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PubMedDate not supplied

Simultaneous determination of thiocyanate and salicylate by a combined UV-spectrophotometric detection principal component artificial neural network.

A modified principle component artificial neural network (PC-ANN) model is developed for simultaneous determination of thiocyanate and salycilate concentration after passing through the bulk of a liquid membrane by tri-phenyl benzyl phosphonium chloride. All calibration, and test samples data were obtained using UV-Vis spectrophotometer. In this way, a modified PC-ANN consisting of three layers of nodes was trained by combination of Bayesian-Levenberg-Marquardt as training rule. Sigmoid and liner transfer functions were used in the hidden and output layers respectively to facilitate nonlinear calibration. The model could accurately estimate the concentration of components with acceptable precision and accuracy, for mixtures. The PC-ANN model exhibits a good ability for the simultaneous determination of the thiocyanate and salycilate in concentration range 0.5 x 10(-4) mol.l(-1) up to 5.0 x 10(-4) mol.l(-1) with Root Mean square error (2.22% and 2.20%, for thiocyanate and salycilate, respectively) and high correlation coefficients (R2= 0.998 or greater). Results obtained with modified trained PC-ANN were compared with stepwise linear regression (SMLR) model. Validation of the two models shows a better ability in estimation of the modified PC-ANN as compared with the SMLR model (MSRE given are 3.12%, 6.31%.).

Neural Networks, Computer↗
PubMedDate not supplied

Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics.

Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets.

Algorithms↗
PubMed2003

Application of principal component analysis for the estimation of source of heavy metal contamination in surface sediments from the Rybnik Reservoir.

The concentrations of metals, loss of ignition and nutrient (N, P) were determined in the bottom sediments of the Rybnik Reservoir (southern Poland). The mean concentrations of the metals in the bottom sediments were: Cd 25.8 microgram/g, Cu 451.7 microgram/g, Zn 1583.4 microgram/g, Ni 71.1 microgram/g, Pb 118.6 microgram/g, Cr 129.8 microgram/g, Fe 38782 microgram/g and Mn 2018.7 microgram/g. The bottom sediments are very heavily loaded with zinc, manganese, copper, nickel, phosphorus and lead (percentage enrichment factor), and cadmium, phosphorus and zinc (index of geoaccumulation). The increase of cadmium, lead, nickel and zinc concentrations was connected with the inflow of the contaminated water of the river Ruda and long-range transport. The contamination of the reservoir with copper and manganese resulted mainly from atmospheric precipitation. The variability of the bottom sediment loading with metals during the investigations was affected in the first place by changes in the concentration of iron, but also those elements whose concentrations in the bottom sediment were elevated compared to the concentrations in shale--cadmium, nickel and lead.

Environmental Monitoring↗
PubMed2001

Oriented principal component analysis for large margin classifiers.

Large margin classifiers (such as MLPs) are designed to assign training samples with high confidence (or margin) to one of the classes. Recent theoretical results of these systems show why the use of regularisation terms and feature extractor techniques can enhance their generalisation properties. Since the optimal subset of features selected depends on the classification problem, but also on the particular classifier with which they are used, global learning algorithms for large margin classifiers that use feature extractor techniques are desired. A direct approach is to optimise a cost function based on the margin error, which also incorporates regularisation terms for controlling capacity. These terms must penalise a classifier with the largest margin for the problem at hand. Our work shows that the inclusion of a PCA term can be employed for this purpose. Since PCA only achieves an optimal discriminatory projection for some particular distribution of data, the margin of the classifier can then be effectively controlled. We also propose a simple constrained search for the global algorithm in which the feature extractor and the classifier are trained separately. This allows a degree of flexibility for including heuristics that can enhance the search and the performance of the computed solution. Experimental results demonstrate the potential of the proposed method.

Algorithms↗
PubMed2005

Generalized principal component analysis (GPCA).

This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point. When the number of subspaces is known, we show that these polynomials can be estimated linearly from data; hence, subspace segmentation is reduced to classifying one point per subspace. We select these points optimally from the data set by minimizing certain distance function, thus dealing automatically with moderate noise in the data. A basis for the complement of each subspace is then recovered by applying standard PCA to the collection of derivatives (normal vectors). Extensions of GPCA that deal with data in a high-dimensional space and with an unknown number of subspaces are also presented. Our experiments on low-dimensional data show that GPCA outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as K-subspaces and Expectation Maximization. We also present applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views.

Algorithms↗
PubMed2005

Principal component analysis for predicting transcription-factor binding motifs from array-derived data.

