obtained 'black-box' state-space models indeed can be compared with the commonplace ideal magnetohydrodynamics (MHD) resistive thin-shell model in cylindrical geometry. It is possible to directly map the most unstable autodetected empirical system pole to the corresponding theoretical resistive shell MHD eigenmode.

On distributed wavefront reconstruction for large-scale adaptive optics systems.

PubMed

de Visser, Cornelis C; Brunner, Elisabeth; Verhaegen, Michel

2016-05-01

The distributed-spline-based aberration reconstruction (D-SABRE) method is proposed for distributed wavefront reconstruction with applications to large-scale adaptive optics systems. D-SABRE decomposes the wavefront sensor domain into any number of partitions and solves a local wavefront reconstruction problem on each partition using multivariate splines. D-SABRE accuracy is within 1% of a global approach with a speedup that scales quadratically with the number of partitions. The D-SABRE is compared to the distributed cumulative reconstruction (CuRe-D) method in open-loop and closed-loop simulations using the YAO adaptive optics simulation tool. D-SABRE accuracy exceeds CuRe-D for low levels of decomposition, and D-SABRE proved to be more robust to variations in the loop gain.

Power independent EMG based gesture recognition for robotics.

PubMed

Li, Ling; Looney, David; Park, Cheolsoo; Rehman, Naveed U; Mandic, Danilo P

2011-01-01

A novel method for detecting muscle contraction is presented. This method is further developed for identifying four different gestures to facilitate a hand gesture controlled robot system. It is achieved based on surface Electromyograph (EMG) measurements of groups of arm muscles. The cross-information is preserved through a simultaneous processing of EMG channels using a recent multivariate extension of Empirical Mode Decomposition (EMD). Next, phase synchrony measures are employed to make the system robust to different power levels due to electrode placements and impedances. The multiple pairwise muscle synchronies are used as features of a discrete gesture space comprising four gestures (flexion, extension, pronation, supination). Simulations on real-time robot control illustrate the enhanced accuracy and robustness of the proposed methodology.

Earth resources data analysis program, phase 2

NASA Technical Reports Server (NTRS)

1974-01-01

The efforts and findings of the Earth Resources Data Analysis Program are summarized. Results of a detailed study of the needs of EOD with respect to an applications development system (ADS) for the analysis of remotely sensed data, including an evaluation of four existing systems with respect to these needs are described. Recommendations as to possible courses for EOD to follow to obtain a viable ADS are presented. Algorithmic development comprised of several subtasks is discussed. These subtasks include the following: (1) two algorithms for multivariate density estimation; (2) a data smoothing algorithm; (3) a method for optimally estimating prior probabilities of unclassified data; and (4) further applications of the modified Cholesky decomposition in various calculations. Little effort was expended on task 3, however, two reports were reviewed.

Model diagnostics in reduced-rank estimation

PubMed Central

Chen, Kun

2016-01-01

Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches. PMID:28003860

Model diagnostics in reduced-rank estimation.

PubMed

Chen, Kun

2016-01-01

Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches.

Dichotic and dichoptic digit perception in normal adults.

PubMed

Lawfield, Angela; McFarland, Dennis J; Cacace, Anthony T

2011-06-01

Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have also been used to assess for disorders of auditory processing. However, with this application, limitations exist and paradigms need to be developed to improve specificity of the diagnosis. Use of matched tasks in multiple sensory modalities is a logical approach to address this issue. Herein, we use dichotic listening and dichoptic viewing of visually presented digits for making this comparison. To evaluate methodological issues involved in using matched tasks of dichotic listening and dichoptic viewing in normal adults. A multivariate assessment of the effects of modality (auditory vs. visual), digit-span length (1-3 pairs), response selection (recognition vs. reproduction), and ear/visual hemifield of presentation (left vs. right) on dichotic and dichoptic digit perception. Thirty adults (12 males, 18 females) ranging in age from 18 to 30 yr with normal hearing sensitivity and normal or corrected-to-normal visual acuity. A computerized, custom-designed program was used for all data collection and analysis. A four-way repeated measures analysis of variance (ANOVA) evaluated the effects of modality, digit-span length, response selection, and ear/visual field of presentation. The ANOVA revealed that performances on dichotic listening and dichoptic viewing tasks were dependent on complex interactions between modality, digit-span length, response selection, and ear/visual hemifield of presentation. Correlation analysis suggested a common effect on overall accuracy of performance but isolated only an auditory factor for a laterality index. The variables used in this experiment affected performances in the auditory modality to a greater extent than in the visual modality. The right-ear advantage observed in the dichotic-digits task was most evident when reproduction mediated response selection was used in conjunction with three-digit pairs. This effect implies that factors such as "speech related output mechanisms" and digit-span length (working memory) contribute to laterality effects in dichotic listening performance with traditional paradigms. Thus, the use of multiple-digit pairs to avoid ceiling effects and the application of verbal reproduction as a means of response selection may accentuate the role of nonperceptual factors in performance. Ideally, tests of perceptual abilities should be relatively free of such effects. American Academy of Audiology.

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

Sui, Jing; Adali, Tülay; Li, Yi-Ou; Yang, Honghui; Calhoun, Vince D.

2010-03-01

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

A Hybrid Nanomaterial for the Controlled Generation of Free Radicals and Oxidative Destruction of Hypoxic Cancer Cells.

PubMed

Shen, Song; Zhu, Chunlei; Huo, Da; Yang, Miaoxin; Xue, Jiajia; Xia, Younan

2017-07-17

Anticancer modalities based on oxygen free radicals, including photodynamic therapy and radiotherapy, have emerged as promising treatments in the clinic. However, the hypoxic environment in tumor tissue prevents the formation of oxygen free radicals. Here we introduce a novel strategy that employs oxygen-independent free radicals generated from a polymerization initiator for eradicating cancer cells. The initiator is mixed with a phase-change material and loaded into the cavities of gold nanocages. Upon irradiation by a near-infrared laser, the phase-change material is melted due to the photothermal effect of gold nanocages, leading to the release and decomposition of the loaded initiator to generate free radicals. The free radicals produced in this way are highly effective in inducing apoptosis in hypoxic cancer cells. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Eclogite-Garnetite transformation in the MORB + H 2O system

NASA Astrophysics Data System (ADS)

Okamoto, Kazuaki; Maruyama, Shigenori

2004-08-01

To decipher phase relations of oceanic crust in the coldest slab at the mantle transition zone, multi-anvil experiments were conducted in the MORB+H 2O system at pressures of 10-19 GPa, and temperatures of 700-1500 °C. Garnet and stishovite were recognized in all run charges. Above 15 GPa, garnet drastically increases NaSi (Na 2MSi 5O 12) component (M = Ca, Mg, Fe 2+), jadeite occurs instead of omphacite. Na-, K-hollandite containing 7 mol% NaAlSi 3O 8 and Ca-perovskite with 60 mol% CaTiO 3, were observed at P>17 GPa. With decomposition of omphacite and increase of modal ratio of garnet, there is a sharp increase of density at 440 km. The density increase due to appearance of Ca-perovskite at 570 km, is estimated approximately 100 km shallower than that of previous estimation.

Unsteady flow sensing and optimal sensor placement using machine learning

NASA Astrophysics Data System (ADS)

Semaan, Richard

2016-11-01

Machine learning is used to estimate the flow state and to determine the optimal sensor placement over a two-dimensional (2D) airfoil equipped with a Coanda actuator. The analysis is based on flow field data obtained from 2D unsteady Reynolds averaged Navier-Stokes (uRANS) simulations with different jet blowing intensities and actuation frequencies, characterizing different flow separation states. This study shows how the "random forests" algorithm is utilized beyond its typical usage in fluid mechanics estimating the flow state to determine the optimal sensor placement. The results are compared against the current de-facto standard of maximum modal amplitude location and against a brute force approach that scans all possible sensor combinations. The results show that it is possible to simultaneously infer the state of flow and to determine the optimal sensor location without the need to perform proper orthogonal decomposition. Collaborative Research Center (CRC) 880, DFG.

International Space Station Future Correlation Analysis Improvements

NASA Technical Reports Server (NTRS)

Laible, Michael R.; Pinnamaneni, Murthy; Sugavanam, Sujatha; Grygier, Michael

2018-01-01

Ongoing modal analyses and model correlation are performed on different configurations of the International Space Station (ISS). These analyses utilize on-orbit dynamic measurements collected using four main ISS instrumentation systems: External Wireless Instrumentation System (EWIS), Internal Wireless Instrumentation System (IWIS), Space Acceleration Measurement System (SAMS), and Structural Dynamic Measurement System (SDMS). Remote Sensor Units (RSUs) are network relay stations that acquire flight data from sensors. Measured data is stored in the Remote Sensor Unit (RSU) until it receives a command to download data via RF to the Network Control Unit (NCU). Since each RSU has its own clock, it is necessary to synchronize measurements before analysis. Imprecise synchronization impacts analysis results. A study was performed to evaluate three different synchronization techniques: (i) measurements visually aligned to analytical time-response data using model comparison, (ii) Frequency Domain Decomposition (FDD), and (iii) lag from cross-correlation to align measurements. This paper presents the results of this study.

Design of a linear projector for use with the normal modes of the GLAS 4th order GCM

NASA Technical Reports Server (NTRS)

Bloom, S. C.

1984-01-01

The design of a linear projector for use with the normal modes of a model of atmospheric circulation is discussed. A central element in any normal mode initialization scheme is the process by which a set of data fields - winds, temperatures or geopotentials, and surface pressures - are expressed ("projected') in terms of the coefficients of a model's normal modes. This process is completely analogous to the Fourier decomposition of a single field (indeed a FFT applied in the zonal direction is a part of the process). Complete separability in all three spatial dimensions is assumed. The basis functions for the modal expansion are given. An important feature of the normal modes is their coupling of the structures of different fields, thus a coefficient in a normal mode expansion would contain both mass and momentum information.

The Short-Term and Intermediate-Term Risk of Second Neoplasms After Diagnosis and Treatment of Unilateral Vestibular Schwannoma: Analysis of 9460 Cases

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

Carlson, Matthew L., E-mail: carlson.matthew@mayo.edu; Department of Neurologic Surgery, Mayo Clinic School of Medicine, Rochester, Minnesota; Glasgow, Amy E.

Purpose: To determine the incidence of second intracranial neoplasms after the diagnosis and treatment of sporadic vestibular schwannoma (VS). Methods and Materials: Analysis of the Surveillance, Epidemiology, and End Results (SEER) database including all patients identified with a diagnosis of VS and a second intracranial tumor. The Kaplan-Meier method was used to determine the incidence of second tumors while allowing for censoring at loss to follow-up or death. Multivariable associations between treatment modality and second tumor formation were explored using Cox proportional hazards regression analysis. Two illustrative cases are also presented. Results: In all, 9460 patients with unilateral VS weremore » identified between 2004 and 2012. Overall, 66 (0.7%) patients experienced a separate intracranial tumor, benign or malignant, after treatment of VS. Kaplan-Meier estimates for time to second neoplasm at 1, 3, and 5 years were 0.3%, 0.7%, and 0.8%, respectively. Multivariable comparison between VS treatment modalities revealed that the risk of second tumor formation was similar between radiation and surgery (hazard ratio [HR] 0.74; 95% confidence interval [CI] 0.36-1.51; P=.93) but greater for tumors managed with observation alone compared with radiation (HR 2.48; 95% CI 1.31-4.71; P<.01). A total of 6 (0.06%) intracranial malignancies were diagnosed after VS treatment. Kaplan-Meier estimates for time to malignancy at 1, 3, and 5 years were 0%, 0.1%, and 0.1%, respectively. After adjustment for age at diagnosis, sex, and treatment modality, the probability of malignancy after radiation was not greater than after observation alone or microsurgery (HR 4.88; 95% CI 0.85-28.14; P=.08) during the study period. Conclusions: The risk for the development of a second intracranial neoplasm, benign or malignant, at 5 years after treatment of unilateral VS is approximately 0.8%, whereas the risk of acquiring a separate malignancy is 0.1%, or approximately 1 per 1000 cases. The short-term and intermediate-term incidence of second neoplasm after radiation of VS is not greater than the incidence after microsurgery or observation.« less

Characterization of a Heated Liquid Jet in Crossflow

NASA Astrophysics Data System (ADS)

Wiest, Heather K.

The liquid jet in crossflow (LJICF) is a widely utilized fuel injection method for airbreathing propulsion devices such as low NO x gas turbine combustors, turbojet afterburners, scramjet/ramjet engines, and rotating detonation engines (RDE's). This flow field allows for efficient fuel-air mixing as aerodynamic forces from the crossflow augment atomization. Additionally, increases in the thermal demands of advanced aeroengines necessitates the use of fuel as a primary coolant. The resulting higher fuel temperatures can cause flash atomization of the liquid fuel as it is injected into a crossflow, potentially leading to a large reduction in the jet penetration. While many experimental works have characterized the overall atomization process of a room temperature liquid jet in an ambient temperature and pressure crossflow, the aggressive conditions associated with flash atomization especially in an air crossflow with elevated temperatures and pressures have been less studied in the community. A successful test campaign was conducted to study the effects of fuel temperature on a liquid jet injected transversely into a steady air crossflow at ambient as well as elevated temperature and pressure conditions. Modifications were made to an existing optically accessible rig, and a new fuel injector was designed for this study. Backlit imaging was utilized to record changes in the overall spray characteristics and jet trajectory as fuel temperature and crossflow conditioners were adjusted. Three primary analysis techniques were applied to the heated LJICF data: linear regression of detected edges to determine trajectory correlations, exploratory study of pixel intensity variations both temporally as well as spatially, and modal decomposition of the data. The overall objectives of this study was to assess the trajectory, breakup, and mixing of the LJICF undery varying jet and crossflow conditions, develop a trajectory correlation to predict changes in jet penetration due to fuel temperature increases, and characterize the changes in underlying physics in the LJICF flow field. Based on visual inspection, the increase in fuel temperature leads to a finer and denser fuel spray. With increasingly elevated liquid temperatures, the penetration of the jet typically decreases. At or near flashing conditions, the jet had a tendency to penetrate upstream before bending over in the crossflow as well as experiences a rapid expansion causing the jet column to increase in width. Two trajectory correlations were determined, one for each set of crossflow conditions, based on normalized axial distance, normalized liquid viscosity, and normalized jet diameter as liquid is vaporized. The pixel intensity analysis showed that the highest temperature jet in the ambient temperature and pressure crossflow exhibited periodic behavior that was also found using various modal techniques including proper orthogonal decomposition and dynamic mode decomposition. Dominant frequencies determined for most test cases were associated with the bulk or flapping motion of the jet. Most notably, the DMD analysis in this study was successful in identifying robust modes across different subgroupings of the data even though the modes identified were not the highest power modes in each DMD spectrum.

Passive simulation of the nonlinear port-Hamiltonian modeling of a Rhodes Piano

NASA Astrophysics Data System (ADS)

Falaize, Antoine; Hélie, Thomas

2017-03-01

This paper deals with the time-domain simulation of an electro-mechanical piano: the Fender Rhodes. A simplified description of this multi-physical system is considered. It is composed of a hammer (nonlinear mechanical component), a cantilever beam (linear damped vibrating component) and a pickup (nonlinear magneto-electronic transducer). The approach is to propose a power-balanced formulation of the complete system, from which a guaranteed-passive simulation is derived to generate physically-based realistic sound synthesis. Theses issues are addressed in four steps. First, a class of Port-Hamiltonian Systems is introduced: these input-to-output systems fulfill a power balance that can be decomposed into conservative, dissipative and source parts. Second, physical models are proposed for each component and are recast in the port-Hamiltonian formulation. In particular, a finite-dimensional model of the cantilever beam is derived, based on a standard modal decomposition applied to the Euler-Bernoulli model. Third, these systems are interconnected, providing a nonlinear finite-dimensional Port-Hamiltonian System of the piano. Fourth, a passive-guaranteed numerical method is proposed. This method is built to preserve the power balance in the discrete-time domain, and more precisely, its decomposition structured into conservative, dissipative and source parts. Finally, simulations are performed for a set of physical parameters, based on empirical but realistic values. They provide a variety of audio signals which are perceptively relevant and qualitatively similar to some signals measured on a real instrument.

Wavelet optimization for content-based image retrieval in medical databases.

PubMed

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data.

PubMed

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-05-15

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Fundamental Insights into Combustion Instability Predictions in Aerospace Propulsion

NASA Astrophysics Data System (ADS)

Huang, Cheng

Integrated multi-fidelity modeling has been performed for combustion instability in aerospace propulsion, which includes two levels of analysis: first, computational fluid dynamics (CFD) using hybrid RANS/LES simulations for underlying physics investigations (high-fidelity modeling); second, modal decomposition techniques for diagnostics (analysis & validation); third, development of flame response model using model reduction techniques for practical design applications (low-order model). For the high-fidelity modeling, the relevant CFD code development work is moving towards combustion instability prediction for liquid propulsion system. A laboratory-scale single-element lean direct injection (LDI) gas turbine combustor is used for configuration that produces self-excited combustion instability. The model gas turbine combustor is featured with an air inlet section, air plenum, swirler-venturi-injector assembly, combustion chamber, and exit nozzle. The combustor uses liquid fuel (Jet-A/FT-SPK) and heated air up to 800K. Combustion dynamics investigations are performed with the same geometry and operating conditions concurrently between the experiment and computation at both high (φ=0.6) and low (φ=0.36) equivalence ratios. The simulation is able to reach reasonable agreement with experiment measurements in terms of the pressure signal. Computational analyses are also performed using an acoustically-open geometry to investigate the characteristic hydrodynamics in the combustor with both constant and perturbed inlet mass flow rates. Two hydrodynamic modes are identified by using Dynamic Mode Decomposition (DMD) analysis: Vortex Breakdown Bubble (VBB) and swirling modes. Following that, the closed geometry simulation results are analyzed in three steps. In step one, a detailed cycle analysis shows two physically important couplings in the combustor: first, the acoustic compression enhances the spray drop breakup and vaporization, and generates more gaseous fuel for reaction; second, the acoustic compression couples with the unsteady hydrodynamics found in the open-geometry simulation, enhances the fuel/air mixing, and triggers a large amount of heat addition. In step two, a modal analysis using DMD extracts the dynamic features of important modes in the combustor, and identifies the presence of Precessing Vortex Core (PVC) mode and its nonlinear interactions with acoustic modes. Moreover, the DMD analysis helps to establish the couplings between the hydrodynamics and acoustics in terms of frequencies. In step 3, Rayleigh index analysis provides a quantitative assessment of acoustics/combustion couplings and identifies local regions for instability driving/damping. Two modal decomposition techniques, Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), are assessed in terms of their capabilities in extracting important information from the original simulation dataset and in validating the computational results using the experiment measurement. A POD analysis provides a series of modes with decreasing energy content and it offers an efficient and optimized way to represent a large dataset. The frequency-based DMD technique provides modes that correspond to all single frequencies. For the low-order modeling, fundamental aspects are examined to study necessary conditions, criteria and approaches to develop a reduced-order model (ROM) that is able to represent generic combustion/flame responses, which then can be used in an engineering level tool to provide efficient predictions of combustion instability for practical design applications. Explorations are focused on model reduction techniques by using the so-called POD/Galerkin method. The method uses the numerical solutions of the model equations as the database for building a set of POD eigen-bases. Specifically, the numerical solutions are calculated by perturbing quantities of interest such as the inlet conditions. The POD-derived eigen-bases are, in turn, used in conjunction with a Galerkin procedure to reduce the governing partial differential equation to an ordinary differential equation, which constitutes the ROM. Once the ROM is established, it can then be used as a lower-order test-bed to predict detailed results within certain parametric ranges at a fraction of the cost of solving the full governing equations. A detailed assessment is performed on the method in two parts. In part one, a one-dimensional scalar reaction-advection model equation is used for fundamental investigations, which include verification of the POD eigen-basis calculation and of the ROM development procedure. Moreover, certain criteria during ROM development are established: 1. a necessary number of POD modes that should be included to guarantee a stable ROM; 2. the need for the numerical discretization scheme to be consistent between the original CFD and the developed ROM. Furthermore, the predictive capabilities of the resulting ROM are evaluated to test its limits and to validate the values of applying broadband forcing in improving the ROM performance. In part two, the exploration is extended to a vector system of equations. Using the one-dimensional Euler equation is used as a model equation. A numerical stability issue is identified during the ROM development, the cause of which is further studied and attributed to the normalization methods implemented to generate coupled POD eigen-bases for vector variables. (Abstract shortened by UMI.).

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

PubMed Central

Levman, Jacob; Takahashi, Emi

2016-01-01

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury’s etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

Hierarchical multivariate covariance analysis of metabolic connectivity

PubMed Central

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-01-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI). PMID:25294129

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

System Characterizations and Optimized Reconstruction Methods for Novel X-ray Imaging Modalities

NASA Astrophysics Data System (ADS)

Guan, Huifeng

In the past decade there have been many new emerging X-ray based imaging technologies developed for different diagnostic purposes or imaging tasks. However, there exist one or more specific problems that prevent them from being effectively or efficiently employed. In this dissertation, four different novel X-ray based imaging technologies are discussed, including propagation-based phase-contrast (PB-XPC) tomosynthesis, differential X-ray phase-contrast tomography (D-XPCT), projection-based dual-energy computed radiography (DECR), and tetrahedron beam computed tomography (TBCT). System characteristics are analyzed or optimized reconstruction methods are proposed for these imaging modalities. In the first part, we investigated the unique properties of propagation-based phase-contrast imaging technique when combined with the X-ray tomosynthesis. Fourier slice theorem implies that the high frequency components collected in the tomosynthesis data can be more reliably reconstructed. It is observed that the fringes or boundary enhancement introduced by the phase-contrast effects can serve as an accurate indicator of the true depth position in the tomosynthesis in-plane image. In the second part, we derived a sub-space framework to reconstruct images from few-view D-XPCT data set. By introducing a proper mask, the high frequency contents of the image can be theoretically preserved in a certain region of interest. A two-step reconstruction strategy is developed to mitigate the risk of subtle structures being oversmoothed when the commonly used total-variation regularization is employed in the conventional iterative framework. In the thirt part, we proposed a practical method to improve the quantitative accuracy of the projection-based dual-energy material decomposition. It is demonstrated that applying a total-projection-length constraint along with the dual-energy measurements can achieve a stabilized numerical solution of the decomposition problem, thus overcoming the disadvantages of the conventional approach that was extremely sensitive to noise corruption. In the final part, we described the modified filtered backprojection and iterative image reconstruction algorithms specifically developed for TBCT. Special parallelization strategies are designed to facilitate the use of GPU computing, showing demonstrated capability of producing high quality reconstructed volumetric images with a super fast computational speed. For all the investigations mentioned above, both simulation and experimental studies have been conducted to demonstrate the feasibility and effectiveness of the proposed methodologies.

Ambient Vibration and Earthquake-Data Analyses of a 62-STORY Building Using System Identification and Seismic Interferometry

NASA Astrophysics Data System (ADS)

Kalkan, E.; Fletcher, J. B.; Ulusoy, H. S.; Baker, L. A.

2014-12-01

A 62-story residential tower in San Francisco—the tallest all-residential building in California—was recently instrumented by the USGS's National Strong Motion Project in collaboration with the Strong Motion Instrumentation Program of the California Geological Survey to monitor the motion of a tall building built with specifically engineered features (including buckling-restrained braces, outrigger columns and a tuned liquid damper) to reduce its sway from seismic and wind loads. This 641-ft tower has been outfitted with 72 uni-axial accelerometers, spanning through 26 different levels of the building. For damage detection and localization through structural health monitoring, we use local micro-earthquake and ambient monitoring (background noises) to define linear-elastic (undamaged) dynamic properties of the superstructure including its modal parameters (fundamental frequencies, mode shapes and modal damping values) and shear-wave propagation profile and wave attenuation inside the building, which need to be determined in advance of strong shaking. In order to estimate the baseline modal parameters, we applied a frequency domain decomposition method. Using this method, the first three bending modes in the reference east-west direction, the first two bending modes in the reference north-south direction, and the first two torsional modes were identified. The shear-wave propagation and wave attenuation inside the building were computed using deconvolution interferometry. The data used for analyses are from ambient vibrations having 20 minutes duration, and earthquake data from a local M4.5 event located just north east of Geyserville, California. We show that application of deconvolution interferometry to data recorded inside a building is a powerful technique for monitoring structural parameters, such as velocities of traveling waves, frequencies of normal modes, and intrinsic attenuation (i.e., damping). The simplicity and similarity of the deconvolved waveforms from ambient vibrations and a small magnitude event also suggest that a one-dimensional shear velocity model is sufficiently accurate to represent the wave propagation charactersistics inside the building.

Reduced rank regression via adaptive nuclear norm penalization

PubMed Central

Chen, Kun; Dong, Hongbo; Chan, Kung-Sik

2014-01-01

Summary We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally non-convex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed non-convex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy. PMID:25045172

Factors Associated with the Choice of Peritoneal Dialysis in Patients with End-Stage Renal Disease.

