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PubMed1988

Principal component analysis of intercusp distances on the lower first molars of three human populations.

The distances between the five main cusps of lower first molars were measured on moiré photographs of casts obtained from Japanese, Dutch and Australian Aboriginal children. Principal component analysis of the intercusp distances, log transformed and standardized so that average tooth size was held constant, revealed three sources of shape variation in cusp topography. All populations were similar for scores on component 1 which was concerned with variations in the position of the hypoconulid. The Dutch had lowest scores on component 2 indicating small buccolingual distances compared with mesiodistal, whereas the Australian Aboriginals had the lowest mean score on component 3, expressing the distances between metaconid, entoconid and hypoconulid.

Asian People↗
PubMed1985

Principal component analysis for a better understanding of the herbicidal effectivity of some benzonitriles.

To gain more insight in mode of action of ten different 4-hydroxy-benzonitrile derivatives, their biological activities in eight bioassays, and their lipophilicity and adsorptivity determined by thin-layer chromatography in nine different systems were subjected to principal component analysis. Four background components explained about 90% of total variance. Only three of eight biological activities, the inhibition of the 2,6-dichlorophenol-indophenol reduction by spinach and wheat chloroplasts and the CO2 fixation of wheat seedlings had not any common background components with the physico-chemical parameters of the compounds. The nonlinear mapping of principal component loadings and variables showed, that the in vivo and in vitro biological activities differed considerably and depended on the object investigated. The effectivity of compounds is governed mainly by the number of substituents and by the presence of free hydroxy group.

Chromatography, High Pressure Liquid↗
PubMed2005

The role of the right hemisphere in processing nonsalient metaphorical meanings: application of principal components analysis to fMRI data.

Some researches indicate that the right hemisphere (RH) has a unique role in comprehending the figurative meaning of metaphors whereas the results of other studies do not support the notion of a selective role for the RH in accessing metaphorical meanings. The present research used fMRI technology to test a theoretical explanation of the above conflicting findings. This theoretical account is derived from the Graded Salience Hypothesis (GSH) [Giora, R. (1997). Understanding figurative and literal language: the Graded Salience Hypothesis. Cognitive Linguistics, 7, 183-206; Giora, R. (2003). On our mind: Salience, context and figurative language. New York: Oxford University Press], according to which the degree of meaning salience, rather than literality or nonliterality primarily affects differences between the LH and RH in linguistic processing. Thus, the GSH predicts a selective RH involvement in comprehension of novel, nonsalient metaphoric meanings and LH involvement in the comprehension of conventional, salient metaphoric meanings. Fifteen normal adults participated in a block designed fMRI experiment that compared the patterns of brain activation induced by processing the meanings of literal, conventional metaphoric, novel metaphoric and unrelated word pairs. The subjects performed a semantic judgment task. We applied the Principal Components Analysis (PCA) technique in order to find different functional networks corresponding to the different stimuli. Our results, obtained from PCA of the fMRI data indicate that the right homologue of Wernicke's area has a special role in processing novel metaphors. We suggest that a unique network, consisting of the right homologue of Wernicke's area, right and left premotor areas, right and left insula and Broca's area, is recruited for the processing of novel metaphors but not for the processing of conventional metaphors.

Adult↗
PubMed2006

A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies--clinical brain studies using [11C]-GR205171, [11C]-L-deuterium-deprenyl, [11C]-5-Hydroxy-L-Tryptophan, [11C]-L-DOPA and Pittsburgh Compound-B.

Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.

Antiemetics↗
PubMed1991

Comparison of two principal component analysis methods to evaluate reversed-phase retention data.

