SEARCH · PubMed Health

Results for “Principal Component Analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19Linked to original sources

PubMedDate not supplied

New semen quality scores developed by principal component analysis of semen characteristics.

The purpose of this study was to determine whether semen characteristics can be reduced to 2 semen quality (SQ) scores and whether these new scores can help the clinician in assessing the reproductive outcome. A cross-sectional sample of 250 patients seeking infertility treatment were analyzed for semen characteristics. In addition, 177 male-factor patients (prostatitis with infection, n = 40; varicocele, n = 77; varicocele with infections, n = 11; and vasectomy reversal, n = 43) were also assessed. Sperm motion kinetics were measured by computer-assisted semen analysis (CASA) (concentration, percent motility, curvilinear velocity [VCL], straight-line velocity [VSL], average path velocity [VAP], linearity [LIN], and amplitude of lateral head displacement [ALH]). Sperm morphology was assessed by both World Health Organization (WHO) guidelines and Tygerberg strict criteria. The principal component analysis model was used to construct an SQ score and a relative semen quality (RQ) score. A separate set of 25 normal donors was included as controls to determine normal ranges of the semen scores. Among the patient samples, SQ and RQ scores (median and 25% and 75% interquartile values) were 89.9, 25.1, and 130.4 and 106.1, 45.2, and 165.9, respectively. The SQ score for the varicocele and varicocele with infection groups was comparable (78.6 +/- 17.4 and 84.8 +/- 20.6) but significantly different from the control (100 +/- 10, P <.001 and.03). Vasectomy reversal patients had an SQ score of 78.2 plus or minus 16.8 that was significantly lower than controls (P <.001). The correlation among semen characteristics allows for the efficient combining of semen measures. The composite scores can summarize overall SQ and quantity. Both SQ and RQ scores provide meaningful information on the quality of semen specimens for the clinician.

Humans↗

PubMed2005

Mechanism of thermal phase transition of a ferroelectric liquid crystal with monotropic transition temperature studied by infrared spectroscopy combined with principal component analysis and sample-sample two-dimensional correlation spectroscopy.

Infrared (IR) spectra of FLC-154 (FLC: ferroelectric liquid crystal) with monotropic phase transition under a nonalignment state with a sample layer thickness of 24.5 microm were measured for heating process from 55 to 90 degrees C and a cooling process from 90 to 55 degrees C in increments of 1 degrees C. The thermal dynamics of FLC-154 were investigated by use of IR spectroscopy combined with principal component analysis (PCA) and sample-sample two-dimensional (2D) correlation spectroscopy. During the cooling, the FLC-154 molecule passes through the monotropic smectic-C* (Sm-C*) phase, which is transformed from the Sm-A phase. The results from PCA suggest that during the heating process, the thermal dynamics of the alkyl chains, core moiety, and C=O groups are similar to each other. Furthermore, PCA and sample-sample 2D correlation spectroscopy indicate that the alkyl chains and C=O groups in the chiral and core moieties are responsible for the emergence of the Sm-C* phase. This conclusion is very important because the IR data have given more evident cause for the emergence of the Sm-C* phase than the theoretical models such as the molecular-statistical theory of ferroelectric ordering and the indigenous polarization theory. Moreover, it has been found that some of the trans conformations of the alkyl chains of FLC-154 change partly to the gauche conformation when the phase transition from the crystalline phase to the Sm-A phase occurs. It has also been found that the intermolecular interactions of the C=O group in the core moiety in the Sm-A phase are weaker than those in the crystalline phase and that the conformational change occurs on the C-O-C bonds in the core moiety upon going from the crystalline to the Sm-A phase.

Algorithms↗

PubMed1990

Application of principal component analysis for characterizing convergence patterns of inputs in interneurones of the cat forelimb segments.

We attempted to quantitatively describe a variety of convergence patterns of inputs from peripheral nerves and descending tracts (13 sources) onto interneurones of the cat forelimb segments (C6-C8). To this end we applied principal component analysis using the latency of firing as the parameter of each input. The first 3 principal components thus obtained explained 65% of the total variance of convergence patterns and characterized the input pattern of each cell. The first principal component correlated mainly with inputs from pads and the median nerve, the second with the cortico- and rubro-spinal tracts and the third with the superficial radial nerve.

Animals↗

PubMed2006

[Application of principal component analysis (PCA) for the estimation of source of heavy metal contamination in marine sediments].

