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At least 109 records · Page 6Linked to original sources
PubMed2006

[Study of variables associated with skin cancer in Chile using principal component analysis].

BACKGROUND: The incidence of skin cancer in Chile has increased in recent years. OBJECTIVE: To associate variables with skin cancer in Chile through indices generated using multivariate descriptive statistical techniques. MATERIAL AND METHOD: During May 2004, information was gathered from demographic, meteorological and clinical data from Chile corresponding to fiscal year 2001, the latest complete, official information available for the country's Health Services as a whole. The variables developed by the following were studied: the National Statistics Institute (INE), the Ministry of Health (MINSAL), the Ministry of Planning and Cooperation (MIDEPLAN), the National Health Fund (FONASA), the Chilean Meteorological Directorate, Federico Santa María Technical University and the Directorate-General for Water. A Principal Component Analysis (PCA) was then performed on the data obtained. RESULTS: The first three principal components were selected, with a cumulative explained variance percentage of 54.48 %. The first principal component explains 24.92 % of the variance, and is related to climatic and geographic variables. The second principal component explains 15.77 % of the variance, and is mainly related to FONASA's beneficiary population and the poverty rate. The mortality rate from skin cancer runs significantly against this component. The third principal component explains 13.79 % of the variance, and is related to population characteristics, such as total catchment population, female population and urban population. CONCLUSION: Performing PCA is useful in studying the factors associated with skin cancer.

Adult↗
PubMed2004

Genome screen for a combined bone phenotype using principal component analysis: the Framingham study.

Genetic factors substantially contribute to variation in bone mass. There is a controversy as to whether shared genetic factors exist for bone mass at different sites. We hypothesize that using a composite phenotypic score of several correlated bone mass measures may provide complementary results for linkage studies. In the members of 323 pedigrees from the Framingham Osteoporosis Study, bone mineral density (BMD) was measured at the lumbar spine and three femoral sites (Lunar DPX-L), and quantitative ultrasound (QUS) measured at the calcaneus (Hologic Sahara). Data on age, sex, anthropometry, alcohol and caffeine intake, smoking status, physical activity, menopause, and estrogen use (in females) were also obtained. Principal component analyses of BMD and QUS phenotypes were performed in each sex and generation (parents and offspring). The principal component analyses yielded two components, whose loadings were extracted as principal component scores (PC1 and PC2) for each individual, with PC1 explaining up to 66% of the total variation of all bone mass measurements, and PC2 an additional 24%. Principal component analysis of the three femoral BMD measures resulted in one component (PC_hip) that explained 89-91% of the common variation of hip BMD measures. Quantitative genetic analysis (using the variance components method) revealed that both principal component scores were under significant genetic influences (covariate-adjusted heritabilities of PC1, PC2, and PC_hip were 0.66 +/- 0.07, 0.44 +/- 0.07, and 0.61 +/- 0.06, respectively). For PC1, loci of suggestive linkage were identified on chromosomes 1q21.3 and 8q24.3 with the maximum multipoint LOD scores 2.5 and 2.4, respectively. For PC2, multipoint LOD score was 2.1 on 1p36. Suggestive linkage of PC_hip was found on 8q24.3 and 16p13.2 (LODs>1.9). In conclusion, an approach to linkage analysis using the linear combination of several correlated bone phenotypes suggests that there are chromosomal loci regulating bone mass, with seemingly pleiotropic effects at different skeletal sites.

Adult↗
PubMedDate not supplied

Interpretation of principal components of the reflectance spectra obtained from multispectral images of exposed pig brain.

The spatial variation in reflectance such as the blood-vessel pattern can be observed in the image of cerebral cortex. This spatial variation is mainly caused by the difference in concentrations of oxy- and deoxyhemoglobin in the tissue. We analyze the reflectance spectra obtained from multispectral images of pig cortex by principal component analysis to extract information that relates to physiological parameters such as the concentrations of oxy- and deoxyhemoglobin and physical parameters such as mean optical path length. The light propagation in a model of exposed pig cortex is predicted by Monte Carlo simulation to estimate the interpretation of physiological and physical meanings of the principal components. The spatial variance of reflectance spectra of the pig cortex can be approximately described by the first principal component. The first principal component reflects the spectrum of hemoglobin in the cortical tissue multiplied by the mean optical path length. These results imply that the wavelength dependence of mean optical path length can be experimentally estimated from the first principal component of the reflectance spectra obtained from multispectral image of cortical tissue.

