Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Principal component”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 469 records · Page 26Linked to original sources
PubMed2004

Protein designability analysis in sequence principal component space using 2D lattice model.

The number of proteins that fold into a certain structure differs drastically. The designability of a protein structure, which is defined as the number of sequences that have that structure as their unique lowest energy state, is studied in this paper using a simplified lattice model. The two-letter (HP) code and the pair-contact energy model are employed in the formulation of the relationship between the protein sequences and the compact structures. Due to the correlations between different dimensions, principal component analysis (PCA) is carried out to remove these correlations and develop reliable approximations of probability density functions of the protein sequences and the compact structures. An estimation of designability is derived using these probability density functions. Good correlation between estimated designabilities and those obtained through enumerative calculations is successfully achieved.

Algorithms↗
PubMed2004

Fourier transform infrared-attenuated total reflection nitrate determination of soil pastes using principal component regression, partial least squares, and cross-correlation.

This paper investigates the use of Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy as a fast and simple way for direct determination of nitrate concentration in soil pastes, which would assist precision fertilizer placement and reduce nitrate pollution. Eight types of soils are investigated, with nitrate concentrations ranging from 0 to 1000 ppm-N. The spectral region around the nitrate band (1300-1550 cm(-1)) is analyzed by (1) principal component regression (PCR), (2) partial least squares (PLS), and (3) cross-correlation with reference libraries that include spectra of pure ions and/or soils. The main obstacle to accurate nitrate measurement appears to be an interfering band present in calcareous soils. This band, which may be due to carbonate, is located around 1450 cm(-1) and overlaps with the nitrate band centered around 1370 cm(-1). For non-calcareous soils, and in particular for light sandy agricultural soils, PLS and cross-correlation with a reference library containing only spectra of ions in water give similar results (about 8 ppm-N on dry soil basis), while PCR leads to slightly poorer results. When calcareous soils are included in the analysis, the prediction errors are about twice as large. In this case, the best results are obtained using PLS, followed by PCR, while cross-correlation with reference libraries leads to poorer results.

Algorithms↗
PubMed2004

Principal component analysis of physical, color, and sensory characteristics of chicken breasts deboned at two, four, six, and twenty-four hours postmortem.

The effects of various postchill deboning times on functional, color, yield, and sensory attributes of broiler breast meat were determined. Broiler breast muscles were deboned at 2, 4, 6, and 24 h postmortem, and pH, color change, cooking yield, shear force values, and sensory traits of the breast meat were recorded. Data were examined by multivariate data analysis, namely principal component analysis (PCA). Averages of 13 variables (pH, delta a*, shear force, and sensory attributes of cardboardy, wet feathers, springiness, cohesiveness, hardness, moisture release, particle size, bolus size, chewiness, and metallic aftertaste-afterfeel) decreased gradually as deboning time increased from 2 to 24 h, especially for shear values after 4 h of postmortem aging. Univariate correlation coefficients among 24 variables indicated several significant correlations. Warner-Bratzler shear force had high positive correlations with 5 sensory texture attributes (cohesiveness, hardness, particle size, bolus size, and chewiness). The parameters of pH, delta L*, delta a*, delta b*, and cooking yield were not obviously correlated with shear force values or any of the 18 sensory characteristics. PCA score plot showed no clear separation of the breast muscles deboned at different postmortem times, but it was still possible to differentiate them. The loading biplot suggested that 18 variables were effective in sample differentiation, including delta L*, shear force, cooking yield, 6 sensory flavor attributes (brothy, cardboardy, wet feathers, blood/serumy, salty, and sour), all sensory texture attributes except springiness, and all afterfeel-aftertaste properties.

Animals↗
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↗
PubMed2004

Principal component and volume of interest analyses in depressed patients imaged by 99mTc-HMPAO SPET: a methodological comparison.

