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PubMed2004

Flexibility of beta-sheets: principal component analysis of database protein structures.

Protein folds are built primarily from the packing together of two types of structures: alpha-helices and beta-sheets. Neither structure is rigid, and the flexibility of helices and sheets is often important in determining the final fold (e.g., coiled coils and beta-barrels). Recent work has quantified the flexibility of alpha-helices using a principal component analysis (PCA) of database helical structures (J. Mol. Bio. 2003, 327, pp. 229-237). Here, we extend the analysis to beta-sheet flexibility using PCA on a database of beta-sheet structures. For sheets of varying dimension and geometry, we find two dominant modes of flexibility: twist and bend. The distributions of amplitudes for these modes are found to be Gaussian and independent, suggesting that the PCA twist and bend modes can be identified as the soft elastic normal modes of sheets. We consider the scaling of mode eigenvalues with sheet size and find that parallel beta-sheets are more rigid than antiparallel sheets over the entire range studied. Finally, we discuss the application of our PCA results to modeling and design of beta-sheet proteins.

Databases, Protein↗
PubMed2003

Fatigue among caregivers of chronic renal failure patients: a principal components analysis.

Quality of life for caregivers of ESRD patients has not been well addressed. The physical and psychological status of this overlooked group can be important in the recovery or adaptation of patients with chronic renal failure. One particular symptom of a reduced quality of life of such caregivers is that of fatigue. The study tested the reliability of both existing and newer fatigue measures. Measures with high reliability yielded a single construct of fatigue in a principal components analysis in this study of 99 caregivers. Implications for practice are addressed. Potential for further study is recommended.

Caregivers↗
PubMed2004

Normalization of single-channel DNA array data by principal component analysis.

MOTIVATION: Detailed comparison and analysis of the output of DNA gene expression arrays from multiple samples require global normalization of the measured individual gene intensities from the different hybridizations. This is needed for accounting for variations in array preparation and sample hybridization conditions. RESULTS: Here, we present a simple, robust and accurate procedure for the global normalization of datasets generated with single-channel DNA arrays based on principal component analysis. The procedure makes minimal assumptions about the data and performs well in cases where other standard procedures produced biased estimates. It is also insensitive to data transformation, filtering (thresholding) and pre-screening.

Algorithms↗
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

Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis.

The accuracy of predictions of muscle force based on electromyography (EMG) is an important issue in biomechanics and kinesiology. Since human skeletal muscles show a high diversity and heterogeneity in their fiber architecture, it is difficult to properly align electrodes to the muscle fiber direction. Against this background, we analyzed the effect of different bipolar configuration directions on EMG-based force estimation. In addition, we investigated whether principal component analysis (PCA) can improve this estimation. High-density surface-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) between predicted and measured force was determined. We found the best bipolar configuration direction to cause a 13% lower RMSD relative to the worst direction. Optimal results were obtained with electrodes aligned with the expected main muscle fiber direction. We found that PCA reduced RMSD by about 40% compared to conventional bipolar electrodes and by about 12% compared to optimally aligned multiple bipolar electrodes. Thus, PCA contributes to the accuracy of EMG-based estimation of muscle force when using a high-density EMG grid.

Algorithms↗
PubMed2005

QSAR by LFER model of cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives using principal component factor analysis and genetic function approximation.

Cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives were subjected to quantitative structure-activity relationship (QSAR) study using linear free energy related (LFER) model of Hansch using electronic (Hammett sigma), hydrophobicity (pi) and steric (molar refractivity and STERIMOL L, B1, B2, B3 and B4) parameters of phenyl ring substituents of the compounds, along with appropriate indicator variables. Principal component factor analysis (FA) was used as the data-preprocessing step to identify the important predictor variables contributing to the response variable and to avoid collinearities among them. The generated multiple linear regression (MLR) equations were statistically validated using leave-one-out technique. Genetic function approximation (GFA) was also used on the same data set to develop QSAR equations, which produced the same best equation as obtained with FA-MLR. The final equation is of acceptable statistical quality (explained variance 80.2%) and predictive potential (leave-one-out predicted variance 74%). The analysis explores the structural and physicochemical contributions of the compounds for cytotoxicity. A thiol substituent at 2 position of the imidazole nucleus decreases cytotoxicity when compared to the corresponding unsubstituted congener. Presence of hydrogen bond donor group at meta position of the phenyl ring present at 5 position of the imidazole nucleus also reduces cytotoxicity. Additionally, absence of any substituent at 2 and 3 positions of the phenyl ring of 1-phenylamino fragment reduces the cytotoxicity. The negative coefficient of sigmap indicates that presence of electron-withdrawing substituents at the para position of the phenyl ring of the 1-phenylamino fragment is not favourable for the cytotoxicity. Again, lipophilicity of meta substituents of the 5-phenyl ring increases cytotoxicity. The coefficients of molar refractivity (MRm) and STERIMOL parameters for meta substituents (Lm, B1m and B4m) of the phenyl ring of 1-phenylamino fragment indicate that the length, width and overall size of meta substituents are conducive factors for the cytotoxicity.