BACKGROUND: The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD to a promoter matrix defined from regulatory DNA sequences, we derived a novel method to predict the critical set of TFBMs. RESULTS: The promoter matrix was defined to establish a quantitative relationship between the IL-1-driven mRNA alteration and genomic DNA sequences of the IL-1 responsive genes. The matrix was decomposed with SVD, and the effects of 8 potential TFBMs (5'-CAGGC-3', 5'-CGCCC-3', 5'-CCGCC-3', 5'-ATGGG-3', 5'-GGGAA-3', 5'-CGTCC-3', 5'-AAAGG-3', and 5'-ACCCA-3') were predicted from a pool of 512 random DNA sequences. The prediction included matches to the core binding motifs of biologically known TFBMs such as AP2, SP1, EGR1, KROX, GC-BOX, ABI4, ETF, E2F, SRF, STAT, IK-1, PPARgamma, STAF, ROAZ, and NFkappaB, and their significance was evaluated numerically using Monte Carlo simulation and genetic algorithm. CONCLUSION: The described SVD-based prediction is an analytical method to provide a set of potential TFBMs involved in transcriptional regulation. The results would be useful to evaluate analytically a contribution of individual DNA sequences.

Algorithms↗
PubMed2005

Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks.

OBJECTIVE: To evaluate the effectiveness and comparability of PCA-based simplifications of ERP waveforms versus their reference-free Laplacian transformations for separating task- and response-related ERP generator patterns during auditory oddball tasks. METHODS: Nose-referenced ERPs (31 sites total) were recorded from 66 right-handed adults during oddball tasks using syllables or tones. Response mode (left press, right press, silent count) and task was varied within subjects. Spherical spline current source density (CSD) waveforms were computed to sharpen ERP scalp topographies and eliminate volume-conducted contributions. ERP and CSD data were submitted to separate covariance-based, unrestricted temporal PCAs (Varimax) to disentangle temporally and spatially overlapping ERP and CSD components. RESULTS: Corresponding ERP and CSD factors were unambiguously related to known ERP components. For example, the dipolar organization of a central N1 was evident from factorized anterior sinks and posterior sources encompassing the Sylvian fissure. Factors associated with N2 were characterized by asymmetric frontolateral (tonal: frontotemporal R > L) and parietotemporal (phonetic: parietotemporal L > R) sinks for targets. A single ERP factor summarized parietal P3 activity, along with an anterior negativity. In contrast, two CSD factors peaking at 360 and 560 ms distinguished a parietal P3 source with an anterior sink from a centroparietal P3 source with a sharply localized Fz sink. A smaller parietal but larger left temporal P3 source was found for silent count compared to button press. Left or right press produced opposite, region-specific asymmetries originating from central sites, modulating the N2/P3 complex. CONCLUSIONS: CSD transformation is shown to be a valuable preprocessing step for PCA of ERP data, providing a unique, physiologically meaningful solution to the ubiquitous reference problem. By reducing ERP redundancy and producing sharper, simpler topographies, and without losing or distorting any effects of interest, the CSD-PCA solution replicated and extended previous task- and response-related findings. SIGNIFICANCE: Eliminating ambiguities of the recording reference, the combined CSD-PCA approach systematically bridges between montage-dependent scalp potentials and distinct, anatomically-relevant current generators, and shows promise as a comprehensive, generic strategy for ERP analysis.

Acoustic Stimulation↗
PubMed2005

Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates.

OBJECTIVE: To evaluate the comparability of high- and low-density surface Laplacian estimates for determining ERP generator patterns of group data derived from a typical ERP sample size and paradigm. METHODS: High-density ERP data (129 sites) recorded from 17 adults during tonal and phonetic oddball tasks were converted to a 10-20-system EEG montage (31 sites) using spherical spline interpolations. Current source density (CSD) waveforms were computed from the high- and low-density, but otherwise identical, ERPs, and correlated at corresponding locations. CSD data were submitted to separate covariance-based, unrestricted temporal PCAs (Varimax of covariance loadings) to identify and effectively summarize temporally and spatially overlapping CSD components. Solutions were compared by correlating factor loadings and scores, and by plotting ANOVA F statistics derived from corresponding high- and low-resolution factor scores using representative sites. RESULTS: High- and low-density CSD waveforms, PCA solutions, and F statistics were remarkably similar, yielding correlations of .9 < or = r < or = .999 between waveforms, loadings, and scores for almost all comparisons at low-density locations except for low-signal CSD waveforms at occipital sites. Each of the first 10 high-density factors corresponded precisely to one factor of the first 10 low-density factors, with each 10-factor set accounting for the meaningful CSD variance (> 91.6%). CONCLUSIONS: Low-density surface Laplacian estimates were shown to be accurate approximations of high-density CSDs at these locations, which adequately and quite sufficiently summarized group data. Moreover, reasonable approximations of many high-density scalp locations were obtained for group data from interpolations of low-density data. If group findings are the primary objective, as typical for cognitive ERP research, low-resolution CSD topographies may be as efficient, given the effective spatial smoothing when averaging across subjects and/or conditions. SIGNIFICANCE: Conservative recommendations for restricting surface Laplacians to high-density recordings may not be appropriate for all ERP research applications, and should be re-evaluated considering objective, costs and benefits.

Acoustic Stimulation↗
PubMed2004

Perceived cognitive function is a major determinant of health related quality of life in a non-selected population of patients with coronary artery disease--a principal components analysis.

