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Results for “Principal Component Analysis”

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PubMed2000

Application of Multiple Linear Regression and Extended Principal-Component Analysis to Determination of the Acid Dissociation Constant of 7-Hydroxycoumarin in Water/AOT/Isooctane Reverse Micelles.

The apparent pK(a) of dyes in water-in-oil microemulsions depends on the charge of the acid and base forms of the buffers present in the water pool. Extended principal-component analysis allows the precise determination of the apparent pK(a) and of the spectra of the acid and base forms of the dye. Combination with multiple linear regression increases the precision. The pK(a) of 7-hydroxycoumarin (umbelliferone) was spectrophotometrically measured in a water/AOT/isooctane microemulsion in the presence of a series of buffers carrying different charges at various different water/surfactant ratios. The spectra of the acid and base forms of the dye in the microemulsion are very similar to those in bulk water in the presence of Tris and ammonia. The presence of carbonate changes somewhat the spectrum of the acid form. Results are discussed taking into account the profile of the electrostatic potential drop in the water pool and the possible partition of umbelliferone between the aqueous core and the surfactant. The pK(a) values corrected for these effects are independent of w(0) and are close to the value of the pK(a) in bulk water. Copyright 2000 Academic Press.

Journal Article↗

PubMed1993

A principal component analysis of morphogenetic field in the root of Japanese dentition.

Teeth extracted from Japanese male cadavers were analyzed from the morphogenetic point of view. Variables were buccolingual crown diameter, mesiodistal crown diameter, crown height, root length and total length. Each dimension was analyzed separately by means of principal component analysis with varimax rotation. Components extracted from crown dimensions and total length showed 3 or 4 of 5 underlying components for morphogenetic field, anterior group, molar, premolar, canine and incisor. However, for crown height and root length, the components were less distinct.

Adult↗

PubMed1990

Principal components analysis of the inventory of drinking situations: empirical categories of drinking by alcoholics.

Male alcoholics (n = 336) were given the Inventory of Drinking Situations (IDS), a 100-item questionnaire that asks subjects to rate the frequency with which they drank in various situations during the previous year. A principal components analysis of the responses suggests there are three major categories of situations in which alcoholics are likely to drink: negative affect states, positive affect states combined with social cues to drink, and attempts to test one's ability to control one's drinking. These categories are compared with recent empirical attempts to define categories of alcohol and smoking relapse.

Adult↗

PubMed2004

Principal component analysis of the T wave in patients with chest pain and conduction disturbances.

There is a need for markers reflecting the increased risk in patients with conduction disturbances. Conduction disturbances presumably cause inhomogeneous repolarization that may create an arrhythmogenic substrate. In patients with normal conduction, parameters derived from principal components analysis (PCA) of the T wave contain prognostic information. The nondipolar PCA components are assumed to reflect repolarization inhomogeneity. This study examined the PCA parameters in relation to conduction disturbances. PCA was performed on continuously recorded 12-lead ECGs in 800 patients with chest pain and nondiagnostic ECG on admission. The patients with conduction disturbance on admission were classified into separate groups and related to comparison groups without conduction disturbance recruited from the same series. For each patient, the dipolar and nondipolar components were quantified by medians of the ratio of the two largest eigenvalues (S2/S1 Median), the residue that summarizes the eigenvalues S4-S8 (TWRabsMedian) and the ratio of this residue to the total power of the T wave (TWRrelMedian). The parameters were assessed with respect to common clinical and ECG parameters, discharge diagnosis, and total mortality during a 35-month follow-up. TWRabsMedian increased with increasing conduction disturbance. In 135 patients with conduction disturbances, ROC curves for TWRabsMedian as indicator of mortality exhibited areas under a curve of 0.66, 0.65, and 0.56 at 6-month, 24-month, and 35-month follow-up. Conduction disturbances were associated with increased nondipolar PCA component and, thus, with increased repolarization inhomogeneity. The nondipolar PCA component contained a moderate amount of prognostic information not present in a simple ECG diagnosis of a conduction disturbance.

Aged↗

PubMed2000

EEG bands during wakefulness, slow-wave and paradoxical sleep as a result of principal component analysis in man.