The retention of twelve 2-nitro-4-cyanophenyl esters showing marked herbicidal activity was determined in 23 reversed-phase thin-layer chromatographic systems. The retention data set was evaluated by principal component analysis (PCA). To assess the effect of the information loss caused by normalization, PCA was separately carried out on the covariance (method A) and on the correlation matrix (method B). The ratio of the variances explained was very similar for both methods, however, the PC loadings and the coordinates of the two-dimensional nonlinear maps showed poor correlation. The distribution of the 2-nitro-4-cyanophenyl esters and that of chromatographic systems showed differences on the two-dimensional nonlinear maps of PC loadings and PC variables, however, the general trend was similar independently of the application of method A or B. The findings indicate that the application of the correlation matrix as basis for the PCA calculations may lead to slightly distorted results that strongly advocates the use of covariance matrix in PCA.

Chromatography, Thin Layer↗
PubMed2005

Distribution of PCDDs and PCDFs in soils collected from the Denver Front Range--principal components analysis of diffuse dioxin sources.

BACKGROUND, AIMS AND SCOPE: The Rocky Mountain Arsenal (RMA) is a U.S. Army facility located northeast of Denver, Colorado that has been listed on the National Priorities List (NPL). It is currently being re-mediated under the authority of the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA) and the Superfund Amendments and Reauthorization Act of 1986 (SARA). As part of the remediation activities at RMA, indications were found that a source of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) had existed on the RMA. As a result, investigations were undertaken to assess the possible nature and extent of any potential sources of PCDDs and PCDFs on the RMA site. In addition, other studies were conducted that examined PCDD/PCDF contamination in the Denver area. The goal of these studies was to examine nature and extent of PCDD/PCDF contamination both on the RMA as well as in the surrounding Denver area. The intent of this study was to characterize sources of dioxins (PCDDs) and dibenzofurans (PCDFs) at low environmental concentrations which might have originated from diffuse sources in the Denver Colorado area and in particular, the Rocky Mountain Arsenal (RMA) using Principal Component Analysis (PCA). METHODS: Over 200 soil samples were collected from the RMA and the Denver area. From the RMA, soil was collected as part of three studies that included a (1) random site-wide sampling of the RMA, (2) soils from the Western Tier Parcel (WTP), and (3) soils from Historic Use areas. Denver area soil samples were collected from five different land use categories: Residential, Agricultural, Open Space, Commercial, and Industrial. PCA was conducted on concentrations of 17 2,3,7,8-substituted PCDD and PCDF congeners in 220 soil samples collected from the RMA and the Denver Front Range region. RESULTS AND DISCUSSION: PCA demonstrated the presence of possible minor sources of dioxins on the RMA. Current remediation efforts on RMA will result in the removal of these sources. Samples from the RMA were identified by the presence of a congener profile containing higher chlorinated PCDFs while the Denver Front Range areas were characterized by the presence of higher chlorinated PCDD congeners. The presence of a PCDF signature for the RMA samples does not necessarily indicate a major source of these contaminants on-site. Indeed, the relatively diffuse nature of the sample clusters would argue strongly against the presence of a single large source. Instead, the predominance of the PCDF congeners probably indicates the mixed industrial activities that took place on and near the site. CONCLUSION: PCA results indicate that PCDD/PCDF profiles in soils collected from the RMA differed from those collected from the outlying Denver areas but that a major source of these contaminants was not present. Rather, the diffuse nature of sample clusters from the PCA indicated that the congener profile of RMA samples was most likely a result of the mixed industrial activities that historically have taken place on and near the site. PCA also indicated that many of the 'open area' (peripheral site-wide) RMA soils samples did not differ from Denver are reference congener profiles. This finding was also true for samples collected from the WTP that were essentially indistinguishable from Off-RMA reference samples. In addition, total TEQ concentrations in soils collected from WTP were similar to those measured in soils collected from the Denver Front Range areas indicating that lack of a major source of PCDD/PCDF within the WTP zones of the RMA. RECOMMENDATION AND OUTLOOK: Analytical as well as statistical results of the soil congener data indicate that the WTP soils are indistinguishable from soils collected from non-industrial areas in the Denver area. This finding would support the recent 'de-listing' of the WTP from the other RMA areas and its transfer to other authorities in the Denver area.

