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- SPRINGER-VERLAG BERLIN
- Biblioteca Digital da Unicamp
- Associação Brasileira de Divulgação Científica
- Associação Brasileira de Saúde Coletiva
- National Academy of Sciences
- Public Library of Science
- Oxford University Press
- Elsevier Science BV
- Universidade Cornell
- Associação Brasileira de Pós -Graduação em Saúde Coletiva
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## Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Fonte: SPRINGER-VERLAG BERLIN
Publicador: SPRINGER-VERLAG BERLIN

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

95.78%

#Dependent Component Analysis#Independent Component Analysis#Mixture of signals#Recover the source signals#Signal of interest#fMRI#GLM#ICA#TIME-SERIES#FUNCTIONAL MRI#INFORMATION

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM)...

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## Reconhecimento e classificação de fáceis geológicas através da análise de componentes independentes; Recognition and classification of geological facies based on independent component analysis

Fonte: Biblioteca Digital da Unicamp
Publicador: Biblioteca Digital da Unicamp

Tipo: Dissertação de Mestrado
Formato: application/pdf

Publicado em 02/12/2010
Português

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#Análise multivariada#Fácies (Geologia)#Reconhecimento de padrões#Classificação#Multivariate analysis#Facies (Geology)#Recognition of patterns#Classification

O uso método de análise multivariada ICA (Análise de Componentes Independentes), mais o método K-NN (K-vizinhos mais Próximos) aplicados em dados de poços e em dados sísmicos buscando classificar fácies geológicas e suas características. Esses dois métodos foram aplicados em dados retirados do Campo de Namorado, na Bacia de Campos, Brasil. A ICA encontra as componentes independentes dos dados, que quando treinadas pelo método K-NN para reconhecer padrões nos dados, predizem fácies geológicas e outras informações sobre as rochas, como as características de reservatório. Essas componentes independentes configuram uma nova opção de interpretação das informações disponíveis, pois nessas novas variáveis, o espaço de análise não apresenta dimensões dependentes e exclui informações repetidas ou dúbias da interpretação dos resultados. Além disso, a maior parte da informação é resumida em poucas dimensões, resultando em uma possível redução de variáveis referentes ao problema. Um abundante número de testes foi feito procurando a taxa de sucesso desse método. Como taxa de sucesso, é compreendida a divisão do número de predições corretas dividido pelo número total de tentativas. O que se observa é uma taxa de sucesso alta...

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## EEG spike source localization before and after surgery for temporal lobe epilepsy: a BOLD EEG-fMRI and independent component analysis study

Fonte: Associação Brasileira de Divulgação Científica
Publicador: Associação Brasileira de Divulgação Científica

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/06/2009
Português

Relevância na Pesquisa

45.65%

Simultaneous measurements of EEG-functional magnetic resonance imaging (fMRI) combine the high temporal resolution of EEG with the distinctive spatial resolution of fMRI. The purpose of this EEG-fMRI study was to search for hemodynamic responses (blood oxygen level-dependent - BOLD responses) associated with interictal activity in a case of right mesial temporal lobe epilepsy before and after a successful selective amygdalohippocampectomy. Therefore, the study found the epileptogenic source by this noninvasive imaging technique and compared the results after removing the atrophied hippocampus. Additionally, the present study investigated the effectiveness of two different ways of localizing epileptiform spike sources, i.e., BOLD contrast and independent component analysis dipole model, by comparing their respective outcomes to the resected epileptogenic region. Our findings suggested a right hippocampus induction of the large interictal activity in the left hemisphere. Although almost a quarter of the dipoles were found near the right hippocampus region, dipole modeling resulted in a widespread distribution, making EEG analysis too weak to precisely determine by itself the source localization even by a sophisticated method of analysis such as independent component analysis. On the other hand...

