Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Single trial event-related cerebral potentials (ERPs) in response to skin stimuli of various intensities and qualities in man were investigated in respect to their nociceptive information content. Electrical constant current stimuli (20 msec, 2 - 8 mA) and mechanical force controlled stimuli (20 msec, 0.8 - 3.2 N) were applied to the tip of the left middle finger. Four intensities of each stimulus quality were given, each intensity appearing 40 times in standardized randomized order. EEG segments (between 5 sec before and 500 msec after stimulus onset) were subjected to computer analysis. ERP wave form was shown to depend upon the amount of alpha waves in the prestimulus EEG. For analysis, only subjects with low power in the alpha band were selected. Principal component analysis was applied to all single trial ERPs measured using the variance-covariance matrix of association. Six principal components (PCs) were extracted accounting for about 90% of total variance. Five of the extracted PCs had well located loading maxima: PC1 (50 - 80 msec), PC4 (140 - 160 msec), PC3 (200 - 250 msec), PC4 (280 - 360 msec), PC5 (400 - 500 msec); PC6 appeared polyphasic. Analysis of variance of the mean PC scores revealed that one PC (PC1) discriminated between quality, and 4 PCs (PC1 - PC4) between quantity of stimulation. Eliminating effects of stimulus intensity resulted in two PCs (PC2, PC4) which distinguished exclusively between non-pain and pain. PCA applied to disjunctive subsets of ERPs, corresponding to the different experimental conditions, yielded practically identical sets of PCs, such that no specific ERP component emerged when pain was reported.
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PMID:Principal component analysis of pain-related cerebral potentials to mechanical and electrical stimulation in man. 617 4

Successive projections algorithm (SPA) was employed to select the optimal combination of principal components (PCs) which were obtained by principal component analysis. Short-wave near infrared spectra of milk powder was firstly analyzed by PCA, and the optimal combination of obtained first eight PCs was determined by SPA. The optimal PC combination of fat content prediction was PC1 , PC2, PC 4, PC5, PC6 and PC7, and the combination for protein content prediction was PC1, PC2, PC3, PC4, PC5 and PC8. Least-squares support vector machine models inputted by different PC combination were established to predict fat and protein content, respectively. Both the fat and protein content prediction results of the PC combination selected by SPA were better than those of first four PCs to first eight PCs. Rp2, and root mean square errors for prediction and residual predictive deviation of prediction results of the PC combination selected by SPA were 0.989, 0.1703 and 9.5343, respectively for fat, and 0.9876, 0.1348 and 8.9274 for protein. The overall results demonstrate that SPA can fast and effectively select the optimal PC combination. The selecting process is simple and does not need abundant parameter debugging.
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PMID:[Study on combinatorial optimization of spectral principal components using successive projections algorithm]. 2003 49

Exposures to persistent organohalogen chemicals during pregnancy are associated with adverse health effects. Low-income, minority women with pre-existing co-morbidities may be particularly vulnerable to these exposures, but have historically been understudied. We aimed to characterize exposures to multiple chemical classes among a sample of ethnically diverse, lower income, overweight or obese pregnant women. Serum concentrations of polybrominated diphenyl ethers (PBDEs) and their hydroxylated metabolites (OH-PBDEs), polychlorinated biphenyls (PCBs), and poly- and perfluoroalkyl substances (PFASs) were measured in 98 pregnant women (California; 2011-2013). Aggregate exposures were evaluated using correlational clustering, a "chemical burden" score, and PCA. Associations between sociodemographic characteristics and individual and aggregate exposures were evaluated using multivariable linear regression. Clustering and PCA both produced four groupings: (PC1) PBDEs/OH-PBDEs, (PC2) PCBs, (PC3) PFNA/PFOA/PFDeA, (PC4) PFHxS/PFOS. Race/ethnicity and prepregnancy BMI were associated with PBDEs, OH-PBDEs and PC1. Maternal age was associated with PCBs and PC2. Parity was associated with PBDEs, OH-PBDEs and PC2. Poverty was negatively associated with PCBs, whereas food insecurity was positively associated with PFOS. We observed variations in sociodemographic profiles of exposures by chemical class and weak across-class correlations. These findings have implications for epidemiologic studies of chemical mixtures and for exposure reduction strategies.
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PMID:Associations between sociodemographic characteristics and exposures to PBDEs, OH-PBDEs, PCBs, and PFASs in a diverse, overweight population of pregnant women. 3154 25