Concentrations of heavy metals and organic matter in the bottom sediments of Jiaozhou Bay were determined and the average enrichment factors (AEFs) were used simultaneously to evaluate the extent of metal enrichment-contamination. Results show that heavy metal contamination in this bay could be divided into three groups: negligible to low contamination (AEFs < 2), which is the case of Zn (AEF = 1.11), Pb (AEF = 1.15), Cr (AEF = 1.52), Mn (AEF = 0.80) and Fe (AEF = 0.45); moderate contamination (AEFs = 2 - 3), which is the case of Cu (AEF = 2.79) and Cd (AEF = 2.52); certain to severe contamination (AEFs > 3), As (AEF = 3.03) and Hg (AEF = 8.08) being included in this group. Principal component analysis (PCA) was applied to estimate the sources of heavy metal contamination. Results that the first three components accounted for 52.61%, 17.37% and 15.60% of the total variance respectively exhibited that industrial wastewater, degradation of organic matter and erosion of rocks were the main sources of heavy metal contamination. The Q-analysis of PCA indicated that 14 stations could be divided into five groups. This result not only reflected the pollution characteristic of surface sediments, but also provided fundamental evidences for the putative analysis that industrial discharge is the main source of heavy metal contamination in Jiaozhou Bay.

Geologic Sediments↗

PubMed2003

Principal component analysis as a method to facilitate fast detection of transient-evoked otoacoustic emissions.

Transient-evoked otoacoustic emissions (TEOAE) are acoustic signals coming from the inner ear (outer hair cells of the cochlea) after acoustic stimulation by clicks. They can be used to investigate the status of the peripheral hearing system. Some of their potential applications (e.g., their use as a tool in newborn hearing screening programs) are deeply related to the duration of each recording session. This duration can be strongly reduced by applying a principal component analysis approach to a set of TEOAE recorded from the same ear at different stimulus levels averaging only a few sweeps (a maximum of 100 versus the classical 260). The PCA approach is shown to be able to enhance the signal-to-noise ratio and, in turn, to allow a correct detection of the responses. Results of the application of this approach in comparison with responses recorded from the same subjects with the classical technique will be shown.

Acoustic Stimulation↗

PubMed1988

A principal components analysis of human odontometrics.

It has long been recognized that tooth crown diameters in hominoids are all positively intercorrelated one with another. This study reports on sex-specific correlation matrices derived from 2,650 individuals from the Solomon Islands, Melanesia. Mesiodistal and buccolingual diameters of all permanent teeth from one side are used, excluding third molars. Analysis discloses significant sex dimorphism in the strengths of the intercorrelations, with females being better integrated. Principal components analysis (PCA) provides an objective means of data reduction (shown here to be preferable to simple size summation methods) and decorrelation of the resulting linear combinations. Four components are extracted (with results being virtually identical in the two sexes) and arguments are put forth that varimax rotation to "a simpler solution" may be counterproductive. Before rotation, the four components are 1) overall size, 2) buccolingual widths contrasted with mesiodistal lengths, 3) anterior (I,C) contrasted with posterior (P,M) teeth, and 4) premolars contrasted with molars. Most of the explained (shared) variance (63%) extracted by PCA is in overall size of the dentition. There is a strong urge to view the results of these principal components analyses as reflective of biologically and genetically meaningful entities.

Female↗

PubMed1995

EP component identification and measurement by principal components analysis.

Between the acquisition of Evoked Potential (EP) data and their interpretation lies a major problem: What to measure? An approach to this kind of problem is outlined here in terms of Principal Components Analysis (PCA). An important second theme is that experimental manipulation is important to functional interpretation. It would be desirable to have a system of EP measurement with the following characteristics: (1) represent the data in a concise, parsimonous way; (2) determine EP components from the data without assuming in advance any particular waveforms for the components; (3) extract components which are independent of each other; (4) measure the amounts (contributions) of various components in observed EPs; (5) use measures that have greater reliability than measures at any single time point or peak; and (6) identify and measure components that overlap in time. PCA has these desirable characteristics. Simulations are illustrated. PCA's beauty also has some warts that are discussed. In addition to discussing the usual two-mode model of PCA, an extension of PCA to a three-mode model is described that provides separate parameters for (1) waveforms over time, (2) coefficients for spatial distribution, and (3) scores telling the amount of each component in each EP. PCA is compared with more traditional approaches. Some biophysical considerations are briefly discussed. Choices to be made in applying PCA are considered. Other issues include misallocation of variance, overlapping components, validation, and latency changes.

Brain↗

PubMed1998

Diagnostic subgrouping of depressed patients by principal component analysis and visualized pattern recognition.