OBJECTIVE: To assess health related quality of life (HRQL) and explore its underlying structure in a non-selected population of patients with coronary artery disease (CAD). DESIGN, SETTING AND SUBJECTS: HRQL was estimated by the disease specific Cardiac Health Profile (CHP) questionnaire and the EuroQol-VAS (EQ) in 253 consecutive unselected CAD patients in Södertälje, Stockholm County, Sweden. Explorative factor analysis was used to identify independent dimensions of HRQL. Current angina was ranked according to Canadian Cardiovascular Society Classification (CCS). RESULTS: Four independent principal factors representing perceived cognitive, physical, social and emotional functions underlying the patients' HRQL were found. Identical factors were recognized with an alternate technique. The major factor--explaining 43 % of HRQL--was perceived cognitive function reflecting ability to concentrate, activity drive, memory and problem solving. Cognitive function correlated to EQ but not to CCS. Perceived physical function/general health explained 9% of HRQL and was as expected related both to EQ and CCS. Total CHP scores differed significantly to those of healthy controls. CONCLUSIONS: Perceived cognitive function seems to be a major determinant of HRQL in CAD patients. This, in addition to earlier reports of possible prognostic information of reduced cognitive function, would prompt us to propose that HRQL assessments should include questions aimed to assess cognitive function.

Aged↗
PubMed1985

Principal-components analysis of Brazilian Indian anthropometric data.

Analysis of nine characteristics on 1,205 males and 932 females from 12 tribes or groups of tribes indicated a poor relationship between morphology and language, as well as moderate agreement with the variability expected considering geography only. Two samples in the Xingu area studied during an interval of half a century (1897-1947) showed remarkable similarity. The conformity of the Caingang morphology with those of other tribes and the distinctiveness of the Xavante and Tenetehara has been amply confirmed.

Anthropometry↗
PubMed1997

Equilin, a principal component of the estrogen replacement therapy premarin, increases the growth of cortical neurons via an NMDA receptor-dependent mechanism.

Regulation of both the outgrowth and the survival of neurons involved in cognitive function can have a significant impact on the function of neural networks involved in memory and other cognitive processes. Results of this investigation demonstrated that 17beta-estradiol and the estrogenic steroids estrone, estriol, mestranol, and equilin induced significant increases in cortical nerve cell growth. Of the neurotrophic estrogenic steroids, equilin was most efficacious. We therefore conducted an extensive analysis of equilin-induced neurotrophism. Equilin induced highly significant increases in the growth of both the macro and micro features of cortical nerve cell morphology. The growth-promoting effects of equilin were present in both serum-containing and serum-free media, indicating that the growth-promoting effect of equilin is direct and not dependent upon factors present in serum. Analysis of the regional selectivity of equilin-induced neurotrophism in the cerebral cortex demonstrated that equilin significantly increased the growth of neurons from the frontal, temporal, and occipital regions, with neurons from the parietal region also influenced, though more modestly. We pursued the mechanism of equilin-induced neurotrophism and found that the growth-promoting effects of equilin were completely abolished in the presence of the glutamatergic NMDAreceptor antagonist AP5. Equilin is a major component of Premarin, the leading prescribed pharmaceutical for estrogen replacement therapy for postmenopausal women in the United States. Results of this investigation have the potential of influencing the application and design of therapeutic agents for the prevention of cognitive decline in estrogen deficient women and for the prevention of Alzheimer's disease in postmenopausal women, a group that comprises a large sector of the population, the size of which will continue to grow in the coming decades.

2-Amino-5-phosphonovalerate↗
PubMed2000

The decay of linkage disequilibrium under random union of gametes: how to calculate Bennett's principal components.

How rapidly does an arbitrary pattern of statistical association among a set of loci decay under meiosis and random union of gametes? This problem is non-trivial, even in the case of an infinitely large population where selection and other forces are absent. J. H. Bennett (1954, Ann. Hum. Genet. 18, 311-317) found that, for an arbitrary number of loci with an arbitrary linkage map, it is possible to define measures of linkage disequilibrium that decay geometrically with time. He found a recursive method for deriving expressions for these variables in terms of "allelic moments" (the factorial moments about the origin of the "allelic indicators"), and expressions for the allelic moments in terms of his new variables. However, Bennett no where stated his recursive algorithm explicitly, nor did he give a general formula for his measures of linkage disequilibrium, for an arbitrary number of loci. Recursive definitions of Bennett's variables were obtained by Lyubich. However, the expressions generated by these recursions are not the same as those found by Bennett. (They do not express Bennett's variables as functions of the allelic moments.) Lyubich's derivations employ genetic algebras. Here, I present a method for obtaining explicit expressions for Bennett's variables in terms of the allelic moments. I show that the transformation from the allelic moments to Bennett's variables and the inverse transformation always have the form that Bennett claimed. (This transformation and its inverse have essentially the same form.) I present general recursions for calculating the coefficients in the forward transformation and the coefficients in the inverse transformation. My derivations involve combinatorial arguments and ordinary algebra only. The special case of unlinked loci is briefly discussed.

Algorithms↗