Human electroencephalogram (EEG) has been divided in bands established by visual inspection that frequently do not correspond with EEG generators nor with functional meaning of EEG rhythms. Power spectra from wakefulness, stage 2, stage 4 and paradoxical sleep of 8 young adults were submitted to Principal Component Analyses to investigate which frequencies covaried together. Two identical eigenvectors were identified for stage 2 and stage 4: 1 to 8 Hz and 5 to 15 Hz (87.95 and 84.62 % of the total variance respectively). Two eigenvectors were extracted for PS: 1 to 9 Hz and 10 to 15 Hz (81.62% of the total variance). Three eigenvectors were obtained for W: with frequencies between 1 to 7 Hz, 7 to 11 Hz, and 12 to 15 Hz (78.32% of the total variance). Power for all frequencies showed significant differences among vigilance states. These results indicate that slow wave activity can oscillate at higher frequencies, up to 8 Hz, and that spindle oscillations have a wider range down to 5 Hz. No theta band was independently identified, suggesting either that delta and theta oscillations are two rhythms under the same global influence, or that the traditional division of theta band in the human cortical EEG is artificial. Alpha as a band was identified only during wakefulness. Principal component analysis upon spectral densities extracted broad bands different for each vigilance state and from traditional bands, consistent with functional significance of EEG and with frequencies of generators of rhythmic activity obtained in cellular studies in animals.

Adult↗

PubMed2004

Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks.

Theoretical models relating atom-based structural descriptors to 13C NMR chemical shifts were used to accurately simulate 13C NMR spectra of lignin model compounds (poly-substituted phenols). The structure-activity relationship (SAR) studies for 15 lignins using pattern recognition methods of principal component analysis (PCA) and artificial neural networks (ANNs) were performed in this work. The most important parameters affecting the 13C chemical shifts of different carbons were descriptors consisting of the charge density of the atoms at different distances from the center carbon. Among the large number of parameters, these descriptors were selected using PCA and were used as ANN input. The least square regression analyses of the results indicate correlation coefficient (R) values in excess of 0.983 for the total data set.

Carbon Isotopes↗

PubMed2001

NMR of biofluids and pattern recognition: assessing the impact of NMR parameters on the principal component analysis of urine from rat and mouse.

The ability to interpret metabolic responses to toxic insult as expressed in altered urine composition and measured by NMR spectroscopy is dependent upon a database of proton NMR spectra of urine collected from both control and treated animals. Pattern recognition techniques, such as principal component analysis (PCA), can be used to establish whether the spectral data cluster according to a dose response. However, PCA will be sensitive to other variables that might exist in the data, such as those arising from the NMR instrument itself. Thus, studies were conducted to determine the impact that NMR-related variables might impart on the data, with a view towards understanding and minimizing variables that could interfere with the interpretation of a biological effect. This study has focused on solvent suppression methods, as well as instrument-to-instrument variability, including field strength. The magnitude of the NMR-induced variability was assessed in the presence of an established response to the nephrotoxin bromoethanamine. Changes caused by the model toxin were larger and easily distinguished from those caused by using different solvent suppression methods and field strengths.

Animals↗

PubMed1994

Tests of human olfactory function: principal components analysis suggests that most measure a common source of variance.

It is not known whether nominally different olfactory tests actually measure dissimilar perceptual attributes. In this study, we administered nine olfactory tests, including tests of odor identification, discrimination, detection, memory, and suprathreshold intensity and pleasantness perception, to 97 healthy subjects. A principal components analysis performed on the intercorrelation matrix revealed four meaningful components. The first was comprised of strong primary loadings from most of the olfactory test measures, whereas the second was comprised of primary loadings from intensity ratings given to a set of suprathreshold odorant concentrations. The third and fourth components had primary loadings that reflected, respectively, mean suprathreshold pleasantness ratings and a response bias measure derived from a yes/no odor identification signal detection task. In an effort to adjust for potential confounding influences of age, gender, smoking, and years of schooling on the component structure, a matrix of residuals from a multiple regression analysis, which included these variables, was also analyzed. A similar component pattern emerged. Overall, these findings suggest, in healthy subjects spanning a wide range, that (1) a number of nominally distinct tests of olfactory function are measuring a common source of variance, and (2) some suprathreshold odor intensity and pleasantness rating tests may be measuring sources of variance different from this common source.