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## The complex dynamics of diabetes modeled as a fractal complex-adaptive-system (FCAS)

Fonte: Associação Brasileira de Saúde Coletiva
Publicador: Associação Brasileira de Saúde Coletiva

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/12/1998
Português

Relevância na Pesquisa

45.72%

#Power law#Fractals#Diabetes mellitus#insulin-dependent#Diabetes mellitus, non-insulin dependent#Nonlinear dynamics#Principal component analysis#Complex-adaptive-system modeling#Allometry

An approach is suggested in this paper that has successfully been applied in physics, ecology, and the biomedical sciences. This is called fractal-complex-adaptive-system (FCAS) modeling. The objective of this type of analysis is to reconstruct the dynamics of the pathological process that has been leading to the disease. Diabetes, a complexdisease, has been used to test the methodology. Biometrical analyses were undertaken on subjects diagnosed with overt diabetes (hereafter called IDDM), chemical diabetes (NIDDM), and a group of normal subjects. The studied variables were plasma glucose, insulin concentration, and insulin sensitivity. FCAS modeling consists in fitting a power-law function to the bivariate lognormal distribution of the variables. The power-law exponent is estimated by principal component analysis (PCA). Analyses have shown that glucose disposal can be considered a fractal process, thereby implying a complex hierarchy of interacting scales and mechanisms in glucose handling. The first principal component represents quantitative glucose disposal, and the second component is compatible with insulin efficiency. PCA further retrieved distinct ongoing pathological processes within clinical groups of subjects. The IDDM insulin production defect had a high (absolute value) exponent of -3.5 that confirms a crude defect scanning the whole fractal hierarchy. Definite insulin resistance has been detected in clinically normal subjects with a low exponent of -0.5...

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## Principal component analysis of the pH-dependent conformational transitions of bovine β-lactoglobulin monitored by heteronuclear NMR

Fonte: National Academy of Sciences
Publicador: National Academy of Sciences

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.68%

To clarify the pH-dependent conformational transitions of proteins, we propose an approach in which structural changes monitored by heteronuclear sequential quantum correlation (HSQC) spectroscopy were analyzed by using a principal component analysis (PCA). We use bovine β-lactoglobulin, a protein widely used in protein folding studies, as a target. First, we measured HSQC spectra at various pH values and subjected them to a PCA. The analysis revealed three apparent transitions with pKa values of 2.9, 4.9, and 6.8, consistent with previous reports using different methods. Next, Gdn-HCl-induced unfolding was examined by measuring tryptophan fluorescence at various pH values. Between pH 2 and 8, β-lactoglobulin exhibited a number of structural transitions as well as changes in stability represented by the free energy change of unfolding, ΔGU. By combining the NMR and fluorescence results, the change in ΔGU was suggested to result from the decreased pKa of some acidic residues. Notably, the native state at neutral pH is destabilized by deprotonation of Glu-89, leading to an increase in the relative population of the intermediate. Thus, the PCA of pH-dependent HSQC spectra provides a more comprehensive understanding of the stability and function of proteins.

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## On the application of (topographic) independent and tree-dependent component analysis for the examination of DCE-MRI data

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Publicado em /07/2009
Português

Relevância na Pesquisa

65.7%

In this contribution we investigate the applicability of different methods from the field of independent component analysis (ICA) for the examination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data from breast cancer research. DCE-MRI has evolved in recent years as a powerful complement to X-ray based mammography for breast cancer diagnosis and monitoring. In DCE-MRI the time related development of the signal intensity after the administration of a contrast agent can provide valuable information about tissue states and characteristics. To this end, techniques related to ICA, offer promising options for data integration and feature extraction at voxel level. In order to evaluate the applicability of ICA, topographic ICA and tree-dependent component analysis (TCA), these methods are applied to twelve clinical cases from breast cancer research with a histopathologically confirmed diagnosis. For ICA these experiments are complemented by a reliability analysis of the estimated components. The outcome of all algorithms is quantitatively evaluated by means of receiver operating characteristics (ROC) statistics whereas the results for specific data sets are discussed exemplarily in terms of reification, score-plots and score images.

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## Na+/K+-ATPase Inhibition Partially Mimics the Ethanol-Induced Increase of the Golgi Cell-Dependent Component of the Tonic GABAergic Current in Rat Cerebellar Granule Cells