Algorithms↗
PubMed2003

Studies on organoleptic properties of food products from fresh egg and egg powder through principal component analysis.

The population which is below the poverty line is devoid of nutritious diet. Egg and milk are categorized as complete foods. The defensive organizations are situated in such remote places where fresh food material is not available. Keeping in view these problems, the study of organoleptic variables, viz., color, appearance, aroma, texture and taste in the food products of cake, omelet doughnut, coconut macaroon and mayonnaise from fresh egg and egg powder, was conducted. Principal component analysis was carried out. Organoleptic properties of doughnut prepared from egg powder were superior compared to fresh egg which had better sensory traits for coconut macaroon. The sensory traits like taste, texture and aroma were the most influential traits studied to pronouncing as a panel decision. It is proposed that fresh egg and egg powder should be preferred in the process of preparation of coconut macaroon and doughnut, respectively.

Color↗
PubMed2003

Detection of single unit activity from the rat vagus using cluster analysis of principal components.

In vivo recordings from subdiaphragmatic vagal afferent nerves generally lack the resolution to distinguish single unit activity. Several methods for data acquisition and analysis were combined to produce a high degree of reliability in recording electrophysiological signals from gastrointestinal and hepatic afferent fibers in the rat. Recordings with low noise were achieved by paralysis of the respiratory muscles and by pinning the nerve to a recording platform. Single unit activity was isolated using principal component (PC) analysis and cluster cutting of data in multi-dimensional space (1-3 PCs). Cluster assignments were determined by a semi-automated approach using the k-means algorithm. The accuracy of single unit classification was assessed by checking inter-spike intervals (ISIs) to determine the length of the refractory period, and by cross-correlation analysis to assess whether single units were mistakenly split into more than one cluster. These analyses produced up to four isolated single units from each nerve filament (a bundle of nerve fibers), and typically it was possible to further increase yield by recording from several nerve filaments simultaneously using an array of electrodes.

Action Potentials↗
PubMed2005

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

Antiviral Agents↗
PubMed2005

Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis.

A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters.

Algorithms↗
PubMed2005

A construction of an averaged representation of human cortical gyri using non-linear principal component analysis.

Because of the complex shape of human cortical gyri and great variation between individuals, development of effective representation schemes which allow establishment of correspondence between individuals, extraction of average structure of a population, and co-registration has proved very difficult. We introduce an approach which extracts line representations of gyri at different depths from high resolution MRI, labels main gyri semi-automatically, and extracts a template from a population using non-linear principal component analysis. The method has been tested on data from 96 healthy human volunteers. The model captures the most salient shape features of all major cortical gyri, and can be used for inter-subject registration, for investigating regionalized inter-subject variability, and for inter-hemispheric comparisons.

Algorithms↗
PubMed2005

Neuropathological changes in ten cases of neuronal intermediate filament inclusion disease (NIFID): a study using alpha-internexin immunohistochemistry and principal components analysis (PCA).

Ten cases of neuronal intermediate filament inclusion disease (NIFID) were studied quantitatively. The alpha-internexin positive neurofilament inclusions (NI) were most abundant in the motor cortex and CA sectors of the hippocampus. The densities of the NI and the swollen achromatic neurons (SN) were similar in laminae II/III and V/VI but glial cell density was greater in V/VI. The density of the NI was positively correlated with the SN and the glial cells. Principal components analysis (PCA) suggested that PC1 was associated with variation in neuronal loss in the frontal/temporal lobes and PC2 with neuronal loss in the frontal lobe and NI density in the parahippocampal gyrus. The data suggest: 1) frontal and temporal lobe degeneration in NIFID is associated with the widespread formation of NI and SN, 2) NI and SN affect cortical laminae II/III and V/VI, 3) the NI and SN affect closely related neuronal populations, and 4) variations in neuronal loss and in the density of NI were the most important sources of pathological heterogeneity.

Adult↗
PubMedDate not supplied

The late somatosensory evoked potential in premature and term infants. I. Principal component topography.

