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Results for “Principal Component Analysis”

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PubMed1985

Subclassification of neurons in the subthalamic nucleus of the lesser bushbaby (Galago senegalensis): a quantitative Golgi study using principal components analysis.

The morphology of neurons in the subthalamic nucleus (STN) of the lesser bushbaby (Galago senegalensis) is described in coronal brain sections processed by Golgi- and Nissl-staining techniques. Quantitative and statistical methods are used to evaluate (1) soma size and shape, (2) dendritic field size, shape, and branch frequency, (3) the number of dendritic and somatic spines per neuron, and (4) neuron location within the STN. Principal components analysis of these variables suggests that three classes of neurons are present. Two of these classes are considered to be projection cells, referred to as elongate-fusiform and radiate neurons, respectively. Elongatefusiform neurons have somata and dendritic fields which are large in diameter, extremely fusiform in shape, and give rise to few appendages. Somata and dendritic fields of radiate neurons are smaller in diameter, more rounded in shape, and support more spines than the elongate-fusiform neurons. The third class of cells in Galago STN is tentatively identified as consisting of interneurons on the basis of small soma and dendritic field size, thin and varicose dendritic morphology, and the presence of multilobulated dendritic appendages.

Animals↗

PubMed2000

Determination of the major dimensions of femoral implants using morphometrical data and principal component analysis.

This paper describes the work that leads to the establishment of a set of major parameters for the design of symmetrical prosthetic implants for the Asian population. In the study, 62 sets of femurs harvested from cadavers were used. The morphometrical data obtained are compared with known results and found to be in good agreement with Asian knees. Subsequently, the data are treated and analysed using the principal component analysis, a statistical technique for analysing multivariate data. The analysis has resulted in the establishment of the major design parameters for six different sizes of femoral implants. Details of the analysis are presented. The major parameters obtained in this work are compared with those of existing implants. Results of the comparison are presented. The relationship between the anterio-posterior and medio-lateral dimensions is also examined and reported.

Aged↗

PubMed1996

Differentiation of resin-modified paints by pyrolysis-gas chromatography/mass spectrometry and principal component analysis.

The combination of pyrolysis (Py) with gas chromatography/mass spectrometry (GC/MS) is already well established for polymer analysis. A first approach is reported using this method for detailed quality monitoring of a complex technical polymer system. Six similar solvent-based paints (one standard and five modifications) have been used for evaluation. The major pyrolysis products were identified and qualitative and quantitative modifications were detected and specified. Principal component analysis (PCA) was applied for visualization of differences and similarities.

Journal Article↗

PubMed1995

[Whole mouth gustatory test (Part 1)--basic considerations and principal component analysis].

The whole mouth method gustatory test is a simple gustatory test. It can be applied easily, and can be used to assess overall taste which a subject is supposed to be feeling. In the present study, a new series of taste solutions for the use in the whole mouth gustatory test was prepared. In order to determine the normal range of gustation and relationships between each taste solution, gustatory threshold tests were conducted on 123 healthy volunteers (17-22 years old). The series of taste solutions represented 4 tastes, i.e., sweet, salty, sour, and bitter, and was prepared using sucrose, salt, tartaric acid and quinine, respectively. Recognition thresholds measured for each taste solution yielded normal ranges i.e., 0.0165 mol/l for the sweet solution, 0.0316 mol/l for the salty solution, 0.000734 mol/l for the sour solution, and 0.0000203 mol/l for the bitter solution which were almost the same as those published. The average thresholds obtained for all solutions were at almost always at the middle concentration level, i.e., level 6. Thus, this method can serve as a standard method of performing the whole mouth taste test. Principal component analysis of the thresholds obtained revealed that the primary component mainly demonstrated taste detection function, and that it could more or less be represented by the simple sum of each detection threshold. The analysis also showed that approximately 90% of taste threshold variation is explained by the 4 principal components. These findings provide further evidence that these four tastes comprise the basic elements of taste.

Adolescent↗

PubMed2006

Biomechanical features of gait waveform data associated with knee osteoarthritis: an application of principal component analysis.

