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Results for “Principal Component Analysis”

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PubMed2004

In situ spectral monitoring of mRNA translation in embryonic stem cells during differentiation in vitro.

Raman microspectroscopy was used to determine biochemical markers during the differentiation of embryonic murine stem cells (mES) in vitro. Such markers are useful to determine the differentiation status of ES cells cultured on biomaterials. Raman spectra of mES cells as undifferentiated, spontaneously differentiated (4 days), and differentiated cells via formation of embryoid bodies (16, 20 days) were analyzed. Unsupervised hierarchical cluster analysis and principal component analysis were used to determine biochemical differences between mES cells in various states of differentiation. The undifferentiated cells were characterized by high scores of the first principal component (PC1, 49% variance). Similarity between the PC1 loading and the Raman spectrum of RNA indicated a high concentration of RNA in mES cells compared to differentiated cells. The ratio between the peak areas of RNA and proteins was used as a measure of mRNA translation. Using the same peak area ratio, it was possible to differentiate even between mES as undifferentiated and in early stages of differentiation (4 days). These findings were correlated with biological studies reporting high levels of nontranslated mRNA during early embryonic development. Therefore, the RNA translation obtained from the Raman spectra can be used as marker of differentiation state of mES cells.

Animals↗

PubMed2002

A comprehensive and novel predictive modeling technique using detailed pathology factors in men with localized prostate carcinoma.

BACKGROUND: The purpose of the current study was to evaluate modeling strategies using sextant core prostate biopsy specimen data that would best predict biochemical control in patients with localized prostate carcinoma treated with permanent prostate brachytherapy (PPB). METHODS: One thousand four hundred seventy-seven patients underwent PPB between 1992 and 2000. The authors restricted analysis to those patients who had sextant biopsies (n = 1073). A central pathology review was undertaken on all specimens. Treatment consisted of PPB with either I-125 or Pd-103 prescribed to 144 Gy or 140 Gy, respectively. Two hundred twenty-eight patients (21%) received PPB in combination with external radiotherapy and 333 patients (31%) received neoadjuvant hormones. In addition to clinical stage, biopsy Gleason sum, and pretreatment prostate specific antigen (pretx-PSA), the following detailed biopsy variables were considered: mean percentage of cancer in an involved core; maximum percentage of cancer; mean primary and secondary Gleason grades; maximum Gleason grade (primary or secondary); percentage of cancer in the apex, mid, and base; percent of cores positive; maximum primary and secondary Gleason grades in apex, mid, and base; maximum percent cancer in apex, mid, and base; maximum Gleason grade in apex, mid, and base; maximum primary Gleason grade; and maximum secondary Gleason grade. In all, 23 biopsy variables were considered. Four modeling strategies were compared. As a base model, the authors considered the pretx-PSA, clinical stage, and biopsy Gleason sum as predictors. For the second model, the authors added percent of cores positive. The third modeling strategy was to use stepwise variable selection to select only those variables (from the total pool of 26) that were statistically significant. The fourth strategy was to apply principal components analysis, which has theoretical advantages over the other strategies. Principal components analysis creates component scores that account for maximum variance in the predictors. RESULTS: The median followup of the study cohort was 36 months (range, 6-92), and the Kattan modification of the American Society for Therapeutic Radiology and Oncology (ASTRO) definition was used to define PSA freedom from recurrence (PSA-FFR). The four models were compared in their ability to predict PSA-FFR as measured by the Somers D rank correlation coefficient. The Somers D rank correlation coefficients were then corrected for optimism with use of bootstrapping. The results for the four models were 0.32, 0.34, 0.37, and 0.39, respectively. CONCLUSIONS: The current study shows that the use of principal components analysis with additional pathology data is a more discriminating model in predicting outcome in prostate carcinoma than other conventional methods and can also be used to model outcome predictions for patients treated with radical prostatectomy and external beam.

Adult↗

PubMed1996

Linear dimension reduction of sequences of medical images: III. Factor analysis in signal space.

A method is presented for improving the precision of factor analysis by utilizing physiological information. The first step is an optimal linear dimension reduction, whereby the data are projected onto a low-dimensional signal space. Then principal component analysis is performed in the signal space rather than in the entire data space. This improves the precision of the principal components. Unlike ordinary principal component analysis, the present method is not degraded when the time intervals are subdivided, provided that the signal space is correct. Alternatively, but with identical results, the covariance matrix can be calculated from the whole data space. The covariance matrix is then transformed and principal component analysis is performed in either a low-rank matrix or a low-dimensional submatrix instead of in the whole covariance matrix. Factor analysis using the intersection method with a theory space may be improved by employing the present method. In simulations based on a [11C]flumazenil study with 27 frames, the proposed method required only 58 per cent of the radioactivity to produce the same precision as the intersection method and only 27 per cent when compared to ordinary principal component analysis.

