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At least 163 records · Page 9Linked to original sources
PubMed1999

Psychopathology in the Dogon Plateau: an assessment using the QDSM and principal components analysis.

BACKGROUND: The present paper reports the findings of principal components analysis performed on the basis of answers to the Questionnaire pour le Depistage en Santé Mentale (QDSM) administered to subjects from the Bandiagara plateau (Mali), who had been evaluated in a previously published report. METHODS: The study sample was made up of 466 subjects (253 males, 213 females), 273 of whom belonged to the Dogon ethnic group, 163 were Peul and the remaining 30 belonged to other groups (Sonrai, Bozo, Tuareg, Bambara). All subjects were submitted to QDSM, a structured interview derived from the Self Reporting Questionnaire. Data obtained were processed by means of principal components analysis, in order to obtain syndromic aggregations. RESULTS: Eight factors with an Eigen value greater than 1 were extracted, which provided sufficient explanation for the overall variance observed among the 23 items. These factors may be termed as follows: Sadness (factor 1); Dysphoria (factor 2); Nightmares (factor 3); Persecution (factor 4); Somatic symptoms (factor 5); Special powers (factor 6); Hopelessness (factor 7); Loss of Interest (factor 8). CONCLUSIONS: The findings from this study support the hypothesis of an independence of "psychosomatic" from depressive symptoms. In particular, contrary to some evidence derived from other African studies, the present research appears to suggest a possible counterposition of these two ways of expressing depression, commonly considered as autonomous.

Adolescent↗
PubMed2005

Bivariate genome scans incorporating factor and principal component analyses to identify common genetic components of alcoholism, event-related potential, and electroencephalogram phenotypes.

Genetic components significantly contribute to the susceptibilities of alcoholism and its endophenotypes, such as event-related potential measures and electroencephalogram. An endophenotype is a correlated trait which identifies individuals at risk. Correlated traits could be influenced by shared genes. This study is intended to identify chromosome regions that may harbor common genetic loci contributing to alcoholism, event related potential measures and electroencephalogram. All 143 Collaborative Study on the Genetics of Alcoholism families with 1,614 individuals provided by the Genetic Analysis Workshop 14 were used for the analysis with aldx1 as an alcoholism diagnosis. We carried out factor and principal component analyses on the 12 event-related potentials, then bivariate genome scans on aldx1 and electroencephalogram (ecb21), as well as alcoholism and the principal component scores of the event-related potential measures. A univariate genome scan was also carried out on each trait. Factor and principal component analysis on the event-related potential measures showed that the 4 ttths and 4 ntths belong to one cluster (cluster 1), while the 4 ttdts belonged to another (cluster 2). From each cluster, one principal component was extracted and saved as pc1 (for cluster 1) and pc2 (for cluster 2). The results of genome scans revealed only one chromosome region, chromosome 4 q at about 100 cM, identified by several univariate genome scans including aldx1, ecb21, and pc2, and the evidence of linkage increased significantly in the bivariate genome scans of aldx1 and ecb21 and aldx1 and pc2. Our study suggests that the same quantitative trait locus on the chromosome 4 q region, where ADH3 is located, may influence the risk of alcoholism, variations of electroencephalogram, and the 4 ttdts of the event-related potential measures.

Alcoholism↗
PubMed1984

Principal components analysis of haematological data from F344 rats with bladder cancer fed N-(ethyl)-all-trans-retinamide.

Several multivariate statistical methods are available which can alleviate the problems of analysing the large volumes of data generated from toxicological experiments. One such technique, principal components analysis, provides a method for exploring the relationships between a number of variables (such as blood parameters) and for eliminating redundant data if strong correlations exist between the characters. It also provides a method for clustering individuals, which may reveal similarities between animals in a treatment group or highlight individual 'outliers'. The application of principal components analysis to a set of haematological data from a trial evaluating the efficacy of a synthetic retinoid against carcinogen-induced bladder cancer in the rat has clearly shown, in two bivariate plots, that while some animals in the carcinogen-treated groups were normal, others were anaemic and that animals fed the synthetic retinoid and killed at 1 year had a microcytic anaemia. A full exploration of the data using conventional univariate statistical analysis would have involved at least 28 graphic representations of the data, as well as the interpretation of more than 130 means and SDs. Principal components analysis provides a valuable additional tool for the statistical analysis and exploration of toxicological data, but it must be used in conjunction with univariate or other multivariate methods if hypothesis testing is required. The use of multivariate techniques in toxicology may best be assessed by their practical application to toxicological data, and this paper presents such an evaluation with the aim of encouraging further exploration of the usefulness of principal components analysis. The raw data on which most analyses have been carried out are given.

