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At least 199 records · Page 11Linked to original sources
PubMed2001

One-carbon metabolism and other biochemical correlates of cognitive impairment as visualized by principal component analysis.

In the present report, 101 ambulatory elderly patients complaining about cognitive disturbances were investigated using the Mini-Mental State Examination (MMSE). Laboratory investigations, brain imaging, and electroencephalography were performed. Twelve patients were diagnosed with subjective memory complaints (SMC), 32 with mild cognitive impairment (MCI), 43 with dementia of the Alzheimer type (DAT), and 14 with vascular dementia (VAD). Thirty-three percent of the SMC group, 31% of the MCI group, 45% of the DAT group, and 62% of the VAD group had increased serum homocysteine (s-HCY). Principal component analysis of 19 variables showed 3 significant principal components by cross-validation. The cognitive impairment in the patients (MMSE) was explained to 50%. According to the principal component analysis, the population followed two different routes to cognitive impairment: one correlated with disturbance of one-carbon metabolism (cerebrospinal fluid vitamin B12, plasma B12, plasma folate, and s-HCY) and the other correlated with more classic dementia, as marked by cerebrospinal fluid tau, vascular risk factors, atrophy on brain imaging, possession of the apolipoprotein E4 allele, and age. There was poor discrimination between DAT and VAD.

Aged↗
PubMed2001

Enzyme specificity under dynamic control II: Principal component analysis of alpha-lytic protease using global and local solvent boundary conditions.

The contributions of conformational dynamics to substrate specificity have been examined by the application of principal component analysis to molecular dynamics trajectories of alpha-lytic protease. The wild-type alpha-lytic protease is highly specific for substrates with small hydrophobic side chains at the specificity pocket, while the Met190-->Ala binding pocket mutant has a much broader specificity, actively hydrolyzing substrates ranging from Ala to Phe. Based on a combination of multiconformation analysis of cryo-X-ray crystallographic data, solution nuclear magnetic resonance (NMR), and normal mode calculations, we had hypothesized that the large alteration in specificity of the mutant enzyme is mainly attributable to changes in the dynamic movement of the two walls of the specificity pocket. To test this hypothesis, we performed a principal component analysis using 1-nanosecond molecular dynamics simulations using either a global or local solvent boundary condition. The results of this analysis strongly support our hypothesis and verify the results previously obtained by in vacuo normal mode analysis. We found that the walls of the wild-type substrate binding pocket move in tandem with one another, causing the pocket size to remain fixed so that only small substrates are recognized. In contrast, the M190A mutant shows uncoupled movement of the binding pocket walls, allowing the pocket to sample both smaller and larger sizes, which appears to be the cause of the observed broad specificity. The results suggest that the protein dynamics of alpha-lytic protease may play a significant role in defining the patterns of substrate specificity. As shown here, concerted local movements within proteins can be efficiently analyzed through a combination of principal component analysis and molecular dynamics trajectories using a local solvent boundary condition to reduce computational time and matrix size.

Amino Acid Substitution↗
PubMed2005

Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. II. Multivariate and principal component analyses.

The merits of complementary multivariate techniques to identify QTL associated with multiple traits were evaluated. Records from 806 F2 pigs pertaining to a Berkshire x Duroc three-generation population were available. Six multitrait groups on SSC 2, 6, 13, and 18 with information on 30 markers were studied. Multivariate techniques studied included multivariate models and principal components analysis of each multitrait group. All models included, in addition to systematic effects, additive, dominance, and imprinting coefficients corresponding to a one-QTL model and a random family effect. Multivariate analysis identified QTL associated with genomewise significant variation in four of the multitrait groups. The majority of the multivariate analysis provided greater precision of parameter estimates and higher statistical significance in some cases than univariate approaches, because of the greater parameterization of the multivariate models and moderate information content of the data. Principal component analysis results were consistent with univariate and multivariate analyses and recovered the levels of statistical significance observed in univariate analyses on the original data. In addition, principal component analysis was able to provide a location associated with LM area not detected by other analyses. The relative advantage of multivariate over the univariate approaches varied with the level of genetic covariance between traits because of the modeled QTL effect and information contained in the data; however, multivariate approaches have the unique capability to identify pleiotropic effects or multiple linked QTL.

Animals↗
PubMed2000

Symptom dimensions in recent-onset schizophrenia and mania: a principal components analysis of the 24-item Brief Psychiatric Rating Scale.