Very little is known about the topographic distribution of the cortical somatosensory evoked potential in premature infants. Principal component analysis (PCA) was applied to the wave forms generated from right median nerve stimuli over a relatively long sweep (483 msec post stimulus) at 16 electrodes in 53 infants with postconceptual ages from 31 to 40 weeks, subdivided into 5 groups by 2 week increments. Factor scores were averaged across subjects, within groups and displayed as topographical maps. Four factors accounted for 71-76% of the variance in each of the 5 groups and the factors extracted from the PCA performed independently in each group were markedly consistent. The first factor (N1/P1) had a left posterior minimum and a left frontal-central maximum and probably represents a tangential dipole located in the post-central gyrus. The second factor (N2) was characterized by a consistent left central minimum with a systematic developmental change in the maximum that seemed to imply that its neural generator was changing in orientation as the infants matured. A third factor (N3) accounted for the most variance and appeared to represent the first evidence of activity in the ipsilateral cortex. Finally, a very late fourth factor appeared only in the more mature groups, with uncertain localization. The topographic maps of the factor scores for these 4 factors appear to account for independent generators in the SEP of the premature and term infant.

Brain Mapping↗
PubMed1993

Multicomponent determination of flavour enhancers in food preparations by partial least squares and principal component regression modelling of spectrophotometric data.

Three multivariate calibration methods, partial least squares (PLS-1 and PLS-2) and principal component regression (PCR), were applied to the simultaneous determination of three flavour enhancers (inosine 5'-monophosphate, guanosine 5'-monophosphate and monosodium glutamate), in mixtures by ultraviolet/visible absorption spectrophotometry. The absorption and first- and second-derivative absorption spectra of the ternary mixtures were used to perform the optimization of the calibration matrices by the PLS and PCR methods. The results obtained by the application of the different chemometric approaches are discussed and compared. No significant advantages were found for the prior differentiation step. The proposed method was applied satisfactorily to the determination of inosine 5'-monophosphate and guanosine 5'-monophosphate in several food preparations, in the presence of monosodium glutamate.

Flavoring Agents↗
PubMed2004

Sensitivity, principal component and flux analysis applied to signal transduction: the case of epidermal growth factor mediated signaling.

MOTIVATION: Novel high-throughput genomic and proteomic tools are allowing the integration of information from a range of biological assays into a single conceptual framework. This framework is often described as a network of biochemical reactions. We present strategies for the analysis of such networks. RESULTS: The direct differential method is described for the systematic evaluation of scaled sensitivity coefficients in reaction networks. Principal component analysis, based on an eigenvalue-eigenvector analysis of the scaled sensitivity coefficient matrix, is applied to rank individual reactions in the network based on their effect on system output. When combined with flux analysis, sensitivity analysis allows model reduction or simplification. Using epidermal growth factor (EGF) mediated signaling and trafficking as an example of signal transduction, we demonstrate that sensitivity analysis quantitatively reveals the dependence of dual-phosphorylated extracellular signal-regulated kinase (ERK) concentration on individual reaction rate constants. It predicts that EGF mediated reactions proceed primarily via an Shc-dependent pathway. Further, it suggests that receptor internalization and endosomal signaling are important features regulating signal output only at low EGF dosages and at later times.

Algorithms↗
PubMed2005

Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis.

MOTIVATION: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to 'real world' scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified. RESULTS: We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups. AVAILABILITY: Neuroshell 2 is commercially available from Ward Systems.

Algorithms↗
PubMed2005

Serum 1H-nuclear magnetic spectroscopy followed by principal component analysis and hierarchical cluster analysis to demonstrate effects of statins on hyperlipidemic patients.

Use of statins for prevention of coronary heart disease is based on the decrease of serum cholesterol and LDL cholesterol. To better investigate the changes in lipid profile after statin treatment, we propose here to use an analysis of serum by proton nuclear magnetic resonance (NMR) spectroscopy associated with a multivariate analysis of the main spectral components. Sera were obtained from 60 male patients treated for 6 weeks with simvastatin (30 patients) or atorvastatin (30 patients) for who LDL cholesterol decreased by over 45% in all selected patients. Proton nuclear magnetic resonance spectra were obtained and the region of methyl resonance from lipids was separated into six consecutive lines attributed to lipids which were analyzed by principal component analysis (PCA) and clustering by hierarchical cluster analysis (HCA) based on Euclidian distance coupled with the Ward's minimum variance method. PCA and HCA gave a map discriminating the 120 samples into five clusters, three clusters containing samples obtained at baseline and two others containing samples obtained after treatment. Both statins produced a decrease in lower-density lipoprotein components and an increase in higher density lipoprotein components. Patients with a coronary heart disease history could be discriminated after treatment by the increase in the component containing the highest proportion of HDL. Proton NMR spectroscopy of sera coupled with a PCA and an HCA was able to detect variations in the metabolism of lipids resulting from statin treatments.

Aged↗
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↗