Analysis of Variance↗
PubMed2004

[Adopting the method of principal components analysis combined with correlation coefficient to increase the predicted concentration's accuracy of benzene and its homology mixture].

The concentrations of benzene and its homology mixture were measured by near infrared spectra, and the emphasis was put on the character of the principal component and its physical significance. It is pointed out that the anterior principal components are very similar to the correlation coefficient of the multi-component solution and the theoretical proof for the right condition is given. The high frequency noise of the system can be checked out by principal component combined with the correlation coefficient. Removing the noise can greatly increase the accuracy of the prediction model.

Benzene↗
PubMed2006

Interpretation of the repetitive nerve stimulation test results using principal component analysis.

OBJECTIVE: Assessment of the repetitive nerve stimulation (RNS) test parameters has some inherent difficulties, as too many co-dependent variables are involved. To circumvent these problems, we have employed the principal component analysis (PCA) for evaluating the RNS test. METHODS: We performed the RNS test on the abductor digiti quinti (ADQ), flexor carpi ulnaris (FCU) and orbicularis oculi (OO) muscles of 23 myasthenia gravis (MG) patients and 50 controls. For each group, following parameters were chosen for PCA: decremental response of amplitude and area on 2, 3 and 5Hz stimulation rate, including 5Hz stimulation, 4min following tetanus; decremental and incremental response of amplitude and area on 50Hz stimulation. RESULTS: Two principal components (PC1 and PC2) for ADQ and FCU muscles and 1 principal component (PC1) for OO muscle were extracted. The mean values of PC1 were significantly increased for all three muscles in the MG group compared to controls (p<0.01). No significant difference between PC2 values of the MG and control groups was observed (p>0.05). PC1 was the most sensitive test in detecting an abnormality on low rates of stimulation. CONCLUSIONS: PCA, which has the advantage of studying a small number of independent parameters on RNS test, seems to be useful for detecting neuromuscular transmission defects. SIGNIFICANCE: By markedly decreasing the number of assessed variables, PCA can give insight to the direction of data distribution abnormalities in the RNS test, which can prove particularly useful in research studies.

Action Potentials↗
PubMed2001

Is principal components analysis necessary to characterise dietary behaviour in studies of diet and disease?

OBJECTIVE: To assess the relative ability of principal components analysis (PCA)-derived dietary patterns to correctly identify cases and controls compared with other methods of characterising food intake. SUBJECTS: Participants in this study were 232 endometrial cancer cases and 639 controls from the Western New York Diet Study, 1986-1991, frequency-matched to cases on age and county of residence. DESIGN: Usual intake in the year preceding interview of 190 foods and beverages was collected during a personal interview using a detailed food-frequency questionnaire. Principal components analysis identified two major dietary patterns which we labelled 'healthy' and 'high fat'. Classification on disease status was assessed with separate discriminant analyses (DAs) for four different characterisation schemes: stepwise DA of 168 food items to identify the subset of foods that best discriminated between cases and controls; foods associated with each PCA-derived dietary pattern; fruits and vegetables (47 items); and stepwise DA of USDA-defined food groups (fresh fruit, canned/frozen fruit, raw vegetables, cooked vegetables, red meat, poultry, fish and seafood, processed meats, snacks and sweets, grain products, dairy, and fats). RESULTS: In general, classification of disease status was somewhat better among cases (54.7% to 67.7%) than controls (54.0% to 63.1%). Correct classification was highest for fruits and vegetables (67.7% and 62.9%, respectively) but comparable to that of the other schemes (49.5% to 66.8%). CONCLUSIONS: Our results suggest that the use of principal components analysis to characterise dietary behaviour may not provide substantial advantages over more commonly used, less sophisticated methods of characterising diet.

Case-Control Studies↗
PubMed2004

Direct estimation of genetic principal components: simplified analysis of complex phenotypes.

Estimating the genetic and environmental variances for multivariate and function-valued phenotypes poses problems for estimation and interpretation. Even when the phenotype of interest has a large number of dimensions, most variation is typically associated with a small number of principal components (eigen-vectors or eigenfunctions). We propose an approach that directly estimates these leading principal components; these then give estimates for the covariance matrices (or functions). Direct estimation of the principal components reduces the number of parameters to be estimated, uses the data efficiently, and provides the basis for new estimation algorithms. We develop these concepts for both multivariate and function-valued phenotypes and illustrate their application in the restricted maximum-likelihood framework.

