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PubMed1991

[Aging effects on blood biochemical parameters in aged women: a longitudinal study using principal component analysis].

In order to examine the aging effects on blood biochemical parameters, 25 aged females (70 +/- 8 years old at the beginning of this investigation) were studied prospectively for 7 years. Biochemical parameters were measured in 1982, 1985, 1987, and 1989. Component scores were obtained by principal component analysis applied to all of these 100 data. The data of 13 biochemical parameters and 4 component scores were analyzed for aging, cohort, and time effects by utilizing 3 types of approach i.e., longitudinal, cross-sectional, and time-series. Positive aging effects were observed on blood urea nitrogen and 4th component score. Fourth component score had positive relations to blood urea nitrogen and total bilirubin, and a negative relation to total protein. This 4th component score increased with longitudinal age changes among all cohorts divided by decade, and it increased markedly over 70 years old. These results suggest that the aging effects on biochemical parameters are related with both the decrease in glomerular and hepatocellular functions and the increase in breakdown of tissue protein.

Aged↗
PubMed1994

Principal Components Analysis of the General Purpose Abbreviated Battery of the Stanford-Binet, Fourth Edition, for Young Children

With increased emphasis on preschool assessment, the technology and theoretical basis of intelligence measures suitable for this age group is of increasing importance. One method of examining the appropriateness of preschool measures involves verification of the factor structure of the measure as an indication of construct validity. Factor analytic studies of the Binet 4 have not resulted in consistent findings, and more specifically, the number of factors identified have demonstrated variability across age levels. In that many clinicians use abbreviated forms of the Binet 4, this study conducted a principal component analysis of the General Purpose Abbreviated Battery (GPAB) for a sample of 107 children (ages 2-7 years) who were referred for evaluation to a community-based pediatric psychoeducational outpatient clinic. The structure of the GPAB resulted in a single general intelligence component that mitigates against Binet Area Score interpretation for this age group when the GPAB is used. Implications of previous research as well as the results of this study are discussed relative to the interpretation and utility of the GPAB with preschool and young children.

Journal Article↗
PubMed2002

A new whole-mouth gustatory test procedure. 1. Thresholds and principal components analysis in healthy men and women.

Gustatory testing using the whole-mouth method was performed in 123 healthy young male and female subjects. The average thresholds for detection and recognition of the four basic tastes were not greatly different from the normal thresholds previously reported in Japan: a 0.0165 M solution of sucrose for sweet taste, a 0.0316 M solution of table salt for salty taste, a 0.000743 M solution of tartaric acid for sour taste and a 0.0000203 M solution of quinine hydrochloride for bitter taste. These results indicate that the whole-mouth gustatory test procedure employed in this study may be useful for evaluating gustatory function clinically. Principal components analysis confirmed that sweet, salty, sour and bitter are indeed the four basic tastes and revealed that the sensation of taste is detected before the specific taste is recognized, regardless of the specific taste tested.

Adolescent↗
PubMed1997

Identification of mitochondrial deficiency using principal component analysis.

The mitochondrial pathologies are a heterogeneous group of metabolic disorders that are characterized by anomalies of oxidative phosphorylation, especially in the respiratory chain. The diagnosis of these pathologies involves many investigations among which biochemical study is at present the main tool. However, the analysis of the results obtained during such study remains complex and often does not make it possible to conclude clearly if a patient is affected or not by a biochemical and/or bioenergetic deficiency. This arises from two main problems: 1. The determination of control values from the whole set of variable values (affected and unaffected people). 2. The small size of the population studied and the large number of variables collected which present a rather large variability. To cope with these problems, the principal component analysis method is applied to the results obtained during our biochemical studies. This analysis makes it possible for each respiratory chain complex, to distinguish clearly two subsets of the whole population (affected and unaffected people) as well as to detect the variables which are the most discriminative.

Adolescent↗
PubMed1985

Subclassification of neurons in the ventrobasal complex of the dog: quantitative Golgi study using principal components analysis.

The neuronal architecture of the ventrobasal complex (VB) in dog is examined in coronal and horizontal brain sections processed by Golgi- and Nissl-staining methods. Presumed projection and intrinsic neurons are identified by differences in soma size and shape, dendritic branch pattern, the morphology and distribution of appendages, and the appearance of axons. Forty-five projection neurons are examined by quantifying (1) soma cross-sectional area, (2) dendritic field extent and shape, (3) appendages on the soma, primary dendrites, and in a defined major dendritic branch zone, and (4) location in the VB. When considered independently, each variable offers little evidence for separation into morphological classes. However, several of the variables have wide ranges and show significant correlation with other parameters. Using the multivariate descriptive methods of principal components analysis and cluster analysis, a separation of the projection neurons into three morphological classes designated as large, medium, and small neurons is indicated. The features most critical in distinguishing between the groups are, in descending order of importance: (1) dendritic field extent; (2) number of primary dendrites; (3) soma cross-sectional area; (4) number of appendages per major branch point (MBP); (5) number of appendages on the soma; and, (6) number of appendages on the primary dendrites. Dendritic field shape and neuron location have little influence in determining classification.