Previous regional cerebral blood flow (rCBF) studies on patients with unipolar major depressive disorder (MDD) have analysed clusters of voxels or single regions and yielded conflicting results, showing either higher or lower rCBF in MDD as compared to normal controls (CTR). The aim of this study was to assess rCBF distribution changes in 68 MDD patients, investigating the data set with both volume of interest (VOI) analysis and principal component analysis (PCA). The rCBF distribution in 68 MDD and 66 CTR, at rest, was compared. Technetium-99m d, l-hexamethylpropylene amine oxime single-photon emission tomography was performed and the uptake in 27 VOIs, bilaterally, was assessed using a standardising brain atlas. Data were then grouped into factors by means of PCA performed on rCBF of all 134 subjects and based on all 54 VOIs. VOI analysis showed a significant group x VOI x hemisphere interaction ( P<0.001). rCBF in eight VOIs (in the prefrontal, temporal, occipital and central structures) differed significantly between groups at the P<0.05 level. PCA identified 11 anatomo-functional regions that interacted with groups ( P<0.001). As compared to CTR, MDD rCBF was relatively higher in right associative temporo-parietal-occipital cortex ( P<0.01) and bilaterally in prefrontal ( P<0.005) and frontal cortex ( P<0.025), anterior temporal cortex and central structures ( P<0.05 and P<0.001 respectively). Higher rCBF in a selected group of MDD as compared to CTR at rest was found using PCA in five clusters of regions sharing close anatomical and functional relationships. At the single VOI level, all eight regions showing group differences were included in such clusters. PCA is a data-driven method for recasting VOIs to be used for group evaluation and comparison. The appearance of significant differences absent at the VOI level emphasises the value of analysing the relationships among brain regions for the investigation of psychiatric disease.

Algorithms↗
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↗
PubMedDate not supplied

Monitoring of an industrial process by multivariate control charts based on principal component analysis.

The control and monitoring of an industrial process is performed in this paper by the multivariate control charts. The process analysed consists of the bottling of the entire production of 1999 of the sparkling wine "Asti Spumante". This process is characterised by a great number of variables that can be treated with multivariate techniques. The monitoring of the process performed with classical Shewhart charts is very dangerous because they do not take into account the presence of functional relationships between the variables. The industrial process was firstly analysed by multivariate control charts based on Principal Component Analysis. This approach allowed the identification of problems in the process and of their causes. Successively, the SMART Charts (Simultaneous Scores Monitoring And Residual Tracking) were built in order to study the process in its whole. In spite of the successful identification of the presence of problems in the monitored process, the Smart chart did not allow an easy identification of the special causes of variation which casued the problems themselves.

Fermentation↗
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↗
PubMed2004

Aggregate eco-efficiency indices for New Zealand--a principal components analysis.

Eco-efficiency has emerged as a management response to waste issues associated with current production processes. Despite the popularity of the term in both business and government circles, limited attention has been paid to measuring and reporting eco-efficiency to government policy makers. Aggregate measures of eco-efficiency are needed, to complement existing measures and to help highlight important patterns in eco-efficiency data. This paper aims to develop aggregate measures of eco-efficiency for use by policy makers. Specifically, this paper provides a unique analysis by applying principal components analysis (PCA) to eco-efficiency indicators in New Zealand. The study reveals that New Zealand's overall eco-efficiency improved for two out of the five aggregate measures over the period 1994/1995-1997/1998. The worsening of the other aggregate measures reflects, among other things, the relatively poor performance of the primary production and related processing sectors. These results show PCA is an effective approach for aggregating eco-efficiency indicators and assisting decision makers by reducing redundancy in an eco-efficiency indicators matrix.

Conservation of Natural Resources↗
PubMed2006

Identification of latent variables in a semantic odor profile database using principal component analysis.

Many classifications of odors have been proposed, but none of them have yet gained wide acceptance. Odor sensation is usually described by means of odor character descriptors. If these semantic profiles are obtained for a large diversity of compounds, the resulting database can be considered representative of odor perception space. Few of these comprehensive databases are publicly available, being a valuable source of information for fragrance research. Their statistical analysis has revealed that the underlying structure of odor space is high dimensional and not governed by a few primary odors. In a new effort to study the underlying sensory dimensions of the multivariate olfactory perception space, we have applied principal component analysis to a database of 881 perfume materials with semantic profiles comprising 82 odor descriptors. The relationships identified between the descriptors are consistent with those reported in similar studies and have allowed their classification into 17 odor classes.