Biophysical Phenomena↗

PubMed1992

Relationships between the chromatographic retention data and the effects of nucleoside derivatives in highly metastatic 3LL cells.

The effect of 21 nucleoside derivatives on the [3H]-thymidine cellular uptake and on the incorporation into DNA of highly metastatic 3LL (Lewis lung carcinoma) cells has been measured. Hydrophobic and hydrophilic molecular parameters (the adsorption capacity, specific adsorption surface, lipophilicity and specific hydrophobic surface area) have been determined by using thin-layer chromatography. Stepwise linear regression analysis and principal component analysis have been applied in order to reveal the relationships between the molecular parameters and the effect of the nucleoside derivatives on highly metastatic 3LL cells. The first principal component obtained from the measured activity data could be attributed to the change of [3H]-thymidine cellular uptake caused by the nucleoside, while the second principal component could be regarded as the measure of the effect on the DNA incorporation of [3H]-thymidine. The effect of the nucleosides on the [3H]-thymidine uptake could be explained by the specific hydrophobic and adsorption surface area of the nucleoside, on the other hand the effect on the DNA incorporation could be described by the adsorption characteristics (specific hydrophilic surface area and adsorption capacity) of the derivatives.

Animals↗

PubMed2006

Determination of PM2.5 sources using time-resolved integrated source and receptor models.

Multivariate statistical techniques are applied to particulate matter (PM) and meteorological data to identify the sources responsible for evening PM spikes at Sunland Park, NM (USA). The statistical techniques applied are principal components analysis (PCA), redundancy analysis (RDA), and absolute principal components scores analysis (APCSA), and the data evaluated are 3-h average (6-9 p.m.) PM(2.5) mass and chemical composition and 1-h average PM(2.5) and PM(10) mass and environmental data collected in the winter of 2002. Although the interpretation of the data was complicated by the presence of sources which are likely changing in time (e.g. brick kilns), the multivariate analyses indicate that the evening high PM(2.5) is associated with burning-activities occurring to the south of Sunland Park, and these emissions are characterized by elevated Sb, Cl(-), and elemental carbon; approximately 68% of the PM(2.5) mass can be attributed to this source. The PM(10) evening peaks, on the other hand, are mainly caused by resuspended dust generated by vehicular movements south of the site and transported by the local terrain-induced drainage flow.

Air Pollutants↗

PubMed2006

Use of multivariate analysis to extract latent variables related to level of production and lactation persistency in dairy cattle.

Multivariate factor analysis and principal component analysis were used to decompose the correlation matrix of test-day milk yields of 48,374 lactations of 21,721 Italian Simmental cows. Two common latent factors related to level of production in early lactation and lactation persistency, and 2 principal components associated with the whole lactation yield and persistency were obtained. Factor and principal component scores were treated as new quantitative phenotypes related to prominent features of lactation curve shape. Genetic parameters were estimated by univariate and bivariate animal models. Estimates of heritability were moderately low for both latent factors (0.13 for persistency and yield early in lactation). Heritabilities of the principal component related to total lactation yield and 305-d yield were similar (0.19 and 0.20, respectively). Finally, heritability was quite low for the principal component related to lactation persistency (0.07). Repeatabilities between lactations were about 0.27 for both latent factors, around 0.4 for the first principal component and 305-d yield, and 0.11 for the second principal component. Moderate genetic correlation among common factors (0.26) and their high genetic correlation with total lactation yield (>0.60) suggest that selection can be used to change the shape of lactation curve as well as improve yield. Scores of the second principal component can be used to genetically improve persistency while maintaining constant total lactation yield.

Analysis of Variance↗

PubMed1996

Comparison of the crown dimensions between the maxillary second deciduous molar and the first permanent molar.