PubMed

Chiang, Pei-Chun; Hou, Jia-Jeng; Jong, Ing-Ching; Hung, Peir-Haur; Hsiao, Chih-Yen; Ma, Tsung-Liang; Hsu, Yueh-Han

2016-01-01

The purpose of this study was to analyze the factors associated with receiving peritoneal dialysis (PD) in patients with incident end-stage renal disease (ESRD) in a hospital in Southern Taiwan. The study included all consecutive patients with incident ESRD who participated in a multidisciplinary predialysis education (MPE) program and started their first dialysis therapy between January 1, 2008, and June 30, 2013, in the study hospital. We provided small group teaching sessions to advanced CKD patients and their family to enhance understanding of various dialysis modalities. Multivariate logistic regression models were used to analyze the association of patient characteristics with the chosen dialysis modality. Of the 656 patients, 524 (80%) chose hemodialysis and 132 chose PD. Our data showed that young age, high education level, and high scores of activities of daily living (ADLs) were positively associated with PD treatment. Patients who received small group teaching sessions had higher percentages of PD treatment (30.5% versus 19.5%; P = 0.108) and preparedness for dialysis (61.1% versus 46.6%; P = 0.090). Young age, high education level, and high ADL score were positively associated with choosing PD. Early creation of vascular access may be a barrier for PD.

Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation.

PubMed

Mathew, Boby; Holand, Anna Marie; Koistinen, Petri; Léon, Jens; Sillanpää, Mikko J

2016-02-01

A novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented. Multi-trait genetic parameter estimation is a relevant topic in animal and plant breeding programs because multi-trait analysis can take into account the genetic correlation between different traits and that significantly improves the accuracy of the genetic parameter estimates. Generally, multi-trait analysis is computationally demanding and requires initial estimates of genetic and residual correlations among the traits, while those are difficult to obtain. In this study, we illustrate how to reparametrize covariance matrices of a multivariate animal model/animal models using modified Cholesky decompositions. This reparametrization-based approach is used in the Integrated Nested Laplace Approximation (INLA) methodology to estimate genetic parameters of multivariate animal model. Immediate benefits are: (1) to avoid difficulties of finding good starting values for analysis which can be a problem, for example in Restricted Maximum Likelihood (REML); (2) Bayesian estimation of (co)variance components using INLA is faster to execute than using Markov Chain Monte Carlo (MCMC) especially when realized relationship matrices are dense. The slight drawback is that priors for covariance matrices are assigned for elements of the Cholesky factor but not directly to the covariance matrix elements as in MCMC. Additionally, we illustrate the concordance of the INLA results with the traditional methods like MCMC and REML approaches. We also present results obtained from simulated data sets with replicates and field data in rice.

Brain organization underlying superior mathematical abilities in children with autism.

PubMed

Iuculano, Teresa; Rosenberg-Lee, Miriam; Supekar, Kaustubh; Lynch, Charles J; Khouzam, Amirah; Phillips, Jennifer; Uddin, Lucina Q; Menon, Vinod

2014-02-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Probabilistic characterization of wind turbine blades via aeroelasticity and spinning finite element formulation

NASA Astrophysics Data System (ADS)

Velazquez, Antonio; Swartz, R. Andrew

2012-04-01

Wind energy is an increasingly important component of this nation's renewable energy portfolio, however safe and economical wind turbine operation is a critical need to ensure continued adoption. Safe operation of wind turbine structures requires not only information regarding their condition, but their operational environment. Given the difficulty inherent in SHM processes for wind turbines (damage detection, location, and characterization), some uncertainty in conditional assessment is expected. Furthermore, given the stochastic nature of the loading on turbine structures, a probabilistic framework is appropriate to characterize their risk of failure at a given time. Such information will be invaluable to turbine controllers, allowing them to operate the structures within acceptable risk profiles. This study explores the characterization of the turbine loading and response envelopes for critical failure modes of the turbine blade structures. A framework is presented to develop an analytical estimation of the loading environment (including loading effects) based on the dynamic behavior of the blades. This is influenced by behaviors including along and across-wind aero-elastic effects, wind shear gradient, tower shadow effects, and centrifugal stiffening effects. The proposed solution includes methods that are based on modal decomposition of the blades and require frequent updates to the estimated modal properties to account for the time-varying nature of the turbine and its environment. The estimated demand statistics are compared to a code-based resistance curve to determine a probabilistic estimate of the risk of blade failure given the loading environment.

Utilization of services in a randomized trial testing phone- and web-based interventions for smoking cessation.

PubMed

Zbikowski, Susan M; Jack, Lisa M; McClure, Jennifer B; Deprey, Mona; Javitz, Harold S; McAfee, Timothy A; Catz, Sheryl L; Richards, Julie; Bush, Terry; Swan, Gary E

2011-05-01

Phone counseling has become standard for behavioral smoking cessation treatment. Newer options include Web and integrated phone-Web treatment. No prior research, to our knowledge, has systematically compared the effectiveness of these three treatment modalities in a randomized trial. Understanding how utilization varies by mode, the impact of utilization on outcomes, and predictors of utilization across each mode could lead to improved treatments. One thousand two hundred and two participants were randomized to phone, Web, or combined phone-Web cessation treatment. Services varied by modality and were tracked using automated systems. All participants received 12 weeks of varenicline, printed guides, an orientation call, and access to a phone supportline. Self-report data were collected at baseline and 6-month follow-up. Overall, participants utilized phone services more often than the Web-based services. Among treatment groups with Web access, a significant proportion logged in only once (37% phone-Web, 41% Web), and those in the phone-Web group logged in less often than those in the Web group (mean = 2.4 vs. 3.7, p = .0001). Use of the phone also was correlated with increased use of the Web. In multivariate analyses, greater use of the phone- or Web-based services was associated with higher cessation rates. Finally, older age and the belief that certain treatments could improve success were consistent predictors of greater utilization across groups. Other predictors varied by treatment group. Opportunities for enhancing treatment utilization exist, particularly for Web-based programs. Increasing utilization more broadly could result in better overall treatment effectiveness for all intervention modalities.

Reliability-Weighted Integration of Audiovisual Signals Can Be Modulated by Top-down Attention

PubMed Central

Noppeney, Uta

2018-01-01

Abstract Behaviorally, it is well established that human observers integrate signals near-optimally weighted in proportion to their reliabilities as predicted by maximum likelihood estimation. Yet, despite abundant behavioral evidence, it is unclear how the human brain accomplishes this feat. In a spatial ventriloquist paradigm, participants were presented with auditory, visual, and audiovisual signals and reported the location of the auditory or the visual signal. Combining psychophysics, multivariate functional MRI (fMRI) decoding, and models of maximum likelihood estimation (MLE), we characterized the computational operations underlying audiovisual integration at distinct cortical levels. We estimated observers’ behavioral weights by fitting psychometric functions to participants’ localization responses. Likewise, we estimated the neural weights by fitting neurometric functions to spatial locations decoded from regional fMRI activation patterns. Our results demonstrate that low-level auditory and visual areas encode predominantly the spatial location of the signal component of a region’s preferred auditory (or visual) modality. By contrast, intraparietal sulcus forms spatial representations by integrating auditory and visual signals weighted by their reliabilities. Critically, the neural and behavioral weights and the variance of the spatial representations depended not only on the sensory reliabilities as predicted by the MLE model but also on participants’ modality-specific attention and report (i.e., visual vs. auditory). These results suggest that audiovisual integration is not exclusively determined by bottom-up sensory reliabilities. Instead, modality-specific attention and report can flexibly modulate how intraparietal sulcus integrates sensory signals into spatial representations to guide behavioral responses (e.g., localization and orienting). PMID:29527567

Characterization of agricultural land using singular value decomposition

NASA Astrophysics Data System (ADS)

Herries, Graham M.; Danaher, Sean; Selige, Thomas

1995-11-01

A method is defined and tested for the characterization of agricultural land from multi-spectral imagery, based on singular value decomposition (SVD) and key vector analysis. The SVD technique, which bears a close resemblance to multivariate statistic techniques, has previously been successfully applied to problems of signal extraction for marine data and forestry species classification. In this study the SVD technique is used as a classifier for agricultural regions, using airborne Daedalus ATM data, with 1 m resolution. The specific region chosen is an experimental research farm in Bavaria, Germany. This farm has a large number of crops, within a very small region and hence is not amenable to existing techniques. There are a number of other significant factors which render existing techniques such as the maximum likelihood algorithm less suitable for this area. These include a very dynamic terrain and tessellated pattern soil differences, which together cause large variations in the growth characteristics of the crops. The SVD technique is applied to this data set using a multi-stage classification approach, removing unwanted land-cover classes one step at a time. Typical classification accuracy's for SVD are of the order of 85-100%. Preliminary results indicate that it is a fast and efficient classifier with the ability to differentiate between crop types such as wheat, rye, potatoes and clover. The results of characterizing 3 sub-classes of Winter Wheat are also shown.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

PubMed

Aragri, Adriana; Scarabino, Tommaso; Seifritz, Erich; Comani, Silvia; Cirillo, Sossio; Tedeschi, Gioacchino; Esposito, Fabrizio; Di Salle, Francesco

2006-09-01

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity. (c) 2006 Wiley-Liss, Inc.

Decomposing socioeconomic inequality in child vaccination: results from Ireland.

PubMed

Doherty, Edel; Walsh, Brendan; O'Neill, Ciaran

2014-06-05

There is limited knowledge of the extent of or factors underlying inequalities in uptake of childhood vaccination in Ireland. This paper aims to measure and decompose socioeconomic inequalities in childhood vaccination in the Republic of Ireland. The analysis was performed using data from the first wave of the Growing Up in Ireland survey, a nationally representative survey of the carers of over 11,000 nine-month old babies collected in 2008 and 2009. Multivariate analysis was conducted to explore the child and parental factors, including socioeconomic factors that were associated with non-vaccination of children. A concentration index was calculated to measure inequality in childhood vaccination. Subsequent decomposition analysis identified key factors underpinning observed inequalities. Overall the results confirm a strong socioeconomic gradient in childhood vaccination in the Republic of Ireland. Concentration indices of vaccination (CI=-0.19) show a substantial pro-rich gradient. Results from the decomposition analysis suggest that a substantial proportion of the inequality is explained by household level variables such as socioeconomic status, household structure, income and entitlement to publicly funded care (29.9%, 24% 30.6% and 12.9% respectively). Substantial differences are also observed between children of Irish mothers and immigrant mothers from developing countries. Vaccination was less likely in lower than in higher income households. Access to publicly funded services was an important factor in explaining inequalities. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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

Ladd-Lively, Jennifer L

2014-01-01

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

Cascaded systems analysis of noise and detectability in dual-energy cone-beam CT

PubMed Central

Gang, Grace J.; Zbijewski, Wojciech; Webster Stayman, J.; Siewerdsen, Jeffrey H.

2012-01-01

Purpose: Dual-energy computed tomography and dual-energy cone-beam computed tomography (DE-CBCT) are promising modalities for applications ranging from vascular to breast, renal, hepatic, and musculoskeletal imaging. Accordingly, the optimization of imaging techniques for such applications would benefit significantly from a general theoretical description of image quality that properly incorporates factors of acquisition, reconstruction, and tissue decomposition in DE tomography. This work reports a cascaded systems analysis model that includes the Poisson statistics of x rays (quantum noise), detector model (flat-panel detectors), anatomical background, image reconstruction (filtered backprojection), DE decomposition (weighted subtraction), and simple observer models to yield a task-based framework for DE technique optimization. Methods: The theoretical framework extends previous modeling of DE projection radiography and CBCT. Signal and noise transfer characteristics are propagated through physical and mathematical stages of image formation and reconstruction. Dual-energy decomposition was modeled according to weighted subtraction of low- and high-energy images to yield the 3D DE noise-power spectrum (NPS) and noise-equivalent quanta (NEQ), which, in combination with observer models and the imaging task, yields the dual-energy detectability index (d′). Model calculations were validated with NPS and NEQ measurements from an experimental imaging bench simulating the geometry of a dedicated musculoskeletal extremities scanner. Imaging techniques, including kVp pair and dose allocation, were optimized using d′ as an objective function for three example imaging tasks: (1) kidney stone discrimination; (2) iodine vs bone in a uniform, soft-tissue background; and (3) soft tissue tumor detection on power-law anatomical background. Results: Theoretical calculations of DE NPS and NEQ demonstrated good agreement with experimental measurements over a broad range of imaging conditions. Optimization results suggest a lower fraction of total dose imparted by the low-energy acquisition, a finding consistent with previous literature. The selection of optimal kVp pair reveals the combined effect of both quantum noise and contrast in the kidney stone discrimination and soft-tissue tumor detection tasks, whereas the K-edge effect of iodine was the dominant factor in determining kVp pairs in the iodine vs bone task. The soft-tissue tumor task illustrated the benefit of dual-energy imaging in eliminating anatomical background noise and improving detectability beyond that achievable by single-energy scans. Conclusions: This work established a task-based theoretical framework that is predictive of DE image quality. The model can be utilized in optimizing a broad range of parameters in image acquisition, reconstruction, and decomposition, providing a useful tool for maximizing DE-CBCT image quality and reducing dose. PMID:22894440

Understanding perception of active noise control system through multichannel EEG analysis.

PubMed

Bagha, Sangeeta; Tripathy, R K; Nanda, Pranati; Preetam, C; Das, Debi Prasad

2018-06-01

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p < 0.001 ) and 99.31% ( p < 0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

Cardiomyocyte hypertrophy, oncosis, and autophagic vacuolization predict mortality in idiopathic dilated cardiomyopathy with advanced heart failure.

PubMed

Vigliano, Carlos A; Cabeza Meckert, Patricia M; Diez, Mirta; Favaloro, Liliana E; Cortés, Claudia; Fazzi, Lucía; Favaloro, Roberto R; Laguens, Rubén P

2011-04-05

The aim of this study was to identify the remodeling parameters cardiomyocyte (CM) damage or death, hypertrophy, and fibrosis that may be linked to outcomes in patients with advanced heart failure (HF) in an effort to understand the pathogenic mechanisms of HF that may support newer therapeutic modalities. There are controversial results on the influence of fibrosis, CM hypertrophy, and apoptosis on outcomes in patients with HF; other modalities of cell damage have been poorly investigated. In endomyocardial biopsy specimens from 100 patients with idiopathic dilated cardiomyopathy and advanced HF, CM diameter and the extent of fibrosis were determined by morphometry. The proportion of CMs with evidence of apoptosis, autophagic vacuolization (AuV), and oncosis was investigated by immunohistochemical methods and by terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end labeling. Those parameters were correlated with mortality in 3 years of follow-up by univariate analysis and with multivariate models incorporating the clinical variables more relevant to the prediction of outcomes. CM AuV occurred in 28 patients (0.013 ± 0.012%) and oncosis in 41 (0.109 ± 0.139%). Nineteen patients showed both markers. Apoptotic CM nuclei were observed in 3 patients. In univariate analysis, CM diameter and AuV, either alone or associated with oncosis, were predictors of mortality. In multivariate analysis, CM diameter (hazard ratio: 1.37; 95% confidence interval: 1.12 to 1.68; p = 0.002) and simultaneous presence in the same endomyocardial biopsy specimen of AuV and oncosis (hazard ratio: 2.82; 95% confidence interval: 1.12 to 7.13; p = 0.028) were independent predictors of mortality. CM hypertrophy and AuV, especially in association with oncosis, are predictors of outcome in patients with idiopathic dilated cardiomyopathy and severe HF. Copyright © 2011 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

An Incident Cohort Study Comparing Survival on Home Hemodialysis and Peritoneal Dialysis (Australia and New Zealand Dialysis and Transplantation Registry)

PubMed Central

Nadeau-Fredette, Annie-Claire; Hawley, Carmel M.; Pascoe, Elaine M.; Chan, Christopher T.; Clayton, Philip A.; Polkinghorne, Kevan R.; Boudville, Neil; Leblanc, Martine

2015-01-01

Background and objectives Home dialysis is often recognized as a first-choice therapy for patients initiating dialysis. However, studies comparing clinical outcomes between peritoneal dialysis and home hemodialysis have been very limited. Design, setting, participants, & measurements This Australia and New Zealand Dialysis and Transplantation Registry study assessed all Australian and New Zealand adult patients receiving home dialysis on day 90 after initiation of RRT between 2000 and 2012. The primary outcome was overall survival. The secondary outcomes were on-treatment survival, patient and technique survival, and death-censored technique survival. All results were adjusted with three prespecified models: multivariable Cox proportional hazards model (main model), propensity score quintile–stratified model, and propensity score–matched model. Results The study included 10,710 patients on incident peritoneal dialysis and 706 patients on incident home hemodialysis. Treatment with home hemodialysis was associated with better patient survival than treatment with peritoneal dialysis (5-year survival: 85% versus 44%, respectively; log-rank P<0.001). Using multivariable Cox proportional hazards analysis, home hemodialysis was associated with superior patient survival (hazard ratio for overall death, 0.47; 95% confidence interval, 0.38 to 0.59) as well as better on-treatment survival (hazard ratio for on-treatment death, 0.34; 95% confidence interval, 0.26 to 0.45), composite patient and technique survival (hazard ratio for death or technique failure, 0.34; 95% confidence interval, 0.29 to 0.40), and death-censored technique survival (hazard ratio for technique failure, 0.34; 95% confidence interval, 0.28 to 0.41). Similar results were obtained with the propensity score models as well as sensitivity analyses using competing risks models and different definitions for technique failure and lag period after modality switch, during which events were attributed to the initial modality. Conclusions Home hemodialysis was associated with superior patient and technique survival compared with peritoneal dialysis. PMID:26068181

Comparison of Biochemical Relapse-Free Survival Between Primary Gleason Score 3 and Primary Gleason Score 4 for Biopsy Gleason Score 7 Prostate Cancer

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

Burdick, Michael J.; Reddy, Chandana A.; Ulchaker, James

2009-04-01

Purpose: To determine whether the primary grade (PG) of biopsy Gleason score (GS) 7 prostate cancer (CaP) was predictive for biochemical relapse-free survival (bRFS). Most of the present data regarding the PG of GS7 CaP refer to surgical specimens. Our goal was to determine whether the biopsy GS used at the time of medical decision making predicted for the biochemical outcome. Methods and Materials: We reviewed the data from 705 patients with biopsy GS7 CaP, from a prospectively maintained database, who had been treated at our institution between September 1996 and March 2005 with radical prostatectomy (n = 310), externalmore » beam radiotherapy (n = 268), or prostate radioactive seed implantation (n = 127). The bRFS rates were estimated using the Kaplan-Meier method. Cox proportional hazards regression analysis was used for univariate and multivariate analyses examining these factors in relation to bRFS: PG of biopsy GS, initial prostate-specific antigen level, clinical T stage, use of androgen deprivation, risk group (high or intermediate), and treatment modality. Results: The 5-year bRFS rate was 78% and 71% (p = 0.0108) for biopsy GS7 PG3 CaP and biopsy GS7 PG4 CaP, respectively. Comparing PG3 and PG4 within treatment modalities, only prostate implantation patients had a significant difference in the 5-year bRFS rate, 88% vs. 76%, respectively (p = 0.0231). On multivariate analysis, the PG of biopsy GS remained an independent predictor of bRFS, with PG3 having better bRFS than PG4 (relative risk, 0.655; 95% confidence interval, 0.472-0.909; p = 0.0113). Conclusion: Biopsy GS7 PG4 CaP carries a worse bRFS than biopsy GS7 PG3 CaP.« less

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

Wong, May Wai San; Ovchinnikov, Mikhail; Wang, Minghuai

Potential ways of parameterizing vertical turbulent fluxes of hydrometeors are examined using a high-resolution cloud-resolving model. The cloud-resolving model uses the Morrison microphysics scheme, which contains prognostic variables for rain, graupel, ice, and snow. A benchmark simulation with a horizontal grid spacing of 250 m of a deep convection case carried out to evaluate three different ways of parameterizing the turbulent vertical fluxes of hydrometeors: an eddy-diffusion approximation, a quadrant-based decomposition, and a scaling method that accounts for within-quadrant (subplume) correlations. Results show that the down-gradient nature of the eddy-diffusion approximation tends to transport mass away from concentrated regions, whereasmore » the benchmark simulation indicates that the vertical transport tends to transport mass from below the level of maximum to aloft. Unlike the eddy-diffusion approach, the quadri-modal decomposition is able to capture the signs of the flux gradient but underestimates the magnitudes. The scaling approach is shown to perform the best by accounting for within-quadrant correlations, and improves the results for all hydrometeors except for snow. A sensitivity study is performed to examine how vertical transport may affect the microphysics of the hydrometeors. The vertical transport of each hydrometeor type is artificially suppressed in each test. Results from the sensitivity tests show that cloud-droplet-related processes are most sensitive to suppressed rain or graupel transport. In particular, suppressing rain or graupel transport has a strong impact on the production of snow and ice aloft. Lastly, a viable subgrid-scale hydrometeor transport scheme in an assumed probability density function parameterization is discussed.« less

Spatial Harmonic Decomposition as a tool for unsteady flow phenomena analysis

NASA Astrophysics Data System (ADS)

Duparchy, A.; Guillozet, J.; De Colombel, T.; Bornard, L.

2014-03-01

Hydropower is already the largest single renewable electricity source today but its further development will face new deployment constraints such as large-scale projects in emerging economies and the growth of intermittent renewable energy technologies. The potential role of hydropower as a grid stabilizer leads to operating hydro power plants in "off-design" zones. As a result, new methods of analyzing associated unsteady phenomena are needed to improve the design of hydraulic turbines. The key idea of the development is to compute a spatial description of a phenomenon by using a combination from several sensor signals. The spatial harmonic decomposition (SHD) extends the concept of so-called synchronous and asynchronous pulsations by projecting sensor signals on a linearly independent set of a modal scheme. This mathematical approach is very generic as it can be applied on any linear distribution of a scalar quantity defined on a closed curve. After a mathematical description of SHD, this paper will discuss the impact of instrumentation and provide tools to understand SHD signals. Then, as an example of a practical application, SHD is applied on a model test measurement in order to capture and describe dynamic pressure fields. Particularly, the spatial description of the phenomena provides new tools to separate the part of pressure fluctuations that contribute to output power instability or mechanical stresses. The study of the machine stability in partial load operating range in turbine mode or the comparison between the gap pressure field and radial thrust behavior during turbine brake operation are both relevant illustrations of SHD contribution.

Social Determinants of Self-Reported Health in Women and Men: Understanding the Role of Gender in Population Health

PubMed Central

Hosseinpoor, Ahmad Reza; Stewart Williams, Jennifer; Amin, Avni; Araujo de Carvalho, Islene; Beard, John; Boerma, Ties; Kowal, Paul; Naidoo, Nirmala; Chatterji, Somnath

2012-01-01

Background Women and men share similar health challenges yet women report poorer health. The study investigates the social determinants of self-reported health in women and men, and male-female differences in health. Methods Data on 103154 men and 125728 women were analysed from 57 countries in the World Health Survey 2002–2004. Item Response Theory was used to construct a composite measure of health. Associations between health and determinants were assessed using multivariate linear regression. Blinder-Oaxaca decomposition partitioned the inequality in health between women and men into an “explained" component that arises because men and women differ in social and economic characteristics, and an “unexplained" component due to the differential effects of these characteristics. Decomposition was repeated for 18 countries in the World Health Organization (WHO) African region and 19 countries in the WHO European region. Results Women's health was significantly lower than men's. Health was associated with education, household economic status, employment, and marital status after controlling for age. In the pooled analysis decomposition showed that 30% of the inequality was “explained", of which almost 75% came from employment, education, marital status. The differential effects of being in paid employment increased the inequality. When countries in Africa and Europe were compared, the “explained" component (31% and 39% respectively) was largely attributed to the social determinants in the African countries and to women's longevity in the European countries. Being in paid employment had a greater positive effect on the health of males in both regions. Conclusions Ways in which age and the social determinants contribute to the poorer health status of women compared with men varies between groups of countries. This study highlights the need for action to address social structures, institutional discrimination and harmful gender norms and roles that differently influence health with ageing. PMID:22514667

Trends of Modern Contraceptive Use among Young Married Women Based on the 2000, 2005, and 2011 Ethiopian Demographic and Health Surveys: A Multivariate Decomposition Analysis

PubMed Central

Worku, Abebaw Gebeyehu; Tessema, Gizachew Assefa; Zeleke, Atinkut Alamirrew

2015-01-01

Introduction Accessing family planning can reduce a significant proportion of maternal, infant, and childhood deaths. In Ethiopia, use of modern contraceptive methods is low but it is increasing. This study aimed to analyze the trends and determinants of changes in modern contraceptive use over time among young married women in Ethiopia. Methods The study used data from the three Demographic Health Surveys conducted in Ethiopia, in 2000, 2005, and 2011. Young married women age 15–24 years with sample sizes of 2,157 in 2000, 1,904 in 2005, and 2,146 in 2011 were included. Logit-based decomposition analysis technique was used for analysis of factors contributing to the recent changes. STATA 12 was employed for data management and analyses. All calculations presented in this paper were weighted for the sampling probabilities and non-response. Complex sampling procedures were also considered during testing of statistical significance. Results Among young married women, modern contraceptive prevalence increased from 6% in 2000 to 16% in 2005 and to 36% in 2011. The decomposition analysis indicated that 34% of the overall change in modern contraceptive use was due to difference in women’s characteristics. Changes in the composition of young women’s characteristics according to age, educational status, religion, couple concordance on family size, and fertility preference were the major sources of this increase. Two-thirds of the increase in modern contraceptive use was due to difference in coefficients. Most importantly, the increase was due to change in contraceptive use behavior among the rural population (33%) and among Orthodox Christians (16%) and Protestants (4%). Conclusions Modern contraceptive use among young married women has showed a remarkable increase over the last decade in Ethiopia. Programmatic interventions targeting poor, younger (adolescent), illiterate, and Muslim women would help to maintain the increasing trend in modern contraceptive use. PMID:25635389

Behavioral Stage of Change and Dialysis Decision-Making

PubMed Central

McGrail, Anna; Lewis, Steven A.; Schold, Jesse; Lawless, Mary Ellen; Sehgal, Ashwini R.; Perzynski, Adam T.