Fonte: Public Library of Science
Publicador: Public Library of Science

Tipo: Artigo de Revista Científica

Publicado em 31/01/2013
Português

Relevância na Pesquisa

45.73%

Cerebellar granule cells (CGNs) are one of many neurons that express phasic and tonic GABAergic conductances. Although it is well established that Golgi cells (GoCs) mediate phasic GABAergic currents in CGNs, their role in mediating tonic currents in CGNs (CGN-Itonic) is controversial. Earlier studies suggested that GoCs mediate a component of CGN-Itonic that is present only in preparations from immature rodents. However, more recent studies have detected a GoC-dependent component of CGN-Itonic in preparations of mature rodents. In addition, acute exposure to ethanol was shown to potentiate the GoC component of CGN-Itonic and to induce a parallel increase in spontaneous inhibitory postsynaptic current frequency at CGNs. Here, we tested the hypothesis that these effects of ethanol on GABAergic transmission in CGNs are mediated by inhibition of the Na+/K+-ATPase. We used whole-cell patch-clamp electrophysiology techniques in cerebellar slices of male rats (postnatal day 23–30). Under these conditions, we reliably detected a GoC-dependent component of CGN-Itonic that could be blocked with tetrodotoxin. Further analysis revealed a positive correlation between basal sIPSC frequency and the magnitude of the GoC-dependent component of CGN-Itonic. Inhibition of the Na+/K+-ATPase with a submaximal concentration of ouabain partially mimicked the ethanol-induced potentiation of both phasic and tonic GABAergic currents in CGNs. Modeling studies suggest that selective inhibition of the Na+/K+-ATPase in GoCs can...

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## Spatiotemporal Segregation of Neural Response to Auditory Stimulation: An fMRI Study Using Independent Component Analysis and Frequency-Domain Analysis

Fonte: Public Library of Science
Publicador: Public Library of Science

Tipo: Artigo de Revista Científica

Publicado em 18/06/2013
Português

Relevância na Pesquisa

45.69%

Although auditory processing has been widely studied with conventional parametric methods, there have been a limited number of independent component analysis (ICA) applications in this area. The purpose of this study was to examine spatiotemporal behavior of brain networks in response to passive auditory stimulation using ICA. Continuous broadband noise was presented binaurally to 19 subjects with normal hearing. ICA was performed to segregate spatial networks, which were subsequently classified according to their temporal relation to the stimulus using power spectrum analysis. Classification of separated networks resulted in 3 stimulus-activated, 9 stimulus-deactivated, 2 stimulus-neutral (stimulus-dependent but not correlated with the stimulation timing), and 2 stimulus-unrelated (fluctuations that did not follow the stimulus cycles) components. As a result of such classification, spatiotemporal subdivisions were observed in a number of cortical structures, namely auditory, cingulate, and sensorimotor cortices, where parts of the same cortical network responded to the stimulus with different temporal patterns. The majority of the classified networks seemed to comprise subparts of the known resting-state networks (RSNs); however, they displayed different temporal behavior in response to the auditory stimulus...

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## Independent Component Analysis (ICA) of Generalized Spike Wave Discharges in fMRI: Comparison with General Linear Model-Based EEG-fMRI

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Publicado em /02/2011
Português

Relevância na Pesquisa

45.72%

Most EEG-fMRI studies in epileptic patients are analyzed using the general linear model (GLM), which assumes a known hemodynamic response function (HRF) to epileptic spikes. In contrast, independent component analysis (ICA) can extract blood-oxygenation level dependent (BOLD) responses without imposing constraints on the HRF. ICA might therefore detect responses that vary in time and shape, and that are not detected in the GLM analysis. In this study, we compared the findings of ICA and GLM analyses in 12 patients with idiopathic generalized epilepsy. Spatial ICA was used to extract independent components from the functional magnetic resonance imaging (fMRI) data. A deconvolution method identified component time courses significantly related to the generalized EEG discharges, without constraining the shape of the HRF. The results from the ICA analysis were compared to those from the GLM analysis. GLM maps and ICA maps showed significant correlation and revealed BOLD responses in the thalamus, caudate nucleus, and default mode areas. In patients with a low rate of discharges per minute, the GLM analysis detected BOLD signal changes within the thalamus and the caudate nucleus that were not revealed by the ICA. In conclusion, ICA is a viable alternative technique to GLM analyses in EEG-fMRI studies related to generalized discharges. This study demonstrated that the BOLD response largely resembles the standard HRF and that GLM analysis is adequate. However...