Adult↗

PubMed2000

Three-mode principal components analysis: choosing the numbers of components and sensitivity to local optima.

A method that indicates the numbers of components to use in fitting the three-mode principal components analysis (3MPCA) model is proposed. This method, called DIFFIT, aims to find an optimal balance between the fit of solutions for the 3MPCA model and the numbers of components. The achievement of DIFFIT is compared with that of two other methods, both based on two-way PCAs, by means of a simulation study. It was found that DIFFIT performed considerably better than the other methods in indicating the numbers of components. The 3MPCA model can be estimated by the TUCKALS3 algorithm, which is an alternating least squares algorithm. In a study of how sensitive TUCKALS3 is at hitting local optima, it was found that, if the numbers of components are specified correctly, TUCKALS3 never hits a local optimum. The occurrence of local optima increased as the difference between the numbers of underlying components and the numbers of components as estimated by TUCKALS3 increased. Rationally initiated TUCKALS3 runs hit local optima less often than randomly initiated runs.

Algorithms↗

PubMed2007

Component retention in principal component analysis with application to cDNA microarray data.

Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given.

Journal Article↗

PubMed2001

Principal component analysis and cluster analysis for measuring the local organisation of human atrial fibrillation.

The distribution of atrial electrogram types has been proposed to characterise human atrial fibrillation. The aim of this study was to provide computer procedures for evaluating the local organisation of intracardiac recordings during AF as an alternative to off-line manual classification. Principal component analysis (PCA) reduced the data set to a few representative activations, and cluster analysis (CA) measured the average dissimilarity between consecutive activations of an intracardiac signal. The data set consisted of 106 bipolar signals recorded on 11 patients during electrophysiological studies for catheter ablation. Performances of PCA and CA in distinguishing between organised (type I) and disorganised (type II/III, Wells criteria) were assessed, in comparison with manual reading, by evaluating the predictive parameters of the classification analysis. Both methods gave high accuracy (92% for PCA and 89% for CA), confirming the feasibility of on-line characterisation of AF. Sensitivity was lower than specificity (81% against 98% for PCA, and 77% against 97% for CA), with seven out of eight misclassifications of PCA in common with CA. Differences between manual and computer analysis may be related to the higher resolution of PCA and CA in the measurement of the organisation of atrial activations. These procedures are suitable for providing automatic (by CA) or semi-automatic (by PCA) measures of the extent of local organisation of AF in the pre-ablation treatment phase.

Atrial Fibrillation↗

PubMed1991

Principal component analysis to detect the similarity of distantly related proteins; its application to cytochromes c, c1 and f.

A new method has been developed for detecting the similarity between distantly related families of proteins. The amino acid sequences of each family of proteins are vertically aligned by a homologous alignment method and the physico-chemical properties of the amino acid residues at the corresponding site are evaluated simultaneously, by method of the principal component analysis. Taking into account the species diversity of each family of proteins, we assign the similar regions between the different families of proteins by the overlapping degree of the standard deviations around the mean values of the first principal component. To investigate the homologous relationship between the electron transport proteins in photosynthetic and O2 respiratory systems, this method has been applied to 70 species of mitochondrial cytochrome c, 4 species of cytochrome c1 and 7 species of cytochrome f. This analysis reveals that both cytochrome f and cytochrome c1 have large regions which are similar to those of cytochrome c. Assuming that these similar regions have the same stereochemical structures as those in cytochrome c, we can predict the outlines of the tertiary structures of cytochrome c1 and cytochrome f, respectively, each able to interact with its electron acceptor, cytochrome c and plastocyanin.

Amino Acid Sequence↗

PubMed1997

Obstructive sleep apnoea syndrome: results and conclusions of a principal component analysis.

A cephalometric analysis according to Hasund, supplemented by special obstructive sleep apnoea syndrome (OSAS) parameters, was performed on 169 patients who had been referred from the sleep laboratory. Statistical analysis showed a correlation between specific cephalometric landmarks including posterior airway space (PAS), a soft palate length, hyoid position and posterior growth development of the mandible and OSAS severity. A principal component analysis differentiated between four subgroups of OSAS patients: (1) orthognathic obese subjects; (2) patients with a long soft palate and low-positioned hyoid; (3) retrognathic patients with narrow PAS; and (4) prognathic ones. Lateral cephalometry is an important contribution to OSAS diagnostics and oral and maxillofacial therapy procedures.