The morphologic relationship of the crown structures of the maxillary second deciduous molar (m2) and the first permanent molar (M1) was investigated with odontometric methods. Materials used were 124 male dental casts taken from Chinese living in Kaohsiung (Taiwan). The mesiodistal and buccolingual diameters, cusp sizes, and intercusp distances were measured. The mean crown diameters were larger in M1 than in m2. The tooth crown proportions were similar in m2 and M1. The intercusp distance was compressed buccolingually in m2. With respect to the relative cusp size, the paracone was not significantly different between m2 and M1. The metacone was relatively larger in m2 than M1, while lingual cusps (protocone and hypocone) were relatively smaller in m2 than in M1. Principal component analysis was performed to investigate factors influencing the variation in crown dimensions of the two teeth. Four components were extracted: (1) buccolingual intercusp distances, (2) crown proportion (mesiodistal and buccolingual diameters), (3) mesiobuccal and distolingual compression, and (4) protocone and mesiodistal intercusp distance. Analysis of principal component scores indicated that the crown proportions of m2 and M1 were similar. The m2 had a smaller protocone and more buccolingually compressed intercusp distances than did M1. The morphologic characteristics of the crown of m2 indicate that it is more primitive and has developed less than M1.

Humans↗

PubMed2002

Prediction of vapour pressures for halogenated diphenyl ether congeners from molecular descriptors.

BACKGROUND, AIM AND SCOPE: Polychlorinated diphenyl ethers (PCDE) and polybrominated diphenyl ethers (PBDE) have both been identified as environmental contaminants. The physical properties are important in determining the distribution and fate of organic contaminants in the environment. The purpose of the present investigation was to characterise halogenated diphenyl ethers using computationally derived descriptors, and to develop calibration models for the vapour pressure from published experimental data. METHODS: Experimental data for vapour pressures were obtained from the literature. The chemical structure of each PCDE and PBDE congener was optimised prior to descriptor generation. The data analysis was performed using principal component analysis (PCA) and partial least squares regression (PLSR). The calibration models were validated with external test sets. RESULTS AND DISCUSSION: All congeners of PCDEs and PBDEs were characterised by 795 molecular descriptors and two principal components could account for about two thirds of the variance within each group. Bilinear calibration models were developed that could explain 99.4% of the variance in the external validation test sets. Vapour pressures were subsequently predicted for all congeners that were adequately described by these calibration models. The type and number of halogen atoms in the molecule were the main factors influencing the vapour pressures of halogen substituted diphenyl ethers, but the variations in substitution pattern was also shown to be a significant factor. CONCLUSIONS: The molecular descriptor patterns of halogenated aromatic compounds such as diphenyl ethers can be described and interpreted using principal component analysis (PCA). The major sources of variation in the descriptor spaces for PCDEs and PBDEs are the same as those contributing to the differences in vapour pressure, similar to what has previously been reported for the PCBs. The bilinear calibration models for vapour pressure presented here, has a standard error of prediction that is lower than what is reported as the experimental uncertainty or observed as deviations between experimental investigations. The estimated prediction errors are expected to be within the reported boundaries when the models are applied to new objects within the same molecular descriptor space, and model predictions can hence extend the current database of experimental values. RECOMMENDATIONS AND OUTLOOK: The results from this investigation and others show that the establishment of quantitative structure-property relationships (QSPR) is a viable approach to estimate physical properties for halogenated diphenyl ethers. It is easy to foresee an increased need for using QSPR estimation methods in the future, for evaluation of the environmental fate for organic pollutants. Despite method developments and automation, it is unlikely that laboratory determinations can cope with the pace that new pollutants are identified.

Calibration↗

PubMed2000

Principal components in three-ball cascade juggling.

To uncover the underlying control structure of three-ball cascade juggling, we studied its spatiotemporal properties in detail. Juggling patterns, performed at fast and preferred speeds, were recorded in the frontal plane and subsequently analyzed using principal component analysis and serial correlation techniques. As was expected on theoretical grounds, the principal component analysis revealed that maximally four instead of the original six dimensions (3 balls x 2 planar coordinates) are sufficient for describing the juggling dynamics. Juggling speed was shown to affect the number of dimensions (four for the fast condition, two for the preferred condition) as well as the smoothness of the time evolution of the eigenvectors of the principal component analysis, particularly around the catches. Contrary to the throws and the zeniths, and regardless of juggling speed, consecutive catches of the same hand showed a markedly negative lag-one serial correlation, suggesting that the catches are timed so as to preserve the temporal integrity of the juggling act.

Biomechanical Phenomena↗

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

A directed essential dynamics simulation of peptide folding.

We present a directed essential dynamics (DED) method for peptide and protein folding. DED is a molecular dynamics method based on the essential dynamics sampling and the principal component analysis. The main idea of DED is to use principal component analysis to determine the direction of the most active collective motion of peptides at short intervals of time (20 fs) during the folding process and then add an additional force along it to adjust the folding direction. This method can make the peptides avoid being trapped in the local minima for a long time and enhance the sampling efficiency in conformational space during the simulation. An S-peptide with 15 amino acids is used to demonstrate the DED method. The results show that DED can lead the S-peptide to fold quickly into the native state, whereas traditional molecular dynamics needs more time to do this.