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## Longitudinal epigenetic and gene expression profiles analyzed by three-component analysis reveal down-regulation of genes involved in protein translation in human aging

Fonte: Oxford University Press
Publicador: Oxford University Press

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.68%

Data on biological mechanisms of aging are mostly obtained from cross-sectional study designs. An inherent disadvantage of this design is that inter-individual differences can mask small but biologically significant age-dependent changes. A serially sampled design (same individual at different time points) would overcome this problem but is often limited by the relatively small numbers of available paired samples and the statistics being used. To overcome these limitations, we have developed a new vector-based approach, termed three-component analysis, which incorporates temporal distance, signal intensity and variance into one single score for gene ranking and is combined with gene set enrichment analysis. We tested our method on a unique age-based sample set of human skin fibroblasts and combined genome-wide transcription, DNA methylation and histone methylation (H3K4me3 and H3K27me3) data. Importantly, our method can now for the first time demonstrate a clear age-dependent decrease in expression of genes coding for proteins involved in translation and ribosome function. Using analogies with data from lower organisms, we propose a model where age-dependent down-regulation of protein translation-related components contributes to extend human lifespan.

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## Characterization of human fetal cord blood steroid profiles in relation to fetal sex and mode of delivery using temperature-dependent inclusion chromatography and principal component analysis (PCA)

Fonte: Elsevier Science BV
Publicador: Elsevier Science BV

Tipo: Artigo de Revista Científica

Publicado em //2007
Português

Relevância na Pesquisa

55.66%

In the present work, human male and female fetal cord blood samples were purified, selectively extracted and separated to examine a fraction of steroids ranging from polar estetrol to relatively non-polar progesterone using solid phase extraction based on C-18 tubes and β-cyclodextrin driven temperature dependent inclusion chromatography. Resulting UV diode array chromatographic patterns revealed the presence of 27 peaks. Chromatographic patterns of UV detected steroids were analyzed using principal components analysis which revealed differences between male/female and labour/not-in-labour clusters. Quantitative analysis of nine identified steroids including: estetrol, 17β-estradiol, estrone, estriol, cortisol, cortisone, progesterone, 20greek small letter alpha-hydroxyprogesterone and 17greek small letter alpha-hydroxyprogesterone were not significantly different between males and females. Significant differences between male and female fetuses were related to as yet unidentified compounds. Four peaks were significantly different with labour which corresponded with cortisol, cortisone and two unidentified compounds. This protocol may distinguish significant differences between clinical groups that are not readily identifiable using univariate measurements of single steroids or different low molecular mass biomarkers. Moreover...

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## Overview of NPPs Component Reliability Data Collection with Regards to Time-dependent Reliability Analysis Applications - EC JRC Network on Use of Probabilistic Safety Assessments (PSA) for Evaluation of Aging Effects to the Safety of Energy Facilities - Task 4

Fonte: OPOCE
Publicador: OPOCE

Tipo: EUR - Scientific and Technical Research Reports
Formato: Printed

Português

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This report presents the state of the art of existed NPPs component reliability data collection systems which aimed to elaborate components reliability parameters to be used in Probabilistic Safety Assessements (PSA). A specific emphasis was done to the possible application of data in time-dependent reliability analysis.
The report was prepared by JRC IE in the frame of EC JRC Ageing PSA Network Task 4 activities and is based on analysis of responses of Network participants to the questionnaire.
Main conclusions and recommendations are presented in the report and they addressed to the data availability and accessibility, as well as to possible improvements of data collection and important issues to be considered in Ageing PSA Network work plan.; JRC.F.4-Nuclear design safety

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## Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/08/2015
Português

Relevância na Pesquisa

55.66%

#Computer Science - Computational Engineering, Finance, and Science#Computer Science - Learning#Computer Science - Numerical Analysis

With the increasing availability of various sensor technologies, we now have
access to large amounts of multi-block (also called multi-set,
multi-relational, or multi-view) data that need to be jointly analyzed to
explore their latent connections. Various component analysis methods have
played an increasingly important role for the analysis of such coupled data. In
this paper, we first provide a brief review of existing matrix-based (two-way)
component analysis methods for the joint analysis of such data with a focus on
biomedical applications. Then, we discuss their important extensions and
generalization to multi-block multiway (tensor) data. We show how constrained
multi-block tensor decomposition methods are able to extract similar or
statistically dependent common features that are shared by all blocks, by
incorporating the multiway nature of data. Special emphasis is given to the
flexible common and individual feature analysis of multi-block data with the
aim to simultaneously extract common and individual latent components with
desired properties and types of diversity. Illustrative examples are given to
demonstrate their effectiveness for biomedical data analysis.; Comment: 20 pages, 11 figures, Proceedings of the IEEE, 2015