Previous four- and five-factor solutions of the 18-item Brief Psychiatric Rating Scale (BPRS) suggested the possibility of an affective dimension in psychosis. A principal components analysis was used to analyze psychiatric symptom data rated on an expanded 24-item version of the BPRS. BPRS data were collected during a period of acute psychotic and affective illness with 114 young adult, recent-onset schizophrenia and schizoaffective patients and 27 bipolar manic patients. Principal components analyses of the 18-item and 24-item BPRS indicated a four-factor solution was the most interpretable. Principal components analysis of the 24-item BPRS produced a clear mania factor characterized by high loadings from items added to the 18-item BPRS, which included elevated mood, motor hyperactivity, and distractibility. This factor solution suggests that the 24-item BPRS allows for an expanded assessment of affective symptoms relating to a manic dimension. Potentially important symptoms that were added to the traditional 18-item version, namely suicidality, bizarre behavior, and self-neglect, also make clear contributions to other factors.

Adult↗
PubMed1990

Application of principal component analysis for characterizing convergence patterns of inputs in interneurones of the cat forelimb segments.

We attempted to quantitatively describe a variety of convergence patterns of inputs from peripheral nerves and descending tracts (13 sources) onto interneurones of the cat forelimb segments (C6-C8). To this end we applied principal component analysis using the latency of firing as the parameter of each input. The first 3 principal components thus obtained explained 65% of the total variance of convergence patterns and characterized the input pattern of each cell. The first principal component correlated mainly with inputs from pads and the median nerve, the second with the cortico- and rubro-spinal tracts and the third with the superficial radial nerve.

Animals↗
PubMed1999

Extraction of principal components from biosignals by neural net.

This contribution gives the information on a useful application of principal component analysis (PCA) in the field of electroencephalogram (EEG) and laser-Doppler signal processing. The principal components are estimated by a neural network (NN) approach.

Algorithms↗
PubMed2003

Performance evaluation of principal component analysis in dynamic FDG-PET studies of recurrent colorectal cancer.

Performance evaluation of principal component analysis (PCA) of dynamic F-18-FDG-PET studies of patients with recurrent colorectal cancer. Principal component images (PCI) of 17 iteratively reconstructed data sets were visually and quantitatively evaluated. The F-18-FDG compartment model parameters were estimated using polynomial regression. All structures were present in PCI1. PCI2 was correlated with the vascular component and PCI3 with the tumor. The vessel density in the tumor was estimated with a correlation coefficient equal to 0.834. PCA supports the visual interpretation of dynamic F-18-FDG-PET studies, facilitates the application of compartment modeling and is a promising quantification technique.

Colorectal Neoplasms↗
PubMed2006

Use of three tristimulus values from surface reflectance spectra to calculate the principal components for reconstructing these spectra by using only three eigenvectors.

The division of Color Space into ten zones, corresponding to the ten Munsell hues, allows a good reconstruction of surface reflectance spectra using just three eigenvectors, obtained by applying principal components analysis to the reflectance spectra of the Munsell Atlas specimens (model group), although the basis vectors obtained are different for each subspace. The use of the tristimulus values from each measured spectrum, calculated with the Illuminant D65 and the Standard Observer CIE64 to obtain the principal components necessary to reconstruct the spectrum, allows a very high degree of metamerism to be attained between the two spectra (measured and reconstructed). Furthermore, this method of calculating the principal components allows reconstruction of the spectra of specimens from other sample sets that differ from the model group used in the PCA. The colorimetric accuracy obtained in the new sample sets is similar to that obtained for the model group.

Journal Article↗
PubMed2001

Eigenvalues and principal component loadings of heavily overlapped vibrational spectra.

In order to illustrate the possibilities of principal component analysis in determining the number of components in a system of heavily overlapped spectra, several numerical spectral models were formed of bands with very close parameters. The models consisted of three bands, whose peak positions were locally shifted and noise added. For all the cases the relations between eigenvalues and principal component loadings were considered. It was shown that for those complex spectra for which peak positions and band halfwidths can be determined with high accuracy, eigenvalues criteria could easily indicate the number of components. For all analyzed models, the consideration of the shape of loadings was proven to have a high importance. In limited number of cases the shape of a loading can make the results of eigenvalue analysis more understandable. It has been shown that the noise can be treated as a main limitation in the application of the method to this type of the spectra.

Carbon Tetrachloride↗
PubMed2001

Linkage analysis of human systemic lupus erythematosus-related traits: a principal component approach.

