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PubMed2005

Personality disorders in substance abusers: validation of the DIP-Q through principal components factor analysis and canonical correlation analysis.

BACKGROUND: Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. METHODS: The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a) a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133), and (b) a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. RESULTS: It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. CONCLUSION: The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Adult↗
PubMed2006

Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis.

AIMS/HYPOTHESIS: We devised a practical continuous score to assess the metabolic syndrome, and assessed whether this syndrome score predicts incident diabetes and cardiovascular disease. SUBJECTS AND METHODS: Among 5,024 participants of the Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.) cohort, we defined a metabolic syndrome score by the first principal component (PC1), using only the correlations between continuous metabolic syndrome measures (glucose, waist circumference, triglycerides, and systolic blood pressure). This metabolic syndrome score was highly correlated with a similar score also including insulin and HDL cholesterol (r ( s )=0.94). Over 9 years of follow-up, incident diabetes and cardiovascular disease (CVD) were predicted by logistic regression using the simpler metabolic syndrome score. RESULTS: The means of the metabolic syndrome measures differed between men and women. Nevertheless, as the degree of variance explained and the PC1 coefficients were remarkably similar, we used a common metabolic syndrome score. The metabolic syndrome score explained 50% of the variance of the metabolic syndrome measures, and waist circumference had the highest correlation (0.59) with this score. Each standard deviation increase in the metabolic syndrome score was associated with a markedly increased age-adjusted risk of developing diabetes (odds ratios: men 3.4 [95% CI 2.6-4.4]; women 5.1 [3.6-7.2]) and with increased incident CVD of 1.7 (1.4-2.1) in men and 1.7 (1.0-2.7) in women. CONCLUSIONS/INTERPRETATION: Our results, which should be confirmed in other populations, suggest that it is possible to evaluate the risk of the metabolic syndrome in a pragmatic fashion with a continuous score, obtained from principal components analysis of the basic, continuous syndrome measures.

Adult↗
PubMed2001

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering.

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy o-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Algorithms↗
PubMed2000

Principal component analysis of the power developed in the flexion/extension muscles of the hip in able-bodied gait.

This study was undertaken to demonstrate how principal component analysis (PCA) can be used: (a) to detect the main functional structure of actions taken by hip extensors and flexors during two consecutive gait cycles of able-bodied subjects, and (b) to determine whether or not symmetrical behaviour exists between right and left hip muscle power activity. Twenty young, healthy male subjects walked along a 13 m path at a freely-chosen speed. Applying curve structure detection methods such as PCA to walking patterns can provide insight into the functional tasks accomplished by the lower limbs of able-bodied and disabled subjects. PCA was applied as a classification and curve structure detection method to hip sagittal muscle power calculated for the right and left lower limbs. Over 70% of the information provided by the first four principal components (PCs) was chosen for further biomechanical interpretation. PC1 for both right and left sides mainly described the action taken by the hip extensors/flexors corresponding to the vertical component of ground force on the respective limbs during mid-stance. Propulsion and limb preparation were identified as the second and third tasks attributed to right hip muscle power, while between limb co-ordination was recognised as the second and third functional tasks of the left hip extensors/flexors. Balance was identified as the fourth main functional contribution of the hip extensors/flexors at the right limb while for the left limb, these muscles were mainly responsible for preparing the limb to enter into new gait cycle. PCA was able to identify the four main functional contributions of hip sagittal muscle power during able-bodied gait. PCA was also able to examine the existence of functional asymmetry in gait by highlighting different task priorities at the hip level for the right and left lower limbs.

Adult↗
PubMed2006

Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction.

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.

Amino Acids↗
PubMed2003

Morphological classification of the rat lateral cerebellar nuclear neurons by principal component analysis.