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## Least Dependent Component Analysis Based on Mutual Information

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

75.73%

#Physics - Computational Physics#Computer Science - Information Theory#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Quantitative Methods

We propose to use precise estimators of mutual information (MI) to find least
dependent components in a linearly mixed signal. On the one hand this seems to
lead to better blind source separation than with any other presently available
algorithm. On the other hand it has the advantage, compared to other
implementations of `independent' component analysis (ICA) some of which are
based on crude approximations for MI, that the numerical values of the MI can
be used for:
(i) estimating residual dependencies between the output components;
(ii) estimating the reliability of the output, by comparing the pairwise MIs
with those of re-mixed components;
(iii) clustering the output according to the residual interdependencies.
For the MI estimator we use a recently proposed k-nearest neighbor based
algorithm. For time sequences we combine this with delay embedding, in order to
take into account non-trivial time correlations. After several tests with
artificial data, we apply the resulting MILCA (Mutual Information based Least
dependent Component Analysis) algorithm to a real-world dataset, the ECG of a
pregnant woman.
The software implementation of the MILCA algorithm is freely available at
http://www.fz-juelich.de/nic/cs/software; Comment: 18 pages...

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## Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 20/01/2006
Português

Relevância na Pesquisa

45.65%

#Physics - Chemical Physics#Condensed Matter - Statistical Mechanics#Computer Science - Information Theory#Mathematics - Probability#Mathematics - Statistics Theory#Physics - Computational Physics#Physics - Data Analysis, Statistics and Probability

We propose a simulated annealing algorithm (called SNICA for "stochastic
non-negative independent component analysis") for blind decomposition of linear
mixtures of non-negative sources with non-negative coefficients. The de-mixing
is based on a Metropolis type Monte Carlo search for least dependent
components, with the mutual information between recovered components as a cost
function and their non-negativity as a hard constraint. Elementary moves are
shears in two-dimensional subspaces and rotations in three-dimensional
subspaces. The algorithm is geared at decomposing signals whose probability
densities peak at zero, the case typical in analytical spectroscopy and
multivariate curve resolution. The decomposition performance on large samples
of synthetic mixtures and experimental data is much better than that of
traditional blind source separation methods based on principal component
analysis (MILCA, FastICA, RADICAL) and chemometrics techniques (SIMPLISMA, ALS,
BTEM)
The source codes of SNICA, MILCA and the MI estimator are freely available
online at http://www.fz-juelich.de/nic/cs/software

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## Likelihood Component Analysis

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 05/11/2015
Português

Relevância na Pesquisa

45.66%

Independent component analysis (ICA) is popular in many applications,
including cognitive neuroscience and signal processing. Due to computational
constraints, principal component analysis is used for dimension reduction prior
to ICA (PCA+ICA), which could remove important information. The problem is that
interesting independent components (ICs) could be mixed in several principal
components that are discarded and then these ICs cannot be recovered. To
address this issue, we propose likelihood component analysis (LCA), a novel
methodology in which dimension reduction and latent variable estimation is
achieved simultaneously by maximizing a likelihood with Gaussian and
non-Gaussian components. We present a parametric LCA model using the logistic
density and a semi-parametric LCA model using tilted Gaussians with cubic
B-splines. We implement an algorithm scalable to datasets common in
applications (e.g., hundreds of thousands of observations across hundreds of
variables with dozens of latent components). In simulations, our methods
recover latent components that are discarded by PCA+ICA methods. We apply our
method to dependent multivariate data and demonstrate that LCA is a useful data
visualization and dimension reduction tool that reveals features not apparent
from PCA or PCA+ICA. We also apply our method to an experiment from the Human
Connectome Project with state-of-the-art temporal and spatial resolution and
identify an artifact using LCA that was missed by PCA+ICA. We present
theoretical results on identifiability of the LCA model and consistency of our
estimator.