2015-01-01

Background and objectives Behavioral stage of change (SoC) algorithms classify patients’ readiness for medical treatment decision-making. In the precontemplation stage, patients have no intention to take action within 6 months. In the contemplation stage, action is intended within 6 months. In the preparation stage, patients intend to take action within 30 days. In the action stage, the change has been made. This study examines the influence of SoC on dialysis modality decision-making. Design, setting, participants, & measurements SoC and relevant covariates were measured, and associations with dialysis decision-making were determined. In-depth interviews were conducted with 16 patients on dialysis to elicit experiences. Qualitative interview data informed the survey design. Surveys were administered to adults with CKD (eGFR≤25 ml/min/1.73 m2) from August, 2012 to June, 2013. Multivariable logistic regression modeled dialysis decision-making with predictors: SoC, provider connection, and dialysis knowledge score. Results Fifty-five patients completed the survey (71% women, 39% white, and 59% black), and median annual income was $17,500. In total, 65% of patients were in the precontemplation/contemplation (thinking) and 35% of patients were in the preparation/maintenance (acting) SoC; 62% of patients had made dialysis modality decisions. Doctors explaining modality options, higher dialysis knowledge scores, and fewer lifestyle barriers were associated with acting versus thinking SoC (all P<0.02). Patients making modality decisions had doctors who explained dialysis options (76% versus 43%), were in the acting versus the thinking SoC (50% versus 10%), had higher dialysis knowledge scores (1.4 versus 0.5), and had lower eGFR (13.9 versus 16.8 ml/min/1.73 m2; all P<0.05). In adjusted analyses, dialysis knowledge was significantly associated with decision-making (odds ratio, 4.2; 95% confidence interval, 1.4 to 12.9; P=0.01), and SoC was of borderline significance (odds ratio, 5.8; 95% confidence interval, 1.0 to 32.6; P=0.05). The model C statistic was 0.87. Conclusions Dialysis decision-making was associated with SoC, dialysis knowledge, and physicians discussing treatment options. Future studies determining ways to assist patients with CKD in making satisfying modality decisions are warranted. PMID:25591499

Tumor Volume Is a Prognostic Factor in Non-Small-Cell Lung Cancer Treated With Chemoradiotherapy

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

Alexander, Brian M.; Othus, Megan; Caglar, Hale B.

2011-04-01

Purpose: To investigate whether primary tumor and nodal volumes defined on radiotherapy planning scans are correlated with outcome (survival and recurrence) after combined-modality treatment. Methods and Materials: A retrospective review of patients with Stage III non-small-cell lung cancer treated with chemoradiation at Brigham and Women's Hospital/Dana-Farber Cancer Institute from 2000 to 2006 was performed. Tumor and nodal volume measurements, as computed by Eclipse (Varian, Palo Alto, CA), were used as independent variables, along with existing clinical factors, in univariate and multivariate analyses for association with outcomes. Results: For patients treated with definitive chemoradiotherapy, both nodal volume (hazard ratio [HR], 1.09;more » p < 0.01) and tumor volume (HR, 1.03; p < 0.01) were associated with overall survival on multivariate analysis. Both nodal volume (HR, 1.10; p < 0.01) and tumor volume (HR, 1.04; p < 0.01) were also associated with local control but not distant metastases. Conclusions: In addition to traditional surgical staging variables, disease burden, measured by primary tumor and nodal metastases volume, provides information that may be helpful in determining prognosis and identifying groups of patients for which more aggressive local therapy is warranted.« less

Early visual analysis tool using magnetoencephalography for treatment and recovery of neuronal dysfunction.

PubMed

Rasheed, Waqas; Neoh, Yee Yik; Bin Hamid, Nor Hisham; Reza, Faruque; Idris, Zamzuri; Tang, Tong Boon

2017-10-01

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modal Decomposition of TTV: Inferring Planet Masses and Eccentricities

NASA Astrophysics Data System (ADS)

Linial, Itai; Gilbaum, Shmuel; Sari, Re’em

2018-06-01

Transit timing variations (TTVs) are a powerful tool for characterizing the properties of transiting exoplanets. However, inferring planet properties from the observed timing variations is a challenging task, which is usually addressed by extensive numerical searches. We propose a new, computationally inexpensive method for inverting TTV signals in a planetary system of two transiting planets. To the lowest order in planetary masses and eccentricities, TTVs can be expressed as a linear combination of three functions, which we call the TTV modes. These functions depend only on the planets’ linear ephemerides, and can be either constructed analytically, or by performing three orbital integrations of the three-body system. Given a TTV signal, the underlying physical parameters are found by decomposing the data as a sum of the TTV modes. We demonstrate the use of this method by inferring the mass and eccentricity of six Kepler planets that were previously characterized in other studies. Finally we discuss the implications and future prospects of our new method.

Matrix eigenvalue method for free-oscillations modelling of spherical elastic bodies

NASA Astrophysics Data System (ADS)

Zábranová, E.; Hanyk, L.; Matyska, C.

2017-11-01

Deformations and changes of the gravitational potential of pre-stressed self-gravitating elastic bodies caused by free oscillations are described by means of the momentum and Poisson equations and the constitutive relation. For spherically symmetric bodies, the equations and boundary conditions are transformed into ordinary differential equations of the second order by the spherical harmonic decomposition and further discretized by highly accurate pseudospectral difference schemes on Chebyshev grids; we pay special attention to the conditions at the centre of the models. We thus obtain a series of matrix eigenvalue problems for eigenfrequencies and eigenfunctions of the free oscillations. Accuracy of the presented numerical approach is tested by means of the Rayleigh quotients calculated for the eigenfrequencies up to 500 mHz. Both the modal frequencies and eigenfunctions are benchmarked against the output from the Mineos software package based on shooting methods. The presented technique is a promising alternative to widely used methods because it is stable and with a good capability up to high frequencies.

New contrasts for x-ray imaging and synergy with optical imaging

NASA Astrophysics Data System (ADS)

Wang, Ge

2017-02-01

Due to its penetrating power, fine resolution, unique contrast, high-speed, and cost-effectiveness, x-ray imaging is one of the earliest and most popular imaging modalities in biomedical applications. Current x-ray radiographs and CT images are mostly on gray-scale, since they reflect overall energy attenuation. Recent advances in x-ray detection, contrast agent, and image reconstruction technologies have changed our perception and expectation of x-ray imaging capabilities, and generated an increasing interest in imaging biological soft tissues in terms of energy-sensitive material decomposition, phase-contrast, small angle scattering (also referred to as dark-field), x-ray fluorescence and luminescence properties. These are especially relevant to preclinical and mesoscopic studies, and potentially mendable for hybridization with optical molecular tomography. In this article, we review new x-ray imaging techniques as related to optical imaging, suggest some combined x-ray and optical imaging schemes, and discuss our ideas on micro-modulated x-ray luminescence tomography (MXLT) and x-ray modulated opto-genetics (X-Optogenetics).

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.

Mode detection in turbofan inlets from near field sensor arrays.

PubMed

Castres, Fabrice O; Joseph, Phillip F

2007-02-01

Knowledge of the modal content of the sound field radiated from a turbofan inlet is important for source characterization and for helping to determine noise generation mechanisms in the engine. An inverse technique for determining the mode amplitudes at the duct outlet is proposed using pressure measurements made in the near field. The radiated sound pressure from a duct is modeled by directivity patterns of cut-on modes in the near field using a model based on the Kirchhoff approximation for flanged ducts with no flow. The resulting system of equations is ill posed and it is shown that the presence of modes with eigenvalues close to a cutoff frequency results in a poorly conditioned directivity matrix. An analysis of the conditioning of this directivity matrix is carried out to assess the inversion robustness and accuracy. A physical interpretation of the singular value decomposition is given and allows us to understand the issues of ill conditioning as well as the detection performance of the radiated sound field by a given sensor array.

Multiplexing of spatial modes in the mid-IR region

NASA Astrophysics Data System (ADS)

Gailele, Lucas; Maweza, Loyiso; Dudley, Angela; Ndagano, Bienvenu; Rosales-Guzman, Carmelo; Forbes, Andrew

2017-02-01

Traditional optical communication systems optimize multiplexing in polarization and wavelength both trans- mitted in fiber and free-space to attain high bandwidth data communication. Yet despite these technologies, we are expected to reach a bandwidth ceiling in the near future. Communications using orbital angular momentum (OAM) carrying modes offers infinite dimensional states, providing means to increase link capacity by multiplexing spatially overlapping modes in both the azimuthal and radial degrees of freedom. OAM modes are multiplexed and de-multiplexed by the use of spatial light modulators (SLM). Implementation of complex amplitude modulation is employed on laser beams phase and amplitude to generate Laguerre-Gaussian (LG) modes. Modal decomposition is employed to detect these modes due to their orthogonality as they propagate in space. We demonstrate data transfer by sending images as a proof-of concept in a lab-based scheme. We demonstrate the creation and detection of OAM modes in the mid-IR region as a precursor to a mid-IR free-space communication link.

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

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

NASA Astrophysics Data System (ADS)

Schaffrin, Burkhard; Felus, Yaron A.

2008-06-01

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

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

Multivariate curve resolution based chromatographic peak alignment combined with parallel factor analysis to exploit second-order advantage in complex chromatographic measurements.

PubMed

Parastar, Hadi; Akvan, Nadia

2014-03-13

In the present contribution, a new combination of multivariate curve resolution-correlation optimized warping (MCR-COW) with trilinear parallel factor analysis (PARAFAC) is developed to exploit second-order advantage in complex chromatographic measurements. In MCR-COW, the complexity of the chromatographic data is reduced by arranging the data in a column-wise augmented matrix, analyzing using MCR bilinear model and aligning the resolved elution profiles using COW in a component-wise manner. The aligned chromatographic data is then decomposed using trilinear model of PARAFAC in order to exploit pure chromatographic and spectroscopic information. The performance of this strategy is evaluated using simulated and real high-performance liquid chromatography-diode array detection (HPLC-DAD) datasets. The obtained results showed that the MCR-COW can efficiently correct elution time shifts of target compounds that are completely overlapped by coeluted interferences in complex chromatographic data. In addition, the PARAFAC analysis of aligned chromatographic data has the advantage of unique decomposition of overlapped chromatographic peaks to identify and quantify the target compounds in the presence of interferences. Finally, to confirm the reliability of the proposed strategy, the performance of the MCR-COW-PARAFAC is compared with the frequently used methods of PARAFAC, COW-PARAFAC, multivariate curve resolution-alternating least squares (MCR-ALS), and MCR-COW-MCR. In general, in most of the cases the MCR-COW-PARAFAC showed an improvement in terms of lack of fit (LOF), relative error (RE) and spectral correlation coefficients in comparison to the PARAFAC, COW-PARAFAC, MCR-ALS and MCR-COW-MCR results. Copyright © 2014 Elsevier B.V. All rights reserved.

Utilization of Services in a Randomized Trial Testing Phone- and Web-Based Interventions for Smoking Cessation

PubMed Central

Jack, Lisa M.; McClure, Jennifer B.; Deprey, Mona; Javitz, Harold S.; McAfee, Timothy A.; Catz, Sheryl L.; Richards, Julie; Bush, Terry; Swan, Gary E.

2011-01-01

Introduction: Phone counseling has become standard for behavioral smoking cessation treatment. Newer options include Web and integrated phone–Web treatment. No prior research, to our knowledge, has systematically compared the effectiveness of these three treatment modalities in a randomized trial. Understanding how utilization varies by mode, the impact of utilization on outcomes, and predictors of utilization across each mode could lead to improved treatments. Methods: One thousand two hundred and two participants were randomized to phone, Web, or combined phone–Web cessation treatment. Services varied by modality and were tracked using automated systems. All participants received 12 weeks of varenicline, printed guides, an orientation call, and access to a phone supportline. Self-report data were collected at baseline and 6-month follow-up. Results: Overall, participants utilized phone services more often than the Web-based services. Among treatment groups with Web access, a significant proportion logged in only once (37% phone–Web, 41% Web), and those in the phone–Web group logged in less often than those in the Web group (mean = 2.4 vs. 3.7, p = .0001). Use of the phone also was correlated with increased use of the Web. In multivariate analyses, greater use of the phone- or Web-based services was associated with higher cessation rates. Finally, older age and the belief that certain treatments could improve success were consistent predictors of greater utilization across groups. Other predictors varied by treatment group. Conclusions: Opportunities for enhancing treatment utilization exist, particularly for Web-based programs. Increasing utilization more broadly could result in better overall treatment effectiveness for all intervention modalities. PMID:21330267

Trainees May Add Value to Patient Care by Decreasing Addendum Utilization in Radiology Reports.

PubMed

Balthazar, Patricia; Konstantopoulos, Christina; Wick, Carson A; DeSimone, Ariadne K; Tridandapani, Srini; Simoneaux, Stephen; Applegate, Kimberly E

2017-11-01

The purpose of this study was to evaluate the impact of trainee involvement and other factors on addendum rates in radiology reports. This retrospective study was performed in a tertiary care pediatric hospital. From the institutional radiology data repository, we extracted all radiology reports from January 1 to June 30, 2016, as well as trainee (resident or fellow) involvement, imaging modality, patient setting (emergency, inpatient, or outpatient), order status (routine vs immediate), time of interpretation (regular work hours vs off-hours), radiologist's years of experience, and sex. We grouped imaging modalities as advanced (CT, MRI, and PET) or nonadvanced (any modality that was not CT, MRI, or PET) and radiologist experience level as ≤ 20 years or > 20 years. Our outcome measure was the rate of addenda in radiology reports. Statistical analysis was performed using multivariate logistic regression. From 129,033 reports finalized during the study period, 418 (0.3%) had addenda. Reports generated without trainees were 12 times more likely than reports with trainee involvement to have addenda (odds ratio [OR] = 12.2, p < 0.001). Advanced imaging studies were more likely than nonadvanced studies to be associated with addendum use (OR = 4.7, p < 0.001). Reports generated for patients in emergency or outpatient settings had a slightly higher likelihood of addendum use than those in an inpatient setting (OR = 1.5, p = 0.04; and OR = 1.3, p = 0.04, respectively). Routine orders had a slightly higher likelihood of addendum use compared with immediate orders (OR = 1.3, p = 0.01). We found no difference in addendum use by radiologist's sex, radiologist's years of experience, emergency versus outpatient setting, or time of interpretation. Trainees may add value to patient care by decreasing addendum rates in radiology reports.

Geographic region: Does it matter in cutaneous melanoma of the head and neck?

PubMed

Kılıç, Suat; Unsal, Aykut A; Chung, Sei Y; Samarrai, Ruwaa; Kılıç, Sarah S; Baredes, Soly; Eloy, Jean Anderson

2017-12-01

The head and neck are two of the most common locations for cutaneous melanoma. We present the first population-based analysis of geographic differences in anatomic subsite, clinicopathologic and demographical traits, histopathologic subtype, treatment modality, and disease-specific survival (DSS) of cutaneous head and neck melanoma (CHNM). Retrospective database analysis. The Surveillance, Epidemiology, and End Results database was queried for cases of CHNM reported between 2000 and 2013. Patients were grouped into East, Midwest, South, and West regions of the United States. Overall incidence, demographic traits, primary tumor site, clinicopathologic traits, histopathologic subtype, treatment modality, and DSS were compared among regions. There were 49,365 patients with CHNM identified. The West (4.60) and the South (4.42) had significantly higher incidence (per 100,000) than the East (3.84) and Midwest (3.65) (P < .05). DSS was significantly different among regions (P < .0066). The East (5 years: 89.4%, 10 years: 84.1%) had the highest DSS rate, and the South (5 years: 87.0%, 10 years: 81.8%) had the lowest DSS rate. The Midwest (5 years: 88.4%, 10 years: 84.3%) and West (5 years: 88.3%, 10 years: 83.5%) had intermediate DSS. On multivariate analysis, the South had an elevated hazard ratio (1.17, 95% confidence interval: 1.05-1.30) when compared to the West. Geographic region may play a significant role in CHNM. Incidence is higher in the South and the West. Incidence, histologic subtype, treatment modality, and DSS vary among regions. DSS is lower in the South than the West, even after accounting for other major prognostic factors. 4. Laryngoscope, 127:2763-2769, 2017. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

PubMed Central

Hao, Xiaoke; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Zhang, Daoqiang; Shen, Li

2016-01-01

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation. PMID:27277494

A randomized controlled trial of the effects of muscle stretching, manual therapy and steroid injections in addition to 'stay active' care on health-related quality of life in acute or subacute low back pain.

PubMed

Grunnesjö, Marie I; Bogefeldt, Johan P; Blomberg, Stefan I E; Strender, Lars-Erik; Svärdsudd, Kurt F

2011-11-01

To evaluate the health-related quality of life effects of muscle stretching, manual therapy and steroid injections in addition to 'stay active' care in acute or subacute low back pain patients. A randomized, controlled trial during 10 weeks with four treatment groups. Nine primary health care and one outpatient orthopaedic hospital department. One hundred and sixty patients with acute or subacute low back pain. Ten weeks of 'stay active' care only (group 1), or 'stay active' and muscle stretching (group 2), or 'stay active', muscle stretching and manual therapy (group 3), or 'stay active', muscle stretching, manual therapy and steroid injections (group 4). The Gothenburg Quality of Life instrument subscales Well-being score and Complaint score. In a multivariate analysis adjusted for possible outcome affecting variables other than the treatment given Well-being score was 68.4 (12.5), 72.1 (12.4), 72,3 (12.4) and 72.7 (12.5) in groups 1-4, respectively (P for trend <0.05). There were significant trends for the well-being components patience (P < 0.005), energy (P < 0.05), mood (P < 0.05) and family situation (P < 0.05). The remaining two components and Complaint score showed a non-significant trend towards improvement. The effects on health-related quality of life were greater the larger the number of treatment modalities available. The 'stay active' treatment group, with the most restricted number of modalities, had the most modest health-related quality of life improvement, while group 4 with the most generous choice of treatment modalities, had the greatest improvement.

The role of tactile sensation in online and offline hierarchical control of multi-finger force synergy.

PubMed

Koh, Kyung; Kwon, Hyun Joon; Yoon, Bum Chul; Cho, Yongseok; Shin, Joon-Ho; Hahn, Jin-Oh; Miller, Ross H; Kim, Yoon Hyuk; Shim, Jae Kun

2015-09-01

The hand, one of the most versatile but mechanically redundant parts of the human body, must overcome imperfect motor commands and inherent noise in both the sensory and motor systems in order to produce desired motor actions. For example, it is nearly impossible to produce a perfectly consistent note during a single violin stroke or to produce the exact same note over multiple strokes, which we denote online and offline control, respectively. To overcome these challenges, the central nervous system synergistically integrates multiple sensory modalities and coordinates multiple motor effectors. Among these sensory modalities, tactile sensation plays an important role in manual motor tasks by providing hand-object contact information. The purpose of this study was to investigate the role of tactile feedback in individual finger actions and multi-finger interactions during constant force production tasks. We developed analytical techniques for the linear decomposition of the overall variance in the motor system in both online and offline control. We removed tactile feedback from the fingers and demonstrated that tactile sensors played a critical role in the online control of synergistic interactions between fingers. In contrast, the same sensors did not contribute to offline control. We also demonstrated that when tactile feedback was removed from the fingers, the combined motor output of individual fingers did not change while individual finger behaviors did. This finding supports the idea of hierarchical control where individual fingers at the lower level work together to stabilize the performance of combined motor output at the higher level.

Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

NASA Astrophysics Data System (ADS)

Mehta, P. M.; Linares, R.

2017-12-01

This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization

PubMed Central

Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu

2012-01-01

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance. PMID:22778600

Modal and thermal analysis of Les Arches unstable rock column (Vercors massif, French Alps)

NASA Astrophysics Data System (ADS)

Bottelin, P.; Lévy, C.; Baillet, L.; Jongmans, D.; Guéguen, P.

2013-08-01

A potentially unstable limestone column (˜1000 m3, Vercors, French Alps) delineated by an open rear fracture was continuously instrumented with two three-component seismic sensors from mid-May 2009 to mid-October 2011. Spectral analysis of seismic noise allowed several resonance frequencies to be determined, ranging from 6 to 21 Hz. The frequency domain decomposition (FDD) technique was applied to the ambient vibrations recorded on the top of the rock column. Three vibration modes were identified at 6, 7.5 and 9 Hz, describing the upper part of corresponding modal shapes. Finite element numerical modelling of the column dynamic response confirmed that the first two modes are bending modes perpendicular and parallel to the fracture, respectively, while the third one corresponds to torsion. Seismic noise monitoring also pointed out that resonance frequencies fluctuate with time, under thermomechanical control. For seasonal cycles, changes in frequency are due to the variations of the bulk elastic properties with temperature. At daily scale, increase in fundamental frequency with temperature has been interpreted as resulting from the rock expansion inducing a closure of the rear fracture rock bridges, hence stiffening the contact between the column and the rock mass. Conversely, the rock contraction induces a fracture opening and a decrease in resonance frequency. In winter, when the temperature drops below 0 °C, a dramatic increase in fundamental frequency is observed from 6 Hz to more than 25 Hz, resulting from ice formation in the fracture. During spring, the resonance frequency gradually diminishes with ice melting to reach the value measured before winter.

Palmprint and face multi-modal biometric recognition based on SDA-GSVD and its kernelization.

PubMed

Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu

2012-01-01

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Chin, Alexander W.; Marvis, Dimitri N.

2014-01-01

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

How well can morphology assess cell death modality? A proteomics study

PubMed Central

Chernobrovkin, Alexey L; Zubarev, Roman A

2016-01-01

While the focus of attempts to classify cell death programs has finally shifted in 2010s from microscopy-based morphological characteristics to biochemical assays, more recent discoveries have put the underlying assumptions of many such assays under severe stress, mostly because of the limited specificity of the assays. On the other hand, proteomics can quantitatively measure the abundances of thousands of proteins in a single experiment. Thus proteomics could develop a modern alternative to both semiquantitative morphology assessment as well as single-molecule biochemical assays. Here we tested this hypothesis by analyzing the proteomes of cells dying after been treated with various chemical agents. The most striking finding is that, for a multivariate model based on the proteome changes in three cells lines, the regulation patterns of the 200–500 most abundant proteins typically attributed to household type more accurately reflect that of the proteins directly interacting with the drug than any other protein subset grouped by common function or biological process, including cell death. This is in broad agreement with the 'rigid cell death mechanics' model where drug action mechanism and morphological changes caused by it are bijectively linked. This finding, if confirmed, will open way for a broad use of proteomics in death modality assessment. PMID:27752363

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Chin, Alexander W.; Mavris, Dimitri N.

2016-01-01

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.

PubMed

Oosterhof, Nikolaas N; Connolly, Andrew C; Haxby, James V

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

PubMed Central

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA PMID:27499741

Effects of imidacloprid on the ecology of sub-tropical freshwater microcosms.