The deep cerebellar nuclei (DCN) constitute the major structures by which the cerebellum forwards its output to the rest of the brain. Although the connectivity of the DCN has been well studied, little is known about the interface-the neurons' soma and dendrites-between the DCN's inputs and outputs. We therefore decided to analyze the neurons' somatic and dendritic morphology by applying a multivariate approach (principal component analysis; PCA), in order to define morphological groups possibly related to distinct positions in the nuclear microcircuitry. The PCA was based on intracellularly stained neurons from the rat's lateral DCN and on 19 parameters that described the neurons' morphology. The PCA yielded two principal components that accounted for 46% of the variance. The first component, correlated with soma size, separated the majority of neurons (type I) from a population of small neurons (type II). The second component showed negative correlation with larger cells with more numerous primary dendrites and a more multipolar appearance (type Ia) and positive correlation with smaller neurons with asymmetric dendritic fields and tufted dendrites (type Ib). The preponderance of small somata in our type Ib neurons suggests that these neurons probably correspond to the inferior olive projection neurons. In summary, our results are in agreement with previous classifications, which distinguished projection neurons (type I) from local neurons (type II); furthermore, our results point to a hitherto undescribed dendritic morphological difference in the projection neurons. The latter may be important for understanding the phylogenetic changes seen in the mammalian lateral cerebellar nucleus.

Animals↗
PubMed2002

Genetic basis for systems of skeletal quantitative traits: principal component analysis of the canid skeleton.

Evolution of mammalian skeletal structure can be rapid and the changes profound, as illustrated by the morphological diversity of the domestic dog. Here we use principal component analysis of skeletal variation in a population of Portuguese Water Dogs to reveal systems of traits defining skeletal structures. This analysis classifies phenotypic variation into independent components that can be used to dissect genetic networks regulating complex biological systems. We show that unlinked quantitative trait loci associated with these principal components individually promote both correlations within structures (e.g., within the skull or among the limb bones) and inverse correlations between structures (e.g., skull vs. limb bones). These quantitative trait loci are consistent with regulatory genes that inhibit growth of some bones while enhancing growth of others. These systems of traits could explain the skeletal differences between divergent breeds such as Greyhounds and Pit Bulls, and even some of the skeletal transformations that characterize the evolution of hominids.

Animals↗
PubMed2006

Assessing newborn body composition using principal components analysis: differences in the determinants of fat and skeletal size.

BACKGROUND: Birth weight is a composite of skeletal size and soft tissue. These components are likely to have different growth patterns. The aim of this paper is to investigate the association between established determinants of birth weight and these separate components. METHODS: Weight, length, crown-rump, knee-heel, head circumference, arm circumference, and skinfold thicknesses were measured at birth in 699 healthy, term, UK babies recruited as part of the Exeter Family Study of Childhood Health. Corresponding measurements were taken on both parents. Principal components analysis with varimax rotation was used to reduce these measurements to two independent components each for mother, father and baby: one highly correlated with measures of fat, the other with skeletal size. RESULTS: Gestational age was significantly related to skeletal size, in both boys and girls (r = 0.41 and 0.52), but not fat. Skeletal size at birth was also associated with parental skeletal size (maternal: r = 0.24 (boys), r = 0.39 (girls) ; paternal: r = 0.16 (boys), r = 0.25 (girls)), and maternal smoking (0.4 SD reduction in boys, 0.6 SD reduction in girls). Fat was associated with parity (first borns smaller by 0.45 SD in boys; 0.31 SD in girls), maternal glucose (r = 0.18 (boys); r = 0.27 (girls)) and maternal fat (r = 0.16 (boys); r = 0.36 (girls)). CONCLUSION: Principal components analysis with varimax rotation provides a useful method for reducing birth weight to two more meaningful components: skeletal size and fat. These components have different associations with known determinants of birth weight, suggesting fat and skeletal size may have different regulatory mechanisms, which would be important to consider when studying the associations of birth weight with later adult disease.

Adiposity↗
PubMed2001

Identification of principal components in cortical evoked potentials by brief surface cooling.