A data-analytical method is described for identifying behavioral and biological variables in psychiatric patients with predictive value in defining clinical subgroups. The procedure, based on principal component analysis (PCA) and graphical analysis, was applied in a group of 28 depressed patients. The 28 depressed patients of unipolar type were observed for up to 15 years for re-evaluation of the diagnoses at the start of the study. Platelet monoamine oxidase activity, post-dexamethasone serum cortisol and serum melatonin predicted two main clinical subgroups as well as a smaller subgroup of bipolar patients. The selection procedure revealed which of several variables were predictive of subgroups that were not possible to identify by univariate methods. The three biological variables may thus be useful in further assessment of clinical subgroups of unipolar depressed patients studied by other research groups.

Adult↗

PubMed2002

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment.

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Algorithms↗

PubMed2002

Validity of the depressive dimension extracted from principal component analysis of the PANSS in drug-free patients with schizophrenia.

Depressive symptoms frequently occur during the course of schizophrenia. This study explored the relationships between the schizophrenia symptomatology and three measures of depression. Eighty-one drug-free inpatients with acute schizophrenia were assessed with the positive and negative syndrome scale (PANSS), the Calgary depression scale for schizophrenia (CDSS), and the Hamilton rating scale for depression (HAM-D). The depressive subscale of PANSS (PANSS-D) was also considered as a third scale for measuring depression. A principal component analysis (PCA) of PANSS items identified five clinical dimensions of schizophrenia called 'negative', 'positive', 'anxio-depressive', 'excitement', and 'disorganisation and others'. Our anxio-depressive dimension (PANSS-ad) was strictly identical with the PANSS-D. Scores on CDSS and HAM-D were highly inter-correlated and highly correlated with the PANSS-ad. Furthermore, while scores on CDSS were correlated only with this dimension, scores at HAM-D were also positively correlated with the negative dimension and negatively correlated with the excitement dimension. In conclusion, our results suggest that PANSS evaluation itself may be sufficient to give a correct approximation of the depression in patients with schizophrenia. However, depression scales are of course needed to assess specifically depressive symptoms in patients with schizophrenia; hence, the CDSS could be a more specific instrument than HAM-D.

Acute Disease↗

PubMed2003

Water characterization and seasonal heavy metal distribution in the Odiel River (Huelva, Spain) by means of principal component analysis.

The Iberian Pyrite Belt is the largest mass of sulfide and manganese ores in Western Europe. Its sulfide oxidation is the origin of a heavily acidic drainage that affects the Odiel River in southwestern Huelva (Spain). To assess physicochemical, contamination parameters, heavy metal distribution and its seasonal variation in the upper Odiel River and in El Lomero mines, three water samplings were undertaken and analyzed between July 1998 and November 1999. Water from the Odiel River in the polluted zone showed low pH values (2.76-3.51), high heavy metal content, and high values of conductivity (1410-3648 microS/cm) and dissolved solids (1484-5602 mg/L). Principal Component Analysis (PCA) showed that variables related with the products of the pyrite oxidation and the salts that are solubilized by the high acidity generated in the oxidation of sulfides, grouped in the first component, accounted for 40.88% of total variance, and were the main influential factor in physicochemical water sample properties. The second influential factor was minority metals (nickel, cobalt, cadmium). Heavy metals showed three different seasonal patterns, closely related with saline efflorescences formed next to the river bed: majority metals (iron, copper, manganese, zinc); minority metals (lead, nickel, cobalt, cadmium); and chromium, which had a distinctive behavior.

Environmental Monitoring↗

PubMed2004

[A principal component analysis of the AGGIR scale in demented elderly patients].

AGGIR grid is the national standardized instrument determining aimed at the dependency of old people in France living in institutions as well as in the community. Attribution of the governmental financial assistance APA (Allocation Personnalisée d'Autonomie) depends essentially on the classification of frail old people in 6 degrees of dependency (GIR1 to GIR6). The aim of the present study was to test the reliability of this grid to evaluate the degree of dependency in demented elderly people. Mild, moderate or severe demented patients were included in the study (n= 120). A factorial validation of the A GGIR grid was performed by principal components analysis (PCA). This analysis showed a 5-factor solution: factor 1 named the property factor (27 percent of the variance), factor 2 named the dynamic factor (21 percent),factor 3 named the cognitive factor (20 percent), factor 4 named the external mobility factor (11 percent) and factor 5 named the communication factor (11 percent). The result showed that the AGGIR grid takes physical dependency more into account than psychological and behavioral dependency. This result suggests a need for readjustment of the AGGIR grid for demented patients by adding new variables taking into account psychosocial and behavioral disorders.