Animals↗
PubMed2002

Main functional roles of knee flexors/extensors in able-bodied gait using principal component analysis (I).

This study was undertaken to demonstrate how principal component analysis (PCA) can be used to detect the main functional structure of actions taken by knee flexors/extensors during able-bodied gait. PCA was applied as a classification and curve structure detection method for knee sagittal muscle moment developed during walking of 20 young, healthy male subjects. Over 90% of the information provided by the first three principal components (PCs) was chosen for further biomechanical interpretation. PCA was able to identify the three main functional contributions of knee sagittal muscle moment during able-bodied gait, namely control balance, foot clearance/limb preparation and shock absorption.

Adolescent↗
PubMed2004

Coupled principal component analysis.

A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.

Principal Component Analysis↗
PubMed2006

Principal component analysis of lifting waveforms.

BACKGROUND: One limiting factor in lifting research design has been the inability to effectively analyze waveform data, especially when differences in body mass, height, and load magnitude influence the derived kinetic variables. The purpose of this study was to demonstrate the sensitivity of principal component analysis to quantify clinically relevant differences in kinetic lifting waveforms over three load magnitudes and between two separate populations. METHODS: Principal component analysis was applied to five kinetic lifting waveforms. The derived principal component scores were used as the dependent measures in a two-way (clinical status x load magnitude) MANOVA. FINDINGS: Significant low back pain group differences (P<0.05) were found for three of the principal component scores on extension moment generation in the sacral and thoracic regions and for trunk compression. Significant differences were found for each variable with respect to the magnitude across the entire lift time between the three load conditions, as well as four significant differences related to inferred mechanical changes that resulted from lifting increasingly heavier loads. INTERPRETATION: Principal component analysis of kinetic lifting waveforms was shown to be insensitive to a confounding factor of different load magnitudes when attempting to identify previously determined clinically relevant differences in the waveform trajectories. The analysis was able to partition the variability attributed to the direct influence of different external load magnitudes, versus those differences in spinal loading that arose from the variations in the lifting mechanics of increasing loads. The technique could be beneficial for other kinetic analyses where confounding magnitude modifiers like body size are present.

Adult↗
PubMed2005

Iterative kernel principal component analysis for image modeling.

In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution and denoising performance are comparable to existing methods.

Algorithms↗
PubMed2004

Metabolomic differentiation of Cannabis sativa cultivars using 1H NMR spectroscopy and principal component analysis.

The metabolomic analysis of 12 Cannabis sativa cultivars was carried out by 1H NMR spectroscopy and multivariate analysis techniques. Principal component analysis (PCA) of the 1H NMR spectra showed a clear discrimination between those samples by principal component 1 (PC1) and principal component 3 (PC3) in cannabinoid fraction. The loading plot of PC value obtained from all 1)H NMR signals shows that Delta9-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) are important metabolites to differentiate the cultivars from each other. The discrimination of the cultivars could also be obtained from a water extract containing carbohydrates and amino acids. The level of sucrose, glucose, asparagine, and glutamic acid are found to be major discriminating metabolites of these cultivars. This method allows an efficient differentiation between cannabis cultivars without any prepurification steps.

Asparagine↗
PubMedDate not supplied

QSAR modeling of flotation collectors using principal components extracted from topological indices.

Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

Journal Article↗
PubMed2005

Location and intensity discrimination in the leech local bend response quantified using optic flow and principal components analysis.