Algorithms↗
PubMed2001

Principal component analysis of polarity and interaction parameters in inverse gas chromatography.

Inverse gas chromatography is used in the characterization of aliphatic-aromatic and aromatic ketones, their oximes, and ketone-oxime or oxime-oxime mixtures. All these organic materials are used as liquid stationary phases in gas chromatographic columns. A series of polarity and Flory-Huggins interaction parameters are determined and used to describe the physicochemical properties of examined materials, metal extractants, and products of their degradation. Principal component analysis (PCA) is performed on a data matrix consisting of polarity and interaction parameters for ketones, their oximes, and mixtures. The calculations are carried out on the correlation matrix. It is found that seven principal components account for more than 95% of the total variance in the data, indicating that the polarity (interaction) parameters are not correlating well. Physical meanings are attributed to the principal components, the most influential ones being that the first and the second principal components account for several Flory-Huggins interaction parameters, whereas the fifth is correlated with criterion "A". The plots of component loadings show characteristic groupings of polarity indicators, whereas that of component scores show several groupings of stationary phases. Cluster analysis provides mainly the same groupings. PCA allows for the grouping of polarity and solubility parameters based on the information carried within those parameters. There is no need to use more than one parameter from each cluster. McReynolds polarity and the partial molar excess Gibbs free energy of solution per methylene group carry the same information. The groups of ketones, oximes, and their mixtures can be distinguished with the use of PCA on the basis of the measured polarity, solubility parameters, or both.

Chemical Phenomena↗
PubMed1998

Quantitative Golgi study of the rat cerebellar molecular layer interneurons using principal component analysis.

In this study, we applied for the first time a multivariate analysis to describe the anatomy of cerebellar molecular layer interneurons. Forty variables extending over a variety of morphological features (geometrical, topological, and metrical) were obtained from a three-dimensional reconstruction of 26 rat rapid Golgi-stained neurons. The subsequent principal component analysis showed that the first principal component was strongly correlated with variables related to the depth of each cell's soma in the molecular layer. The second principal component was strongly correlated with parameters describing axonal morphology. Finally, an analysis of the distribution of these anatomical features suggested that these cells cannot be classified into distinct groups but, instead, represent one continuously varying population. Thus, the classical division of molecular layer neurons into deep basket cells and superficial stellate cells is not supported by our analysis. These results have important implications for the development of the cerebellar cortex as well as for the expected patterns of Purkinje cell activity following activation of the granule cell layer.

Animals↗
PubMed2006

Principal components null space analysis for image and video classification.

We present a new classification algorithm, principal component null space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i," an Mi-dimensional subspace along which the class' intraclass variance is the smallest. We call this subspace an approximate null space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class' mean in the class' ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video--an action retrieval problem and abnormal activity detection.

Algorithms↗
PubMed1994

Evaluation of acid-base disorders in dairy cows using principal component analysis and empiric equations.

Acid-base disorders were studied in two groups of dairy cows. According to conventional interpretation in the 1st group of dairy cows (n = 10) the results of the acid-base parameters indicated respiratory alkalosis and in the 2nd group (n = 10) metabolic acidosis. The data of the two groups were examined using principal component analysis. So called "reduced variables"--principal components for each animal were calculated. Each experimental animal was projected in the coordinates of 3 principal components K1, K2 and K3. The components K1, K2 and K3 comprised a 90% data variability. The importance of variables for a dividing of groups (1 and 2) was quantified. The most important were pCO2, (HCO3)' and BE. It was proved according to mathematico-empiric equations that dairy cows of 1st group suffered from acute respiratory alkalosis. In the 2nd group of dairy cows the combined acid-base disorder was confirmed using the equations for metabolic acidosis and chronic respiratory acidosis. The use of PCA method and empiric equations for acid-base imbalances shows the possibilities for gaining new knowledge how to make the diagnostic process more exact.

Acid-Base Imbalance↗
PubMedDate not supplied

Comparison of multidimensional scaling and principal component analysis of interspecific variation in bacteria.

Multidimensional scaling (MDS) and principal component analysis (PCA) were applied to bacterial taxonomy. The biochemical profiles of 42 isolates consisting of four species of Enterobacteriaceae were used. Both MDS and PCA use proximity measures such as the correlation coefficient or Euclidean distance to generate a spatial configuration (map) of points in multidimensional space where distances between points reflect the similarity among isolates. Multidimensional scaling and principal component analysis were able to discriminate organisms in two dimensions. The test components of the MDS and PCA factors (derived variables composed of linear combination of biochemical tests) were different for a two-dimensional solution.