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## Principal Component Analysis of the Time- and Position-Dependent Point Spread Function of the Advanced Camera for Surveys

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/10/2007
Português

Relevância na Pesquisa

45.69%

We describe the time- and position-dependent point spread function (PSF)
variation of the Wide Field Channel (WFC) of the Advanced Camera for Surveys
(ACS) with the principal component analysis (PCA) technique. The time-dependent
change is caused by the temporal variation of the $HST$ focus whereas the
position-dependent PSF variation in ACS/WFC at a given focus is mainly the
result of changes in aberrations and charge diffusion across the detector,
which appear as position-dependent changes in elongation of the astigmatic core
and blurring of the PSF, respectively. Using >400 archival images of star
cluster fields, we construct a ACS PSF library covering diverse environments of
the $HST$ observations (e.g., focus values). We find that interpolation of a
small number ($\sim20$) of principal components or ``eigen-PSFs'' per exposure
can robustly reproduce the observed variation of the ellipticity and size of
the PSF. Our primary interest in this investigation is the application of this
PSF library to precision weak-lensing analyses, where accurate knowledge of the
instrument's PSF is crucial. However, the high-fidelity of the model judged
from the nice agreement with observed PSFs suggests that the model is
potentially also useful in other applications such as crowded field stellar
photometry...

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## Tree-dependent Component Analysis

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/12/2012
Português

Relevância na Pesquisa

65.68%

We present a generalization of independent component analysis (ICA), where
instead of looking for a linear transform that makes the data components
independent, we look for a transform that makes the data components well fit by
a tree-structured graphical model. Treating the problem as a semiparametric
statistical problem, we show that the optimal transform is found by minimizing
a contrast function based on mutual information, a function that directly
extends the contrast function used for classical ICA. We provide two
approximations of this contrast function, one using kernel density estimation,
and another using kernel generalized variance. This tree-dependent component
analysis framework leads naturally to an efficient general multivariate density
estimation technique where only bivariate density estimation needs to be
performed.; Comment: Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002)

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## Spectral Mixture Decomposition by Least Dependent Component Analysis

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

55.57%

#Physics - Data Analysis, Statistics and Probability#Computer Science - Information Theory#Physics - Chemical Physics

A recently proposed mutual information based algorithm for decomposing data
into least dependent components (MILCA) is applied to spectral analysis, namely
to blind recovery of concentrations and pure spectra from their linear
mixtures. The algorithm is based on precise estimates of mutual information
between measured spectra, which allows to assess and make use of actual
statistical dependencies between them. We show that linear filtering performed
by taking second derivatives effectively reduces the dependencies caused by
overlapping spectral bands and, thereby, assists resolving pure spectra. In
combination with second derivative preprocessing and alternating least squares
postprocessing, MILCA shows decomposition performance comparable with or
superior to specialized chemometrics algorithms. The results are illustrated on
a number of simulated and experimental (infrared and Raman) mixture problems,
including spectroscopy of complex biological materials.
MILCA is available online at http://www.fz-juelich.de/nic/cs/software; Comment: 27 pages, 7 figures, 1 table; uses elsart.cls

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## The complex dynamics of diabetes modeled as a fractal complex-adaptive-system (FCAS)

Fonte: Associação Brasileira de Pós -Graduação em Saúde Coletiva
Publicador: Associação Brasileira de Pós -Graduação em Saúde Coletiva

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/12/1998
Português

Relevância na Pesquisa

45.72%

#Power law#Fractals#Diabetes mellitus#insulin-dependent#Diabetes mellitus, non-insulin dependent#Nonlinear dynamics#Principal component analysis#Complex-adaptive-system modeling#Allometry

An approach is suggested in this paper that has successfully been applied in physics, ecology, and the biomedical sciences. This is called fractal-complex-adaptive-system (FCAS) modeling. The objective of this type of analysis is to reconstruct the dynamics of the pathological process that has been leading to the disease. Diabetes, a complexdisease, has been used to test the methodology. Biometrical analyses were undertaken on subjects diagnosed with overt diabetes (hereafter called IDDM), chemical diabetes (NIDDM), and a group of normal subjects. The studied variables were plasma glucose, insulin concentration, and insulin sensitivity. FCAS modeling consists in fitting a power-law function to the bivariate lognormal distribution of the variables. The power-law exponent is estimated by principal component analysis (PCA). Analyses have shown that glucose disposal can be considered a fractal process, thereby implying a complex hierarchy of interacting scales and mechanisms in glucose handling. The first principal component represents quantitative glucose disposal, and the second component is compatible with insulin efficiency. PCA further retrieved distinct ongoing pathological processes within clinical groups of subjects. The IDDM insulin production defect had a high (absolute value) exponent of -3.5 that confirms a crude defect scanning the whole fractal hierarchy. Definite insulin resistance has been detected in clinically normal subjects with a low exponent of -0.5...

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