Biophysics↗

PubMed2007

The coupling between upper and lower extremity synergies during whole body reaching.

When performing whole body reaching movements, all four limbs participate in the task. We hypothesized that the synergies that characterize upper and lower extremity movement are flexible and become coupled into one functional unit to transport the body towards the target. To test this hypothesis, subjects reached to three targets, one within and two beyond arm's length. In addition, subjects reached at two speeds and either stopped at the target or returned to the original start position. To assess the coupling during the various whole body reaches, a principal component analysis was performed on the displacements of the five primary joints used to accomplish the task (ankle, knee, hip, shoulder and elbow). Analysis of the loadings from the principal component analysis indicated that the first component represented the reaching element of the task, while the second and third components represented the postural element. When reaching within arm's length the variance explained by the joint coupling was distributed between the first three principal components. However, as reach distance increased, the distribution shifted with most of the variance being explained by the first principal component. Neither movement velocity nor final joint configuration affected the coupling between the joints. Analysis of center of mass indicated that it shifted progressively forward as reached distance increased. We conclude that as target distance increased, the reach and postural synergies became coupled resulting in the arms, legs and trunk working together as one functional unit to move the whole body forward.

Adult↗

PubMed2004

Flow microcalorimetric studies of phenol and its chlorinated derivatives and a theoretical evaluation of their possible inhibition mode on Chromobacterium violaceum respiration.

The general belief that chemical structure determines the biological effect of drugs has led to several techniques to establish structure-activity relationships (SAR) that is useful in the development of more active compounds. Predicting toxic effects based on SAR, one can obtain toxicological data with a low cost-benefit ratio. Chlorophenols that represent a class of toxic agents frequently used in industrial processes are not satisfactorily described in the literature in relation to their toxicity. The main objective of this work is to relate the microbial activities of phenol, anisole and their chlorinated derivatives on Chromobacterium violaceum respiration with their physicochemical properties. Anisole and its chlorinated derivatives were used to evaluate the influence of phenol acidity on biological activity. The calculations were carried out at the semi-empirical AM1 and ab initio DFT levels employing the basis sets CEP-31G, CEP-31+Ge CEP-31G** that were parameterized using the continuum-solvation model COSMO for solvent contribution. Both empirical and theoretical properties were evaluated by chemometric analyses (hierarchical cluster analysis (HCA) and principal component analysis (PCA)), to correlate the physicochemical properties of the phenol, anisole and their chlorinated derivatives with their biological activities. The results obtained for the current work indicate that the biological activities of these compounds increase as the n-octanol/water (logP) partition coefficients, ionization energies (IP), melting points (mp) and dissociation constants increase and the solvent effects (SE), enthalpies of formation (DeltafH degrees ) and proton affinities (PA) decrease.

Anisoles↗

PubMed2005

Functional connectivity reveals load dependent neural systems underlying encoding and maintenance in verbal working memory.

One of the main challenges in working memory research has been to understand the degree of separation and overlap between the neural systems involved in encoding and maintenance. In the current study we used a variable load version of the Sternberg item recognition test (two, four, six, or eight letters) and a functional connectivity method based on constrained principal component analysis to extract load-dependent neural systems underlying encoding and maintenance, and to characterize their anatomical overlap and functional interaction. Based on the pattern of functional connectivity, constrained principal component analysis identified a load-dependent encoding system comprising bilateral occipital (Brodmann's area (BA) 17, 18), bilateral superior parietal (BA 7), bilateral dorsolateral prefrontal (BA 46), and dorsal anterior cingulate (BA 24, 32) regions. For maintenance, in contrast, constrained principal component analysis identified a system that was characterized by both load-dependent increases and decreases in activation. The structures in this system jointly activated by maintenance load involved left posterior parietal (BA 40), left inferior prefrontal (BA 44), left premotor and supplementary motor areas (BA 6), and dorsal cingulate regions (BA 24, 32), while the regions displaying maintenance-load-dependent activity decreases involved bilateral occipital (BA 17, 18), posterior cingulate (BA 23) and rostral anterior cingulate/orbitofrontal (BA 10, 11, 32) regions. The correlation between the encoding and maintenance systems was strong and negative (Pearson's r = -.55), indicting that some regions important for visual processing during encoding displayed reduced activity during maintenance, while subvocal rehearsal and phonological storage regions important for maintenance showed a reduction in activity during encoding. In summary, our analyses suggest that separable and complementary subsystems underlie encoding and maintenance in verbal working memory, and they demonstrate how constrained principal component analysis can be employed to characterize neuronal systems and their functional contributions to higher-level cognition.