Adult↗

PubMed1997

Inclusion of the standard deviation of data in principal component analysis. A graphical approximation.

The adsorption capacity and specific adsorption surface area of 13 anti-hypoxia drugs were determined in three chromatographic systems using methanol-carbon tetrachloride, chloroform-carbon tetrachloride and acetonitrile-carbon tetrachloride mixtures as eluents. The retention behaviours of the anti-hypoxia drugs were compared using principal component analysis (PCA). A graphical approximation was used for the inclusion of the standard deviations of both the variables and observations in PCA and the results were visualized by two-dimensional nonlinear mapping and cluster analysis. The results indicated that the graphical approximation can be successfully used for the inclusion of the standard deviation of data in PCA calculations. Nonlinear mapping and cluster analysis resulted in similar, but not identical, classification of drugs and chromatographic systems, indicating that each multivariate method can be successfully used for the comparison of solutes and chromatographic systems.

Chromatography↗

PubMedDate not supplied

Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

Cerebrovascular Disorders↗

PubMed2006

Evaluation of ischemic injury of the cardiac tissue by using the principal component analysis of an epicardial electrogram.

Monitoring and control of the heart tissue viability is of crucial importance during heart surgery operations. In most cases the heart tissue suffers from an ischemic injury that causes a decrease in the velocity of electrical excitation propagation in it and influences the shape of the excitation wave front that spreads over the injured area. It is reflected in a more complex shape of the registered epicardial electrogram as compared to normal. A method for quantitative evaluation of the complexity of the shape of the epicardial electrogram based on the principal component analysis is here proposed for evaluation of the ischemic injury of the cardiac tissue. A minimal, yet sufficient, number of the principal components (the optimal basis functions) for truncated expansion of the epicardial electrogram signals could be used as an estimate of signal complexity. The method for determination of such a minimal, yet sufficient, number of principal components were developed by using epicardial electrograms registered during in situ experiments on dogs in which local ischemia was evoked by ligation of a coronary vessel.

Animals↗

PubMed1998

A multisensor array for visualizing continuous state transitions in biopharmaceutical processes using principal component analysis.

An array of sensors with varying sensitivities, a so-called multisensor array, has been used for monitoring the growth and production states of biopharmaceutical processes. The sensor array produced continuous and characteristic response patterns from the processes due to the differences of the sensors. By analysing these patterns with the multivariate method principal component analysis, the state as well as the change of state of the bioprocesses could be visualized. The sensors used in the array were well-known semiconductor and optical gas sensors and the array was connected in an on-line set-up to the bioreactor's headspace effluent. The sensor array was applied to the monitoring of two recombinant bioprocesses, the production of human growth hormone in Escherichia coli and human factor VIII in Chinese ovary hamster cells. The sensor array could clearly visualize the characteristic transitions during the main growth or production phases of these two bioprocesses.

Animals↗

PubMedDate not supplied

Regional cerebral blood volume measured by dynamic susceptibility contrast MR imaging in Alzheimer's disease: a principal components analysis.

Dynamic susceptibility contrast (DSC) MRI is an alternative to positron emission tomography (PET) and single photon emission computed tomography (SPECT) for the evaluation of cerebral hemodynamics in patients with Alzheimer's disease. DSC MRI allows the construction of high resolution images of cerebral blood volume (CBV) without the use of radionuclides or ionizing radiation. In this study, DSC MRI data were collected from 16 patients with probable Alzheimer's disease and 16 age-matched control subjects. Characteristic patterns of regional CBV variation were found using principal component analysis. Three such patterns were identified: a global variation pattern, an anterior-to-posterior CBV gradient, and a temporoparietal pattern. Group differences in the principal component scores associated with the global and temporoparietal patterns (P = .08 and P = .007, respectively) suggest that these deficits reflect characteristic CBV abnormalities in Alzheimer's disease. Using only these two scores, the Alzheimer's disease group was classified with a sensitivity of 81% and a specificity of 88%. Additionally, disease severity, as measured by the Mini-Mental State Examination (MMSE), was correlated significantly with the third principal component score (Pearson's r = .50, P = .05).

Aged↗