OBJECTIVE: To identify chromosomal regions containing genes involved in the susceptibility to human systemic lupus erythematosus (SLE)-related traits. METHODS: In the context of a genome scan, we analyzed 101 SLE-affected sibpairs with respect to dermatologic, renal, immunologic, hematologic, neurologic, cardiopulmonary, and arthritic characteristics. Phenotypes were redefined in terms of principal components, which are synthetic variables composed of linear combinations of the original traits. Using 9 principal components obtained from these 7 traits plus age at SLE onset and race, we analyzed genome scan data with the multivariate version of the new Haseman-Elston regression model. RESULTS: The largest linkage for an individual trait was on chromosome 2 at 228 cM (immunologic; P = 0.00048). The most significant linkage to an individual principal component was on chromosome 4 at 208 cM (P = 0.00007). The largest multivariate linkage was on chromosome 7 at 69 cM (P = 0.0001). Of the individual organ systems, dermatologic involvement had the largest effect (P = 0.0083) at this peak at 7p13 on chromosome 7. Further analyses revealed that malar rash, a subtype of dermatologic involvement, was linked significantly (P = 0.00458) to this location. CONCLUSION: These results provide evidence of the presence and locations of genes that are involved in the genetic susceptibility to SLE-related traits in humans.

Adult↗
PubMed2006

The principal components of natural images revisited.

This paper investigates the principal components (PCs) of natural gray and color images. A horizontal and vertical typology of PCs is found which leads to the identification of groups of basis functions for steerable bandpass filters. Using this system, the contribution of spatio-chromatic structure to the total variance can be quantified for selected spatial frequencies.

Algorithms↗
PubMed2003

Chemical rank estimation by noise perturbation in functional principal component analysist.

Some kinds of chemical data are not only univariate or multivariate observations of classical statistics, but also functions observed continuously. Such special characters of the data, if being handled efficiently, will certainly improve the predictive accuracy. In this paper, a novel method, named noise perturbation in functional principal component analysis (NPFPCA), was proposed to determine the chemical rank of two-way data. In NPFPCA, after noise addition to the measured data, the smooth eigenvectors can be obtained by functional principal component analysis (FPCA). The eigenvectors representing noise are sensitive to the perturbation, on the other hand, those representing chemical components are not. Therefore, by comparing the difference of eigenvectors obtained by FPCA with noise perturbation and by traditional principal component analysis (PCA), the chemical rank of the system can be achieved accurately. Several simulated and real chemical data sets were analyzed to demonstrate the efficiency of the proposed method.

Journal Article↗
PubMed2005

Principal components and factorial approaches for estimating genetic correlations in international sire evaluation.

The increasing number of participating countries and the lack of genetic links among some of them lead to statistical and computational difficulties in estimating the genetic (co)variance matrix needed for international sire evaluation of milk yield. Reparameterization using principal components or factorial approaches is proposed to exploit patterns in the genetic correlation matrix in order to reduce the number of parameters to be estimated without much loss of information. A 2-step approach was used. First, the genetic matrix between 8 or 9 "base" countries was used to determine a reduced number of principal components or factors. Then, the contributions of the remaining countries to these principal components or factors were computed. The resulting genetic correlations for the 18 countries were compared with the "reference" genetic correlations obtained with a classical model. The impact of using reparameterized genetic correlation matrices on breeding value prediction was investigated for both approaches. A better agreement between predicted breeding values and stability of their rankings was found when an approximate factor analysis was used, whatever the number of factors considered. The estimation of genetic correlations among 18 countries using an approximate factorial approach with 5 factors taken into account led to a reduction of the number of parameters to estimate from 171 to 80. The average absolute deviation of the correlations estimated with an approximate factorial approach from the "reference" genetic correlations was 0.014, which is considered very satisfactory in light of the computational ease.

Analysis of Variance↗
PubMed2006

Factors associated with periodontal diseases in Jordan: principal component and factor analysis approach.

This study was conducted to identify factors associated with periodontal disease in a Jordanian population using principal component and factor analysis techniques. Subjects were 603 dentate patients aged 15-65 years attending dental teaching clinics at the Jordan University of Science and Technology. Their oral hygiene and periodontal status were assessed using plaque index, gingival index, probing pocket depth, clinical attachment level, gingival recession, and number of missing teeth. Factor and principal component analysis and binary logistic regression were conducted to identify factors related to periodontal disease. Probing pocket depth, clinical attachment level, gingival recession, and number of missing teeth were sorted as the same factor and could be combined in one scale to measure the severity of periodontal disease. On the other hand, plaque index and gingival index were sorted as another factor and could be combined in another scale to correlate between oral hygiene and gingival status. The results demonstrated that increased age, low level of education, increased plaque index score, not brushing teeth, smoking more than 15 pack-years, and having diabetes were significantly associated with increased severity of periodontal disease. In conclusion, it was possible to form a standard scale, based on linear combinations of periodontal indices and parameters, to measure the severity of periodontal disease and determine its risk indicators.