Benzofurans↗
PubMed1984

Principal component analysis of event-related potentials: simulation studies demonstrate misallocation of variance across components.

Simulated event-related potential (ERP) components were used to investigate the ability of principal component analysis (PCA), Varimax rotation and univariate analysis of variance (ANOVA) to reconstruct component wave shapes, to allocate variance correctly across components, and to identify the correct locus of simulated experimental treatments. The simulated ERPs consisted of 800 randomly weighted combinations of three 64-point components, corresponding to a 2 X 2 X 10 repeated-measures design with 20 subjects. Covariance PCAs, Varimax rotations and univariate ANOVAs were performed on each of 400 such simulations, 100 with no effect of any experimental treatment and 100 each with main effects on each of the 3 components. Eight hundred additional simulations were performed to investigate the effects of systematic variations in the size of the experimental treatments and the number of subjects per experiment. The wave shapes of the simulated components were reconstructed reasonably well, although not completely, by the rotated principal component (PC) loadings. However, comparison of rotated PC scores with the random weights used to generate the simulated ERPs indicated that PCA incorrectly allocated variance across overlapping components, producing dramatic increases in type I error (the largest in excess of 80%) for ANOVAs on one component when the true treatment effect was on another. Although these results should not be overgeneralized, they clearly demonstrate that the PCA-Varimax-ANOVA strategy can incorrectly distribute variance across components, resulting in serious misinterpretation of treatment effects. Additional simulation studies are needed to determine the generality of the variance misallocation problem; pending the outcome of such studies, results obtained with the PCA-Varimax-ANOVA strategy should be interpreted cautiously.

Analysis of Variance↗
PubMed2001

Evaluation of the lipophilicity of bile acids and their derivatives by thin-layer chromatography and principal component analysis.

The lipophilic character of bile acids and their glyco- and tauro-conjugates was studied. The classical R(Mo) values were measured by means of reversed thin-layer chromatography using a mixture of methanol-water as the solvent system and compared with the factors scores obtained by principal component analysis based also onto the TLC-retention data. The reliability of the factor scores values as lipophilic indices are shown by their high correlation with the classical R(Mo) values. In addition, a better correlation was observed between scores corresponding to the first principal components and the partition coefficients (log P) of bile acids. Finally, the "lipophilicity chart" described by the first two components has the effect of separating compounds from each other most effectively from the congeneric aspect point of view.

Bile Acids and Salts↗
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↗
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↗
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↗
PubMed1994

Principal component analysis of Fourier transform infrared and/or circular dichroism spectra of proteins applied in a calibration of protein secondary structure.

Gaining information on the secondary structure of a protein from its spectra is presented as a calibration problem. The secondary structures known from X-ray studies and the spectra of 21 proteins are represented by a linear model. Fourier transform infrared (FTIR) spectra from 1700 to 1600 cm-1, circular dichroism (CD) spectra from 178 to 260 nm, and combined spectra are used; the secondary structure classes of interest are alpha-helices, antiparallel beta-sheets, parallel beta-sheets, beta-turns, and "other." The calibration is solved in two steps: (i) the dependencies between the structures and the spectra of reference proteins are found using the least-squares estimator, and (ii) the secondary structure of a protein is predicted from its spectra using the information gained in the first step and principal component analysis. The problem of information content of the reference spectra is analyzed using the linearly independent pieces of information, the so-called principal components, provided by singular value decomposition. Attention is paid to a number of the principal components sufficient for the prediction, which may be less than the total number. A relative estimable parameter is used to determine unambiguously the number of the components corresponding to the minimum mean square error of the predictor. The analysis gives the solutions to this linear calibration relevant to the underlying protein problem, thus reducing subjective assessments as well as computations.

Calibration↗
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↗
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↗
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↗