PubMed

Sumon, Kizar Ahmed; Ritika, Afifat Khanam; Peeters, Edwin T H M; Rashid, Harunur; Bosma, Roel H; Rahman, Md Shahidur; Fatema, Mst Kaniz; Van den Brink, Paul J

2018-05-01

The neonicotinoid insecticide imidacloprid is used in Bangladesh for a variety of crop protection purposes. Imidacloprid may contaminate aquatic ecosystems via spray drift, surface runoff and ground water leaching. The present study aimed at assessing the fate and effects of imidacloprid on structural (phytoplankton, zooplankton, macroinvertebrates and periphyton) and functional (organic matter decomposition) endpoints of freshwater, sub-tropical ecosystems in Bangladesh. Imidacloprid was applied weekly to 16 freshwater microcosms (PVC tanks containing 400 L de-chlorinated tap water) at nominal concentrations of 0, 30, 300, 3000 ng/L over a period of 4 weeks. Results indicated that imidacloprid concentrations from the microcosm water column declined rapidly. Univariate and multivariate analysis showed significant effects of imidacloprid on the zooplankton and macroinvertebrate community, some individual phytoplankton taxa, and water quality variables (i.e. DO, alkalinity, ammonia and nitrate), with Cloeon sp., Diaptomus sp. and Keratella sp. being the most affected species, i.e. showing lower abundance values in all treatments compared to the control. The observed high sensitivity of Cloeon sp. and Diaptomus sp. was confirmed by the results of single species tests. No significant effects were observed on the species composition of the phytoplankton, periphyton biomass and organic matter decomposition for any of the sampling days. Our study indicates that (sub-)tropical aquatic ecosystems can be much more sensitive to imidacloprid compared to temperate ones. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Decomposing Kenyan socio-economic inequalities in skilled birth attendance and measles immunization

PubMed Central

2013-01-01

Introduction Skilled birth attendance (SBA) and measles immunization reflect two aspects of a health system. In Kenya, their national coverage gaps are substantial but could be largely improved if the total population had the same coverage as the wealthiest quintile. A decomposition analysis allows identifying the factors that influence these wealth-related inequalities in order to develop appropriate policy responses. The main objective of the study was to decompose wealth-related inequalities in SBA and measles immunization into their contributing factors. Methods Data from the Kenyan Demographic and Health Survey 2008/09 were used. The study investigated the effects of socio-economic determinants on [1] coverage and [2] wealth-related inequalities of SBA utilization and measles immunization. Techniques used were multivariate logistic regression and decomposition of the concentration index (C). Results SBA utilization and measles immunization coverage differed according to household wealth, parent’s education, skilled antenatal care visits, birth order and father’s occupation. SBA utilization further differed across provinces and ethnic groups. The overall C for SBA was 0.14 and was mostly explained by wealth (40%), parent’s education (28%), antenatal care (9%), and province (6%). The overall C for measles immunization was 0.08 and was mostly explained by wealth (60%), birth order (33%), and parent’s education (28%). Rural residence (−19%) reduced this inequality. Conclusion Both health care indicators require a broad strengthening of health systems with a special focus on disadvantaged sub-groups. PMID:23294938

A multiplexed analysis approach identifies new association of inflammatory proteins in patients with overactive bladder

PubMed Central

Ma, Emily; Vetter, Joel; Bliss, Laura; Lai, H. Henry; Mysorekar, Indira U.

2016-01-01

Overactive bladder (OAB) is a common debilitating bladder condition with unknown etiology and limited diagnostic modalities. Here, we explored a novel high-throughput and unbiased multiplex approach with cellular and molecular components in a well-characterized patient cohort to identify biomarkers that could be reliably used to distinguish OAB from controls or provide insights into underlying etiology. As a secondary analysis, we determined whether this method could discriminate between OAB and other chronic bladder conditions. We analyzed plasma samples from healthy volunteers (n = 19) and patients diagnosed with OAB, interstitial cystitis/bladder pain syndrome (IC/BPS), or urinary tract infections (UTI; n = 51) for proinflammatory, chemokine, cytokine, angiogenesis, and vascular injury factors using Meso Scale Discovery (MSD) analysis and urinary cytological analysis. Wilcoxon rank-sum tests were used to perform univariate and multivariate comparisons between patient groups (controls, OAB, IC/BPS, and UTI). Multivariate logistic regression models were fit for each MSD analyte on 1) OAB patients and controls, 2) OAB and IC/BPS patients, and 3) OAB and UTI patients. Age, race, and sex were included as independent variables in all multivariate analysis. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic potential of a given analyte. Our findings demonstrate that five analytes, i.e., interleukin 4, TNF-α, macrophage inflammatory protein-1β, serum amyloid A, and Tie2 can reliably differentiate OAB relative to controls and can be used to distinguish OAB from the other conditions. Together, our pilot study suggests a molecular imbalance in inflammatory proteins may contribute to OAB pathogenesis. PMID:27029431

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Impact of the pretransplant dialysis modality on kidney transplantation outcomes: a nationwide cohort study.

PubMed

Lin, Huan-Tang; Liu, Fu-Chao; Lin, Jr-Rung; Pang, See-Tong; Yu, Huang-Ping

2018-06-04

Most patients with uraemia must undergo chronic dialysis while awaiting kidney transplantation; however, the role of the pretransplant dialysis modality on the outcomes of kidney transplantation remains obscure. The objective of this study was to clarify the associations between the pretransplant dialysis modality, namely haemodialysis (HD) or peritoneal dialysis (PD), and the development of post-transplant de novo diseases, allograft failure and all-cause mortality for kidney-transplant recipients. Retrospective nationwide cohort study. Data retrieved from the Taiwan National Health Insurance Research Database. The National Health Insurance database was explored for patients who received kidney transplantation in Taiwan during 1998-2011 and underwent dialysis >90 days before transplantation. The pretransplant characteristics, complications during kidney transplantation and post-transplant outcomes were statistically analysed and compared between the HD and PD groups. Cox regression analysis was used to evaluate the HR of the dialysis modality on graft failure and all-cause mortality. The primary outcomes were long-term post-transplant death-censored allograft failure and all-cause mortality started after 90 days of kidney transplantation until the end of follow-up. The secondary outcomes were events during kidney transplantation and post-transplant de novo diseases adjusted by propensity score in log-binomial model. There were 1812 patients included in our cohort, among which 1209 (66.7%) and 603 (33.3%) recipients received pretransplant HD and PD, respectively. Recipients with chronic HD were generally older and male, had higher risks of developing post-transplant de novo ischaemic heart disease, tuberculosis and hepatitis C after adjustment. Pretransplant HD contributed to higher graft failure in the multivariate analysis (HR 1.38, p<0.05) after adjustment for the recipient age, sex, duration of dialysis and pretransplant diseases. There was no significant between-group difference in overall survival. Pretransplant HD contributed to higher risks of death-censored allograft failure after kidney transplantation when compared with PD. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Cognitive Impairment in Non-Dialysis-Dependent CKD and the Transition to Dialysis: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

PubMed

Harhay, Meera N; Xie, Dawei; Zhang, Xiaoming; Hsu, Chi-Yuan; Vittinghoff, Eric; Go, Alan S; Sozio, Stephen M; Blumenthal, Jacob; Seliger, Stephen; Chen, Jing; Deo, Rajat; Dobre, Mirela; Akkina, Sanjeev; Reese, Peter P; Lash, James P; Yaffe, Kristine; Tamura, Manjula Kurella

2018-05-02

Advanced chronic kidney disease is associated with elevated risk for cognitive impairment. However, it is not known whether and how cognitive impairment is associated with planning and preparation for end-stage renal disease. Retrospective observational study. 630 adults participating in the CRIC (Chronic Renal Insufficiency Cohort) Study who had cognitive assessments in late-stage CKD, defined as estimated glome-rular filtration rate ≤ 20mL/min/1.73m 2 , and subsequently initiated maintenance dialysis therapy. Predialysis cognitive impairment, defined as a score on the Modified Mini-Mental State Examination lower than previously derived age-based threshold scores. Covariates included age, race/ethnicity, educational attainment, comorbid conditions, and health literacy. Peritoneal dialysis (PD) as first dialysis modality, preemptive permanent access placement, venous catheter avoidance at dialysis therapy initiation, and preemptive wait-listing for a kidney transplant. Multivariable-adjusted logistic regression. Predialysis cognitive impairment was present in 117 (19%) participants. PD was the first dialysis modality among 16% of participants (n=100), 75% had preemptive access placed (n=473), 45% avoided using a venous catheter at dialysis therapy initiation (n=279), and 20% were preemptively wait-listed (n=126). Predialysis cognitive impairment was independently associated with 78% lower odds of PD as the first dialysis modality (adjusted OR [aOR], 0.22; 95% CI, 0.06-0.74; P=0.02) and 42% lower odds of venous catheter avoidance at dialysis therapy initiation (aOR, 0.58; 95% CI, 0.34-0.98; P=0.04). Predialysis cognitive impairment was not independently associated with preemptive permanent access placement or wait-listing. Potential unmeasured confounders; single measure of cognitive function. Predialysis cognitive impairment is associated with a lower likelihood of PD as a first dialysis modality and of venous catheter avoidance at dialysis therapy initiation. Future studies may consider addressing cognitive function when testing strategies to improve patient transitions to dialysis therapy. Copyright © 2018 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Factors controlling bark decomposition and its role in wood decomposition in five tropical tree species

PubMed Central

Dossa, Gbadamassi G. O.; Paudel, Ekananda; Cao, Kunfang; Schaefer, Douglas; Harrison, Rhett D.

2016-01-01

Organic matter decomposition represents a vital ecosystem process by which nutrients are made available for plant uptake and is a major flux in the global carbon cycle. Previous studies have investigated decomposition of different plant parts, but few considered bark decomposition or its role in decomposition of wood. However, bark can comprise a large fraction of tree biomass. We used a common litter-bed approach to investigate factors affecting bark decomposition and its role in wood decomposition for five tree species in a secondary seasonal tropical rain forest in SW China. For bark, we implemented a litter bag experiment over 12 mo, using different mesh sizes to investigate effects of litter meso- and macro-fauna. For wood, we compared the decomposition of branches with and without bark over 24 mo. Bark in coarse mesh bags decomposed 1.11–1.76 times faster than bark in fine mesh bags. For wood decomposition, responses to bark removal were species dependent. Three species with slow wood decomposition rates showed significant negative effects of bark-removal, but there was no significant effect in the other two species. Future research should also separately examine bark and wood decomposition, and consider bark-removal experiments to better understand roles of bark in wood decomposition. PMID:27698461

Factors controlling bark decomposition and its role in wood decomposition in five tropical tree species.

PubMed

Dossa, Gbadamassi G O; Paudel, Ekananda; Cao, Kunfang; Schaefer, Douglas; Harrison, Rhett D

2016-10-04

Organic matter decomposition represents a vital ecosystem process by which nutrients are made available for plant uptake and is a major flux in the global carbon cycle. Previous studies have investigated decomposition of different plant parts, but few considered bark decomposition or its role in decomposition of wood. However, bark can comprise a large fraction of tree biomass. We used a common litter-bed approach to investigate factors affecting bark decomposition and its role in wood decomposition for five tree species in a secondary seasonal tropical rain forest in SW China. For bark, we implemented a litter bag experiment over 12 mo, using different mesh sizes to investigate effects of litter meso- and macro-fauna. For wood, we compared the decomposition of branches with and without bark over 24 mo. Bark in coarse mesh bags decomposed 1.11-1.76 times faster than bark in fine mesh bags. For wood decomposition, responses to bark removal were species dependent. Three species with slow wood decomposition rates showed significant negative effects of bark-removal, but there was no significant effect in the other two species. Future research should also separately examine bark and wood decomposition, and consider bark-removal experiments to better understand roles of bark in wood decomposition.

Meddling with middle modalities: a decomposition approach to mental health inequalities between intersectional gender and economic middle groups in northern Sweden

PubMed Central

Gustafsson, Per E.; Sebastián, Miguel San; Mosquera, Paola A.

2016-01-01

Background Intersectionality has received increased interest within population health research in recent years, as a concept and framework to understand entangled dimensions of health inequalities, such as gender and socioeconomic inequalities in health. However, little attention has been paid to the intersectional middle groups, referring to those occupying positions of mixed advantage and disadvantage. Objective This article aimed to 1) examine mental health inequalities between intersectional groups reflecting structural positions of gender and economic affluence and 2) decompose any observed health inequalities, among middle groups, into contributions from experiences and conditions representing processes of privilege and oppression. Design Participants (N=25,585) came from the cross-sectional ‘Health on Equal Terms’ survey covering 16- to 84-year-olds in the four northernmost counties of Sweden. Six intersectional positions were constructed from gender (woman vs. men) and tertiles (low vs. medium vs. high) of disposable income. Mental health was measured through the General Health Questionnaire-12. Explanatory variables covered areas of material conditions, job relations, violence, domestic burden, and healthcare contacts. Analysis of variance (Aim 1) and Blinder-Oaxaca decomposition analysis (Aim 2) were used. Results Significant mental health inequalities were found between dominant (high-income women and middle-income men) and subordinate (middle-income women and low-income men) middle groups. The health inequalities between adjacent middle groups were mostly explained by violence (mid-income women vs. men comparison); material conditions (mid- vs. low-income men comparison); and material needs, job relations, and unmet medical needs (high- vs. mid-income women comparison). Conclusions The study suggests complex processes whereby dominant middle groups in the intersectional space of economic affluence and gender can leverage strategic resources to gain mental health advantage relative to subordinate middle groups. PMID:27887668

Simultaneous head tissue conductivity and EEG source location estimation.

PubMed

Akalin Acar, Zeynep; Acar, Can E; Makeig, Scott

2016-01-01

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm(2)-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm(2)-scale accurate 3-D functional cortical imaging modality. Copyright © 2015 Elsevier Inc. All rights reserved.

A decentralized linear quadratic control design method for flexible structures

NASA Technical Reports Server (NTRS)

Su, Tzu-Jeng; Craig, Roy R., Jr.

1990-01-01

A decentralized suboptimal linear quadratic control design procedure which combines substructural synthesis, model reduction, decentralized control design, subcontroller synthesis, and controller reduction is proposed for the design of reduced-order controllers for flexible structures. The procedure starts with a definition of the continuum structure to be controlled. An evaluation model of finite dimension is obtained by the finite element method. Then, the finite element model is decomposed into several substructures by using a natural decomposition called substructuring decomposition. Each substructure, at this point, still has too large a dimension and must be reduced to a size that is Riccati-solvable. Model reduction of each substructure can be performed by using any existing model reduction method, e.g., modal truncation, balanced reduction, Krylov model reduction, or mixed-mode method. Then, based on the reduced substructure model, a subcontroller is designed by an LQ optimal control method for each substructure independently. After all subcontrollers are designed, a controller synthesis method called substructural controller synthesis is employed to synthesize all subcontrollers into a global controller. The assembling scheme used is the same as that employed for the structure matrices. Finally, a controller reduction scheme, called the equivalent impulse response energy controller (EIREC) reduction algorithm, is used to reduce the global controller to a reasonable size for implementation. The EIREC reduced controller preserves the impulse response energy of the full-order controller and has the property of matching low-frequency moments and low-frequency power moments. An advantage of the substructural controller synthesis method is that it relieves the computational burden associated with dimensionality. Besides that, the SCS design scheme is also a highly adaptable controller synthesis method for structures with varying configuration, or varying mass and stiffness properties.

Bimodal Biometric Verification Using the Fusion of Palmprint and Infrared Palm-Dorsum Vein Images

PubMed Central

Lin, Chih-Lung; Wang, Shih-Hung; Cheng, Hsu-Yung; Fan, Kuo-Chin; Hsu, Wei-Lieh; Lai, Chin-Rong

2015-01-01

In this paper, we present a reliable and robust biometric verification method based on bimodal physiological characteristics of palms, including the palmprint and palm-dorsum vein patterns. The proposed method consists of five steps: (1) automatically aligning and cropping the same region of interest from different palm or palm-dorsum images; (2) applying the digital wavelet transform and inverse wavelet transform to fuse palmprint and vein pattern images; (3) extracting the line-like features (LLFs) from the fused image; (4) obtaining multiresolution representations of the LLFs by using a multiresolution filter; and (5) using a support vector machine to verify the multiresolution representations of the LLFs. The proposed method possesses four advantages: first, both modal images are captured in peg-free scenarios to improve the user-friendliness of the verification device. Second, palmprint and vein pattern images are captured using a low-resolution digital scanner and infrared (IR) camera. The use of low-resolution images results in a smaller database. In addition, the vein pattern images are captured through the invisible IR spectrum, which improves antispoofing. Third, since the physiological characteristics of palmprint and vein pattern images are different, a hybrid fusing rule can be introduced to fuse the decomposition coefficients of different bands. The proposed method fuses decomposition coefficients at different decomposed levels, with different image sizes, captured from different sensor devices. Finally, the proposed method operates automatically and hence no parameters need to be set manually. Three thousand palmprint images and 3000 vein pattern images were collected from 100 volunteers to verify the validity of the proposed method. The results show a false rejection rate of 1.20% and a false acceptance rate of 1.56%. It demonstrates the validity and excellent performance of our proposed method comparing to other methods. PMID:26703596

Simultaneous head tissue conductivity and EEG source location estimation

PubMed Central

Acar, Can E.; Makeig, Scott

2015-01-01

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3 cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15 cm2-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm2-scale accurate 3-D functional cortical imaging modality. PMID:26302675

Self-force via m-mode regularization and 2+1D evolution: Foundations and a scalar-field implementation on Schwarzschild spacetime

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

Dolan, Sam R.; Barack, Leor

2011-01-15

To model the radiative evolution of extreme mass-ratio binary inspirals (a key target of the LISA mission), the community needs efficient methods for computation of the gravitational self-force (SF) on the Kerr spacetime. Here we further develop a practical 'm-mode regularization' scheme for SF calculations, and give the details of a first implementation. The key steps in the method are (i) removal of a singular part of the perturbation field with a suitable 'puncture' to leave a sufficiently regular residual within a finite worldtube surrounding the particle's worldline, (ii) decomposition in azimuthal (m) modes, (iii) numerical evolution of the mmore » modes in 2+1D with a finite-difference scheme, and (iv) reconstruction of the SF from the mode sum. The method relies on a judicious choice of puncture, based on the Detweiler-Whiting decomposition. We give a working definition for the ''order'' of the puncture, and show how it determines the convergence rate of the m-mode sum. The dissipative piece of the SF displays an exponentially convergent mode sum, while the m-mode sum for the conservative piece converges with a power law. In the latter case, the individual modal contributions fall off at large m as m{sup -n} for even n and as m{sup -n+1} for odd n, where n is the puncture order. We describe an m-mode implementation with a 4th-order puncture to compute the scalar-field SF along circular geodesics on Schwarzschild. In a forthcoming companion paper we extend the calculation to the Kerr spacetime.« less

Bimodal Biometric Verification Using the Fusion of Palmprint and Infrared Palm-Dorsum Vein Images.

PubMed

Lin, Chih-Lung; Wang, Shih-Hung; Cheng, Hsu-Yung; Fan, Kuo-Chin; Hsu, Wei-Lieh; Lai, Chin-Rong

2015-12-12

In this paper, we present a reliable and robust biometric verification method based on bimodal physiological characteristics of palms, including the palmprint and palm-dorsum vein patterns. The proposed method consists of five steps: (1) automatically aligning and cropping the same region of interest from different palm or palm-dorsum images; (2) applying the digital wavelet transform and inverse wavelet transform to fuse palmprint and vein pattern images; (3) extracting the line-like features (LLFs) from the fused image; (4) obtaining multiresolution representations of the LLFs by using a multiresolution filter; and (5) using a support vector machine to verify the multiresolution representations of the LLFs. The proposed method possesses four advantages: first, both modal images are captured in peg-free scenarios to improve the user-friendliness of the verification device. Second, palmprint and vein pattern images are captured using a low-resolution digital scanner and infrared (IR) camera. The use of low-resolution images results in a smaller database. In addition, the vein pattern images are captured through the invisible IR spectrum, which improves antispoofing. Third, since the physiological characteristics of palmprint and vein pattern images are different, a hybrid fusing rule can be introduced to fuse the decomposition coefficients of different bands. The proposed method fuses decomposition coefficients at different decomposed levels, with different image sizes, captured from different sensor devices. Finally, the proposed method operates automatically and hence no parameters need to be set manually. Three thousand palmprint images and 3000 vein pattern images were collected from 100 volunteers to verify the validity of the proposed method. The results show a false rejection rate of 1.20% and a false acceptance rate of 1.56%. It demonstrates the validity and excellent performance of our proposed method comparing to other methods.

A multiple-fan active control wind tunnel for outdoor wind speed and direction simulation

NASA Astrophysics Data System (ADS)

Wang, Jia-Ying; Meng, Qing-Hao; Luo, Bing; Zeng, Ming

2018-03-01

This article presents a new type of active controlled multiple-fan wind tunnel. The wind tunnel consists of swivel plates and arrays of direct current fans, and the rotation speed of each fan and the shaft angle of each swivel plate can be controlled independently for simulating different kinds of outdoor wind fields. To measure the similarity between the simulated wind field and the outdoor wind field, wind speed and direction time series of two kinds of wind fields are recorded by nine two-dimensional ultrasonic anemometers, and then statistical properties of the wind signals in different time scales are analyzed based on the empirical mode decomposition. In addition, the complexity of wind speed and direction time series is also investigated using multiscale entropy and multivariate multiscale entropy. Results suggest that the simulated wind field in the multiple-fan wind tunnel has a high degree of similarity with the outdoor wind field.

Hydrothermal carbonization of Opuntia ficus-indica cladodes: Role of process parameters on hydrochar properties.

PubMed

Volpe, Maurizio; Goldfarb, Jillian L; Fiori, Luca

2018-01-01

Opuntia ficus-indica cladodes are a potential source of solid biofuel from marginal, dry land. Experiments assessed the effects of temperature (180-250°C), reaction time (0.5-3h) and biomass to water ratio (B/W; 0.07-0.30) on chars produced via hydrothermal carbonization. Multivariate linear regression demonstrated that the three process parameters are critically important to hydrochar solid yield, while B/W drives energy yield. Heating value increased together with temperature and reaction time and was maximized at intermediate B/W (0.14-0.20). Microscopy shows evidence of secondary char formed at higher temperatures and B/W ratios. X-ray diffraction, thermogravimetric data, microscopy and inductively coupled plasma mass spectrometry suggest that calcium oxalate in the raw biomass remains in the hydrochar; at higher temperatures, the mineral decomposes into CO 2 and may catalyze char/tar decomposition. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Distinction between added-energy and phase-resetting mechanisms in non-invasively detected somatosensory evoked responses.

PubMed

Fedele, T; Scheer, H-J; Burghoff, M; Waterstraat, G; Nikulin, V V; Curio, G

2013-01-01

Non-invasively recorded averaged event-related potentials (ERP) represent a convenient opportunity to investigate human brain perceptive and cognitive processes. Nevertheless, generative ERP mechanisms are still debated. Two previous approaches have been contested in the past: the added-energy model in which the response raises independently from the ongoing background activity, and the phase-reset model, based on stimulus-driven synchronization of oscillatory ongoing activity. Many criteria for the distinction of these two models have been proposed, but there is no definitive methodology to disentangle them, owing also to the limited information at the single trial level. Here, we propose a new approach combining low-noise EEG technology and multivariate decomposition techniques. We present theoretical analyses based on simulated data and identify in high-frequency somatosensory evoked responses an optimal target for the distinction between the two mechanisms.

Otolith patterns of rockfishes from the northeastern Pacific.

PubMed

Tuset, Victor M; Imondi, Ralph; Aguado, Guillermo; Otero-Ferrer, José L; Santschi, Linda; Lombarte, Antoni; Love, Milton

2015-04-01

Sagitta otolith shape was analysed in twenty sympatric rockfishes off the southern California coast (Northeastern Pacific). The variation in shape was quantified using canonical variate analysis based on fifth wavelet function decomposition of otolith contour. We selected wavelets because this representation allow the identifications of zones or single morphological points along the contour. The entire otoliths along with four subsections (anterior, ventral, posterodorsal, and anterodorsal) with morphological meaning were examined. Multivariate analyses (MANOVA) showed significant differences in the contours of whole otolith morphology and corresponding subsection among rockfishes. Four patterns were found: fusiform, oblong, and two types of elliptic. A redundancy analysis indicated that anterior and anterodorsal subsections contribute most to define the entire otolith shape. Complementarily, the eco-morphological study indicated that the depth distribution and strategies for capture prey were correlated to otolith shape, especially with the anterodorsal zone. © 2014 Wiley Periodicals, Inc.

An expert support system for breast cancer diagnosis using color wavelet features.

PubMed

Issac Niwas, S; Palanisamy, P; Chibbar, Rajni; Zhang, W J

2012-10-01

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

Detecting spatio-temporal modes in multivariate data by entropy field decomposition

NASA Astrophysics Data System (ADS)

Frank, Lawrence R.; Galinsky, Vitaly L.