OBJECTIVES: The evoked potential recorded by a single electrode in rat's barrel cortex after whisker stimulation was shown to be composed of two main principal components shifted in time by about 3 ms. The purpose of this study was to verify the hypothesis that these components represent activity of supra- and infragranular pyramidal cell classes. RESULTS: Our results show that a brief cooling pulse applied to the cortical surface abolishes the shorter latency component, which may therefore be attributed to the response of supragranular pyramidal cells. CONCLUSIONS: The longer latency principal component, which disappears only with strong cooling pulses, is proposed to represent postsynaptic activity of infragranular pyramidal neurons.

Animals↗
PubMed2003

Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis.

PURPOSE: To explore the possibilities of combining multispectral magnetic resonance (MR) images of different patients within one data matrix. MATERIALS AND METHODS: Principal component and linear discriminant analysis was applied to multispectral MR images of 12 patients with different brain tumors. Each multispectral image consisted of T1-weighted, T2-weighted, proton-density-weighted, and gadolinium-enhanced T1-weighted MR images, and a calculated relative regional cerebral blood volume map. RESULTS: Similar multispectral image regions were clustered, while dissimilar multispectral image regions were scattered in a single plot. Both principal component and linear discriminant analysis allowed discrimination between healthy and tumor regions on the image. In addition, linear discriminant analysis allowed discrimination between oligodendrogliomas and astrocytomas. However, the discriminant analysis method was partially capable of recognizing the tumor identity in unknown multispectral images. CONCLUSION: The proposed method may help the radiologist in comparing multispectral MR images of different patients in a more easy and objective way.

Brain↗
PubMed2006

Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography-diode array detection data.

Related organic impurities generally have approximately similar molar absorption coefficients (epsilon) due to their structural similarities. On the assumption that all peaks in an impurity profiling chromatogram have approximately the same maximum molar absorption coefficients (epsilon(max)) and the chromatogram contains one major peak and several much smaller ones, all of which are completely separated, integration of the summed score vectors from the principal component analysis (PCA) decomposition of high-performance liquid chromatography-diode array detection (HPLC-DAD) data will give areas that are quantitatively proportional to the actual content of the compounds. Due to the sequential nature of PCA, the first principal component (PC) will primarily be related to the main compound and all peaks showing a similar spectrum, while the second PC will be related to the impurities with a spectrum different from the main peak. Summing the two score vectors thus makes it possible to take account of different spectra in the score chromatogram, which make the method proposed give better quantitative estimates of the impurities than any single wavelength chromatogram. Multivariate curve resolution alternating least squares (MCR-ALS) is used for comparison. The results are presented for two examples of simulated HPLC-DAD data as well as for three examples of real HPLC-DAD data from impurity profiling. The results show that integration of the score chromatograms can handle differences in the unknown epsilon(max) of the peaks and take account of the different spectra of the impurity peaks, giving quantitative estimates of the content of the impurities that closely correspond to the reference values. The results obtained are also better than integration with the best possible separate wavelength. The method could be a straightforward approach to impurity profiling in order to obtain a good estimate of the content or relative response factors of small chromatographic impurity peaks without knowledge of their molar absorption coefficients and without any precalibration.

Chromatography, High Pressure Liquid↗
PubMed2025

WinPCA: a package for windowed principal component analysis.

SUMMARY: With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, contemporary studies often include characterizations of the genomic landscape as it varies along chromosomes, commonly termed genome scans. While traditional summary statistics like FST and dXY between pre-assigned populations remain integral to characterizing the genomic divergence profile, PCA differs by providing single-sample resolution, thereby supporting the identification of polymorphic inversions, introgression and other types of divergent sequence that may not be fully aligned with global population structure. Here, we introduce WinPCA, a user-friendly package to compute, polarize and visualize genetic principal components in windows along the genome. To accommodate low-coverage whole genome-sequencing datasets, WinPCA can optionally make use of PCAngsd methods to compute principal components in a genotype likelihood framework. WinPCA accepts variant data in either VCF or BEAGLE format and can generate rich plots for interactive data exploration and downstream presentation. AVAILABILITY AND IMPLEMENTATION: WinPCA is implemented in Python and freely available at https://github.com/MoritzBlumer/winpca and https://doi.org/10.5281/zenodo.15614979.