Adolescent↗
PubMed2000

Neuropathological heterogeneity in Alzheimer's disease: a study of 80 cases using principal components analysis.

Three hypotheses have been proposed to explain neuropathological heterogeneity in Alzheimer's disease (AD): the presence of distinct subtypes ('subtype hypothesis'), variation in the stage of the disease ('phase hypothesis') and variation in the origin and progression of the disease ('compensation hypothesis'). To test these hypotheses, variation in the distribution and severity of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in 80 cases of AD using principal components analysis (PCA). Principal components analysis using the cases as variables (Q-type analysis) suggested that individual differences between patients were continuously distributed rather than the cases being clustered into distinct subtypes. In addition, PCA using the abundances of SP and NFT as variables (R-type analysis) suggested that variations in the presence and abundance of lesions in the frontal and occipital lobes, the cingulate gyrus and the posterior parahippocampal gyrus were the most important sources of heterogeneity consistent with the presence of different stages of the disease. In addition, in a subgroup of patients, individual differences were related to apolipoprotein E (ApoE) genotype, the presence and severity of SP in the frontal and occipital cortex being significantly increased in patients expressing apolipoprotein (Apo)E allele epsilon4. It was concluded that some of the neuropathological heterogeneity in our AD cases may be consistent with the 'phase hypothesis'. A major factor determining this variation in late-onset cases was ApoE genotype with accelerated rates of spread of the pathology in patients expressing allele epsilon4.

Age of Onset↗
PubMed2005

[Quantitative gait evaluation using principal component analysis].

Evaluation of human gait function is of great significance in clinical medicine and rehabilitation engineering. A quantitative gait evaluation method using principal component analysis was proposed. The evaluation steps included that a series of characteristic index was performed by the gait parameters with a gait detection, and the index was normalized, quantified and summarized by principal component analysis. Then the evaluation results were shown in formulation, figures and tables. The examples showed that this system could evaluate the recovery of the gait by treatment.

Evaluation Studies as Topic↗
PubMed2006

Two-step cluster procedure after principal component analysis identifies sperm subpopulations in canine ejaculates and its relation to cryoresistance.

A 2-step clustering procedure, using indexes derived from principal component analysis, was used to disclose sperm subpopulations within the canine ejaculate and its relationship to sperm cryoresistance. Semen from 4 dogs was frozen-thawed by a standard protocol: before freezing, computer-assisted sperm analysis of motility and morphometry were performed; after thawing, motility analysis was performed again; and cryoresistance was estimated as the percent changes in progressive motility and sperm velocities after thawing. We used indexes derived from principal component analysis (sperm velocity index [SVI] and sperm motility index [SMI]) and the SPSS 2-step cluster method to disclose sperm subpopulations. The 2-step clustering procedure revealed the existence of 6 subpopulations. Subpopulations 4 and 6 were characterized by high values of both SVI (>200 arbitrary units) and SMI (>90 arbitrary units), subpopulations 2 and 3 were characterized by medium values (SVI 100 to 130; and SMI 30 to 40), and subpopulations 1 and 5 were characterized by low values (SVI < 100; SMI < 30). The distribution of sperm subpopulations was completely different among dogs. Four sperm subpopulations based in morphometric parameters of the sperm head and midpiece were revealed. Models including SVI and SMI indexes explained curvilinear velocity (R(2) = 0.997; P < .001), straight-line velocity (R(2) = 0.98; P < .001), and average velocity (R(2) = 0.99; P < .0001) postthaw.

Animals↗
PubMed2003

Principal component and linear discriminant analyses of free amino acids and biogenic amines in hungarian wines.

Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify 187 Hungarian white and red wines according to wine-making technology, geographic origin (wine-making region), grape variety, and year of vintage based on free amino acid and biogenic amine contents. Determination of free amino acids and biogenic amines was accomplished by ion-exchange chromatography. Six principal components accounted for >77% of the total variance in the data. The plots of component loadings showed significant groupings of free amino acids and biogenic amines. The component scores grouped according to wines made by different wine-making technologies. Using LDA the variables with a major discriminant capacity were determined. Almost complete classification (94.7%) was achieved concerning both white and red wines and wines made by different wine-making technologies. The results of differentiation between white wines according to geographic origin, grape variety, and year of vintage were 70.8, 62.4, and 73.5%, respectively. The same numbers for red wines according to geographic origin, grape variety, and year of vintage were 64.9, 71.6, and 82.4%, respectively.

Amino Acids↗