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PubMed1996

Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance.

The performance of a new, neural network-based image compression method was evaluated on digital radiographs for use in an educational environment. The network uses a mixture of principal components (MPC) representation to effect optimally adaptive transform coding of an image and has significant computational advantages over other techniques. Nine representative digital chest radiographs were compressed 10:1, 20:1, 30:1, and 40:1 with the MPC method. The five versions of each image, including the original, were shown simultaneously, in random order, to each of seven radiologists, who rated each one on a five-point scale for image quality and visibility of pathologic conditions. One radiologist also ranked four versions of each of the nine images in terms of the severity of distortion: The four versions represented 30:1 and 40:1 compression with the MPC method and with the classic Karhunen-Loève transform (KLT). Only for the images compressed 40:1 with the MPC method were there any unacceptable ratings. Nevertheless, the images compressed 40:1 received a top score in 26%-33% of the evaluations. Images compressed with the MPC method were rated better than or as good as images compressed with the KLT technique 17 of 18 times. Four of nine times, images compressed 40:1 with the MPC method were rated as good as or better than images compressed 30:1 with the KLT technique.

Neural Networks, Computer↗
PubMed1999

Major structural determinants of transmembrane proteins identified by principal component analysis.

We identify amino acid characteristics important in determining the secondary structures of transmembrane proteins, and compare them with characteristics important for cytoplasmic proteins. Using information derived from multiple sequence alignments, we perform a principal component analysis (PCA) to identify the directions in the 20-dimensional amino acid frequency space that comprise the most variance within each protein secondary structure. These vectors represent the important position-specific properties of the amino acids for coils, turns, beta sheets, and alpha helices. As expected, the most important axis for most of the datasets was hydrophobicity. Additional axes, distinct from hydrophobicity, are surprising, especially in the case of transmembrane alpha helices, where the effects of aromaticity and beta-branching are the next two most significant characteristics. The axis representing beta-branching also has equal importance in cytoplasmic and transmembrane helices, a finding that contrasts with some experimental results in membrane-like environments. In a further analysis, we examine trends for some of the PCA axes over averaged transmembrane alpha helices, and find interesting results for aromaticity.

Amino Acids↗
PubMed1979

Investigation of the common mechanism of action of antibacterial compounds containing gamma-pyridone-beta-carboxylic acid structure by principal component.

On the basis of 576 in vitro antibacterial positive results of 99 compounds containing gamma-pyridone-beta-carboxylic acid structure conclusions have been drawn on the mechanism of action of the derivatives by principal component analysis. It has been found that the compounds exert their effect essentially by influencing two types of physiological processes or series of processes. The more important of the two "mechanisms", responsible for 50-95% of the activity of the compounds, is common for the compounds investigated, and is associated with the gamma-pyridone-beta-carboxylic acid structure.

Anti-Bacterial Agents↗
PubMedDate not supplied

Improving image contrast using principal component analysis for subsequent image segmentation.

This article presents a technique for improving MR image contrast by linearly combining multiple MR images with different tissue contrast. The weighting coefficients of the linear combination are derived using principal component analysis. The contrast-enhanced composite image is segmented subsequently using gray level-based 1D segmentation methods. The technique reduces a multispectral image set to composite eigenimages and allows application of appropriate 1D segmentation methods that do not have equivalent counterparts in multispectral methods.

Brain↗
PubMed1995

Changes in alertness are a principal component of variance in the EEG spectrum.

Minute-scale fluctuations in the normalized EEG log spectrum, when correlated with concurrent changes in level of performance on a sustained auditory detection task, showed that a single principal component of EEG spectral variance is linearly related to minute-scale changes in detection performance. The particular EEG frequencies at which this coupling is expressed are similar for most subjects under a range of task conditions, and match those recently reported from analysis of verbal self-reports during drowsiness. The one-dimensional relationship between detection performance and the EEG spectrum confirms quantitatively the intuitive assumption that minute-scale changes in behavioral alertness during drowsiness are predominantly linked to changes in global brain dynamics along a single dimension of psychophysiological arousal.

Acoustic Stimulation↗
PubMed1986

Relationships between induction of anesthesia and mitotic spindle disturbances studied by means of principal component analysis.