Software↗
PubMed2004

Simultaneous determination of Fe(II) and Fe(III) in pharmaceutical formulations with chromogenic mixed reagent by using principal component artificial neural network and multivariate calibration.

The use of chemometric approaches for the simultaneous determination of Fe(II) and Fe(III) ions has been explored by means of a two component reagent. Mixed reagents of 1,10-phenanthroline and thiocyanate were used as a selective chromogenic system for speciation of Fe(II) and Fe(III). Although the complexes of Fe(II) and Fe(III) with mixed reagent show a spectral overlap, they have been simultaneously determined with chemometric approaches, such as principal component artificial neural network (PC-ANN), principal component regression (PCR) and partial least squares (PLS). A set of synthetic mixtures of Fe(II) and Fe(III) was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the PC-ANN and PLS methods afforded better precision relatively than its of PCR. PC-ANN and PLS methods were also applied satisfactorily in determination of Fe(II) and Fe(III) in pharmaceutical samples.

Chemistry, Pharmaceutical↗
PubMed2005

Asymmetry analysis of deformable hippocampal model using the principal component in schizophrenia.

The hippocampus is thought to play an important role in learning and memory processing, and impairments in memory, attention, and decision making are found commonly in schizophrenia. Although many studies have reported decreases in hippocampal volume in the left hemisphere in schizophrenia, regionally specific hippocampal volume loss has not been revealed consistently using volume analysis. Recently, many studies have analyzed shape asymmetry using 3-D models; however, inconsistent results have been reported, mainly due to methodologic differences. We therefore used an active, flexible, deformable shape model for surface parameterization, and compared shape asymmetry based on principal component analysis (PCA) in the hippocampi of schizophrenic patients with those of the normal controls. Although the overall pattern of the statistical results did not change according to the number of principal components, the reconstructed results based on six major components were much more distinguishable. Although the left hemispheric hippocampal volume was larger than the right hemispheric was in this study, the difference was not significant. In shape asymmetry analysis, the right hemisphere hippocampus was bilaterally larger than the left hemisphere hippocampus was in the head of the superior CA1 and smaller in the tail and head of the inferior CA1. The asymmetry in the schizophrenia group was statistically smaller than that in the control group through reduction of the left hemisphere hippocampus volume.

Adult↗
PubMed2004

Folding dynamics of proteins from denatured to native state: principal component analysis.

Several trajectories starting from random configurations and ending in the native state for chymotrypsin inhibitor 2, CI2, are generated using a Go-type model where the backbone torsional angles execute random jumps on which a drift towards their native values is superposed. Bond lengths and bond angles are kept fixed, and the size of the backbone atoms and side groups are recognized. The large datasets obtained are analyzed using a particular type of principal component analysis known as Karhunen-Loeve expansion (KLE). Trajectories are decomposed separately into modes in residue space and time space. General features of different folding trajectories are compared in the modal space and relationships between the structure of CI2 and its folding dynamics are obtained. Dynamic scaling and order reduction of the folding trajectories are discussed. A continuous wavelet transform is used to decompose the nonstationary folding trajectories into windows exhibiting different features of folding dynamics. Analysis of correlations confirms the known two-state nature of folding of CI2. All of the conserved residues of the protein are shown to be stationary in the small modes of the residue space. The sequential nature of folding is shown by examining the slow modes of the trajectories. The present model of protein folding dynamics is compared with the simple Rouse model of polymer dynamics. Principal component analysis is shown to be a very effective tool for the characterization of the general folding features of proteins.

Computational Biology↗
PubMed2006

Diagnosing old MI by searching for a linear boundary in the space of principal components.