Analysis of Variance↗
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↗
PubMed1992

Use of principal components analysis for mutation detection with two-dimensional electrophoresis protein separations.

The application of two-dimensional electrophoresis (2-DE) to mutation detection requires the capability to monitor each protein in a 2-DE pattern for significant changes in abundance indicative of a mutation event. Previously, mutation searches were done using a univariate outlier detection method in which each protein spot was considered independently in a classical outlier search. An alternative approach to analysis of 2-DE patterns for quantitative changes is a multivariate procedure which takes advantage of the observation that protein spots in a 2-DE pattern often represent correlated rather than independent measurements. We have compared the efficiency of univariate and multivariate procedures for mutation detection using data from the Argonne National Laboratory 2-DE database of mouse liver proteins. Analyses involving a total of over 1500 gels were performed to compare the performance of a multivariate method based on principal components analysis (PCA) with the univariate method. Up to 279 spots from each pattern were used for PCA. First, a simulation was performed to assess the detection efficiency of PCA for single protein spots decreased in abundance by 50%. Then, the ability to detect actual mutations was tested using eight confirmed mutations. Results show that, compared to a univariate approach to analysis of data from the mouse model system, the multivariate method increases the number of protein spots on each 2-DE pattern that can be monitored for quantitative changes indicative of mutations by compensating for variables that contribute to the background quantitative variability of protein spots.

Animals↗
PubMed1994

Principal component analysis, using the measurements during running and swimming test, in thoroughbred horses.

To investigate whether the running exercise fitness of individual horses could be assessed by a standardized swimming exercise test, the results of multivariate analysis of the exercise parameters measured during incremental running and swimming tests were compared. Ten thoroughbred horses were subjected to different types of exercise tests on a track or in a pool, and the maximum heart rate during and the blood lactate concentration immediately after the exercise were examined. Serial exercise parameters (VLA2, VLA4, LA0, V150, V200, HRS, HRLA2, HRLA4) referred to as the indices related to the adaptation of cardiovascular or metabolic systems were computed using the relationships between these measurements and velocity during each test, and were analyzed by a multivariate procedure, i.e. the principal component analysis. The correlation diagram between the exercise parameters on the first two component axes in running were similar to that in swimming. When the exercise fitness in each horse was compared between running and swimming, three horses trained by short-term endurance exercise were statistically distinguished in both tests and differed as a group from the other horses. Therefore, it is thought that evaluation of the exercise fitness in swimming using the multivariate analysis is useful for predicting poor performing horses on a track.

Animals↗
PubMed1995

Link between emotional memory and anxiety states: a study by principal component analysis.

Numerous theoretical as well as pharmacological arguments lead to the assumption that anxiety and memory are two closely linked concepts. Nevertheless, the study of this relationship is full of complexities because neither memory nor anxiety are unitary phenomena. Indeed, the term memory covers a large number of concepts, and anxiety has been divided in two main classes, "state" and "trait" anxiety. Recently the neophobic responses exhibited by Balb/c mice confronted to the free exploratory paradigm have been proposed as a "trait anxiety" model while response exhibited in the light/dark choice procedure as a "state anxiety" one. The aim of this study was to further clarify the link between these two anxiety types and memory of emotional events assessed in the passive avoidance test. The relationship between the variables measured in these three tests were assessed by a principal component analysis that confirmed that the behavior recorded in the two anxiety tests does not reflect the same psychological state, and showed that emotional memory is linked to "state" but not "trait" anxiety.

Animals↗
PubMed1989

Quantification of benzodiazepine-induced topographic EEG changes by a computerized wave form recognition method: application of a principal component analysis.

Topographic EEG changes with medazepam and diazepam in normals were analyzed by the computerized wave form recognition method. A principal component (PC) analysis, using such EEG elements as wave percent-time (8 bands) and average amplitude (7 bands), resulted in a considerable reduction of variables (4 PCs). In O1, because of high positive loadings in the average amplitude in all bands and a decrease in the mean score with either drug, PC-1 represents a component which reacts in the form of diminution of average amplitude as a whole. In Fp1, C3 and O1, PC-2, with a bipolarity of alpha 2 versus beta 1 and beta 2 in the wave percent-time in loading profile, could be a component showing characteristic changes common to the 2 benzodiazepines. In C3, because of a significant difference in the mean score between the 2 drugs, PC-4 might be a between-drug difference component in which diazepam (medazepam) increases (decreases) slow activity. The relationship between the score at PC-4 in C3 and daytime sleepiness may signify that the slow components are associated with sedation. Based on the correlation at PC-2 in Fp1, a marked increase in beta 1 and beta 2 components (responder) rather means less sleepiness, and relative preservation of alpha 1 and alpha 2 (non-responder) more sleepiness.