A dataset comprising the activity of 30 compounds in 4 biological tests--anesthesia of tadpoles, anesthesia of frog heart, abnormal growth and spindle disturbances in Allium root tips--was re-evaluated by means of principal component analysis. A two-component model is required to explain the variation in biological activity of the compounds. It is found that abnormal growth is different from the other biological responses. When this test is excluded, as much as 90% of the variation is explained by a one-component model, the determining factor most probably being the lipophilic character of the compounds. Mammalian mitotic cells respond in a similar way to mitotic cells of Allium root tips. It is suggested that possible regularities in the dose-response relationships for anesthesia, teratogenic effects and generation of abnormal chromosome numbers require further exploration.

Anesthetics↗
PubMed2000

Functionalization of hydrocolloids: principal component analysis applied to the study of correlations between parameters describing the consistency of hydrogels.

This work was part of a pure research project on the functionalization of three families of hydrocolloids: cellulose derivatives, carrageenates, and alginates. Principal component analysis (PCA), a powerful statistical method, was used to demonstrate the relations existing among these different parameters that describe the consistency of hydrogels and their spreadability. This approach therefore provides a basis for modeling hydrogel consistency. PCA also afforded a classification of hydrogels that demonstrated the remarkable adhesiveness of very stiff gels based on cellulose derivatives and sodium or potassium alginates. The corresponding semi-fluid gels and all the gels based on carrageenates and mixed sodium-calcium alginates, whatever their spreadability, were found to be very poorly adhesive. Generalized to all the many colloids currently marketed, this approach can be used to set up a databank for the formulation of mucoadhesive excipients.

Adhesiveness↗
PubMed1979

Principal components analysis as an aid to classification of renal dynamic studies.

Four hundred renal dynamic studies obtained using 99mTc-(Sn) DTPA were classified clinically into four classes (normal, pre-renal lesion, intrarenal lesion and urinary tract obstruction). Principal components analysis was then applied to the kidney activity/time curves and yielded good class separation using only three components. The separation of normal and obstructed or damaged kidneys using only the first component was better than that obtained using the calculated mean transit time.

Diagnosis, Differential↗
PubMed1979

Principal component analysis of ERP differences related to the meaning of an ambiguous word.

Event-related potentials (ERPs) to the noun and verb meanings of/'led/in the single ambiguous phrase 'it was/'led/' were re-analyzed using principal component analysis (PCA). These data had previously been analyzed by SWDA and reported in this journal. PCA defined 3 meaning-related components, comprising 40.3% of the entire data variance. The N150 component was shown to be larger for the noun meaning than for the verb meaning; the P230 epoch differed in its anterior-posterior distribution according to meaning; and N370 for noun responses was relatively more negative at the right posterior lead and positive at the left anterior. All components taken together, the left anterior lead showed the greatest meaning-related difference. Previous analysis by SWDA had resulted in significant discriminant functions for left hemisphere ERPs, but this analysis did not yield a clear definition of the effects of meaning on specific ERP components or of the scalp distributions of meaning-related components. Thus, while the results of both analyses support the interpretation that the perceived meaning of words has a substantial effect on ERP wave forms, PCA appears to provide the clearest definition of the ERP component effects.

Adult↗
PubMed2003

Tracing the Mediterranean diet through principal components and cluster analyses in the Greek population.

OBJECTIVE: To identify dietary patterns, and their socio-demographic and lifestyle correlates in a large sample of Greek adults, and assess their adherence to the traditional Mediterranean diet. DESIGN: Principal component (PC) analysis was used to identify dietary patterns among 28 034 participants of the Greek branch of the European Prospective Investigation into Cancer and Nutrition. Dietary information was collected through a validated, semiquantitative, food-frequency questionnaire. The extracted PCs were subsequently regressed on sociodemographic and lifestyle variables. Analyses were also performed to classify individuals with similar dietary behavior into clusters. RESULTS: Four PCs were identified: PC1 resembled the Mediterranean diet, PC2 approximated a vegetarian diet with emphasis on seed oils, PC3 reflected a preference for sweets, and PC4 reflected a Western diet. PC1 and PC2 were positively associated with age, education, physical activity, and nonsmoking status. Females, in comparison to males, scored higher on PC1 but lower on PC2. Males, younger, more educated individuals, nonsmokers and residents of Greater Athens (Attica) scored higher on PC3. PC4 was associated with younger age, less education, and current smoking. In cluster analyses, cluster A contrasted clusters B and C in having much higher mean PC1- and PC2-scores and substantially lower PC3- and PC4-scores. PC1 and PC4 were, respectively, positively and inversely correlated with an a priori Mediterranean-diet score; PC2 and PC3 were unrelated to it. CONCLUSION: The Mediterranean-like PC1-score as well as the vegetarian-like PC2 were higher among older, more educated people, and were associated with a healthier lifestyle than PC4, which reflected a Western-type diet. PC1 was strongly positively associated with an a priori Mediterranean-diet score.