Enterobacter↗
PubMed1978

Principal component analysis of neural population response of knee joint proprioceptors in cat.

(1) A means of describing the response of neural populations based on principal component analysis is presented. The analysis produces response descriptions that indicate whether two states are distinguishable and suggest how to best distinguish between states. (2) Analysis of slowly adapting joint receptor data from the cat knee joint indicates that the joint receptors are capable of signalling limb position in the range from 150 degrees to 180 degrees of extension during an extension movement. They also provide information which indicates whether the tibia is twisted inward or outward in this range of angles. (3) The first principal component in the response to a constant velocity extension of 8 simultaneously active units described 86% of the total mean square discharge displayed by all 8 units. The first principal component is qualitatively similar to the responses observed in slowly adapting thalamic joint units.

Animals↗
PubMed2004

Use of principal component analysis in the frequency domain for mapping electroencephalographic activities: comparison with phase-encoded Fourier spectral analysis.

Principal component analysis (PCA) can separate multichannel electroencephalographic (EEG) epochs into linearly independent (temporally and spatially noncorrelated) components. Results of PCA include component time-series waveforms and factors representing the contribution of each component to each electrode; these factors may be displayed as contour maps representing the topographic distribution of each component. However, PCA often does not achieve the most useful separation of components. PCA may be performed in the frequency domain to potentially improve results. After inspecting principal components of the frequency spectra, spectral values in a selected frequency range are multiplied by a chosen factor to emphasize (or de-emphasize) these frequencies and PCA is redone, promoting the separation of different frequencies into different components. Phase-encoded Fourier spectral analysis (PEFSA) uses multichannel complex Fourier spectra (amplitude and phase) to obtain positive or negative (phase-encoded) potentials at each electrode for any selected frequency. These may be displayed as a contour map representing the topographic distribution of the selected frequency. Applying both techniques, we found that EEG activities of differing frequency were readily separated by PEFSA, while standard PCA often mixed activities with different frequencies into a single component. However, frequency-domain PCA gave a component whose spatial distribution well matched PEFSA results. PCA is superior to PEFSA for separating activities with overlapping frequencies but differing spatial distributions. Preservation of phase information is an advantage of PEFSA and PCA over topographic maps that represent only amplitude (or power) at a given frequency. PCA or PEFSA maps can serve as a starting point for source localization.

Brain↗
PubMed1999

Analysis of principal component based quantitative phenotypes for alcoholism.

Principal component analysis was used to construct quantitative phenotypes for alcoholism. These were analyzed for linkage to genomic regions with a variance components approach. The four phenotypes considered were a factor describing medical symptoms of alcohol dependency, a factor describing a psychological profile correlated with susceptibility to alcoholism, monoamine oxidase B (MAOB) activity and an average measurement of the P3 component of event-related potentials (ERP) at the Fp electrode placements. One region (around marker GATA123C09 on chromosome 3) with suggestive evidence for linkage was detected for the P3 (Fp) measurement. For three of the four distinct phenotypes, modest evidence for linkage to a similar region (around marker ADH3 on chromosome 4) was found.

Alcoholism↗
PubMed1993

Spectrophotometric analysis of a pharmaceutical preparation by principal component regression.

The use of a principal component regression procedure for quantitative analytical control of pharmaceutical preparations was investigated. The procedure was applied to the simultaneous quantitation of the active compound and preservative of a syrup by resolving their respective UV spectra. The most suitable conditions for quantitation were established and the results were compared with those obtained by HPLC.

Benzamides↗
PubMed1980

Ipsilateral and contralateral correlations between EEG and EP principal components.

Correlations between EP and EEG principal components are reported. The signs of the correlations appear to depend on 3 factors: frequency of the EEG, whether the EP and EEG were measured from ipsilateral or contralateral leads, and EP latency. Correlations are negative between EP amplitude (at 100-500 msec) and EEG alpha from ipsilateral leads. When the EP measurements are from leads contralateral to the eEG alpha measurements, the correlations are positive. EEG beta, however, shows the opposite pattern (ipsilateral correlations are positive, while contralateral correlations are negative). Finally, there is some indication of higher ipsilateral correlations for earlier EP components (as opposed to lower correlations for later EP components), and higher contralateral correlations for later EP components. The EPs and EEGs were taken during separate experimental conditions as part of the same experimental session. The original EP measurements were amplitudes (N100, P150, N200, and P300) to words flashed on a television screen, as S1 of a CNV paradigm. The EEGs were 4 sec epochs taken immediately upon television presentation of a word or picture problem.

Adolescent↗