2016-09-01

A new data analysis method that addresses a general problem of detecting spatio-temporal variations in multivariate data is presented. The method utilizes two recent and complimentary general approaches to data analysis, information field theory (IFT) and entropy spectrum pathways (ESPs). Both methods reformulate and incorporate Bayesian theory, thus use prior information to uncover underlying structure of the unknown signal. Unification of ESP and IFT creates an approach that is non-Gaussian and nonlinear by construction and is found to produce unique spatio-temporal modes of signal behavior that can be ranked according to their significance, from which space-time trajectories of parameter variations can be constructed and quantified. Two brief examples of real world applications of the theory to the analysis of data bearing completely different, unrelated nature, lacking any underlying similarity, are also presented. The first example provides an analysis of resting state functional magnetic resonance imaging data that allowed us to create an efficient and accurate computational method for assessing and categorizing brain activity. The second example demonstrates the potential of the method in the application to the analysis of a strong atmospheric storm circulation system during the complicated stage of tornado development and formation using data recorded by a mobile Doppler radar. Reference implementation of the method will be made available as a part of the QUEST toolkit that is currently under development at the Center for Scientific Computation in Imaging.

Determination of equilibrium structures of bromothymol blue revealed by using quantum chemistry with an aid of multivariate analysis of electronic absorption spectra

NASA Astrophysics Data System (ADS)

Shimada, Toru; Hasegawa, Takeshi

2017-10-01

The pH dependent chemical structures of bromothymol blue (BTB), which have long been under controversy, are determined by employing a combined technique of multivariate analysis of electronic absorption spectra and quantum chemistry. Principle component analysis (PCA) of the pH dependent spectra apparently reveals that only two chemical species are adequate to fully account for the color changes, with which the spectral decomposition is readily performed by using augmented alternative least-squares (ALS) regression analysis. The quantity variation by the ALS analysis also reveals the practical acid dissociation constant, pKa‧. The determination of pKa‧ is performed for various ionic strengths, which reveals the thermodynamic acid constant (pKa = 7.5) and the number of charge on each chemical species; the yellow form is negatively charged species of - 1 and the blue form that of - 2. On this chemical information, the quantum chemical calculation is carried out to find that BTB molecules take the pure quinoid form in an acid solution and the quinoid-phenolate form in an alkaline solution. The time-dependent density functional theory (TD-DFT) calculations for the theoretically determined chemical structures account for the peak shift of the electronic spectra. In this manner, the structures of all the chemical species appeared in equilibrium have finally been confirmed.

­Understanding Information Flow Interaction along Separable Causal Paths in Environmental Signals

NASA Astrophysics Data System (ADS)

Jiang, P.; Kumar, P.

2017-12-01

Multivariate environmental signals reflect the outcome of complex inter-dependencies, such as those in ecohydrologic systems. Transfer entropy and information partitioning approaches have been used to characterize such dependencies. However, these approaches capture net information flow occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within an interested subsystem through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [2015] to develop a framework for quantifying information decomposition along separable causal paths. Momentary information transfer along causal paths captures the amount of information flow between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique and redundant information flow through separable causal paths. Multivariate analysis using this novel approach reveals precise understanding of causality and feedback. We illustrate our approach with synthetic and observed time series data. We believe the proposed framework helps better delineate the internal structure of complex systems in geoscience where huge amounts of observational datasets exist, and it will also help the modeling community by providing a new way to look at the complexity of real and modeled systems. Runge, Jakob. "Quantifying information transfer and mediation along causal pathways in complex systems." Physical Review E 92.6 (2015): 062829.

Determination of equilibrium structures of bromothymol blue revealed by using quantum chemistry with an aid of multivariate analysis of electronic absorption spectra.

PubMed

Shimada, Toru; Hasegawa, Takeshi

2017-10-05

The pH dependent chemical structures of bromothymol blue (BTB), which have long been under controversy, are determined by employing a combined technique of multivariate analysis of electronic absorption spectra and quantum chemistry. Principle component analysis (PCA) of the pH dependent spectra apparently reveals that only two chemical species are adequate to fully account for the color changes, with which the spectral decomposition is readily performed by using augmented alternative least-squares (ALS) regression analysis. The quantity variation by the ALS analysis also reveals the practical acid dissociation constant, pK a '. The determination of pK a ' is performed for various ionic strengths, which reveals the thermodynamic acid constant (pK a =7.5) and the number of charge on each chemical species; the yellow form is negatively charged species of -1 and the blue form that of -2. On this chemical information, the quantum chemical calculation is carried out to find that BTB molecules take the pure quinoid form in an acid solution and the quinoid-phenolate form in an alkaline solution. The time-dependent density functional theory (TD-DFT) calculations for the theoretically determined chemical structures account for the peak shift of the electronic spectra. In this manner, the structures of all the chemical species appeared in equilibrium have finally been confirmed. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial, temporal, and hybrid decompositions for large-scale vehicle routing with time windows

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

Bent, Russell W

This paper studies the use of decomposition techniques to quickly find high-quality solutions to large-scale vehicle routing problems with time windows. It considers an adaptive decomposition scheme which iteratively decouples a routing problem based on the current solution. Earlier work considered vehicle-based decompositions that partitions the vehicles across the subproblems. The subproblems can then be optimized independently and merged easily. This paper argues that vehicle-based decompositions, although very effective on various problem classes also have limitations. In particular, they do not accommodate temporal decompositions and may produce spatial decompositions that are not focused enough. This paper then proposes customer-based decompositionsmore » which generalize vehicle-based decouplings and allows for focused spatial and temporal decompositions. Experimental results on class R2 of the extended Solomon benchmarks demonstrates the benefits of the customer-based adaptive decomposition scheme and its spatial, temporal, and hybrid instantiations. In particular, they show that customer-based decompositions bring significant benefits over large neighborhood search in contrast to vehicle-based decompositions.« less

Probing consciousness in a sensory-disconnected paralyzed patient.

PubMed

Rohaut, Benjamin; Raimondo, Federico; Galanaud, Damien; Valente, Mélanie; Sitt, Jacobo Diego; Naccache, Lionel

2017-01-01

Diagnosis of consciousness can be very challenging in some clinical situations such as severe sensory-motor impairments. We report the case study of a patient who presented a total "locked-in syndrome" associated with and a multi-sensory deafferentation (visual, auditory and tactile modalities) following a protuberantial infarction. In spite of this severe and extreme disconnection from the external world, we could detect reliable evidence of consciousness using a multivariate analysis of his high-density resting state electroencephalogram. This EEG-based diagnosis was eventually confirmed by the clinical evolution of the patient. This approach illustrates the potential importance of functional brain-imaging data to improve diagnosis of consciousness and of cognitive abilities in critical situations in which the behavioral channel is compromised such as deafferented locked-in syndrome.

A Methodology for Conducting Integrative Mixed Methods Research and Data Analyses

PubMed Central

Castro, Felipe González; Kellison, Joshua G.; Boyd, Stephen J.; Kopak, Albert

2011-01-01

Mixed methods research has gained visibility within the last few years, although limitations persist regarding the scientific caliber of certain mixed methods research designs and methods. The need exists for rigorous mixed methods designs that integrate various data analytic procedures for a seamless transfer of evidence across qualitative and quantitative modalities. Such designs can offer the strength of confirmatory results drawn from quantitative multivariate analyses, along with “deep structure” explanatory descriptions as drawn from qualitative analyses. This article presents evidence generated from over a decade of pilot research in developing an integrative mixed methods methodology. It presents a conceptual framework and methodological and data analytic procedures for conducting mixed methods research studies, and it also presents illustrative examples from the authors' ongoing integrative mixed methods research studies. PMID:22167325

The trait contribution to wood decomposition rates of 15 Neotropical tree species.

PubMed

van Geffen, Koert G; Poorter, Lourens; Sass-Klaassen, Ute; van Logtestijn, Richard S P; Cornelissen, Johannes H C

2010-12-01

The decomposition of dead wood is a critical uncertainty in models of the global carbon cycle. Despite this, relatively few studies have focused on dead wood decomposition, with a strong bias to higher latitudes. Especially the effect of interspecific variation in species traits on differences in wood decomposition rates remains unknown. In order to fill these gaps, we applied a novel method to study long-term wood decomposition of 15 tree species in a Bolivian semi-evergreen tropical moist forest. We hypothesized that interspecific differences in species traits are important drivers of variation in wood decomposition rates. Wood decomposition rates (fractional mass loss) varied between 0.01 and 0.31 yr(-1). We measured 10 different chemical, anatomical, and morphological traits for all species. The species' average traits were useful predictors of wood decomposition rates, particularly the average diameter (dbh) of the tree species (R2 = 0.41). Lignin concentration further increased the proportion of explained inter-specific variation in wood decomposition (both negative relations, cumulative R2 = 0.55), although it did not significantly explain variation in wood decomposition rates if considered alone. When dbh values of the actual dead trees sampled for decomposition rate determination were used as a predictor variable, the final model (including dead tree dbh and lignin concentration) explained even more variation in wood decomposition rates (R2 = 0.71), underlining the importance of dbh in wood decomposition. Other traits, including wood density, wood anatomical traits, macronutrient concentrations, and the amount of phenolic extractives could not significantly explain the variation in wood decomposition rates. The surprising results of this multi-species study, in which for the first time a large set of traits is explicitly linked to wood decomposition rates, merits further testing in other forest ecosystems.

Tree species diversity affects decomposition through modified micro-environmental conditions across European forests.

PubMed

Joly, François-Xavier; Milcu, Alexandru; Scherer-Lorenzen, Michael; Jean, Loreline-Katia; Bussotti, Filippo; Dawud, Seid Muhie; Müller, Sandra; Pollastrini, Martina; Raulund-Rasmussen, Karsten; Vesterdal, Lars; Hättenschwiler, Stephan

2017-05-01

Different tree species influence litter decomposition directly through species-specific litter traits, and indirectly through distinct modifications of the local decomposition environment. Whether these indirect effects on decomposition are influenced by tree species diversity is presently not clear. We addressed this question by studying the decomposition of two common substrates, cellulose paper and wood sticks, in a total of 209 forest stands of varying tree species diversity across six major forest types at the scale of Europe. Tree species richness showed a weak but positive correlation with the decomposition of cellulose but not with that of wood. Surprisingly, macroclimate had only a minor effect on cellulose decomposition and no effect on wood decomposition despite the wide range in climatic conditions among sites from Mediterranean to boreal forests. Instead, forest canopy density and stand-specific litter traits affected the decomposition of both substrates, with a particularly clear negative effect of the proportion of evergreen tree litter. Our study suggests that species richness and composition of tree canopies modify decomposition indirectly through changes in microenvironmental conditions. These canopy-induced differences in the local decomposition environment control decomposition to a greater extent than continental-scale differences in macroclimatic conditions. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Reactivity continuum modeling of leaf, root, and wood decomposition across biomes

NASA Astrophysics Data System (ADS)

Koehler, Birgit; Tranvik, Lars J.

2015-07-01

Large carbon dioxide amounts are released to the atmosphere during organic matter decomposition. Yet the large-scale and long-term regulation of this critical process in global carbon cycling by litter chemistry and climate remains poorly understood. We used reactivity continuum (RC) modeling to analyze the decadal data set of the "Long-term Intersite Decomposition Experiment," in which fine litter and wood decomposition was studied in eight biome types (224 time series). In 32 and 46% of all sites the litter content of the acid-unhydrolyzable residue (AUR, formerly referred to as lignin) and the AUR/nitrogen ratio, respectively, retarded initial decomposition rates. This initial rate-retarding effect generally disappeared within the first year of decomposition, and rate-stimulating effects of nutrients and a rate-retarding effect of the carbon/nitrogen ratio became more prevalent. For needles and leaves/grasses, the influence of climate on decomposition decreased over time. For fine roots, the climatic influence was initially smaller but increased toward later-stage decomposition. The climate decomposition index was the strongest climatic predictor of decomposition. The similar variability in initial decomposition rates across litter categories as across biome types suggested that future changes in decomposition may be dominated by warming-induced changes in plant community composition. In general, the RC model parameters successfully predicted independent decomposition data for the different litter-biome combinations (196 time series). We argue that parameterization of large-scale decomposition models with RC model parameters, as opposed to the currently common discrete multiexponential models, could significantly improve their mechanistic foundation and predictive accuracy across climate zones and litter categories.

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

Rank-based decompositions of morphological templates.

PubMed

Sussner, P; Ritter, G X

2000-01-01

Methods for matrix decomposition have found numerous applications in image processing, in particular for the problem of template decomposition. Since existing matrix decomposition techniques are mainly concerned with the linear domain, we consider it timely to investigate matrix decomposition techniques in the nonlinear domain with applications in image processing. The mathematical basis for these investigations is the new theory of rank within minimax algebra. Thus far, only minimax decompositions of rank 1 and rank 2 matrices into outer product expansions are known to the image processing community. We derive a heuristic algorithm for the decomposition of matrices having arbitrary rank.

The effect of body size on the rate of decomposition in a temperate region of South Africa.

PubMed

Sutherland, A; Myburgh, J; Steyn, M; Becker, P J

2013-09-10

Forensic anthropologists rely on the state of decomposition of a body to estimate the post-mortem-interval (PMI) which provides information about the natural events and environmental forces that could have affected the remains after death. Various factors are known to influence the rate of decomposition, among them temperature, rainfall and exposure of the body. However, conflicting reports appear in the literature on the effect of body size on the rate of decay. The aim of this project was to compare decomposition rates of large pigs (Sus scrofa; 60-90 kg), with that of small pigs (<35 kg), to assess the influence of body size on decomposition rates. For the decomposition rates of small pigs, 15 piglets were assessed three times per week over a period of three months during spring and early summer. Data collection was conducted until complete skeletonization occurred. Stages of decomposition were scored according to separate categories for each anatomical region, and the point values for each region were added to determine the total body score (TBS), which represents the overall stage of decomposition for each pig. For the large pigs, data of 15 pigs were used. Scatter plots illustrating the relationships between TBS and PMI as well as TBS and accumulated degree days (ADD) were used to assess the pattern of decomposition and to compare decomposition rates between small and large pigs. Results indicated that rapid decomposition occurs during the early stages of decomposition for both samples. Large pigs showed a plateau phase in the course of advanced stages of decomposition, during which decomposition was minimal. A similar, but much shorter plateau was reached by small pigs of >20 kg at a PMI of 20-25 days, after which decomposition commenced swiftly. This was in contrast to the small pigs of <20 kg, which showed no plateau phase and their decomposition rates were swift throughout the duration of the study. Overall, small pigs decomposed 2.82 times faster than large pigs, indicating that body size does have an effect on the rate of decomposition. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time.

PubMed

Turksoy, Kamuran; Paulino, Thiago Marques Luz; Zaharieva, Dessi P; Yavelberg, Loren; Jamnik, Veronica; Riddell, Michael C; Cinar, Ali

2015-10-06

Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise. © 2015 Diabetes Technology Society.

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin.

PubMed

Ristivojević, Petar; Trifković, Jelena; Vovk, Irena; Milojković-Opsenica, Dušanka

2017-01-01

Considering the introduction of phytochemical fingerprint analysis, as a method of screening the complex natural products for the presence of most bioactive compounds, use of chemometric classification methods, application of powerful scanning and image capturing and processing devices and algorithms, advancement in development of novel stationary phases as well as various separation modalities, high-performance thin-layer chromatography (HPTLC) fingerprinting is becoming attractive and fruitful field of separation science. Multivariate image analysis is crucial in the light of proper data acquisition. In a current study, different image processing procedures were studied and compared in detail on the example of HPTLC chromatograms of plant resins. In that sense, obtained variables such as gray intensities of pixels along the solvent front, peak area and mean values of peak were used as input data and compared to obtained best classification models. Important steps in image analysis, baseline removal, denoising, target peak alignment and normalization were pointed out. Numerical data set based on mean value of selected bands and intensities of pixels along the solvent front proved to be the most convenient for planar-chromatographic profiling, although required at least the basic knowledge on image processing methodology, and could be proposed for further investigation in HPLTC fingerprinting. Copyright © 2016 Elsevier B.V. All rights reserved.

Impact of Primary Gleason Grade on Risk Stratification for Gleason Score 7 Prostate Cancers

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

Koontz, Bridget F., E-mail: bridget.koontz@duke.edu; Tsivian, Matvey; Mouraviev, Vladimir

Purpose: To evaluate the primary Gleason grade (GG) in Gleason score (GS) 7 prostate cancers for risk of non-organ-confined disease with the goal of optimizing radiotherapy treatment option counseling. Methods: One thousand three hundred thirty-three patients with pathologic GS7 were identified in the Duke Prostate Center research database. Clinical factors including age, race, clinical stage, prostate-specific antigen at diagnosis, and pathologic stage were obtained. Data were stratified by prostate-specific antigen and clinical stage at diagnosis into adapted D'Amico risk groups. Univariate and multivariate analyses were performed evaluating for association of primary GG with pathologic outcome. Results: Nine hundred seventy-nine patientsmore » had primary GG3 and 354 had GG4. On univariate analyses, GG4 was associated with an increased risk of non-organ-confined disease. On multivariate analysis, GG4 was independently associated with seminal vesicle invasion (SVI) but not extracapsular extension. Patients with otherwise low-risk disease and primary GG3 had a very low risk of SVI (4%). Conclusions: Primary GG4 in GS7 cancers is associated with increased risk of SVI compared with primary GG3. Otherwise low-risk patients with GS 3+4 have a very low risk of SVI and may be candidates for prostate-only radiotherapy modalities.« less

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Impact of primary Gleason grade on risk stratification for Gleason score 7 prostate cancers.

PubMed

Koontz, Bridget F; Tsivian, Matvey; Mouraviev, Vladimir; Sun, Leon; Vujaskovic, Zeljko; Moul, Judd; Lee, W Robert

2012-01-01

To evaluate the primary Gleason grade (GG) in Gleason score (GS) 7 prostate cancers for risk of non-organ-confined disease with the goal of optimizing radiotherapy treatment option counseling. One thousand three hundred thirty-three patients with pathologic GS7 were identified in the Duke Prostate Center research database. Clinical factors including age, race, clinical stage, prostate-specific antigen at diagnosis, and pathologic stage were obtained. Data were stratified by prostate-specific antigen and clinical stage at diagnosis into adapted D'Amico risk groups. Univariate and multivariate analyses were performed evaluating for association of primary GG with pathologic outcome. Nine hundred seventy-nine patients had primary GG3 and 354 had GG4. On univariate analyses, GG4 was associated with an increased risk of non-organ-confined disease. On multivariate analysis, GG4 was independently associated with seminal vesicle invasion (SVI) but not extracapsular extension. Patients with otherwise low-risk disease and primary GG3 had a very low risk of SVI (4%). Primary GG4 in GS7 cancers is associated with increased risk of SVI compared with primary GG3. Otherwise low-risk patients with GS 3+4 have a very low risk of SVI and may be candidates for prostate-only radiotherapy modalities. Copyright © 2012 Elsevier Inc. All rights reserved.

Classification of Physical Activity

PubMed Central

Turksoy, Kamuran; Paulino, Thiago Marques Luz; Zaharieva, Dessi P.; Yavelberg, Loren; Jamnik, Veronica; Riddell, Michael C.; Cinar, Ali

2015-01-01

Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise. PMID:26443291

Palliative interventions for hepatocellular carcinoma patients: analysis of the National Cancer Database.

PubMed

Hammad, Abdulrahman Y; Robbins, Jared R; Turaga, Kiran K; Christians, Kathleen K; Gamblin, T Clark; Johnston, Fabian M

2017-01-01

Palliative therapies are provided to a subset of hepatocellular carcinoma (HCC) patients with the aim of providing symptomatic relief, better quality of life and improved survival. The present study sought to assess and compare the efficacy of different palliative therapies for HCC. The National Cancer Database (NCDB), a retrospective national database that captures approximately 70% of all patients treated for cancer in the US, was queried for patients with HCC who were deemed unresectable from 1998-2011. Patients were stratified by receipt of palliative therapy. Survival analysis was examined by log-rank test and Kaplan Meier curves, and a multivariate proportional hazards model was utilized to identify the predictors of survival. A total of 3,267 patients were identified; 287 (8.7%) received surgical palliation, 827 (25.3%) received radiotherapy (RT), 877 (26.8%) received chemotherapy, 1,067 (32.6%) received pain management therapy, while 209 (6.4%) received a combination of the previous three modalities. On multivariate analysis palliative RT was identified as a positive predictor of survival [hazards ratio (HR) 0.65; 95% CI, 0.50-0.83]. Stratifying by disease stage, palliative RT provided a significant survival benefit for patients with stage IV disease. Palliative RT appears to extend survival and should be considered for patients presenting with late stage HCC.

Assessing the effect of different treatments on decomposition rate of dairy manure.

PubMed

Khalil, Tariq M; Higgins, Stewart S; Ndegwa, Pius M; Frear, Craig S; Stöckle, Claudio O

2016-11-01

Confined animal feeding operations (CAFOs) contribute to greenhouse gas emission, but the magnitude of these emissions as a function of operation size, infrastructure, and manure management are difficult to assess. Modeling is a viable option to estimate gaseous emission and nutrient flows from CAFOs. These models use a decomposition rate constant for carbon mineralization. However, this constant is usually determined assuming a homogenous mix of manure, ignoring the effects of emerging manure treatments. The aim of this study was to measure and compare the decomposition rate constants of dairy manure in single and three-pool decomposition models, and to develop an empirical model based on chemical composition of manure for prediction of a decomposition rate constant. Decomposition rate constants of manure before and after an anaerobic digester (AD), following coarse fiber separation, and fine solids removal were determined under anaerobic conditions for single and three-pool decomposition models. The decomposition rates of treated manure effluents differed significantly from untreated manure for both single and three-pool decomposition models. In the single-pool decomposition model, AD effluent containing only suspended solids had a relatively high decomposition rate of 0.060 d(-1), while liquid with coarse fiber and fine solids removed had the lowest rate of 0.013 d(-1). In the three-pool decomposition model, fast and slow decomposition rate constants (0.25 d(-1) and 0.016 d(-1) respectively) of untreated AD influent were also significantly different from treated manure fractions. A regression model to predict the decomposition rate of treated dairy manure fitted well (R(2) = 0.83) to observed data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Shrub encroachment in Arctic tundra: Betula nana effects on above- and belowground litter decomposition.

PubMed

McLaren, Jennie R; Buckeridge, Kate M; van de Weg, Martine J; Shaver, Gaius R; Schimel, Joshua P; Gough, Laura

2017-05-01

Rapid arctic vegetation change as a result of global warming includes an increase in the cover and biomass of deciduous shrubs. Increases in shrub abundance will result in a proportional increase of shrub litter in the litter community, potentially affecting carbon turnover rates in arctic ecosystems. We investigated the effects of leaf and root litter of a deciduous shrub, Betula nana, on decomposition, by examining species-specific decomposition patterns, as well as effects of Betula litter on the decomposition of other species. We conducted a 2-yr decomposition experiment in moist acidic tundra in northern Alaska, where we decomposed three tundra species (Vaccinium vitis-idaea, Rhododendron palustre, and Eriophorum vaginatum) alone and in combination with Betula litter. Decomposition patterns for leaf and root litter were determined using three different measures of decomposition (mass loss, respiration, extracellular enzyme activity). We report faster decomposition of Betula leaf litter compared to other species, with support for species differences coming from all three measures of decomposition. Mixing effects were less consistent among the measures, with negative mixing effects shown only for mass loss. In contrast, there were few species differences or mixing effects for root decomposition. Overall, we attribute longer-term litter mass loss patterns to patterns created by early decomposition processes in the first winter. We note numerous differences for species patterns between leaf and root decomposition, indicating that conclusions from leaf litter experiments should not be extrapolated to below-ground decomposition. The high decomposition rates of Betula leaf litter aboveground, and relatively similar decomposition rates of multiple species below, suggest a potential for increases in turnover in the fast-decomposing carbon pool of leaves and fine roots as the dominance of deciduous shrubs in the Arctic increases, but this outcome may be tempered by negative litter mixing effects during the early stages of encroachment. © 2017 by the Ecological Society of America.

Unraveling the physical meaning of the Jaffe-Manohar decomposition of the nucleon spin

NASA Astrophysics Data System (ADS)

Wakamatsu, M.

2016-09-01

A general consensus now is that there are two physically inequivalent complete decompositions of the nucleon spin, i.e. the decomposition of the canonical type and that of mechanical type. The well-known Jaffe-Manohar decomposition is of the former type. Unfortunately, there is a wide-spread misbelief that this decomposition matches the partonic picture, which states that motion of quarks in the nucleon is approximately free. In the present monograph, we reveal that this understanding is not necessarily correct and that the Jaffe-Manohar decomposition is not such a decomposition, which natively reflects the intrinsic (or static) orbital angular momentum structure of the nucleon.

Exploring Patterns of Soil Organic Matter Decomposition with Students through the Global Decomposition Project (GDP) and the Interactive Model of Leaf Decomposition (IMOLD)

NASA Astrophysics Data System (ADS)

Steiner, S. M.; Wood, J. H.