In response to touches to their skin, medicinal leeches shorten their body on the side of the touch. We elicited local bends by delivering precisely controlled pressure stimuli at different locations, intensities, and durations to body-wall preparations. We video-taped the individual responses, quantifying the body-wall displacements over time using a motion-tracking algorithm based on making optic flow estimates between video frames. Using principal components analysis (PCA), we found that one to three principal components fit the behavioral data much better than did previous (cosine) measures. The amplitudes of the principal components (i.e., the principal component scores) nicely discriminated the responses to stimuli both at different locations and of different intensities. Leeches discriminated (i.e., produced distinguishable responses) between touch locations that are approximately a millimeter apart. Their ability to discriminate stimulus intensity depended on stimulus magnitude: discrimination was very acute for weak stimuli and less sensitive for stronger stimuli. In addition, increasing the stimulus duration improved the leech's ability to discriminate between stimulus intensities. Overall, the use of optic flow fields and PCA provide a powerful framework for characterizing the discrimination abilities of the leech local bend response.

Animals↗
PubMed2003

[Principal component analysis on ultrasound indexes of schistosomiasis and the assessment on prevalence rate].

OBJECTIVE: To explore the synthetical index for diagnosing schistosomiasis with ultrasound and to assess the prevalence rate with the index. METHODS: Ultrasound indexes of schistosomiasis Japonicum were analyzed by principal component analysis, and the synthetical indexes were assessed by ROC curve. RESULTS: Among the abnormal rates of the 6 indexes, the lowest was 1.6% comparing with the highest of 59.5%. Significant difference was noficed among the abnormal rates (chi(2) = 631.1, P < 0.01). The individual correlation of the six indexes to each other as will as with age distribution was significant (P < 0.05). The three principal components reflected the degree of pathological changes on liver and spleen. The first principal component was the factor reflecting the degree of liver pathological changes, and the second and third principal components reflected the degree of pathological changes on spleen. The synthetical index D(1) = 0.047X(1) + 0.428X(2) + 1.247X(3) + 0.095X(4) + 0.002X(5) + 0.213X(6) - 12.837 was found by adding the three weight principal components, and it's area under the ROC curve was 0.957. When -1.70 was taken as the critical value, the abnormal rate of population was 66.3%, close to the resident's actual prevalence rate 66.9%. CONCLUSION: Ultrasonography was considered as a method which could rapidly assessing the resident's prevalence rate in the endemic areas of schisitosomiasis Japonicum, and could also provide powerful information for development of strategy on chemotherapy.

Adolescent↗
PubMed2006

Principal component structure and sport-specific differences in the running one-leg vertical jump.

The aim of this study is to identify the kinetic principal components involved in one-leg running vertical jumps, as well as the potential differences between specialists from different sports. The sample was composed of 25 regional skilled athletes who play different jumping sports (volleyball players, handball players, basketball players, high jumpers and novices), who performed a running one-leg jump. A principal component analysis was performed on the data obtained from the 200 tested jumps in order to identify the principal components summarizing the six variables extracted from the force-time curve. Two principal components including six variables accounted for 78 % of the variance in jump height. Running one-leg vertical jump performance was predicted by a temporal component (that brings together impulse time, eccentric time and vertical displacement of the center of mass) and a force component (who brings together relative peak of force and power, and rate of force development). A comparison made among athletes revealed a temporal-prevailing profile for volleyball players, and a force-dominant profile for Fosbury high jumpers. Novices showed an ineffective utilization of the force component, while handball and basketball players showed heterogeneous and neutral component profiles. Participants will use a jumping strategy in which variables related to either the magnitude or timing of force production will be closely coupled; athletes from different sporting backgrounds will use a jumping strategy that reflects the inherent demands of their chosen sport.

Adult↗
PubMed2006

Principal component, Varimax rotation and cost analysis of volume effects in rectal bleeding in patients treated with 3D-CRT for prostate cancer.

We investigate the utility of principal component analysis as a tool for obtaining dose-volume combinations related to rectal bleeding after radiotherapy for prostate cancer. A direct implementation of principal component analysis reduces the number of degrees of freedom from the patient's dose-volume histograms that are associated with bleeding. However, when low-variance principal components are strongly correlated to outcome, their interpretation is problematic. A Varimax rotation is employed to aid in interpretability of the low-variance principal components. This procedure brings us closer to finding unique dose-volume combinations related to outcome but reintroduces correlation, requiring analysis of the overlap of information contained in such modes. Finally, we present examples of cost-benefit analyses for candidate dose-volume constraints for use in treatment planning.

