Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Principal component”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31Linked to original sources
PubMed1993

Principal component analysis of language performances in Alzheimer's disease.

This report analyzes the performances of a group of 104 mildly to moderately impaired probable Alzheimer's disease patients (Mini Mental Examination 10 to 23) on linguistic tasks exploring written and oral language. A principal component analysis showed a two-factor solution including 14 out of the 15 linguistic tasks. Each factor is characterized by a type of operation required to process language material: "operativeness factor," where verbal material receives a transformation; "transcoding factor," where verbal material is processed without any structural modification. Oral verbal repetition remained isolated from the solution.

Aged↗
PubMed1980

On the rational selection of test series. 1. Principal component method combined with multidimensional mapping.

A method for the rational selection of optimal test series with high data variance and low collinearities is presented (PCMM method). The method combines the technique of multidimensional mapping originally introduced by Wootton and colleagues with the principal component method, and it is superior to other selection methods with respect to its collinearity decreasing power. Two examples of the application of PCMM are given, and the results are compared with corresponding results from other selection techniques.

Chemical Phenomena↗
PubMed1988

A principal-components analysis of the Narcissistic Personality Inventory and further evidence of its construct validity.

We examined the internal and external validity of the Narcissistic Personality Inventory (NPI). Study 1 explored the internal structure of the NPI responses of 1,018 subjects. Using principal-components analysis, we analyzed the tetrachoric correlations among the NPI item responses and found evidence for a general construct of narcissism as well as seven first-order components, identified as Authority, Exhibitionism, Superiority, Vanity, Exploitativeness, Entitlement, and Self-Sufficiency. Study 2 explored the NPI's construct validity with respect to a variety of indexes derived from observational and self-report data in a sample of 57 subjects. Study 3 investigated the NPI's construct validity with respect to 128 subject's self and ideal self-descriptions, and their congruency, on the Leary Interpersonal Check List. The results from Studies 2 and 3 tend to support the construct validity of the full-scale NPI and its component scales.

Adolescent↗
PubMed1990

[The use of the method of principal components for phenogenetic analysis of the integral domestication trait].

In the course of long-term experiment on domestication of silver fox, it was necessary to determine the most important behavioral features, selection for which would give domestication effect. The mathematical method of principal components was used for the analysis of 20 fox behavior traits. The combination of the initial traits which reflected the structure of the integral trait, i.e. domesticated type of behavior, was determined. The practical use of this combination seems to more adequately estimate the level of domestication of a given animal, which is indispensable for subsequent effective selection.

Animals↗
PubMed1989

Laminar cortical interactions during epileptic spikes studied with principal component analysis and physiological modeling.

The direct cortical responses (DCR) to electrical stimulation and electrically evoked interictal penicillin spikes (EIIS) were studied in the same rats using current source-density (CSD) analysis to directly compare regions of neuronal depolarization and hyperpolarization in neocortex. Principal component analysis (PCA) was further used to evaluate patterns of covariance in the CSD that were characteristic of interactions between pyramidal cell populations with spatially and temporally distinct transmembrane currents. A physical model was applied to the physiological interpretation of PCA results and the optimal model parameters used to estimate neuronal generators of recorded laminar field potentials. The data suggested that the DCR and EIIS were produced by the same neuronal circuit which could be represented by two anatomically distinct populations of pyramidal cells. The first of these populations was situated in the upper and middle layers (supragranular pyramidal neurons) and formed a dipolar CSD pattern that reversed polarity in layers II and III. The second deeper population (infragranular pyramidal neurons) extended throughout most of the cortical thickness and formed a dipolar CSD pattern that reversed polarity in layer V. We propose that excitatory intracortical connections of supragranular pyramidal cells may pathologically synchronize depolarization within the epileptic focus. In this way, supragranular pyramidal cells may provide a trigger mechanism for interictal spikes in neocortex.

Animals↗
PubMedDate not supplied

Determination of the acid dissociation constant of bromocresol green and cresol red in water/AOT/isooctane reverse micelles by multiple linear regression and extended principal component analysis.

The pKa of 3',3",5',5"tetrabromo-m-cresolsulfonephtalein (Bromocresol Green) and o-cresolsulphonephtalein (Cresol Red) was spectrophotometrically measured in a water/AOT/isooctane microemulsion in the presence of a series of buffers carrying different charges at different water/surfactant ratios. Extended Principal Component Analysis was used for a precise determination of the apparent pKa and of the spectra of the acid and base forms of the dye. The apparent pKa 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. Combination with multiple linear regression increases the precision. Results are discussed taking into account the profile of the electrostatic potential in the water pool and the possible partition of the indicator between the aqueous core and the surfactant. The pKa corrected for these effects are independent of w0 and are close to the value of the pKa in bulk water. On the basis of a tentative hypothesis it is possible to calculate the true pKa of the buffer in the pool.