This study compared the gait of 50 patients with end-stage knee osteoarthritis to a group of 63 age-matched asymptomatic control subjects. The analysis focused on three gait waveform measures that were selected based on previous literature demonstrating their relevance to knee osteoarthritis (OA): the knee flexion angle, flexion moment, and adduction moment. The objective was to determine the biomechanical features of these gait measures related to knee osteoarthritis. Principal component analysis was used as a data reduction tool, as well as a preliminary step for further analysis to determine gait pattern differences between the OA and the control groups. These further analyses included statistical hypothesis testing to detect group differences, and discriminant analysis to quantify overall group separation and to establish a hierarchy of discriminatory ability among the gait waveform features. The two groups were separated with a misclassification rate (estimated by cross-validation) of 8%. The discriminatory features of the gait waveforms were, in order of their discriminatory ability: the amplitude of the flexion moment, the range of motion of the flexion angle, the magnitude of the flexion moment during early stance, and the magnitude of the adduction moment during stance.

Aged↗

PubMed2000

Chromosome identification using hidden Markov models: comparison with neural networks, singular value decomposition, principal components analysis, and Fisher discriminant analysis.

The analysis of G-banded chromosomes remains the most important tool available to the clinical cytogeneticist. The analysis is laborious when performed manually, and the utility of automated chromosome identification algorithms has been limited by the fact that classification accuracy of these methods seldom exceeds about 80% in routine practice. In this study, we use four new approaches to automated chromosome identification--singular value decomposition (SVD), principal components analysis (PCA), Fisher discriminant analysis (FDA), and hidden Markov models (HMM)--to classify three well-known chromosome data sets (Philadelphia, Edinburgh, and Copenhagen), comparing these approaches with the use of neural networks (NN). We show that the HMM is a particularly robust approach to identification that attains classification accuracies of up to 97% for normal chromosomes and retains classification accuracies of up to 95% when chromosome telomeres are truncated or small portions of the chromosome are inverted. This represents a substantial improvement of the classification accuracy for normal chromosomes, and a doubling in classification accuracy for truncated chromosomes and those with inversions, as compared with NN-based methods. HMMs thus appear to be a promising approach for the automated identification of both normal and abnormal G-banded chromosomes.

Chromosome Mapping↗

PubMed1987

Principal components analysis for source localization of VEPs in man.

This study defines and compares the topologies of the visual evoked potentials to various stimuli such as pattern onset/offset, pattern reversal, pattern motion and high frequency luminance flicker. The responses recorded from 24 occipital derivations were examined using a three sphere conductance model to represent the head, with the assumption that activity from an underlying cortical source is equivalent to a single dipole. Principal components analysis was used to find the dimensionality of the data space. From this analysis could be concluded that all stimuli evoked responses in the primary visual cortex. Only pattern onset, and to a lesser degree pattern offset and pattern reversal, yielded activity in higher visual areas. In particular it has been shown that the CI, CII interval of the pattern onset response has its origins in two different cortical regions. A fast positive (CI)-negative (part of the CII) component arises from area 18 (or 19), a slower negative (initial part of CII) component comes from area 17.

Evoked Potentials, Visual↗

PubMed1998

Determination of lipophilicity of some non-steroidal anti-inflammatory agents and their relationships by using principal component analysis based on thin-layer chromatographic retention data.

The relative lipophilicity of ten non-steroidal anti-inflammatory agents have been determined by reversed-phase thin layer chromatography using different reversed-phase high-performance thin-layer chromatography plates and water-methanol mixtures as eluents. The compounds studied showed regular retention behavior, their RM values decreasing linearly with increasing concentration of methanol in the eluent. Principal component analysis allowed a more rational and objective estimation and comparison of lipophilicity determined by reversed-phase thin-layer chromatography. It also affords a useful graphical tool, since scatterplots of the scores onto the plane described by the first two components will have the effect of separating compounds from each other most effectively, thus obtaining "congeneric lipophilicity chart".

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed1975

Calcitonin treatment of post-menopausal osteoporosis. Evaluation of efficacy by principal components analysis.

The efficacy of long term treatment of senile osteoporosis by low doses of calcitonin was established using five parameters of calcium kinetics and a quantitative pain scale. Under treatment the calcium balance improved, due predominantly to a decrease in bone resorption associated with an increase in bone accretion and intestinal absorption of calcium. In addition, the hormone had a marked analgesic effect, which increased with the length of the treatment. Principal components analysis enables to establish the value of a therapeutic agent for the management of a progressive disease with period of regression like osteoporosis, for which the eficacy of previously advocated treatments had never been proven.

Aging↗

PubMedDate not supplied

Effective dimensionality of large-scale expression data using principal component analysis.