2015-12-01

As decomposition rates are affected by climate change, understanding crucial soil interactions that affect plant growth and decomposition becomes a vital part of contributing to the students' knowledge base. The Global Decomposition Project (GDP) is designed to introduce and educate students about soil organic matter and decomposition through a standardized protocol for collecting, reporting, and sharing data. The Interactive Model of Leaf Decomposition (IMOLD) utilizes animations and modeling to learn about the carbon cycle, leaf anatomy, and the role of microbes in decomposition. Paired together, IMOLD teaches the background information and allows simulation of numerous scenarios, and the GDP is a data collection protocol that allows students to gather usable measurements of decomposition in the field. Our presentation will detail how the GDP protocol works, how to obtain or make the materials needed, and how results will be shared. We will also highlight learning objectives from the three animations of IMOLD, and demonstrate how students can experiment with different climates and litter types using the interactive model to explore a variety of decomposition scenarios. The GDP demonstrates how scientific methods can be extended to educate broader audiences, and data collected by students can provide new insight into global patterns of soil decomposition. Using IMOLD, students will gain a better understanding of carbon cycling in the context of litter decomposition, as well as learn to pose questions they can answer with an authentic computer model. Using the GDP protocols and IMOLD provide a pathway for scientists and educators to interact and reach meaningful education and research goals.

Multimodal Fusion with Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

PubMed Central

Qi, Shile; Calhoun, Vince D.; van Erp, Theo G. M.; Bustillo, Juan; Damaraju, Eswar; Turner, Jessica A.; Du, Yuhui; Chen, Jiayu; Yu, Qingbao; Mathalon, Daniel H.; Ford, Judith M.; Voyvodic, James; Mueller, Bryon A.; Belger, Aysenil; Ewen, Sarah Mc; Potkin, Steven G.; Preda, Adrian; Jiang, Tianzi

2017-01-01

Multimodal fusion is an effective approach to take advantage of cross-information among multiple imaging data to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. To date, there is increasing interest to uncover the neurocognitive mapping of specific behavioral measurement on enriched brain imaging data; hence, a supervised, goal-directed model that enables a priori information as a reference to guide multimodal data fusion is in need and a natural option. Here we proposed a fusion with reference model, called “multi-site canonical correlation analysis with reference plus joint independent component analysis” (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to reference information, such as cognitive scores. In a 3-way fusion simulation, the proposed method was compared with its alternatives on estimation accuracy of both target component decomposition and modality linkage detection. MCCAR+jICA outperforms others with higher precision. In human imaging data, working memory performance was utilized as a reference to investigate the covarying functional and structural brain patterns among 3 modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Interestingly, similar brain maps were identified between the two cohorts, with substantial overlap in the executive control networks in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports, while MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the potential of such results to identify potential neuromarkers for mental disorders. PMID:28708547

Modal analysis of the thermal conductivity of nanowires: examining unique thermal transport features.

PubMed

Samaraweera, Nalaka; Larkin, Jason M; Chan, Kin L; Mithraratne, Kumar

2018-06-06

In this study, unique thermal transport features of nanowires over bulk materials are investigated using a combined analysis based on lattice dynamics and equilibrium molecular dynamics (EMD). The evaluation of the thermal conductivity (TC) of Lenard-Jones nanowires becomes feasible due to the multi-step normal mode decomposition (NMD) procedure implemented in the study. A convergence issue of the TC of nanowires is addressed by the NMD implementation for two case studies, which employ pristine nanowires (PNW) and superlattice nanowires. Interestingly, mode relaxation times at low frequencies of acoustic branches exhibit signs of approaching constant values, thus indicating the convergence of TC. The TC evaluation procedure is further verified by implementing EMD-based Green-Kubo analysis, which is based on a fundamentally different physical perspective. Having verified the NMD procedure, the non-monotonic trend of the TC of nanowires is addressed. It is shown that the principal cause for the observed trend is due to the competing effects of long wavelength phonons and phonon-surface scatterings as the nanowire's cross-sectional width is changed. A computational procedure is developed to decompose the different modal contribution to the TC of shell alloy nanowires (SANWs) using virtual crystal NMD and the Allen-Feldman theory. Several important conclusions can be drawn from the results. A propagons to non-propagons boundary appeared, resulting in a cut-off frequency (ω cut ); moreover, as alloy atomic mass is increased, ω cut shifts to lower frequencies. The existence of non-propagons partly causes the low TC of SANWs. It can be seen that modes with low frequencies demonstrate a similar behavior to corresponding modes of PNWs. Moreover, lower group velocities associated with higher alloy atomic mass resulted in a lower TC of SANWs.

Imaging with hypertelescopes: a simple modal approach

NASA Astrophysics Data System (ADS)

Aime, C.

2008-05-01

Aims: We give a simple analysis of imaging with hypertelescopes, a technique proposed by Labeyrie to produce snapshot images using arrays of telescopes. The approach is modal: we describe the transformations induced by the densification onto a sinusoidal decomposition of the focal image instead of the usual point spread function approach. Methods: We first express the image formed at the focus of a diluted array of apertures as the product R_0(α) X_F(α) of the diffraction pattern of the elementary apertures R_0(α) by the object-dependent interference term X_F(α) between all apertures. The interference term, which can be written in the form of a Fourier Series for an extremely diluted array, produces replications of the object, which makes observing the image difficult. We express the focal image after the densification using the approach of Tallon and Tallon-Bosc. Results: The result is very simple for an extremely diluted array. We show that the focal image in a periscopic densification of the array can be written as R_0(α) X_F(α/γ), where γ is the factor of densification. There is a dilatation of the interference term while the diffraction term is unchanged. After de-zooming, the image can be written as γ2 X_F(α)R_0(γ α), an expression which clearly indicates that the final image corresponds to the center of the Fizeau image intensified by γ2. The imaging limitations of hypertelescopes are therefore those of the original configuration. The effect of the suppression of image replications is illustrated in a numerical simulation for a fully redundant configuration and a non-redundant one.

Modal analysis of the thermal conductivity of nanowires: examining unique thermal transport features

NASA Astrophysics Data System (ADS)

Samaraweera, Nalaka; Larkin, Jason M.; Chan, Kin L.; Mithraratne, Kumar

2018-06-01

In this study, unique thermal transport features of nanowires over bulk materials are investigated using a combined analysis based on lattice dynamics and equilibrium molecular dynamics (EMD). The evaluation of the thermal conductivity (TC) of Lenard–Jones nanowires becomes feasible due to the multi-step normal mode decomposition (NMD) procedure implemented in the study. A convergence issue of the TC of nanowires is addressed by the NMD implementation for two case studies, which employ pristine nanowires (PNW) and superlattice nanowires. Interestingly, mode relaxation times at low frequencies of acoustic branches exhibit signs of approaching constant values, thus indicating the convergence of TC. The TC evaluation procedure is further verified by implementing EMD-based Green–Kubo analysis, which is based on a fundamentally different physical perspective. Having verified the NMD procedure, the non-monotonic trend of the TC of nanowires is addressed. It is shown that the principal cause for the observed trend is due to the competing effects of long wavelength phonons and phonon–surface scatterings as the nanowire’s cross-sectional width is changed. A computational procedure is developed to decompose the different modal contribution to the TC of shell alloy nanowires (SANWs) using virtual crystal NMD and the Allen–Feldman theory. Several important conclusions can be drawn from the results. A propagons to non-propagons boundary appeared, resulting in a cut-off frequency (ω cut); moreover, as alloy atomic mass is increased, ω cut shifts to lower frequencies. The existence of non-propagons partly causes the low TC of SANWs. It can be seen that modes with low frequencies demonstrate a similar behavior to corresponding modes of PNWs. Moreover, lower group velocities associated with higher alloy atomic mass resulted in a lower TC of SANWs.

Mortar and artillery variants classification by exploiting characteristics of the acoustic signature

NASA Astrophysics Data System (ADS)

Hohil, Myron E.; Grasing, David; Desai, Sachi; Morcos, Amir

2007-10-01

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis facilitate the development of a robust classification algorithm that reliably discriminates mortar and artillery variants via acoustic signals produced during the launch/impact events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants. Distinct characteristics arise within the different mortar variants because varying HE mortar payloads and related charges emphasize concussive and shrapnel effects upon impact employing varying magnitude explosions. The different mortar variants are characterized by variations in the resulting waveform of the event. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing techniques can employed to classify a given set. The DWT and other readily available signal processing techniques will be used to extract the predominant components of these characteristics from the acoustic signatures at ranges exceeding 2km. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients, frequency spectrum, and higher frequency details found within different levels of the multiresolution decomposition. The process that will be described herein extends current technologies, which emphasis multi modal sensor fusion suites to provide such situational awareness. A two fold problem of energy consumption and line of sight arise with the multi modal sensor suites. The process described within will exploit the acoustic properties of the event to provide variant classification as added situational awareness to the solider.

Elementary Particle Spectroscopy in Regular Solid Rewrite

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

Trell, Erik

The Nilpotent Universal Computer Rewrite System (NUCRS) has operationalized the radical ontological dilemma of Nothing at All versus Anything at All down to the ground recursive syntax and principal mathematical realisation of this categorical dichotomy as such and so governing all its sui generis modalities, leading to fulfilment of their individual terms and compass when the respective choice sequence operations are brought to closure. Focussing on the general grammar, NUCRS by pure logic and its algebraic notations hence bootstraps Quantum Mechanics, aware that it ''is the likely keystone of a fundamental computational foundation'' also for e.g. physics, molecular biology andmore » neuroscience. The present work deals with classical geometry where morphology is the modality, and ventures that the ancient regular solids are its specific rewrite system, in effect extensively anticipating the detailed elementary particle spectroscopy, and further on to essential structures at large both over the inorganic and organic realms. The geodetic antipode to Nothing is extension, with natural eigenvector the endless straight line which when deployed according to the NUCRS as well as Plotelemeian topographic prescriptions forms a real three-dimensional eigenspace with cubical eigenelements where observed quark-skewed quantum-chromodynamical particle events self-generate as an Aristotelean phase transition between the straight and round extremes of absolute endlessness under the symmetry- and gauge-preserving, canonical coset decomposition SO(3)xO(5) of Lie algebra SU(3). The cubical eigen-space and eigen-elements are the parental state and frame, and the other solids are a range of transition matrix elements and portions adapting to the spherical root vector symmetries and so reproducibly reproducing the elementary particle spectroscopy, including a modular, truncated octahedron nano-composition of the Electron which piecemeal enter into molecular structures or compressed to each other fuse into atomic honeycombs of periodic table signature.« less

Anatomical decomposition in dual energy chest digital tomosynthesis

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Choi, Seungyeon; Kim, Hee-Joung

2016-03-01

Lung cancer is the leading cause of cancer death worldwide and the early diagnosis of lung cancer has recently become more important. For early screening lung cancer, computed tomography (CT) has been used as a gold standard for early diagnosis of lung cancer [1]. The major advantage of CT is that it is not susceptible to the problem of misdiagnosis caused by anatomical overlapping while CT has extremely high radiation dose and cost compared to chest radiography. Chest digital tomosynthesis (CDT) is a recently introduced new modality for lung cancer screening with relatively low radiation dose compared to CT [2] and also showing high sensitivity and specificity to prevent anatomical overlapping occurred in chest radiography. Dual energy material decomposition method has been proposed for better detection of pulmonary nodules as means of reducing the anatomical noise [3]. In this study, possibility of material decomposition in CDT was tested by simulation study and actual experiment using prototype CDT. Furthermore organ absorbed dose and effective dose were compared with single energy CDT. The Gate v6 (Geant4 application for tomographic emission), and TASMIP (Tungsten anode spectral model using the interpolating polynomial) code were used for simulation study and simulated cylinder shape phantom consisted of 4 inner beads which were filled with spine, rib, muscle and lung equivalent materials. The patient dose was estimated by PCXMC 1.5 Monte Carlo simulation tool [4]. The tomosynthesis scan was performed with a linear movement and 21 projection images were obtained over 30 degree of angular range with 1.5° degree of angular interval. The proto type CDT system has same geometry with simulation study and composed of E7869X (Toshiba, Japan) x-ray tube and FDX3543RPW (Toshiba, Japan) detector. The result images showed that reconstructed with dual energy clearly visualize lung filed by removing unnecessary bony structure. Furthermore, dual energy CDT could enhance spine bone hidden by heart effectively. The effective dose in dual energy CDT was slightly higher than single energy CDT, while only 10% of average thoracic CT [5]. Dual energy tomosynthesis is a new technique; therefore, there is little guidance for its integration into the clinical practice and this study can be used to improve diagnosis efficiency of lung field screening using CDT

Product Chemistry and Process Efficiency of Biomass Torrefaction, Pyrolysis and Gasification Studied by High-Throughput Techniques and Multivariate Analysis

NASA Astrophysics Data System (ADS)

Xiao, Li

Despite the great passion and endless efforts on development of renewable energy from biomass, the commercialization and scale up of biofuel production is still under pressure and facing challenges. New ideas and facilities are being tested around the world targeting at reducing cost and improving product value. Cutting edge technologies involving analytical chemistry, statistics analysis, industrial engineering, computer simulation, and mathematics modeling, etc. keep integrating modern elements into this classic research. One of those challenges of commercializing biofuel production is the complexity from chemical composition of biomass feedstock and the products. Because of this, feedstock selection and process optimization cannot be conducted efficiently. This dissertation attempts to further evaluate biomass thermal decomposition process using both traditional methods and advanced technique (Pyrolysis Molecular Beam Mass Spectrometry). Focus has been made on data base generation of thermal decomposition products from biomass at different temperatures, finding out the relationship between traditional methods and advanced techniques, evaluating process efficiency and optimizing reaction conditions, comparison of typically utilized biomass feedstock and new search on innovative species for economical viable feedstock preparation concepts, etc. Lab scale quartz tube reactors and 80il stainless steel sample cups coupled with auto-sampling system were utilized to simulate the complicated reactions happened in real fluidized or entrained flow reactors. Two main high throughput analytical techniques used are Near Infrared Spectroscopy (NIR) and Pyrolysis Molecular Beam Mass Spectrometry (Py-MBMS). Mass balance, carbon balance, and product distribution are presented in detail. Variations of thermal decomposition temperature range from 200°C to 950°C. Feedstocks used in the study involve typical hardwood and softwood (red oak, white oak, yellow poplar, loblolly pine), fast growing energy crops (switchgrass), and popular forage crop (alfalfa), as well as biochar derived from those materials and their mixtures. It demonstrated that Py-MBMS coupled with MVA could be used as fast analytical tools for the study of not only biomass composition but also its thermal decomposition behaviors. It found that the impact of biomass composition heavily depends on the thermal decomposition temperature because at different temperature, the composition of biomass decomposed and the impact of minerals on the decomposition reaction varies. At low temperature (200-500°C), organic compounds attribute to the majority of variation in thermal decomposition products. At higher temperature, inorganics dramatically changed the pyrolysis pathway of carbohydrates and possibly lignin. In gasification, gasification tar formation is also observed to be impacted by ash content in vapor and char. In real reactor, biochar structure also has interactions with other fractions to make the final pyrolysis and gasification product. Based on the evaluation of process efficiencies during torrefaction, temperature ranging from 275°C to 300°C with short residence time (<10min) are proposed to be optimal torrefaction conditions. 500°C is preferred to 700°C as primary pyrolysis temperature in two stage gasification because higher primary pyrolysis temperature resulted in more tar and less gasification char. Also, in terms of carbon yield, more carbon is lost in tar while less carbon is retained in gas product using 700°C as primary pyrolysis temperature. In addition, pyrolysis char is found to produce less tar and more gas during steam gasification compared with gasification of pyrolysis vapor. Thus it is suggested that torrefaction might be an efficient pretreatment for biomass gasification because it can largely improve the yield of pyrolysis char during the primary pyrolysis step of gasification thus reduce the total tar of the overall gasification products. Future work is suggested in the end.

Multivariate Statistical Modelling of Drought and Heat Wave Events

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A copula is a multivariate distribution function which allows one to model the dependence structure of given variables separately from the marginal behaviour. We firstly look at the structure of soil moisture drought over the entire of France using the SAFRAN dataset between 1959 and 2009. Soil moisture is represented using the Standardised Precipitation Evapotranspiration Index (SPEI). Drought characteristics are computed at grid point scale where drought conditions are identified as those with an SPEI value below -1.0. We model the multivariate dependence structure of drought events defined by certain characteristics and compute return levels of these events. We initially find that drought characteristics such as duration, mean SPEI and the maximum contiguous area to a grid point all have positive correlations, though the degree to which they are correlated can vary considerably spatially. A spatial representation of return levels then may provide insight into the areas most prone to drought conditions. As a next step, we analyse the dependence structure between soil moisture conditions preceding the onset of a heat wave and the heat wave itself.

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

PubMed

Chambon, Stanislas; Galtier, Mathieu N; Arnal, Pierrick J; Wainrib, Gilles; Gramfort, Alexandre

2018-04-01

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.

Predictors of Recurrence, Progression, and Retreatment in Basilar Tip Aneurysms: A Location-Controlled Analysis.

PubMed

Abecassis, Isaac Josh; Sen, Rajeev D; Barber, Jason; Shetty, Rakshith; Kelly, Cory M; Ghodke, Basavaraj V; Hallam, Danial K; Levitt, Michael R; Kim, Louis J; Sekhar, Laligam N

2018-06-14

Endovascular treatment of intracranial aneurysms is associated with higher rates of recurrence and retreatment, though contemporary rates and risk factors for basilar tip aneurysms (BTAs) are less well-described. To characterize progression, retreatement, and retreated progression of BTAs treated with microsurgical or endovascular interventions. We retrospectively reviewed records for 141 consecutive BTA patients. We included 158 anterior communicating artery (ACoA) and 118 middle cerebral artery (MCA) aneurysms as controls. Univariate and multivariate analyses were used to calculate rates of progression (recurrence of previously obliterated aneurysms and progression of known residual aneurysm dome or neck), retreatment, and retreated progression. Kaplan-Meier analysis was used to characterize 24-mo event rates for primary outcome prediction. Of 141 BTA patients, 62.4% were ruptured and 37.6% were unruptured. Average radiographical follow-up was 33 mo. Among ruptured aneurysms treated with clipping, there were 2 rehemorrhages due to recurrence (6.1%), and none in any other cohorts. Overall rates of progression (28.9%), retreatment (28.9%), and retreated progression (24.7%) were not significantly different between surgical and endovascular subgroups, though ruptured aneurysms had higher event rates. Multivariate modeling confirmed rupture status (P = .003, hazard ratio = 0.14) and aneurysm dome width (P = .005, hazard ratio = 1.23) as independent predictors of progression requiring retreatment. In a separate multivariate analysis with ACoA and MCA aneurysms, basilar tip location was an independent predictor of progression, retreatment, and retreated progression. BTAs have higher rates of progression and retreated progression than other aneurysm locations, independent of treatment modality. Rupture status and dome width are risk factors for progression requiring retreatment.

The decomposition of fine and coarse roots: their global patterns and controlling factors

PubMed Central

Zhang, Xinyue; Wang, Wei

2015-01-01

Fine root decomposition represents a large carbon (C) cost to plants, and serves as a potential soil C source, as well as a substantial proportion of net primary productivity. Coarse roots differ markedly from fine roots in morphology, nutrient concentrations, functions, and decomposition mechanisms. Still poorly understood is whether a consistent global pattern exists between the decomposition of fine (<2 mm root diameter) and coarse (≥2 mm) roots. A comprehensive terrestrial root decomposition dataset, including 530 observations from 71 sampling sites, was thus used to compare global patterns of decomposition of fine and coarse roots. Fine roots decomposed significantly faster than coarse roots in middle latitude areas, but their decomposition in low latitude regions was not significantly different from that of coarse roots. Coarse root decomposition showed more dependence on climate, especially mean annual temperature (MAT), than did fine roots. Initial litter lignin content was the most important predictor of fine root decomposition, while lignin to nitrogen ratios, MAT, and mean annual precipitation were the most important predictors of coarse root decomposition. Our study emphasizes the necessity of separating fine roots and coarse roots when predicting the response of belowground C release to future climate changes. PMID:25942391

Effects of fire frequency on litter decomposition as mediated by changes to litter chemistry and soil environmental conditions.

PubMed

Ficken, Cari D; Wright, Justin P

2017-01-01

Litter quality and soil environmental conditions are well-studied drivers influencing decomposition rates, but the role played by disturbance legacy, such as fire history, in mediating these drivers is not well understood. Fire history may impact decomposition directly, through changes in soil conditions that impact microbial function, or indirectly, through shifts in plant community composition and litter chemistry. Here, we compared early-stage decomposition rates across longleaf pine forest blocks managed with varying fire frequencies (annual burns, triennial burns, fire-suppression). Using a reciprocal transplant design, we examined how litter chemistry and soil characteristics independently and jointly influenced litter decomposition. We found that both litter chemistry and soil environmental conditions influenced decomposition rates, but only the former was affected by historical fire frequency. Litter from annually burned sites had higher nitrogen content than litter from triennially burned and fire suppression sites, but this was correlated with only a modest increase in decomposition rates. Soil environmental conditions had a larger impact on decomposition than litter chemistry. Across the landscape, decomposition differed more along soil moisture gradients than across fire management regimes. These findings suggest that fire frequency has a limited effect on litter decomposition in this ecosystem, and encourage extending current decomposition frameworks into disturbed systems. However, litter from different species lost different masses due to fire, suggesting that fire may impact decomposition through the preferential combustion of some litter types. Overall, our findings also emphasize the important role of spatial variability in soil environmental conditions, which may be tied to fire frequency across large spatial scales, in driving decomposition rates in this system.

a Novel Two-Component Decomposition for Co-Polar Channels of GF-3 Quad-Pol Data

NASA Astrophysics Data System (ADS)

Kwok, E.; Li, C. H.; Zhao, Q. H.; Li, Y.

2018-04-01

Polarimetric target decomposition theory is the most dynamic and exploratory research area in the field of PolSAR. But most methods of target decomposition are based on fully polarized data (quad pol) and seldom utilize dual-polar data for target decomposition. Given this, we proposed a novel two-component decomposition method for co-polar channels of GF-3 quad-pol data. This method decomposes the data into two scattering contributions: surface, double bounce in dual co-polar channels. To save this underdetermined problem, a criterion for determining the model is proposed. The criterion can be named as second-order averaged scattering angle, which originates from the H/α decomposition. and we also put forward an alternative parameter of it. To validate the effectiveness of proposed decomposition, Liaodong Bay is selected as research area. The area is located in northeastern China, where it grows various wetland resources and appears sea ice phenomenon in winter. and we use the GF-3 quad-pol data as study data, which which is China's first C-band polarimetric synthetic aperture radar (PolSAR) satellite. The dependencies between the features of proposed algorithm and comparison decompositions (Pauli decomposition, An&Yang decomposition, Yamaguchi S4R decomposition) were investigated in the study. Though several aspects of the experimental discussion, we can draw the conclusion: the proposed algorithm may be suitable for special scenes with low vegetation coverage or low vegetation in the non-growing season; proposed decomposition features only using co-polar data are highly correlated with the corresponding comparison decomposition features under quad-polarization data. Moreover, it would be become input of the subsequent classification or parameter inversion.

A density functional theory study of the decomposition mechanism of nitroglycerin.

PubMed

Pei, Liguan; Dong, Kehai; Tang, Yanhui; Zhang, Bo; Yu, Chang; Li, Wenzuo

2017-08-21

The detailed decomposition mechanism of nitroglycerin (NG) in the gas phase was studied by examining reaction pathways using density functional theory (DFT) and canonical variational transition state theory combined with a small-curvature tunneling correction (CVT/SCT). The mechanism of NG autocatalytic decomposition was investigated at the B3LYP/6-31G(d,p) level of theory. Five possible decomposition pathways involving NG were identified and the rate constants for the pathways at temperatures ranging from 200 to 1000 K were calculated using CVT/SCT. There was found to be a lower energy barrier to the β-H abstraction reaction than to the α-H abstraction reaction during the initial step in the autocatalytic decomposition of NG. The decomposition pathways for CHOCOCHONO 2 (a product obtained following the abstraction of three H atoms from NG by NO 2 ) include O-NO 2 cleavage or isomer production, meaning that the autocatalytic decomposition of NG has two reaction pathways, both of which are exothermic. The rate constants for these two reaction pathways are greater than the rate constants for the three pathways corresponding to unimolecular NG decomposition. The overall process of NG decomposition can be divided into two stages based on the NO 2 concentration, which affects the decomposition products and reactions. In the first stage, the reaction pathway corresponding to O-NO 2 cleavage is the main pathway, but the rates of the two autocatalytic decomposition pathways increase with increasing NO 2 concentration. However, when a threshold NO 2 concentration is reached, the NG decomposition process enters its second stage, with the two pathways for NG autocatalytic decomposition becoming the main and secondary reaction pathways.

Effects of fire frequency on litter decomposition as mediated by changes to litter chemistry and soil environmental conditions

PubMed Central

Wright, Justin P.