Cluster Analysis↗
PubMed2005

Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data.

DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples), in which severe collinearity is often observed. This makes it difficult to apply directly the classical statistical methods to investigate microarray data. In this paper, total principal component regression (TPCR) was proposed to classify human tumors by extracting the latent variable structure underlying microarray data from the augmented subspace of both independent variables and dependent variables. One of the salient features of our method is that it takes into account not only the latent variable structure but also the errors in the microarray gene expression profiles (independent variables). The prediction performance of TPCR was evaluated by both leave-one-out and leave-half-out cross-validation using four well-known microarray datasets. The stabilities and reliabilities of the classification models were further assessed by re-randomization and permutation studies. A fast kernel algorithm was applied to decrease the computation time dramatically. (MATLAB source code is available upon request.).

Acute Disease↗
PubMed1983

Principal component analysis: a suitable method for the 3-dimensional study of the shape, dimensions and orientation of dendritic arborizations.

Our study proposes an objective method of describing 3-dimensional dendritic arborizations of neurons in the best possible conditions. The method is based upon a particular exploitation of statistical "principal component analysis". For each arborization, 3 principal axes are calculated which are its axes of inertia. The first two axes define the "principal plane" of the arborization. The shape of the arborization is determined from the statistical distribution of its dendritic points along each of these axes. Shapes are quantified by using an "index of axialization" (a) and an "index of flatness" (p) both of which may vary from zero to 1. The dimensions of the arborization, "length" (1), "width" (w) and "thickness" (t) are also measured along the principal axes. Orientation of arborizations is quantified by considering the orientation of the first principal axis for axialized arborization (a close to 1) and/or the orientation of the principal plane for flattened arborizations (p close to 1). In both cases 2 angles (azimuth and polar angle) are calculated. For spherical arborizations (a and p close to 1), no orientation is significant. The significance level of the defined orientations is evaluated from the values of the shape indices. Several examples are illustrated and other existing methods are discussed.

Animals↗
PubMed1985

Towards automatic analysis of dynamic radionuclide studies using principal-components factor analysis.

A method is proposed for automatic analysis of dynamic radionuclide studies using the mathematical technique of principal-components factor analysis. This method is considered as a possible alternative to the conventional manual regions-of-interest method widely used. The method emphasises the importance of introducing a priori information into the analysis about the physiology of at least one of the functional structures in a study. Information is added by using suitable mathematical models to describe the underlying physiological processes. A single physiological factor is extracted representing the particular dynamic structure of interest. Two spaces "study space, S' and "theory space, T' are defined in the formation of the concept of intersection of spaces. A one-dimensional intersection space is computed. An example from a dynamic 99Tcm DTPA kidney study is used to demonstrate the principle inherent in the method proposed. The method requires no correction for the blood background activity, necessary when processing by the manual method. The careful isolation of the kidney by means of region of interest is not required. The method is therefore less prone to operator influence and can be automated.

Humans↗
PubMed2005

Efficient training of multilayer perceptrons using principal component analysis.

A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior.

Journal Article↗
PubMed2005

Principal components analyses of the MMPI-2 PSY-5 scales: identification of facet subscales.

The Personality Psychopathology Five (PSY-5) is a five-factor personality trait model designed for assessing personality pathology using quantitative dimensions. Harkness, McNulty, and Ben-Porath developed Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales based on the PSY-5 model, and these scales were recently added to the standard MMPI-2 profile. Although the PSY-5 constructs are multidimensional in definition, explicit subscales for the broader PSY-5 scales have not been developed. The primary goals of this study were to empirically derive subscales for the MMPI-2 PSY-5 scales using principal components analysis (PCA) and to replicate these subscales with an independent sample. Individual PSY-5 scales were analyzed using PCA with an initial sample of 4,325 MMPI-2 protocols, and the component structure was replicated with a second sample of 4,277 MMPI-2 protocols. A third sample of 4,327 protocols was used to further evaluate the internal consistency reliabilities of the resulting facet subscales. Overall, replicable facet subscales were identified with content areas that are largely congruent with Harkness and McNulty's model.

Adult↗
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↗
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↗