Large-scale expression data are today measured for thousands of genes simultaneously. This development is followed by an exploration of theoretical tools to get as much information out of these data as possible. One line is to try to extract the underlying regulatory network. The models used thus far, however, contain many parameters, and a careful investigation is necessary in order not to over-fit the models. We employ principal component analysis to show how, in the context of linear additive models, one can get a rough estimate of the effective dimensionality (the number of information-carrying dimensions) of large-scale gene expression datasets. We treat both the lack of independence of different measurements in a time series and the fact that that measurements are subject to some level of noise, both of which reduce the effective dimensionality and thereby constrain the complexity of models which can be built from the data.

Gene Expression Profiling↗

PubMed2003

Infant EEG spectral coherence data during quiet sleep: unrestricted principal components analysis--relation of factors to gestational age, medical risk, and neurobehavioral status.

EEG spectral coherence data in quiet sleep of 312 infants were evaluated, at 42 weeks post-menstrual age. All were medically healthy and living at home by time of evaluation. The sample consisted of prematurely bom infants with a wide spectrum of underlying risk factors, as well as healthy full-term infants. Initial 3040 coherence variables were reduced by principal components analysis in an unrestricted manner, which avoided the folding of spectral and spatial information into among-subject variance. One hundred fifty factors explained 90% of the total variance; 40 Varimax rotated factors explained 65% of the variance yielding a 50:1 data reduction. Factor loading patterns ranged from multiple spectral bands for a single electrode pair to multiple electrode pairs for a single spectral band and all intermediate possibilities. Simple left-right and anterior-posterior pairings were not observed within the factor loadings. By multiple regression analysis, the 40 factors significantly predicted gestational age at birth. By canonical correlation, significant relationships were demonstrated between the coherence factors and medical risk factors as well as neurobehavioral factors. Using discriminant analysis, the coherence factors successfully discriminated between infants with high and low medical risk status and between those with the best and worst neurobehavioral status. The two factors accounting for the most variance, and chosen across several analyses, indicated increased left central-temporal coherence from 6-24 Hz, and increased frontal-occipital coherence at 10 Hz, for the infants born closest to term with lowest medical risk factors and best neurobehavioral performance.

Electroencephalography↗

PubMed1996

Calculation of area of stabilometric signals using principal component analysis.

In stabilometry, the sway of the human body in an upright posture is studied by monitoring the displacement of its centre of pressure in the lateral (x) and anterio-posterior (y) directions. The area covered by this trace has been defined as that of an ellipse fitted to the data. Conventionally, its angle of inclination is found through linear regression (LR) on the data in the x-y plane. In the present paper, principal component analysis (PCA) is proposed as providing a more suitable basis for the estimation of angle and area. Results of simulations and stabilometric tests confirm large differences between area and angle estimates obtained by regression of x over y, and y over x, with PCA generally agreeing with either one or the other of the LRs. The PCA technique is therefore recommended as an improved basis for measuring area and inclination of stabilograms, or similar data sets.

Data Interpretation, Statistical↗

PubMed2000

Principal component analysis and cluster analysis for the characterization of dental composites.

Various experimental dental materials were characterized using chemometric methods. The main aim of the study was to ascertain which composite materials present the best properties for use in restorative dentistry. Bisphenol-alpha-glycidyl methacrylate-based composites containing hydroxyapatite as a filler and a coupling agent were prepared using a photocuring polymerization procedure. Several chemical and mechanical properties of experimental composites were measured and the corresponding data were further studied using principal component analysis and cluster analysis. Results from the characterization allowed the most appropriate materials to be selected. Various composites presented acceptable general properties suggesting their suitability as substitutes for commercial materials in dentistry.

Cluster Analysis↗

PubMed1990

Dynamic electromyography. I. Numerical representation using principal component analysis.

A complete description of human gait requires consideration of linear and temporal gait parameters such as velocity, cadence, and stride length, as well as graphic waveforms such as limb rotations, forces, and moments at the joints and phasic activity of muscles. This results in a large number of interactive parameters, making interpretation of gait data extremely difficult. Statistical pattern recognition techniques can simplify this problem. For this approach to be successful, first it is necessary to reduce the number of interactive parameters to a manageable set. In this study, we present an application of principal component analysis as a means for representing graphic waveforms in a parsimonious manner. In particular, we concentrate on representing the phasic muscle activity recorded using surface electrodes from ten major muscles of the lower extremity of 35 normal subjects during level walking. A 32 point vector is created in which each point of the vector represents the normalized area under the curve of a portion of rectified and smoothed electromyographic signal, expressed as a function of gait cycle. Principal components are computed and the first few weighting coefficients are retained as features to represent the original EMG data. We show that the corresponding basis vectors span parts of the gait cycle where the most variability between individual subjects exists. We also show that the basis vectors can be used to represent the EMG data of subjects not originally used to generate the basis vectors.