Bromcresol Green↗
PubMed2003

A principal components analysis of the Autism Diagnostic Interview-Revised.

OBJECTIVE: To develop factors based on the Autism Diagnostic Interview-Revised (ADI-R) that index separate components of the autism phenotype that are genetically relevant and validated against standard measures of the constructs. METHOD: ADIs and ADI-Rs of 292 individuals with autism were subjected to a principal components analysis using VARCLUS. The resulting variable clusters were validated against standard measures. RESULTS: Six clusters of variables emerged: spoken language, social intent, compulsions, developmental milestones, savant skills and sensory aversions. Five of the factors were significantly correlated with the validating measures and had good internal consistency, face validity, and discriminant and construct validity. Most intraclass correlations between siblings were adequate for use in genetic studies. CONCLUSION: The ADI-R contains correlated clusters of variables that are valid, genetically relevant, and that can be used in a variety of studies.

Adolescent↗
PubMed2001

Interpreting 16S rDNA T-RFLP Data: Application of Self-Organizing Maps and Principal Component Analysis to Describe Community Dynamics and Convergence.

Interpreting the large amount of data generated by rapid profiling techniques, such as T-RFLP, DGGE, and DNA arrays, is a difficult problem facing microbial ecologists. This study compares the ability of two very different ordination methods, principal component analysis (PCA) and self-organizing map neural networks (SOMs), to analyze 16S-DNA terminal restriction-fragment length polymorphism (T-RFLP) profiles from microbial communities in glucose-fed methanogenic bioreactors during startup and changes in operational parameters. Our goal was not only to identify which samples were similar, but also to decipher community dynamics and describe specific phylotypes, i.e., phylogenetically similar organisms, that behaved similarly in different reactors. Fifteen samples were taken over 56 volume changes from each of two bioreactors inoculated from river sediment (S2) and anaerobic digester sludge (M3) and from a well-established control reactor (R1). PCA of bacterial T-RFLP profiles indicated that both the S2 and M3 communities changed rapidly during the first nine volume changes, and then became relatively stable. PCA also showed that an HRT of 8 or 6 days had no effect on either reactor communtity, while an HRT of 2 days changed community structure significantly in both reactors. The SOM clustered the terminal restriction fragments according to when each fragment was most abundant in a reactor community, resulting in four clearly discernible groups. Thirteen fragments behaved similarly in both reactors, eight of which composed a significant proportion of the microbial community as judged by the relative abundance of the fragment in the T-RFLP profiles. Six Bacteria terminal restriction fragments shared between the two communities matched cloned 16S rDNA sequences from the reactors related to Spirochaeta, Aminobacterium, Thermotoga, and Clostridium species. Convergence also occurred within the acetoclastic methanogen community, resulting in a predominance of Methanosarcina siciliae-related organisms. The results demonstrate that both PCA and SOM analysis are useful in the analysis of T-RFLP data; however, the SOM was better at resolving patterns in more complex and variable data than PCA ordination.

Journal Article↗
PubMed2006

Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks.

A Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set. Then, a "leave-more-out" approach, based on the use of a training set, a test set, and a production set, was adopted. The training set was used to build the models, the test set was used to select the number of latent variables or the neural network training endpoint, and the production set was used to produce genuine predictions. ANNs proved to be much more effective in prediction with respect to PLS and PCR and, at least in the case of SO2 and dust, provided a predictive ability comparable with the experimental estimated uncertainty of the response. This showed that it is possible to satisfactorily predict the two responses. Such a prediction will result in the prevention of environmental and legal problems connected to the polluting emissions.

Air Pollution↗
PubMed1975

Reovirus antibody patterns in dogs: a trial for the application of principal component analysis to seroepidemiology.

In 100 dogs in Morioka and its vicinity serologically surveyed for reovirus infection, there were significant correlations between hemagglutination-inhibiting and neutralizing antibody titers of the same virus types. In order to establish a proper index for the evaluation of infection, principal component analysis was applied to the analysis of data, including standard immune sera. Of nine samples of matrix examined, correlation matrix of 6 variables was suggested to afford the most proper result. Essential information from the original data was accounted for by factor loadings of the first 3 components. The 6 attributes of serum antibody were classified into three groups. In a scatter diagram serum samples were classified also into three major groups corresponding to the three reovirus types, and the serotypic pattern of infection was clearly visualized.