Adult↗
PubMed2006

Analyzing large-scale structural change in proteins: comparison of principal component projection and Sammon mapping.

Effective analysis of large-scale conformational transitions in macromolecules requires transforming them into a lower dimensional representation that captures the dominant motions. Herein, we apply and compare two different dimensionality reduction techniques, namely, principal component analysis (PCA), a linear method, and Sammon mapping, which is nonlinear. The two methods are used to analyze four different protein transition pathways of varying complexity, obtained by using either the conjugate peak refinement method or constrained molecular dynamics. For the return-stroke in myosin, both Sammon mapping and PCA show that the conformational change is dominated by a simple rotation of a rigid body. Also, in the case of the T-->R transition in hemoglobin, both methods are able to identify the two main quaternary transition events. In contrast, in the cases of the unfolding transition of staphylococcal nuclease or the signaling switch of Ras p21, which are both more complex conformational transitions, only Sammon mapping is able to identify the distinct phases of motion.

Amino Acid Substitution↗
PubMedDate not supplied

Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm.

We have evaluated combinations of 111 descriptors that were calculated from two-dimensional representations of molecules to classify 455 compounds belonging to seven biological activity classes using a method based on principal component analysis. The analysis was facilitated by application of a genetic algorithm. Using scoring functions that related the number of compounds in pure classes (i.e., compounds with the same biological activity), singletons, and mixed classes, effective descriptor sets were identified. A combination of only four molecular descriptors accounting for aromatic character, hydrogen bond acceptors, estimated polar van der Waals surface area, and a single structural key gave overall best results. At this performance level, approximately 91% of the compounds occurred in pure classes and mixed classes were absent. The results indicate that combinations of only a few critical descriptors are preferred to partition compounds according to their biological activity, at least in the test cases studied here.

Algorithms↗
PubMed1976

Digital computer processing of brain scans using principal components.

Most of the scan processing methods used to date are unable to distinguish between normal and abnormal features. This paper describes a method of constructing a particular orthogonal transform from a set of normal images, the method of principal components, which may be used selectively to filter out normal features, leaving behind abnormal features. The method has been applied to brain scans and has been implemented on a small digital computer system with magnetic disc.

Brain Diseases↗
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↗
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↗
PubMed2005

The phase transition temperatures of a liquid crystal determined from FT-IR spectra explored by principal component analysis.

The FT-IR spectra of a thin layer of pure 4-chloro-2'-hydroxy-4'-pentyloxyazobenzene (CHPAB) were studied as a function of temperature. A detailed analysis of the intensity variations was performed by a method based on principal component analysis (PCA). It was shown that the phase transition temperatures obtained by means of PCA and those determined by differential scanning calorimetry (DSC), the most widely used technique in the field, were nearly identical. The PCA results revealed that the transition from solid to a liquid crystalline (LC) phase (smectic A) is more drastic phase transition in terms of infrared absorption changes. The nematic to isotropic phase transition is much less infrared sensitive. Very much smaller absorption changes are associated with the transition between the smectic and nematic mesophases. The pattern of the intensity changes strictly is correlated with the orientation of the CHPAB molecules towards the surface windows due to the surface-induced homeotropic alignment of LC molecules. The important role of hydrogen bonding interaction on the observed transition is disclosed.

Azo Compounds↗
PubMed2000

Data evaluation for soft drink quality control using principal component analysis and back-propagation neural networks.

This work describes an alternative for chemical data research, with the aim of evaluating finished product quality. Analytical data for additives in soft drinks are interpreted by the use of multivariate data analysis: principal component analysis (PCA), factor analysis, cluster analysis, and artificial neural networks. Taking into account various chemical components like sorbic, benzoic, and ascorbic acids; saccharose; caffeine; Na, K, Ca, Mg, Fe, Zn, Cu, P, and B, soft drinks were characterized and classified. The ratios of Na, K, Ca + Mg, P, and K/Na have been studied. The application of PCA, cluster analysis, and artificial neural networks showed that combination of these chemometric tools offers effective means for modeling and classifying soft drinks in accordance with their contents in additives and heavy metals.

Beverages↗
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↗