Adolescent↗

PubMed1998

Short column gas chromatography-mass spectrometry and principal component analysis for the identification of coeluted substances in doping control analysis.

The identification of four doping control substances in an artificial mixture, using short column gas chromatography-mass spectrometry (GC-MS) analysis was examined. Two chromatographic peaks were recorded in the chromatogram, using a short capillary column (1.8 m) at an oven temperature of 180 degrees C. The first peak was associated with a mixture of a solvent derivative and an artifact. The second one corresponded to the mixture of four control substances. Principal component analysis was applied on a selected GC-MS data set of the latter peak to determine clear full spectra of pure substances from mixture spectra. The time of GC-MS analysis was significantly reduced to less than 1 min from 30 min which is a typical GC-MS analysis time, using standard methods of doping control analysis.

Cocaine↗

PubMed2000

Principal component analysis of the absorption and resonance Raman spectra of the metallochromic indicator antipyrylazo III.

Metallochromic indicators, whose spectral properties are changed in the presence of metal cations, are used mainly in biological studies to monitor Ca2+ and Mg2+ ions. Antipyrylazo III is such indicator, employed for mid-range Ca2+ concentrations (10-1000 microM). The stoichiometry of the interactions of antipyrylazo III with Ca2+, Mg2+, Ba2+, Sr2+ and Zn2+ ions and the relevant binding constants were studied by principal component analysis (PCA) of the absorption spectral changes. The resonance Raman spectra of the above systems were measured as well, and the resolved Raman spectra of the various species were calculated and assigned. The vibrational spectra are more featured, more characteristic of the binding ions and exhibit stronger relative spectral changes upon binding the cations. The basis sets of Raman spectra could thus be used as an analytical tool for these divalent metallic cations.

Barium↗

PubMed2002

Combining selective sequential extractions, X-ray absorption spectroscopy, and principal component analysis for quantitative zinc speciation in soil.

Selective sequential extractions (SSE) and, more recently, X-ray absorption fine-structure IXAFS) spectroscopy have been used to characterize the speciation of metal contaminants in soils and sediments. However, both methods have specific limitations when multiple metal species coexist in soils and sediments. In this study, we tested a combined approach, in which XAFS spectra were collected after each of 6 SSE steps, and then analyzed by multishell fitting, principal component analysis (PCA) and linear combination fits (LCF), to determine the Zn speciation in a smelter-contaminated, strongly acidic soil. In the topsoil, Zn was predominately found in the smelter-emitted minerals franklinite (60%) and sphalerite (30%) and as aqueous or outer-sphere Zn2+ (10%). In the subsoil, aqueous or outer-sphere Zn2+ prevailed (55%), but 45% of Zn was incorporated by hydroxy-Al interlayers of phyllosilicates. Formation of such Zn-bearing hydroxy-interlayers, which has been observed here for the first time, may be an important mechanism to reduce the solubility of Zn in those soils, which are too acidic to retain Zn by formation of inner-sphere sorption complexes, layered double hydroxides or phyllosilicates. The stepwise removal of Zn fractions by SSE significantly improved the identification of species by XAFS and PCA and their subsequent quantification by LCF. While SSE alone provided excellent estimates of the amount of mobile Zn species, it failed to identify and quantify Zn associated with mineral phases because of nonspecific dissolution and the precipitation of Zn oxalate. The systematic combination of chemical extraction, spectroscopy, and advanced statistical analysis allowed us to identify and quantify both mobile and recalcitrant species with high reliability and precision.

Adsorption↗

PubMed2006

Insights into age- and sickle-cell-disease-interaction using principal components analysis.

BACKGROUND: In the context of sickle cell anemia, peripheral blood indexes provide key information that is also potentially influenced by age. Therefore, it is necessary to understand the extent and nature of interactions between sickle cell anemia and age, especially in situations where there is a high prevalence of sickle cell anemia. METHODS: In a cross-sectional study of 374 subjects with varying hemoglobin S (HbS) status, we characterized the interaction between age and sickle hemoglobin using principal components analysis. RESULTS: Factor analysis in subjects with hemoglobin AA identified three orthogonal factors--normal erythropoiesis, presence of thalassemia and the aggregability potential of the blood. These three factors were differentially associated with hemoglobin status. Age influenced the association of factors #2 and #3 with hemoglobin status. CONCLUSION: Our findings suggest that the interaction between age and hemoglobin status needs to be considered in both clinical and public health settings.

Journal Article↗