Adult↗
PubMed2000

Statistical validation of reproducibility of HPLC peptide mapping for the identity of an investigational drug compound based on principal component analysis.

Peptide mapping is a key analytical method for studying the primary structure of proteins. The sensitivity of the peptide map to even the smallest change in the covalent structure of the protein makes it a valuable "fingerprint" for identity testing and process monitoring. We recently conducted a full method validation study of an optimized reverse-phase high-performance liquid chromatography (RP-HPLC) tryptic map of a therapeutic anti-CD4 monoclonal antibody. We have used this method routinely for over a year to test production lots for clinical trials and to support bioprocess development. One of the difficulties in the validation of the peptide mapping method is the lack of proper quantitative measures of its reproducibility. A reproducibility study may include method and system precision study, ruggedness study, and robustness study. In this paper, we discuss the use of principal component analysis (PCA) to quantitate peptide maps properly using its projected scores on the reduced dimensions. This approach allowed us not only to summarize the reproducibility study properly, but also to use the method as a diagnostic tool to investigate any troubles in the reproducibility validation process.

Antibodies, Monoclonal↗
PubMed2005

Enhancing the signal-to-noise ratio of X-ray diffraction profiles by smoothed principal component analysis.

X-ray diffraction is one of the most widely applied methodologies for the in situ analysis of kinetic processes involving crystalline solids. However, due to its relatively high detection limit, it has only limited application in the context of crystallizations from liquids. Methods that can improve the detection limit of X-ray diffraction are therefore highly desirable. Signal processing approaches such as Savitzky-Golay, maximum likelihood, stochastic resonance, and wavelet transforms have been used previously to preprocess X-ray diffraction data. Since all these methods only utilize the frequency information contained in the single X-ray diffraction profile being processed to discriminate between the signals and the noise, they may not successfully identify very weak but important peaks especially when these weak signals are masked by severe noise. Smoothed principal component analysis (SPCA), which takes advantage of both the frequency information and the common variation within a set of profiles, is proposed as a methodology for the preprocessing of the X-ray diffraction data. Two X-ray diffraction data sets are used to demonstrate the effectiveness of the proposed approach. The first was obtained from mannitol-methanol suspensions, and the second data set was generated from slurries of L-glutamic acid (GA) in methanol. The results showed that SPCA can significantly improve the signal-to-noise ratio and hence lower the detection limits (approximately 0.389% g/mL for mannitol-methanol suspensions and 0.4 wt % for beta-form GA in GA-methanol slurries comprising mixtures of both alpha- and beta-forms of GA) thereby providing an important contribution to crystallization process performance monitoring.

Crystallization↗
PubMed2001

Regional cerebral blood flow as assessed by principal component analysis and (99m)Tc-HMPAO SPET in healthy subjects at rest: normal distribution and effect of age and gender.

The increasing implementation of standardisation techniques in brain research and clinical diagnosis has highlighted the importance of reliable baseline data from normal control subjects for inter-subject analysis. In this context, knowledge of the regional cerebral blood flow (rCBF) distribution in normal ageing is a factor of the utmost importance. In the present study, rCBF was investigated in 50 healthy volunteers (25 men, 25 women), aged 31-78 years, who were examined at rest by means of single-photon emission tomography (SPET) using technetium-99m d, l-hexamethylpropylene amine oxime (HMPAO). After normalising the CBF data, 27 left and 27 right volumes of interest (VOIs) were selected and automatically outlined by standardisation software (computerised brain atlas). The heavy load of flow data thus obtained was reduced in number and grouped in factors by means of principal component analysis (PCA). PCA extracted 12 components explaining 81% of the variance and including the vast majority of cortical and subcortical regions. Analysis of variance and regression analyses were performed for rCBF, age and gender before PCA was applied and subsequently for each single extracted factor. There was a significantly higher CBF on the right side than on the left side ( P<0.001). In the overall analysis, a significant decrease was found in CBF ( P=0.05) with increasing age, and this decrease was particularly evident in the left hemisphere ( P=0.006). When gender was specifically analysed, CBF was found to decrease significantly with increasing age in females ( P=0.037) but not in males. Furthermore, a significant decrease in rCBF with increasing age was found in the brain vertex ( P=0.05), left frontotemporal cortex ( P=0.012) and temporocingulate cortex ( P=0.003). By contrast, relative rCBF in central structures increased with age ( P=0.001). The ability of standardisation software and PCA to identify functionally connected brain regions might contribute to a better understanding of the relationships between rCBF at rest, anatomically defined brain structures, ageing and gender.