2017-01-01

Litter quality and soil environmental conditions are well-studied drivers influencing decomposition rates, but the role played by disturbance legacy, such as fire history, in mediating these drivers is not well understood. Fire history may impact decomposition directly, through changes in soil conditions that impact microbial function, or indirectly, through shifts in plant community composition and litter chemistry. Here, we compared early-stage decomposition rates across longleaf pine forest blocks managed with varying fire frequencies (annual burns, triennial burns, fire-suppression). Using a reciprocal transplant design, we examined how litter chemistry and soil characteristics independently and jointly influenced litter decomposition. We found that both litter chemistry and soil environmental conditions influenced decomposition rates, but only the former was affected by historical fire frequency. Litter from annually burned sites had higher nitrogen content than litter from triennially burned and fire suppression sites, but this was correlated with only a modest increase in decomposition rates. Soil environmental conditions had a larger impact on decomposition than litter chemistry. Across the landscape, decomposition differed more along soil moisture gradients than across fire management regimes. These findings suggest that fire frequency has a limited effect on litter decomposition in this ecosystem, and encourage extending current decomposition frameworks into disturbed systems. However, litter from different species lost different masses due to fire, suggesting that fire may impact decomposition through the preferential combustion of some litter types. Overall, our findings also emphasize the important role of spatial variability in soil environmental conditions, which may be tied to fire frequency across large spatial scales, in driving decomposition rates in this system. PMID:29023560

Estimation of beam material random field properties via sensitivity-based model updating using experimental frequency response functions

NASA Astrophysics Data System (ADS)

Machado, M. R.; Adhikari, S.; Dos Santos, J. M. C.; Arruda, J. R. F.

2018-03-01

Structural parameter estimation is affected not only by measurement noise but also by unknown uncertainties which are present in the system. Deterministic structural model updating methods minimise the difference between experimentally measured data and computational prediction. Sensitivity-based methods are very efficient in solving structural model updating problems. Material and geometrical parameters of the structure such as Poisson's ratio, Young's modulus, mass density, modal damping, etc. are usually considered deterministic and homogeneous. In this paper, the distributed and non-homogeneous characteristics of these parameters are considered in the model updating. The parameters are taken as spatially correlated random fields and are expanded in a spectral Karhunen-Loève (KL) decomposition. Using the KL expansion, the spectral dynamic stiffness matrix of the beam is expanded as a series in terms of discretized parameters, which can be estimated using sensitivity-based model updating techniques. Numerical and experimental tests involving a beam with distributed bending rigidity and mass density are used to verify the proposed method. This extension of standard model updating procedures can enhance the dynamic description of structural dynamic models.

Small-scale anisotropy induced by spectral forcing and by rotation in non-helical and helical turbulence

NASA Astrophysics Data System (ADS)

Vallefuoco, Donato; Naso, Aurore; Godeferd, Fabien S.

2018-02-01

We study the effect of large-scale spectral forcing on the scale-dependent anisotropy of the velocity field in direct numerical simulations of homogeneous turbulence. ABC-type forcing and helical or non-helical Euler-type forcing are considered. We propose a scale-dependent characterisation of anisotropy based on a modal decomposition of the two-point velocity tensor spectrum. This produces direction-dependent spectra of energy, helicity and polarisation. We examine the conditions that allow anisotropy to develop in the small scales due to forcing and we show that the theoretically expected isotropy is not exactly obtained, even in the smallest scales, for ABC and helical Euler forcings. When adding rotation, the anisotropy level in ABC-forced simulations is similar to that of lower Rossby number Euler-forced runs. Moreover, even at low rotation rate, the natural anisotropy induced by the Coriolis force is visible at all scales, and two distinct wavenumber ranges appear from our fine-grained characterisation, not separated by the Zeman scale but by a scale where rotation and dissipation are balanced.

A discrete method for modal analysis of overhead line conductor bundles

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

Migdalovici, M.A.; Sireteanu, T.D.; Albrecht, A.A.

The paper presents a mathematical model and a semi-analytical procedure to calculate the vibration modes and eigenfrequencies of single or bundled conductors with spacers which are needed for evaluation of the wind induced vibration of conductors and for optimization of spacer-dampers placement. The method consists in decomposition of conductors in modules and the expansion by polynomial series of unknown displacements on each module. A complete system of polynomials are deduced for this by Legendre polynomials. Each module is considered either boundary conditions at the extremity of the module or the continuity conditions between the modules and also a number ofmore » projections of module equilibrium equation on the polynomials from the expansion series of unknown displacement. The global system of the eigenmodes and eigenfrequencies is of the matrix form: A X + {omega}{sup 2} M X = 0. The theoretical considerations are exemplified on one conductor and on bundle of two conductors with spacers. From this, a method for forced vibration calculus of a single or bundled conductors is also presented.« less

Prediction of dynamic strains on a monopile offshore wind turbine using virtual sensors

NASA Astrophysics Data System (ADS)

Iliopoulos, A. N.; Weijtjens, W.; Van Hemelrijck, D.; Devriendt, C.

2015-07-01

The monitoring of the condition of the offshore wind turbine during its operational states offers the possibility of performing accurate assessments of the remaining life-time as well as supporting maintenance decisions during its entire life. The efficacy of structural monitoring in the case of the offshore wind turbine, though, is undermined by the practical limitations connected to the measurement system in terms of cost, weight and feasibility of sensor mounting (e.g. at muddline level 30m below the water level). This limitation is overcome by reconstructing the full-field response of the structure based on the limited number of measured accelerations and a calibrated Finite Element Model of the system. A modal decomposition and expansion approach is used for reconstructing the responses at all degrees of freedom of the finite element model. The paper will demonstrate the possibility to predict dynamic strains from acceleration measurements based on the aforementioned methodology. These virtual dynamic strains will then be evaluated and validated based on actual strain measurements obtained from a monitoring campaign on an offshore Vestas V90 3 MW wind turbine on a monopile foundation.

Thermal Runaway in Jammed Networks

NASA Astrophysics Data System (ADS)

Lechman, Jeremy; Yarrington, Cole; Bolintineanu, Dan

2017-06-01

The study of thermal explosion has a long history. Names such as Semenov and Frank-Kamenetskii are associated with classical model descriptions under particular assumptions. In this talk we revisit this problem with particular focus on the latter's model for conduction dominated thermal transport and Arrenhius-type reaction chemistry. We extend this description to the case of inhomogeneous microstructure generated by packing mono-sized spheres via a well-defined ``Jamming'' protocol. With these material structures in hand, we recast the Frank-Kamenetskii problem into a reduced-order network form for conduction in particle packs. With this model we can efficiently investigate the variability of the time to ignition due to the random microstructure. Additionally, we propose a modal decomposition and stability analysis of the model akin to stability of linear systems. We highlight the physical insights this approach can give with respect to questions of material dependent performance variability. Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a Lockheed-Martin Company, for the U. S. Department of Energy's National Nuclear Security Administration under Contract No. DE-AC04-94AL85000.

Modeling Coherent Structures in Canopy Flows

NASA Astrophysics Data System (ADS)

Luhar, Mitul

2017-11-01

It is well known that flows over vegetation canopies are characterized by the presence of energetic coherent structures. Since the mean profile over dense canopies exhibits an inflection point, the emergence of such structures is often attributed to a Kelvin-Helmholtz instability. However, though stability analyses provide useful mechanistic insights into canopy flows, they are limited in their ability to generate predictions for spectra and coherent structure. The present effort seeks to address this limitation by extending the resolvent formulation (McKeon and Sharma, 2010, J. Fluid Mech.) to canopy flows. Under the resolvent formulation, the turbulent velocity field is expressed as a superposition of propagating modes, identified via a gain-based (singular value) decomposition of the Navier-Stokes equations. A key advantage of this approach is that it reconciles multiple mechanisms that lead to high amplification in turbulent flows, including modal instability, transient growth, and critical-layer phenomena. Further, individual high-gain modes can be combined to generate more complete models for coherent structure and velocity spectra. Preliminary resolvent-based model predictions for canopy flows agree well with existing experiments and simulations.

Response of a shell structure subject to distributed harmonic excitation

NASA Astrophysics Data System (ADS)

Cao, Rui; Bolton, J. Stuart

2016-09-01

Previously, a coupled, two-dimensional structural-acoustic ring model was constructed to simulate the dynamic and acoustical behavior of pneumatic tires. Analytical forced solutions were obtained and were experimentally verified through laser velocimeter measurement made using automobile tires. However, the two-dimensional ring model is incapable of representing higher order, in-plane modal motion in either the circumferential or axial directions. Therefore, in this paper, a three-dimensional pressurized circular shell model is proposed to study the in-plane shearing motion and the effect of different forcing conditions. Closed form analytical solutions were obtained for both free and forced vibrations of the shell under simply supported boundary conditions. Dispersion relations were calculated and different wave types were identified by their different speeds. Shell surface mobility results under various input distributions were also studied and compared. Spatial Fourier series decompositions were also performed on the spatial mobility results to give the forced dispersion relations, which illustrate clearly the influence of input force spatial distribution. Such a model has practical application in identifying the sources of noise and vibration problems in automotive tires.

Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry.

PubMed

Schwertner, M; Booth, M J; Neil, M A A; Wilson, T

2004-01-01

Confocal or multiphoton microscopes, which deliver optical sections and three-dimensional (3D) images of thick specimens, are widely used in biology. These techniques, however, are sensitive to aberrations that may originate from the refractive index structure of the specimen itself. The aberrations cause reduced signal intensity and the 3D resolution of the instrument is compromised. It has been suggested to correct for aberrations in confocal microscopes using adaptive optics. In order to define the design specifications for such adaptive optics systems, one has to know the amount of aberrations present for typical applications such as with biological samples. We have built a phase stepping interferometer microscope that directly measures the aberration of the wavefront. The modal content of the wavefront is extracted by employing Zernike mode decomposition. Results for typical biological specimens are presented. It was found for all samples investigated that higher order Zernike modes give only a small contribution to the overall aberration. Therefore, these higher order modes can be neglected in future adaptive optics sensing and correction schemes implemented into confocal or multiphoton microscopes, leading to more efficient designs.

Information recovery in propagation-based imaging with decoherence effects

NASA Astrophysics Data System (ADS)

Froese, Heinrich; Lötgering, Lars; Wilhein, Thomas

2017-05-01

During the past decades the optical imaging community witnessed a rapid emergence of novel imaging modalities such as coherent diffraction imaging (CDI), propagation-based imaging and ptychography. These methods have been demonstrated to recover complex-valued scalar wave fields from redundant data without the need for refractive or diffractive optical elements. This renders these techniques suitable for imaging experiments with EUV and x-ray radiation, where the use of lenses is complicated by fabrication, photon efficiency and cost. However, decoherence effects can have detrimental effects on the reconstruction quality of the numerical algorithms involved. Here we demonstrate propagation-based optical phase retrieval from multiple near-field intensities with decoherence effects such as partially coherent illumination, detector point spread, binning and position uncertainties of the detector. Methods for overcoming these systematic experimental errors - based on the decomposition of the data into mutually incoherent modes - are proposed and numerically tested. We believe that the results presented here open up novel algorithmic methods to accelerate detector readout rates and enable subpixel resolution in propagation-based phase retrieval. Further the techniques are straightforward to be extended to methods such as CDI, ptychography and holography.

Dictionary-Based Tensor Canonical Polyadic Decomposition

NASA Astrophysics Data System (ADS)

Cohen, Jeremy Emile; Gillis, Nicolas

2018-04-01

To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.

Comparison of decomposition rates between autopsied and non-autopsied human remains.

PubMed

Bates, Lennon N; Wescott, Daniel J

2016-04-01

Penetrating trauma has been cited as a significant factor in the rate of decomposition. Therefore, penetrating trauma may have an effect on estimations of time-since-death in medicolegal investigations and on research examining decomposition rates and processes when autopsied human bodies are used. The goal of this study was to determine if there are differences in the rate of decomposition between autopsied and non-autopsied human remains in the same environment. The purpose is to shed light on how large incisions, such as those from a thorocoabdominal autopsy, effect time-since-death estimations and research on the rate of decomposition that use both autopsied and non-autopsied human remains. In this study, 59 non-autopsied and 24 autopsied bodies were studied. The number of accumulated degree days required to reach each decomposition stage was then compared between autopsied and non-autopsied remains. Additionally, both types of bodies were examined for seasonal differences in decomposition rates. As temperature affects the rate of decomposition, this study also compared the internal body temperatures of autopsied and non-autopsied remains to see if differences between the two may be leading to differential decomposition. For this portion of this study, eight non-autopsied and five autopsied bodies were investigated. Internal temperature was collected once a day for two weeks. The results showed that differences in the decomposition rate between autopsied and non-autopsied remains was not statistically significant, though the average ADD needed to reach each stage of decomposition was slightly lower for autopsied bodies than non-autopsied bodies. There was also no significant difference between autopsied and non-autopsied bodies in the rate of decomposition by season or in internal temperature. Therefore, this study suggests that it is unnecessary to separate autopsied and non-autopsied remains when studying gross stages of human decomposition in Central Texas and that penetrating trauma may not be a significant factor in the overall rate of decomposition. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Permafrost Thaw, Soil Moisture and Plant Community Change Alter Organic Matter Decomposition in Alaskan Tundra

NASA Astrophysics Data System (ADS)

Natali, S.; Mauritz, M.; Pegoraro, E.; Schuur, E.

2015-12-01

Climate warming in arctic tundra has been associated with increased plant productivity and a shift in plant community composition, specifically an increase in shrub cover, which can impact soil organic matter through changes in the size and composition of the leaf litter pool. Shifts in litter quantity and quality will in turn interact with changes in the soil environment as the climate continues to warm. We examined the effects of permafrost thaw, soil moisture changes, and plant community composition on leaf litter decomposition in an upland tundra ecosystem in Interior Alaska. We present warming and drying effects on decomposition rates of graminoid-dominated and shrub-dominated leaf litter mixtures over three years (2 cm depth), and annual decomposition of a common cellulose substrate (0-10 cm and 10-20 cm) over five years at a permafrost thaw and soil drying experiment. We expected that warming and drying would increase decomposition, and that decomposition would be greater in the shrub litter than in the graminoid litter mix. Decomposition of Betula nana, the dominant shrub, was 50% greater in the shrub-dominated litter mix compared to the graminoid-dominated litter. Surprisingly, there was no significant difference in total litter mass loss between graminoid and shrub litter mixtures, despite significant differences in decomposition rates of the dominant plant species when decomposed alone and in community mixtures. Drying decreased decomposition of B. nana and of the shrub community litter overall, but after two years there was no detected warming effect on shrub-community decomposition. In contrast to leaf litter decomposition, both warming and drying increased decomposition of the common substrate. Warming caused an almost twofold increase in cellulose decomposition in surface soil (0-10cm), and drying caused a twofold increase in cellulose decomposition from deeper organic layer soils (10-20cm). These results demonstrate the importance of interactions among temperature, moisture and vegetation changes on organic matter decomposition, and the potential for increased plant productivity and vegetation changes to alter the size and composition of the soil organic matter pool.

In situ spectroscopic studies on vapor phase catalytic decomposition of dimethyl oxalate.

PubMed

Hegde, Shweta; Tharpa, Kalsang; Akuri, Satyanarayana Reddy; K, Rakesh; Kumar, Ajay; Deshpande, Raj; Nair, Sreejit A

2017-03-15

Dimethyl Oxalate (DMO) has recently gained prominence as a valuable intermediate for the production of compounds of commercial importance. The stability of DMO is poor and hence this can result in the decomposition of DMO under reaction conditions. The mechanism of DMO decomposition is however not reported and more so on catalytic surfaces. Insights into the mechanism of decomposition would help in designing catalysts for its effective molecular transformation. It is well known that DMO is sensitive to moisture, which can also be a factor contributing to its decomposition. The present work reports the results of decomposition of DMO on various catalytic materials. The materials studied consist of acidic (γ-Al 2 O 3 ), basic (MgO), weakly acidic (ZnAl 2 O 4 ) and neutral surfaces such as α-Al 2 O 3 and mesoporous precipitated SiO 2 . Infrared spectroscopy is used to identify the nature of adsorption of the molecule on the various surfaces. The spectroscopy study is done at a temperature of 200 °C, which is the onset of gas phase decomposition of DMO. The results indicate that the stability of DMO is lower than the corresponding acid, i.e. oxalic acid. It is also one of the products of decomposition. Spectroscopic data suggest that DMO decomposition is related to surface acidity and the extent of decomposition depends on the number of surface hydroxyl groups. Decomposition was also observed on α-Al 2 O 3 , which was attributed to the residual surface hydroxyl groups. DMO decomposition to oxalic acid was not observed on the basic surface (MgO).

Exploring Radiotherapy Targeting Strategy and Dose: A Pooled Analysis of Cooperative Group Trials of Combined Modality Therapy for Stage III Non-Small Cell Lung Cancer.

PubMed

Schild, Steven E; Fan, Wen; Stinchcombe, Thomas E; Vokes, Everett E; Ramalingam, Suresh S; Bradley, Jeffrey D; Kelly, Karen; Pang, Herbert H; Wang, Xiaofei

2018-04-21

Concurrent chemoradiotherapy(CRT) is standard therapy for locally-advanced non-small-cell lung cancer(LA-NSCLC)patients. This study was performed to examine thoracic radiotherapy(TRT) parameters and their impact on patient survival. We collected Individual patient data(IPD) from 3600LA-NSCLC patients participating in 16 cooperative group trials of concurrent CRT. The primary TRT parameters examined included field design strategy(elective nodal irradiation(ENI) compared to involved field TRT(IF-TRT)), total dose, and biologically effective dose(BED). Hazard ratios(HRs) for overall survival were calculated with univariable and multivariable Cox models. TRT doses ranged from 60 to 74 Gy with most treatments administered once-daily. ENI was associated with poorer survival than IF-TRT(univariable HR,1.37;95%CI,1.24-1.51,p<0.0001;multivariable HR,1.31;95%CI,1.08-1.59,p=0.002). The median survival of the IF and ENI patients were 24 and 16 months, respectively. Patients were divided into 3 dose groups: low total dose(60 Gy), medium total dose(>60Gy-66Gy) and high total dose(>66Gy-74 Gy). With reference to the low dose group, the multivariable HR's were 1.08 for the medium dose group(95%CI=0.93-1.25) and 1.12 for the high dose group(CI=0.97-1.30).The univariate p=0.054 and multivariable p=0.17. BED was grouped as follows: low(<55.5Gy 10 ), medium(=55.5 Gy 10) , or high(>55.5 Gy 10 ). With reference to the low BED group, the HR was 1.00(95%CI=0.85-1.18) for the medium BED group and 1.10(95%CI=0.93-1.31) for the high BED group. The univariable p=0.076 and multivariable p=0.16. For LA-NSCLC patients treated with concurrent CRT, IF-TRT was associated with significantly better survival than ENI-TRT. TRT total and BED dose levels were not significantly associated with patient survival. Future progress will require research focusing on better systemic therapy and TRT. Copyright © 2018. Published by Elsevier Inc.

Objective measurement of complex multimodal and multidimensional display formats: a common metric for predicting format effectiveness

NASA Astrophysics Data System (ADS)

Marshak, William P.; Darkow, David J.; Wesler, Mary M.; Fix, Edward L.

2000-08-01

Computer-based display designers have more sensory modes and more dimensions within sensory modality with which to encode information in a user interface than ever before. This elaboration of information presentation has made measurement of display/format effectiveness and predicting display/format performance extremely difficult. A multivariate method has been devised which isolates critical information, physically measures its signal strength, and compares it with other elements of the display, which act like background noise. This common Metric relates signal-to-noise ratios (SNRs) within each stimulus dimension, then combines SNRs among display modes, dimensions and cognitive factors can predict display format effectiveness. Examples with their Common Metric assessment and validation in performance will be presented along with the derivation of the metric. Implications of the Common Metric in display design and evaluation will be discussed.

Probability distributions for multimeric systems.

PubMed

Albert, Jaroslav; Rooman, Marianne

2016-01-01

We propose a fast and accurate method of obtaining the equilibrium mono-modal joint probability distributions for multimeric systems. The method necessitates only two assumptions: the copy number of all species of molecule may be treated as continuous; and, the probability density functions (pdf) are well-approximated by multivariate skew normal distributions (MSND). Starting from the master equation, we convert the problem into a set of equations for the statistical moments which are then expressed in terms of the parameters intrinsic to the MSND. Using an optimization package on Mathematica, we minimize a Euclidian distance function comprising of a sum of the squared difference between the left and the right hand sides of these equations. Comparison of results obtained via our method with those rendered by the Gillespie algorithm demonstrates our method to be highly accurate as well as efficient.

Thermal decomposition of high-nitrogen energetic compounds: TAGzT and GUzT

NASA Astrophysics Data System (ADS)

Hayden, Heather F.

The U.S. Navy is exploring high-nitrogen compounds as burning-rate additives to meet the growing demands of future high-performance gun systems. Two high-nitrogen compounds investigated as potential burning-rate additives are bis(triaminoguanidinium) 5,5-azobitetrazolate (TAGzT) and bis(guanidinium) 5,5'-azobitetrazolate (GUzT). Small-scale tests showed that formulations containing TAGzT exhibit significant increases in the burning rates of RDX-based gun propellants. However, when GUzT, a similarly structured molecule was incorporated into the formulation, there was essentially no effect on the burning rate of the propellant. Through the use of simultaneous thermogravimetric modulated beam mass spectrometry (STMBMS) and Fourier-Transform ion cyclotron resonance (FTICR) mass spectrometry methods, an investigation of the underlying chemical and physical processes that control the thermal decomposition behavior of TAGzT and GUzT alone and in the presence of RDX, was conducted. The objective was to determine why GUzT is not as good a burning-rate enhancer in RDX-based gun propellants as compared to TAGzT. The results show that TAGzT is an effective burning-rate modifier in the presence of RDX because the decomposition of TAGzT alters the initial stages of the decomposition of RDX. Hydrazine, formed in the decomposition of TAGzT, reacts faster with RDX than RDX can decompose itself. The reactions occur at temperatures below the melting point of RDX and thus the TAGzT decomposition products react with RDX in the gas phase. Although there is no hydrazine formed in the decomposition of GUzT, amines formed in the decomposition of GUzT react with aldehydes, formed in the decomposition of RDX, resulting in an increased reaction rate of RDX in the presence of GUzT. However, GUzT is not an effective burning-rate modifier because its decomposition does not alter the initial gas-phase decomposition of RDX. The decomposition of GUzT occurs at temperatures above the melting point of RDX. Therefore, the decomposition of GUzT affects reactions that are dominant in the liquid phase of RDX. Although GUzT is not an effective burning-rate modifier, features of its decomposition where the reaction between amines formed in the decomposition of GUzT react with the aldehydes, formed in the decomposition of RDX, may have implications from an insensitive-munitions perspective.

Insight into litter decomposition driven by nutrient demands of symbiosis system through the hypha bridge of arbuscular mycorrhizal fungi.

PubMed

Kong, Xiangshi; Jia, Yanyan; Song, Fuqiang; Tian, Kai; Lin, Hong; Bei, Zhanlin; Jia, Xiuqin; Yao, Bei; Guo, Peng; Tian, Xingjun

2018-02-01

Arbuscular mycorrhizal fungi (AMF) play an important role in litter decomposition. This study investigated how soil nutrient level affected the process. Results showed that AMF colonization had no significant effect on litter decomposition under normal soil nutrient conditions. However, litter decomposition was accelerated significantly under lower nutrient conditions. Soil microbial biomass in decomposition system was significantly increased. Especially, in moderate lower nutrient treatment (condition of half-normal soil nutrient), litters exhibited the highest decomposition rate, AMF hypha revealed the greatest density, and enzymes (especially nitrate reductase) showed the highest activities as well. Meanwhile, the immobilization of nitrogen (N) in the decomposing litter remarkably decreased. Our results suggested that the roles AMF played in ecosystem were largely affected by soil nutrient levels. At normal soil nutrient level, AMF exhibited limited effects in promoting decomposition. When soil nutrient level decreased, the promoting effect of AMF on litter decomposition began to appear, especially on N mobilization. However, under extremely low nutrient conditions, AMF showed less influence on decomposition and may even compete with decomposer microorganisms for nutrients.

Seasonal variation of carcass decomposition and gravesoil chemistry in a cold (Dfa) climate.

PubMed

Meyer, Jessica; Anderson, Brianna; Carter, David O

2013-09-01

It is well known that temperature significantly affects corpse decomposition. Yet relatively few taphonomy studies investigate the effects of seasonality on decomposition. Here, we propose the use of the Köppen-Geiger climate classification system and describe the decomposition of swine (Sus scrofa domesticus) carcasses during the summer and winter near Lincoln, Nebraska, USA. Decomposition was scored, and gravesoil chemistry (total carbon, total nitrogen, ninhydrin-reactive nitrogen, ammonium, nitrate, and soil pH) was assessed. Gross carcass decomposition in summer was three to seven times greater than in winter. Initial significant changes in gravesoil chemistry occurred following approximately 320 accumulated degree days, regardless of season. Furthermore, significant (p < 0.05) correlations were observed between ammonium and pH (positive correlation) and between nitrate and pH (negative correlation). We hope that future decomposition studies employ the Köppen-Geiger climate classification system to understand the seasonality of corpse decomposition, to validate taphonomic methods, and to facilitate cross-climate comparisons of carcass decomposition. © 2013 American Academy of Forensic Sciences.