Gastrointestinal Hemorrhage↗
PubMed1995

[Detection of wall motion abnormality in old myocardial infarction with principal component analysis to body surface potential distribution].

The purpose of this study was to examine the correlation between body surface potential distribution and the extent of abnormally contracting segments (ACS) of the left ventricle in patients with old myocardial infarction (MI). Body surface potential distribution was quantitatively analyzed using principal component analysis. The first 6 principal components were extracted from data set obtained from both 25 normal subjects and 100 patients. The z scores were calculated at every 4 or 8 msec in another 81 patients with previous MI. In conclusion, the principal component analysis on body surface maps successfully condensed mapped data without significant loss of total variance and minute differences in the extent of ACS were sensitively detected on z scores of principal components.

Adolescent↗
PubMed1994

Relation of principal components of ECG maps to loci of wall motion abnormality in old myocardial infarction.

The purpose of this study was to examine the correlation between body surface potential distribution and the extent of abnormally contracting segments (ACS) of the left ventricle in patients with old myocardial infarction (MI). Body surface potential distribution was quantitatively analyzed using principal component analysis. The first six principal components were extracted from data set obtained from both 25 normal subjects and 100 patients. The z scores (principal component weights) were calculated in a time domain at every 4 or 8 ms in another 81 patients with previous MI. In anterior MI, the second z scores on T wave and the fourth z scores on QRS complex were significantly lower in the presence than in the absence of ACS at the lateral wall. In inferior MI, the first z scores on QRS complex, the second on T wave, and the third on both Q and T represented significant differences between the presence and the absence of ACS at the posterior wall. Minute differences in the extent of ACS were sensitively and noninvasively detected on z scores of principal components.

Adolescent↗
PubMed2005

Comparative study of face recognition techniques that use joint transform correlation and principal component analysis.

Face recognition based on principal component analysis (PCA) that uses eigenfaces is popular in face recognition markets. We present a comparison between various optoelectronic face recognition techniques and a PCA-based technique for face recognition. Computer simulations are used to study the effectiveness of the PCA-based technique, especially for facial images with a high level of distortion. Results are then compared with various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical-correlator-based neural networks, and pose-estimation-based correlation.

Algorithms↗
PubMed2006

Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12.

The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant transcripts were identified by analysing the loadings of the principal components (PC) and discriminants (D) in PCA and PCDA, respectively. Even more specifically, the relevant transcripts for a specific phenotype could also be ranked from the loadings under an angle (biplot) obtained after PCDA analysis. The leads identified in this way were compared with those identified using the commonly applied fold-difference and hierarchical clustering approaches. The different data analysis methods gave different results. The methods used were complementary and together resulted in a comprehensive picture of the processes important for the different carbon sources studied. For the more subtle, regulatory processes in a cell, the PCDA approach seemed to be the most effective. Except for glucose and gluconate dehydrogenase, all genes involved in the degradation of glucose, gluconate and fructose were identified. Moreover, the transcriptomics approach resulted in potential new insights into the physiology of the degradation of these carbon sources. Indications of iron limitation were observed with cells grown on glucose, gluconate or succinate but not with fructose-grown cells. Moreover, several cytochrome- or quinone-associated genes seemed to be specifically up- or downregulated, indicating that the composition of the electron-transport chain in P. putida S12 might change significantly in fructose-grown cells compared to glucose-, gluconate- or succinate-grown cells.

Carbohydrate Metabolism↗
PubMed1985

Study of lung function data by principal components analysis.

As a rational approach to the many lung function tests available, we have subjected the results of a battery of six lung function measurements made in 458 coalminers to the statistical technique of principal components analysis. By this means the six test results were reduced to three principal components without important loss of information. The first component appeared to represent lung size and the second the degree of airflow obstruction, and the third detected impairment of gas transfer factor in excess of that explained by the first two components. The values of the first principal component, used to select men with abnormal lung function, identified more younger men with functional abnormalities than a method based on comparison of observed and predicted values of forced expiration volume in one second. The values of the second and third principal components were used to classify types of functional abnormality. It is concluded that this statistical technique provides a sensitive method of identifying men with unusual lung function, particularly younger men, in a population and can be used to define and quantify different aspects of lung function.

Adult↗