Activities of Daily Living↗

PubMed2003

Automated lung outline reconstruction in ventilation-perfusion scans using principal component analysis techniques.

The present work addresses the development of an automated software-based system utilized in order to create an outline reconstruction of lung images from ventilation-perfusion scans for the purpose of diagnosing pulmonary embolism. The proposed diagnostic software procedure would require a standard set of digitized ventilation-perfusion scans in addition to correlated chest X-rays as key components in the identification of an ideal template match used to approximate and reconstruct the outline of the lungs. These reconstructed lung images would then be used to extract the necessary PIOPED-compliant features which would warrant a pulmonary embolism diagnosis. In order to evaluate this issue, two separate principal component analysis (PCA) algorithms were employed independently, including Eigenlungs, which was adapted from the Eigenfaces method, and an artificial neural network. The results obtained through MATLAB(TM) simulation indicated that lung outline reconstruction through the PCA approach carries significant viability.

Algorithms↗

PubMed2006

A novel method for visualizing functional connectivity using principal component analysis.

Functional connectivity is a useful measure of voxel-wise functional magnetic resonance imaging signals that allows for the identification of functionally related brain areas and distributed networks. However, the high dimensionality of functional connectivity makes it difficult to visualize. In most studies, a small percentage of the total functional connectivity is visualized through diagrams that are constructed using individual seed voxels. In the present study describes a new method for visualizing most of the functional connectivity through a single diagram. This method does not rely on seed voxels, but rather employs a reduction of the high-dimensionality of the functional connectivity via a projection onto a three-dimensional color space using principal components analysis. With this new method, most of the information contained in a functional connectivity matrix can be represented through a single color-coded functional connectivity map, thereby facilitating a greater visual appreciation of functional connectivity.

Brain↗

PubMed2005

[FTIR spectra-principal component analysis of phenetic relationships of Huperzia serrata and its closely related species].

Huperzia serrata is an important medicinal plant. This species is rich in inner-specific variation with various closely related species, and their individuals are also small with few identification characters. In the present paper, the method of Fourier transform infrared spectrometer with an OMNI collector was applied to obtaining the infrared spectra of 16 leaf samples including Huperzia serrata and its five closely related species (Huperzia sutchueniana, Phlegmariurus mingchegensis, Lycopodium japonicum, Selaginella doederleinii, Selaginella heterostachys). Based on the indices of wave number-absorbance, the differences of the 16 infrared spectra were compared by the method of Principal Component Analysis (PCA). The results showed that there is good correspondence between the position relationship of PCA three-dimensional plot of the samples based on the indices of wave number-absorbance of FTIR spectra and their phenetic relationship. Therefore, the infrared spectra could be applied to identifying the samples of Huperzia serrata and its closely related species.

Huperzia↗

PubMed2005

Determination of the geographical origin of green coffee by principal component analysis of carbon, nitrogen and boron stable isotope ratios.

In this study we show that the continental origin of coffee can be inferred on the basis of coupling the isotope ratios of several elements determined in green beans. The combination of the isotopic fingerprints of carbon, nitrogen and boron, used as integrated proxies for environmental conditions and agricultural practices, allows discrimination among the three continental areas producing coffee (Africa, Asia and America). In these continents there are countries producing 'specialty coffees', highly rated on the market that are sometimes mislabeled further on along the export-sale chain or mixed with cheaper coffees produced in other regions. By means of principal component analysis we were successful in identifying the continental origin of 88% of the samples analyzed. An intra-continent discrimination has not been possible at this stage of the study, but is planned in future work. Nonetheless, the approach using stable isotope ratios seems quite promising, and future development of this research is also discussed.

Africa↗

PubMed1989

Principal component analysis of the features of concentric needle EMG motor unit action potentials.

Motor unit action potentials (MUAPs) were recorded from the biceps muscle of normal subjects and of patients with nerve or muscle diseases. Principal component analysis of the MUAP amplitude, area, area/amplitude ratio, duration, and the number of turns and phases produced three components that among them contained 90% of the variance of the data set. Thus the dimensionality of data was reduced from six to three. The first component reflected changes in the size of the MU, whereas the second reflected variations in the arrival time at the recording electrode of the action potentials of muscle fibers in the motor unit. The third factor reflected local loss of muscle fibers within the MU territory. Patterns of variations in the three components were different in patients with neuropathy and myopathy.

Action Potentials↗

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↗