Adult↗
PubMed1997

Principal component analysis of event-related potentials: misallocation of variance revisited.

Misallocating variance, in event-related potential analysis, refers to attributing an experimental effect to components not actually affected. A vector interpretation of the relationship between mathematically derived and true underlying components shows that misallocation depends exclusively on incorrect identification of the affected component. Simulations, using seven imperfect rotations, confirmed all predictions from the vector interpretation concerning the presence, direction, and size of misallocated variance. Contrary to principal component analysis (PCA). Möcks's topographic component model (TCM) is not subject to rotation problems. These two methods were compared over 100 simulations in which the components had constant waveforms and topographics across participants. The group effect was always detected, but only PCA and not TCM showed significance on other components, except when their random weights happened to differ between groups.

Analysis of Variance↗
PubMed2001

Application of ICP sector field MS and principal component analysis for studying interdependences among 23 trace elements in Polish beers.

Twenty-three metallic elements, including almost all essential and toxic metals such as lead, cadmium, mercury, arsenic, silver, and thallium, have been quantified in 35 types of bottled and canned Polish beer by using double-focusing sector field inductively coupled plasma mass spectrometry (ICP-MS) with ultrasonic nebulization. The samples were digested using concentrated HNO3 in closed PTFE vessels and applying microwave energy under pressure. The means and medians of the concentrations of Rb, Mn, and Fe were on the order of 200 ng/mL; Cu, Zn, V, Cr, Sn, As, Pb, and Ni were detected at 1-5 ng/mL; Ag, Ga, Cd, Co, Cs, Hg, U, and Sb were found at < 1 ng/mL; and In, Tl, Bi, and Th were present at < 0.1 ng/mL. The concentrations of Hg, Cd, As, Pb, and Zn were 1-3 orders of magnitude lower than proposed tolerance limits. The interdependences among determined trace elements were examined using the principal component analysis (PCA) method. The PCA model explained 74% of the total variance. The metals tend to cluster together (As, Tl, Cs, Sn, Th, Bi, and Hg; Cd and Co; Cs and Cr; Fe and Zn; Mn and V).

Beer↗
PubMed2001

Evaluation of peptide electropherograms by multivariate mathematical-statistical methods. I. Principal component analysis.

Depository effects in slowly metabolised proteins, typically glycation or the estimation of products arising from the reaction of unsaturated long-chain-fatty acid metabolites (possessing aldehydic groups) are very difficult to assess owing to their extremely low concentration in the protein matrix. In order to reveal such alterations we applied deep enzymatic fragmentation resulting in a set of small peptides, which, if modified, are likely to change their electrophoretic properties and can be visualised on the resulting profile. Peptide maps of collagen (a mixture of collagen types I and III digested by bacterial collagenase) were applied as the model protein structure for detecting the nonenzymatic posttranslational changes originating during various physiological conditions like high fructose diet and hypertriglyceridemic state. Capillary electrophoresis in acidic media (sodium phosphate buffer, pH 2.5) was used as the separation method capable of (partial) separation of over 60 peptide peaks. Two to 13 changes were revealed in the profiles obtained reflecting the physiological conditions of the animals tested. Combination of peptide profiling with subsequent t-test evaluation of individual peak areas and principal component analysis based on cumulative peak areas of individual sections of the electropherograms allowed to determine in which section (part) of the electropherogram the physiological state indicating changes occurred. Simultaneously it was possible to reveal the qualitative differences between the four physiological regimes investigated (i.e., which regime affects the collagen molecules most and which affects them least). The approach can be used as guidance for targeted preseparation of the very complex peptide mixture.

Animals↗
PubMed2004

Discrimination of intact and injured Listeria monocytogenes by Fourier transform infrared spectroscopy and principal component analysis.

Fourier transform infrared spectroscopy (FT-IR, 4000-600 cm(-)(1)) was used to discriminate between intact and sonication-injured Listeria monocytogenes ATCC 19114 and to distinguish this strain from other selected Listeria strains (L. innocua ATCC 51742, L. innocua ATCC 33090, and L. monocytogenes ATCC 7644). FT-IR vibrational overtone and combination bands from mid-IR active components of intact and injured bacterial cells produced distinctive "fingerprints" at wavenumbers between 1500 and 800 cm(-)(1). Spectral data were analyzed by principal component analysis. Clear segregations of different intact and injured strains of Listeria were observed, suggesting that FT-IR can detect biochemical differences between intact and injured bacterial cells. This technique may provide a tool for the rapid assessment of cell viability and thereby the control of foodborne pathogens.

Food Contamination↗