Body surface potential mapping (BSPM) is a technique employing multiple electrodes to capture, via noninvasive means, an indication of the heart's condition. An inherent problem with this technique is the resulting high-dimensional recordings and the subsequent problems for diagnostic classifiers. A data set, recorded from a 192-lead BSPM system, containing 74 records is investigated. QRS isointegral maps, offering a summary of the information obtained during ventricular depolarization, were derived from 30 old inferior myocardial infarction and 44 normal recordings. Principal component analysis was applied to reduce the dimensionality of the recordings and a linear classifier was employed for classification. This perceptron-based classifier has been adapted so that the final weight and bias values are estimated prior to the learning process. This estimation process, referred to as the linear hyperplane approach (LHA), derives the estimated weights from a bisector hyperplane, placed orthogonal to the means of two class distributions in an n-dimensional Euclidean space. Estimating weights encourages a network to exhibit better generalization ability. Utilizing a number of different principal components as input features, the LHA achieved an average sensitivity and specificity of 79.58% and 76.45%, respectively, across all experiments. The average accuracy of 76.73% achieved with this approach was significantly better than the other benchmark classifiers evaluated against it.

Algorithms↗
PubMed2001

Use of variable marker density, principal components, and neural networks in the dissection of disease etiology.

Several approaches were taken to identify the loci contributing to the quantitative and qualitative phenotypes in the Genetic Analysis Workshop 12 simulated data set. To identify possible quantitative trait loci (QTL), the quantitative traits were analyzed using SOLAR. The four replicates identified as the "best replicates" by the simulators, 42, 25, 33, and 38, were analyzed separately. Each of the five quantitative phenotypes was analyzed individually in the four replicates. To increase the power to detect QTL with pleiotropic effects, principal component analysis was performed and one new multivariate phenotype was estimated. In each instance, after performing a 10-cM genome screen, fine mapping was completed in the initially identified linked regions to further evaluate the evidence for linkage. This approach of initially performing a coarse marker screen followed by analyses using much higher marker density successfully identified all the QTL playing a role in the quantitative phenotypes. The principal component phenotype did not substantially improve the power of QTL detection or localization. A neural network approach was utilized to identify loci contributing to disease status. The neural network technique identified the strongest gene influencing disease status as well as a locus contributing to quantitative traits 3 and 4; however, the inputs that contributed the greatest information were markers not in QTL regions.

Artificial Intelligence↗
PubMed2006

Clustering of cardiovascular risk factors associated with the insulin resistance syndrome: assessment by principal component analysis in young hyperandrogenic women.

OBJECTIVE: Hyperinsulinemia is often associated with several metabolic abnormalities and increased blood pressure, which are risk factors for cardiovascular disease. It has been hypothesized that insulin resistance may underlie all these features. However, recent data suggest that some links between insulin resistance and these alterations may be indirect. The aim of our study was to further investigate this issue in a sample of young hyperandrogenic women, who often show insulin resistance and other metabolic abnormalities typical of the insulin resistance syndrome. RESEARCH DESIGN AND METHODS: We tested the hypothesis of a single factor underlying these features by principal component analysis, which should recognize one component if a single mechanism explains this association. The analysis was carried out in a sample of 255 young nondiabetic hyperandrogenic women. Variables selected for this analysis included the basic features of the insulin resistance syndrome and some endocrine parameters related to hyperandrogenism. RESULTS: Principal component analysis identified four separate factors, explaining 64.5% of the total variance in the data: the first included fasting and postchallenge insulin levels, BMI, triglycerides, HDL cholesterol, and uric acid; the second, BMI, blood pressure, and serum free testosterone; the third, fasting plasma glucose, postchallenge glucose and insulin levels, serum triglycerides, and free testosterone; and the fourth, postchallenge plasma insulin, serum free testosterone, and gonadotropin-releasing hormone agonist-stimulated 17-hydroxyprogesterone. CONCLUSIONS: These results support the hypothesis of multiple determinants in the clustering of abnormalities in the so-called insulin resistance syndrome.

Adult↗