Animals↗
PubMed1995

Atmospheric trace element deposition: principal component analysis of ICP-MS data from moss samples.

Data from a Norwegian survey on atmospheric deposition, including 33 elements in 495 moss samples collected in 1990, are presented. The biomonitor moss used was Hylocomium splendens, and the analyses were carried out by ICP-MS. Principal component analysis is used to identify possible sources of the elements determined in the mosses. Dominant factors represent long-range atmospheric transported elements (Bi, Pb, Sb, Mo, Cd, V, As, Zn, Tl, Hg, Ga), windblown mineral particles (Y, La, Al, Li, U, Th, Ga, Fe, V, Cr), local emission sources (Ni, Cu, Co, and As; Zn, Cd, and Hg; Fe, Cr, and Al), transport from the marine environment (Mg, B, Na, Sr, Ca), and contribution from higher plants (Cs, Rb, Ba, Mn). Comparison of the results with similar surveys from 1977 and 1985 show a decreasing contribution of most long-range transported elements to southern Norway.

Journal Article↗
PubMed1997

Parallel principal component neural networks for classification of event-related potential waveforms.

Artificial neural networks (ANNs) are discussed in terms of classification of brain auditory event-related potentials (ERPs). A new ANN architecture for the classification of ERPs is proposed. The new architecture is called the parallel principal component neural network (PPCNN). The use of the PPCNN for classification of ERP data obtained from both normal control subjects and chronic schizophrenic patients is discussed. Experimental results are given.

Adult↗
PubMedDate not supplied

Structure-activity relationship studies of carcinogenic activity of polycyclic aromatic hydrocarbons using calculated molecular descriptors with principal component analysis and neural network methods.

Recently a new methodology based on local density of state (LDOS) calculations using topological and semiempirical methods was proposed to identify the carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs). In this work we perform a comparative study of this methodology with principal component analysis (PCA) and neural networks (NN). The PCA and NN results show that LDOS quantum chemical descriptors are relevant descriptors to identify the carcinogenic activity of methylated and non-methylated PAHs. Also, we show that the combination of these distinct methodologies can be an efficient and powerful tool in the structure-activity studies of PAHs compounds. We have studied 81 methylated and non-methylated PAHs, and our study shows that with the use of these methods it is possible to correctly predict the carcinogenic activity of PAHs with accuracy higher than 80%.

Carcinogens↗
PubMed2007

Incremental principal component analysis for image processing.

A simple method for updating the eigenvectors and eigenvalues of a covariance matrix when a new input sample is added is presented. This proposed method will be a solution for both rank-one modification problems of a symmetric matrix and adaptive principal component analysis.

Journal Article↗
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↗
PubMed2004

Principal component for metabolic syndrome risk maps to chromosome 4p in Mexican Americans: the San Antonio Family Heart Study.

Metabolic syndrome refers to the clustering of disease conditions such as insulin resistance, hyperinsulinemia, dyslipidemia, hypertension, and obesity. To explore the genetic predispositions of this complex syndrome, we conducted a principal components analysis using data on 14 phenotypes related to the risk of developing metabolic syndrome. The subjects were 566 nondiabetic Mexican Americans, distributed in 41 extended families from the San Antonio Family Heart Study. The factor scores obtained from these 14 phenotypes were used in multipoint linkage analysis using SOLAR. Factors were identified that accounted for 73% of the total variance of the original variables: body size-adiposity, insulin-glucose, blood pressure, and lipid levels. Each factor exhibited evidence for either significant or suggestive linkage involving four factor-specific chromosomal regions relating to chromosomes 1, 3, 4, and 6. Significant evidence for linkage of the lipid factor was found on chromosome 4 near marker D4S403 (LOD = 3.52), where the cholecystokinin A receptor (CCKAR) and ADP-ribosyl cyclase 1 (CD38) genes are located. Suggestive evidence for linkage of the body size-adiposity factor to chromosome 1 near marker D1S1597 (LOD = 2.53) in the region containing the nuclear receptor subfamily 0, group B, member 2 gene (NROB2) also was observed. The insulin-glucose and blood pressure factors were linked suggestively to regions on chromosome 3 near marker D3S1595 (LOD = 2.20) and on chromosome 6 near marker D6S 1031 (LOD = 2.08), respectively. In summary, our findings suggest that the factor structures for the risk of metabolic syndrome are influenced by multiple distinct genes across the genome.

Adult↗
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↗