Adolescent↗

PubMed2004

Quantitative X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry characterization of the components in DNA.

The great diversity of techniques to synthesize and use DNA microarrays has made them extremely flexible for a variety of applications. This flexibility also has made standardization difficult, leading to problems comparing data from these different systems. In this work, we use the surface science techniques of X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS) to analyze the components of DNA. The atomic ratios of the components of nucleobases, nucleosides, and nucleotides were characterized by XPS. The chemical shifts in the high-resolution XPS spectra allow for their relatively easy resolution. The unique positive and negative ions from the nucleobases, nucleosides, and nucleotides in their TOF-SIMS spectra were identified. This information was used to build a comprehensive table of all of the molecular ions. These standard spectra of DNA components can be used to predict the relative amounts of the bases within more complex molecules either by univariate analysis (i.e., by relating the base molecular ions to the sugar fragment ions within the nucleotides) or by multivariate analysis (e.g., principal component analysis). Our preliminary examination of four oligonucleotides shows promising results in that we can distinguish between two oligomers of similar composition using univariate and multivariate analysis, although additional studies are needed to expand this method to more complex oligomers.

Base Sequence↗

PubMed2004

Validation of 'personal strain' and 'role strain': subscales of the short version of the Japanese version of the Zarit Burden Interview (J-ZBI_8).

The eight-item short version of the Japanese version of the Zarit Burden Interview (J-ZBI_8) has been confirmed for reliability and validity, and its two subscales (personal strain and role strain) are based on the factor structure of the ZBI. It has been demonstrated that these subscales have good reliability. The aim of the present study was to confirm their construct validity. A total of 51 impaired elderly who had been receiving regular nurses' visits in Kyoto Prefecture, Japan and their family primary caregivers, participated in the present study. Each caregiver was asked to complete a questionnaire which included the J-ZBI_8, the hours spent in caregiving, and the physical and cognitive disability of the impaired elderly. A principal component analysis identified the following two principal components of these variables: 'Activities of Daily Living deficits' and 'behavioral disturbances'. Consequently, Barthel Index (BI) and Troublesome Behavior Scale (TBS) were selected as the representative variable, respectively, for each component. We subsequently calculated the Spearman's rank correlations among the subscales of J-ZBI_8, BI and TBS. Personal strain was found to be correlated with TBS (rho = 0.48, P < 0.01), while role strain was correlated with the BI (rho = -0.29, P = 0.04). The correlation between personal strain and BI was not significant, nor was the correlation between role strain and TBS. These findings indicate that the personal strain and role strain measured by J-ZBI_8 correspond to the personal strain and role strain of the ZBI originally described by Whitlatch et al. Therefore, the construct validity of the J-ZBI_8 subscales is confirmed.

Activities of Daily Living↗

PubMed1990

Multivariate data reduction by principal components, with application to neurological scoring instruments.

Principal components analysis is widely used as a practical tool for the analysis of multivariate data. The aim of this analysis is to reduce the dimensionality of a multivariate data set to the smallest number of meaningful and independent dimensions. The analysis can also provide interpretable linear functions of the original measured variables that may serve as valuable indices of variation. A brief introduction to principal components analysis is given herein, followed by an examination of a particular set of multivariate data accruing from a study of acute brain injuries in a pediatric population, in which severity of brain injury had been assessed with the Glasgow Coma Scale (CGS). Principal components analysis reveals that the GCS sum score is a particularly inefficient summarizer of information in this cohort. The determination of an objective weighting of measured variables, as provided through principal components analysis, is essential in the construction of meaningful neurological scoring instruments.

Coma↗

PubMed2002

QSAR of human factor Xa inhibitor N2-aroylanthranilamides using principal component factor analysis.

Quantitative structure-activity relationship (QSAR) study of human factor Xa inhibitor N2-aroylanthranilamides, recently reported by Yee et al. (J. Med. Chem., 43, 873-882), has been performed using principal component factor analysis as the preprocessing step. The study reveals that presence of electron-donating R2 substituent at the para position (with respect to the amide linkage) is conducive to the binding affinity, whereas a meta R2 substituent decreases the affinity. Again, electron-donating R1 substituents with less bulk and optimum hydrophilic-lipophilic balance (particularly, methyl and methoxy groups) favor the activity. The study further suggests that electron-withdrawing R3 substituents are detrimental for the activity, whereas bulkier R4 substituents (particularly NHSO2Me group) increase the activity.

Antithrombin III↗