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.

Climate fails to predict wood decomposition at regional scales

NASA Astrophysics Data System (ADS)

Bradford, Mark A.; Warren, Robert J., II; Baldrian, Petr; Crowther, Thomas W.; Maynard, Daniel S.; Oldfield, Emily E.; Wieder, William R.; Wood, Stephen A.; King, Joshua R.

2014-07-01

Decomposition of organic matter strongly influences ecosystem carbon storage. In Earth-system models, climate is a predominant control on the decomposition rates of organic matter. This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on mean responses can be irrelevant and misleading. We test whether climate controls on the decomposition rate of dead wood--a carbon stock estimated to represent 73 +/- 6 Pg carbon globally--are sensitive to the spatial scale from which they are inferred. We show that the common assumption that climate is a predominant control on decomposition is supported only when local-scale variation is aggregated into mean values. Disaggregated data instead reveal that local-scale factors explain 73% of the variation in wood decomposition, and climate only 28%. Further, the temperature sensitivity of decomposition estimated from local versus mean analyses is 1.3-times greater. Fundamental issues with mean correlations were highlighted decades ago, yet mean climate-decomposition relationships are used to generate simulations that inform management and adaptation under environmental change. Our results suggest that to predict accurately how decomposition will respond to climate change, models must account for local-scale factors that control regional dynamics.

Detecting Spatio-Temporal Modes in Multivariate Data by Entropy Field Decomposition

PubMed Central

Frank, Lawrence R.; Galinsky, Vitaly L.

2016-01-01

A new data analysis method that addresses a general problem of detecting spatio-temporal variations in multivariate data is presented. The method utilizes two recent and complimentary general approaches to data analysis, information field theory (IFT) and entropy spectrum pathways (ESP). Both methods reformulate and incorporate Bayesian theory, thus use prior information to uncover underlying structure of the unknown signal. Unification of ESP and IFT creates an approach that is non-Gaussian and non-linear by construction and is found to produce unique spatio-temporal modes of signal behavior that can be ranked according to their significance, from which space-time trajectories of parameter variations can be constructed and quantified. Two brief examples of real world applications of the theory to the analysis of data bearing completely different, unrelated nature, lacking any underlying similarity, are also presented. The first example provides an analysis of resting state functional magnetic resonance imaging (rsFMRI) data that allowed us to create an efficient and accurate computational method for assessing and categorizing brain activity. The second example demonstrates the potential of the method in the application to the analysis of a strong atmospheric storm circulation system during the complicated stage of tornado development and formation using data recorded by a mobile Doppler radar. Reference implementation of the method will be made available as a part of the QUEST toolkit that is currently under development at the Center for Scientific Computation in Imaging. PMID:27695512

Brain shaving: adaptive detection for brain PET data

NASA Astrophysics Data System (ADS)

Grecchi, Elisabetta; Doyle, Orla M.; Bertoldo, Alessandra; Pavese, Nicola; Turkheimer, Federico E.

2014-05-01

The intricacy of brain biology is such that the variation of imaging end-points in health and disease exhibits an unpredictable range of spatial distributions from the extremely localized to the very diffuse. This represents a challenge for the two standard approaches to analysis, the mass univariate and the multivariate that exhibit either strong specificity but not as good sensitivity (the former) or poor specificity and comparatively better sensitivity (the latter). In this work, we develop an analytical methodology for positron emission tomography that operates an extraction (‘shaving’) of coherent patterns of signal variation while maintaining control of the type I error. The methodology operates two rotations on the image data, one local using the wavelet transform and one global using the singular value decomposition. The control of specificity is obtained by using the gap statistic that selects, within each eigenvector, a subset of significantly coherent elements. Face-validity of the algorithm is demonstrated using a paradigmatic data-set with two radiotracers, [11C]-raclopride and [11C]-(R)-PK11195, measured on the same Huntington's disease patients, a disorder with a genetic based diagnosis. The algorithm is able to detect the two well-known separate but connected processes of dopamine neuronal loss (localized in the basal ganglia) and neuroinflammation (diffusive around the whole brain). These processes are at the two extremes of the distributional envelope, one being very sparse and the latter being perfectly Gaussian and they are not adequately detected by the univariate and the multivariate approaches.

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

PubMed

Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

2018-05-01

Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

Effect of petroleum on decomposition of shrub-grass litters in soil in Northern Shaanxi of China.

PubMed

Zhang, Xiaoxi; Liu, Zengwen; Yu, Qi; Luc, Nhu Trung; Bing, Yuanhao; Zhu, Bochao; Wang, Wenxuan

2015-07-01

The impacts of petroleum contamination on the litter decomposition of shrub-grass land would directly influence nutrient cycling, and the stability and function of ecosystem. Ten common shrub and grass species from Yujiaping oil deposits were studied. Litters from these species were placed into litterbags and buried in petroleum-contaminated soil with 3 levels of contamination (slight, moderate and serious pollution with petroleum concentrations of 15, 30 and 45 g/kg, respectively). A decomposition experiment was then conducted in the lab to investigate the impacts of petroleum contamination on litter decomposition rates. Slight pollution did not inhibit the decomposition of any litters and significantly promoted the litter decomposition of Hippophae rhamnoides, Caragana korshinskii, Amorpha fruticosa, Ziziphus jujuba var. spinosa, Periploca sepium, Medicago sativa and Bothriochloa ischaemum. Moderate pollution significantly inhibited litter decomposition of M. sativa, Coronilla varia, Artemisia vestita and Trrifolium repens and significantly promoted the litter decomposition of C. korshinskii, Z. jujuba var. spinosa and P. sepium. Serious pollution significantly inhibited the litter decomposition of H. rhamnoides, A. fruticosa, B. ischaemum and A. vestita and significantly promoted the litter decomposition of Z. jujuba var. spinosa, P. sepium and M. sativa. In addition, the impacts of petroleum contamination did not exhibit a uniform increase or decrease as petroleum concentration increased. Inhibitory effects of petroleum on litter decomposition may hinder the substance cycling and result in the degradation of plant communities in contaminated areas. Copyright © 2015. Published by Elsevier B.V.

Decomposition patterns of buried remains at different intervals in the Central Highveld region of South Africa.

PubMed

Marais-Werner, A; Myburgh, J; Meyer, A; Nienaber, W C; Steyn, M

2017-07-01

Burial of remains is an important factor when one attempts to establish the post-mortem interval as it reduces, and in extreme cases, excludes oviposition by Diptera species. This in turn leads to modification of the decomposition process. The aim of this study was to record decomposition patterns of buried remains using a pig model. The pattern of decomposition was evaluated at different intervals and recorded according to existing guidelines. In order to contribute to our knowledge on decomposition in different settings, a quantifiable approach was followed. Results indicated that early stages of decomposition occurred rapidly for buried remains within 7-33 days. Between 14 and 33 days, buried pigs displayed common features associated with the early to middle stages of decomposition, such as discoloration and bloating. From 33 to 90 days advanced decomposition manifested on the remains, and pigs then reached a stage of advanced decomposition where little change was observed in the next ±90-183 days after interment. Throughout this study, total body scores remained higher for surface remains. Overall, buried pigs followed a similar pattern of decomposition to those of surface remains, although at a much slower rate when compared with similar post-mortem intervals in surface remains. In this study, the decomposition patterns and rates of buried remains were mostly influenced by limited insect activity and adipocere formation which reduces the rate of decay in a conducive environment (i.e. burial in soil).

Residue decomposition of submodel of WEPS

USDA-ARS?s Scientific Manuscript database

The Residue Decomposition submodel of the Wind Erosion Prediction System (WEPS) simulates the decrease in crop residue biomass due to microbial activity. The decomposition process is modeled as a first-order reaction with temperature and moisture as driving variables. Decomposition is a function of ...

Short-term standard litter decomposition across three different ecosystems in middle taiga zone of West Siberia

NASA Astrophysics Data System (ADS)

Filippova, Nina V.; Glagolev, Mikhail V.

2018-03-01

The method of standard litter (tea) decomposition was implemented to compare decomposition rate constants (k) between different peatland ecosystems and coniferous forests in the middle taiga zone of West Siberia (near Khanty-Mansiysk). The standard protocol of TeaComposition initiative was used to make the data usable for comparisons among different sites and zonobiomes worldwide. This article sums up the results of short-term decomposition (3 months) on the local scale. The values of decomposition rate constants differed significantly between three ecosystem types: it was higher in forest compared to bogs, and treed bogs had lower decomposition constant compared to Sphagnum lawns. In general, the decomposition rate constants were close to ones reported earlier for similar climatic conditions and habitats.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

Sparse and redundant representations for inverse problems and recognition

NASA Astrophysics Data System (ADS)

Patel, Vishal M.

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.

Development Of Polarimetric Decomposition Techniques For Indian Forest Resource Assessment Using Radar Imaging Satellite (Risat-1) Images

NASA Astrophysics Data System (ADS)

Sridhar, J.

2015-12-01

The focus of this work is to examine polarimetric decomposition techniques primarily focussed on Pauli decomposition and Sphere Di-Plane Helix (SDH) decomposition for forest resource assessment. The data processing methods adopted are Pre-processing (Geometric correction and Radiometric calibration), Speckle Reduction, Image Decomposition and Image Classification. Initially to classify forest regions, unsupervised classification was applied to determine different unknown classes. It was observed K-means clustering method gave better results in comparison with ISO Data method.Using the algorithm developed for Radar Tools, the code for decomposition and classification techniques were applied in Interactive Data Language (IDL) and was applied to RISAT-1 image of Mysore-Mandya region of Karnataka, India. This region is chosen for studying forest vegetation and consists of agricultural lands, water and hilly regions. Polarimetric SAR data possess a high potential for classification of earth surface.After applying the decomposition techniques, classification was done by selecting region of interests andpost-classification the over-all accuracy was observed to be higher in the SDH decomposed image, as it operates on individual pixels on a coherent basis and utilises the complete intrinsic coherent nature of polarimetric SAR data. Thereby, making SDH decomposition particularly suited for analysis of high-resolution SAR data. The Pauli Decomposition represents all the polarimetric information in a single SAR image however interpretation of the resulting image is difficult. The SDH decomposition technique seems to produce better results and interpretation as compared to Pauli Decomposition however more quantification and further analysis are being done in this area of research. The comparison of Polarimetric decomposition techniques and evolutionary classification techniques will be the scope of this work.

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

Gill, Beant S.; Lin, Jeff F.; Krivak, Thomas C.

Purpose: To utilize the National Cancer Data Base to evaluate trends in brachytherapy and alternative radiation therapy utilization in the treatment of cervical cancer, to identify associations with outcomes between the various radiation therapy modalities. Methods and Materials: Patients with International Federation of Gynecology and Obstetrics stage IIB-IVA cervical cancer in the National Cancer Data Base who received treatment from January 2004 to December 2011 were analyzed. Overall survival was estimated by the Kaplan-Meier method. Univariate and multivariable analyses were performed to identify factors associated with type of boost radiation modality used and its impact on survival. Results: A totalmore » of 7654 patients had information regarding boost modality. A predominant proportion of patients were Caucasian (76.2%), had stage IIIB (48.9%) disease with squamous (82.0%) histology, were treated at academic/research centers (47.7%) in the South (34.8%), and lived 0 to 5 miles (27.9%) from the treating facility. A majority received brachytherapy (90.3%). From 2004 to 2011, brachytherapy use decreased from 96.7% to 86.1%, whereas intensity modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) use increased from 3.3% to 13.9% in the same period (P<.01). Factors associated with decreased brachytherapy utilization included older age, stage IVA disease, smaller tumor size, later year of diagnosis, lower-volume treatment centers, and facility type. After controlling for significant factors from survival analyses, IMRT or SBRT boost resulted in inferior overall survival (hazard ratio, 1.86; 95% confidence interval, 1.35-2.55; P<.01) as compared with brachytherapy. In fact, the survival detriment associated with IMRT or SBRT boost was stronger than that associated with excluding chemotherapy (hazard ratio, 1.61′ 95% confidence interval, 1.27-2.04′ P<.01). Conclusions: Consolidation brachytherapy is a critical treatment component for locally advanced cervical cancer; however, there has been declining utilization of brachytherapy. Increased use of IMRT and SBRT boost coupled with increased mortality risk should raise concerns about utilizing these approaches over brachytherapy.« less

Treatment deintensification in human papillomavirus-positive oropharynx cancer: Outcomes from the National Cancer Data Base.

PubMed

Cheraghlou, Shayan; Yu, Phoebe K; Otremba, Michael D; Park, Henry S; Bhatia, Aarti; Zogg, Cheryl K; Mehra, Saral; Yarbrough, Wendell G; Judson, Benjamin L

2018-02-15

The growing epidemic of human papillomavirus-positive (HPV+) oropharyngeal cancer and the favorable prognosis of this disease etiology have led to a call for deintensified treatment for some patients with HPV+ cancers. One of the proposed methods of treatment deintensification is the avoidance of chemotherapy concurrent with definitive/adjuvant radiotherapy. To the authors' knowledge, the safety of this form of treatment de-escalation is unknown and the current literature in this area is sparse. The authors investigated outcomes after various treatment combinations stratified by American Joint Committee on Cancer (AJCC) eighth edition disease stage using patients from the National Cancer Data Base. A retrospective study of 4443 patients with HPV+ oropharyngeal cancer in the National Cancer Data Base was conducted. Patients were stratified into AJCC eighth edition disease stage groups. Multivariate Cox regressions as well as univariate Kaplan-Meier analyses were conducted. For patients with stage I disease, treatment with definitive radiotherapy was associated with diminished survival compared with chemoradiotherapy (hazard ratio [HR], 1.798; P = .029), surgery with adjuvant radiotherapy (HR, 2.563; P = .002), or surgery with adjuvant chemoradiotherapy (HR, 2.427; P = .001). For patients with stage II disease, compared with treatment with chemoradiotherapy, patients treated with a single-modality (either surgery [HR, 2.539; P = .009] or radiotherapy [HR, 2.200; P = .030]) were found to have poorer survival. Among patients with stage III disease, triple-modality therapy was associated with improved survival (HR, 0.518; P = .024) compared with treatment with chemoradiotherapy. Deintensification of treatment from chemoradiotherapy to radiotherapy or surgery alone in cases of HPV+ AJCC eighth edition stage I or stage II disease may compromise patient safety. Treatment intensification to triple-modality therapy for patients with stage III disease may improve survival in this group. Cancer 2018;124:717-26. © 2017 American Cancer Society. © 2017 American Cancer Society.

Influence of aging on thermal and vibratory thresholds of quantitative sensory testing.

PubMed

Lin, Yea-Huey; Hsieh, Song-Chou; Chao, Chi-Chao; Chang, Yang-Chyuan; Hsieh, Sung-Tsang

2005-09-01

Quantitative sensory testing has become a common approach to evaluate thermal and vibratory thresholds in various types of neuropathies. To understand the effect of aging on sensory perception, we measured warm, cold, and vibratory thresholds by performing quantitative sensory testing on a population of 484 normal subjects (175 males and 309 females), aged 48.61 +/- 14.10 (range 20-86) years. Sensory thresholds of the hand and foot were measured with two algorithms: the method of limits (Limits) and the method of level (Level). Thresholds measured by Limits are reaction-time-dependent, while those measured by Level are independent of reaction time. In addition, we explored (1) the correlations of thresholds between these two algorithms, (2) the effect of age on differences in thresholds between algorithms, and (3) differences in sensory thresholds between the two test sites. Age was consistently and significantly correlated with sensory thresholds of all tested modalities measured by both algorithms on multivariate regression analysis compared with other factors, including gender, body height, body weight, and body mass index. When thresholds were plotted against age, slopes differed between sensory thresholds of the hand and those of the foot: for the foot, slopes were steeper compared with those for the hand for each sensory modality. Sensory thresholds of both test sites measured by Level were highly correlated with those measured by Limits, and thresholds measured by Limits were higher than those measured by Level. Differences in sensory thresholds between the two algorithms were also correlated with age: thresholds of the foot were higher than those of the hand for each sensory modality. This difference in thresholds (measured with both Level and Limits) between the hand and foot was also correlated with age. These findings suggest that age is the most significant factor in determining sensory thresholds compared with the other factors of gender and anthropometric parameters, and this provides a foundation for investigating the neurobiologic significance of aging on the processing of sensory stimuli.

Effect of dialysis modality on frailty phenotype, disability, and health-related quality of life in maintenance dialysis patients.

PubMed

Kang, Seok Hui; Do, Jun Young; Lee, So-Young; Kim, Jun Chul

2017-01-01

Health-related quality of life (HRQoL) surveys are needed to evaluate regional and ethnic specificies. The aim of the present study was to evaluate the differences in HRQoL, frailty, and disability according to dialysis modality in the Korean population. We enrolled relatively stable maintenance dialysis patients. A total of 1,616 patients were recruited into our study. The demographic and laboratory data collected at enrollment included age, sex, comorbidities, frailty, disability, and HRQoL scales. A total of 1,250 and 366 participants underwent hemodialysis (HD) and peritoneal dialysis (PD), respectively. The numbers of participants with pre-frailty and frailty were 578 (46.2%) and 422 (33.8%) in HD patients, and 165 (45.1%) and 137 (37.4%) in PD patients, respectively (P = 0.349). Participants with a disability included 195 (15.6%) HD patients and 109 (29.8%) PD patients (P < 0.001). On multivariate analysis, the mean physical component scale (PCS) and mental component scale (MCS), symptom/problems, and sleep scores were higher in HD patients than in PD patients. Cox regression analyses showed that an increased PCS in both HD and PD patients was positively associated with patient survival and first hospitalization-free survival. An increased MCS in both HD and PD patients was positively associated with first hospitalization-free survival only. There was no significant difference in frailty between patients treated with the two dialysis modalities; however, disability was more common in PD patients than in HD patients. The MCS and PCS were more favorable in HD patients than in PD patients. Symptom/problems, sleep, quality of social interaction, and social support were more favorable in HD patients than in PD patients; however, patient satisfaction and dialysis staff encouragement were more favorable in PD patients than in HD patients.

Effect of dialysis modality on frailty phenotype, disability, and health-related quality of life in maintenance dialysis patients

PubMed Central

Kang, Seok Hui; Do, Jun Young; Lee, So-Young; Kim, Jun Chul

2017-01-01

Background Health-related quality of life (HRQoL) surveys are needed to evaluate regional and ethnic specificies. The aim of the present study was to evaluate the differences in HRQoL, frailty, and disability according to dialysis modality in the Korean population. Patients and methods We enrolled relatively stable maintenance dialysis patients. A total of 1,616 patients were recruited into our study. The demographic and laboratory data collected at enrollment included age, sex, comorbidities, frailty, disability, and HRQoL scales. Results A total of 1,250 and 366 participants underwent hemodialysis (HD) and peritoneal dialysis (PD), respectively. The numbers of participants with pre-frailty and frailty were 578 (46.2%) and 422 (33.8%) in HD patients, and 165 (45.1%) and 137 (37.4%) in PD patients, respectively (P = 0.349). Participants with a disability included 195 (15.6%) HD patients and 109 (29.8%) PD patients (P < 0.001). On multivariate analysis, the mean physical component scale (PCS) and mental component scale (MCS), symptom/problems, and sleep scores were higher in HD patients than in PD patients. Cox regression analyses showed that an increased PCS in both HD and PD patients was positively associated with patient survival and first hospitalization–free survival. An increased MCS in both HD and PD patients was positively associated with first hospitalization–free survival only. Conclusion There was no significant difference in frailty between patients treated with the two dialysis modalities; however, disability was more common in PD patients than in HD patients. The MCS and PCS were more favorable in HD patients than in PD patients. Symptom/problems, sleep, quality of social interaction, and social support were more favorable in HD patients than in PD patients; however, patient satisfaction and dialysis staff encouragement were more favorable in PD patients than in HD patients. PMID:28467472

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming

DOE PAGES

Feng, Wenting; Liang, Junyi; Hale, Lauren E.; ...

2017-06-09

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon–climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming.more » Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.« less

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming

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

Feng, Wenting; Liang, Junyi; Hale, Lauren E.

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon–climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming.more » Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.« less

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming.

PubMed

Feng, Wenting; Liang, Junyi; Hale, Lauren E; Jung, Chang Gyo; Chen, Ji; Zhou, Jizhong; Xu, Minggang; Yuan, Mengting; Wu, Liyou; Bracho, Rosvel; Pegoraro, Elaine; Schuur, Edward A G; Luo, Yiqi

2017-11-01

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon-climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming. Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO 2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change. © 2017 John Wiley & Sons Ltd.

Factors Influencing Decomposition of Surface Litter from the Cerrado in Central Brazil

EPA Science Inventory

It is well-established that light-induced decomposition can accelerate the decomposition of the organic matter in aquatic environments. In this study, using the production of carbon monoxide as an indicator of decomposition, we investigated the wavelength dependence of the photod...

Scoring of Decomposition: A Proposed Amendment to the Method When Using a Pig Model for Human Studies.

PubMed

Keough, Natalie; Myburgh, Jolandie; Steyn, Maryna

2017-07-01

Decomposition studies often use pigs as proxies for human cadavers. However, differences in decomposition sequences/rates relative to humans have not been scientifically examined. Descriptions of five main decomposition stages (humans) were developed and refined by Galloway and later by Megyesi. However, whether these changes/processes are alike in pigs is unclear. Any differences can have significant effects when pig models are used for human PMI estimation. This study compared human decomposition models to the changes observed in pigs. Twenty pigs (50-90 kg) were decomposed over five months and decompositional features recorded. Total body scores (TBS) were calculated. Significant differences were observed during early decomposition between pigs and humans. An amended scoring system to be used in future studies was developed. Standards for PMI estimation derived from porcine models may not directly apply to humans and may need adjustment. Porcine models, however, remain valuable to study variables influencing decomposition. © 2016 American Academy of Forensic Sciences.

Theoretical studies of the decomposition mechanisms of 1,2,4-butanetriol trinitrate.

PubMed

Pei, Liguan; Dong, Kehai; Tang, Yanhui; Zhang, Bo; Yu, Chang; Li, Wenzuo

2017-12-06

Density functional theory (DFT) and canonical variational transition-state theory combined with a small-curvature tunneling correction (CVT/SCT) were used to explore the decomposition mechanisms of 1,2,4-butanetriol trinitrate (BTTN) in detail. The results showed that the γ-H abstraction reaction is the initial pathway for autocatalytic BTTN decomposition. The three possible hydrogen atom abstraction reactions are all exothermic. The rate constants for autocatalytic BTTN decomposition are 3 to 10 40 times greater than the rate constants for the two unimolecular decomposition reactions (O-NO 2 cleavage and HONO elimination). The process of BTTN decomposition can be divided into two stages according to whether the NO 2 concentration is above a threshold value. HONO elimination is the main reaction channel during the first stage because autocatalytic decomposition requires NO 2 and the concentration of NO 2 is initially low. As the reaction proceeds, the concentration of NO 2 gradually increases; when it exceeds the threshold value, the second stage begins, with autocatalytic decomposition becoming the main reaction channel.

Decomposition rate of Rhizopora stylosa litter in Tanjung Rejo Village, Deli Serdang Regency, North Sumatera Province

NASA Astrophysics Data System (ADS)

Rambey, R.; Delvian; Sianturi, S. D.

2018-02-01

Research on the decomposition rate of Rhizopora stylosa litter in Tanjung Rejo village, Deli Serdang Regency, North Sumatera Province was conducted from September 2016 to May 2017. The objectives of this research were (1) to measure the decomposition rate of Rhizophora stylosa litter and (2) to determine the type of functional fungi in decomposition of litter. R. stylosa litter decomposition is characterized by a reduction in litter weight per observation period. Decomposition rate tended to increase every week, which was from 0.238 in the seventh day and reached 0.302 on the fiftysixthth day. The decomposition rate of R. stylosa litter of leaf was high with the value of k per day > 0,01 caused by macrobentos and fungi, and also the decomposition of R. stylosa litter conducted in the pond area which is classified far from the coast. Therefore, to enable the high population of fungi which affect the decomposition rate of the litter. The types of fungi decomposers were: Aspergillus sp.-1, Aspergillus sp.-2, Aspergillus sp.-3, Rhizophus sp.-1., Rhizophus sp.-2, Penicillium sp., Syncephalastrum sp. and Fusarium sp.

Through-wall image enhancement using fuzzy and QR decomposition.

PubMed

Riaz, Muhammad Mohsin; Ghafoor, Abdul

2014-01-01

QR decomposition and fuzzy logic based scheme is proposed for through-wall image enhancement. QR decomposition is less complex compared to singular value decomposition. Fuzzy inference engine assigns weights to different overlapping subspaces. Quantitative measures and visual inspection are used to analyze existing and proposed techniques.