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At least 739 records · Page 41Linked to original sources
PubMed2004

Analysis of heavy metal distribution in superficial estuarine sediments (estuary of Bilbao, Basque Country) by open-focused microwave-assisted extraction and ICP-OES.

Open-focused microwave-assisted extraction and ICP-OES determination of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn from surface sediments of the estuary of Bilbao (Basque Country, North of Spain) was carried out. All the samples were collected at three different tributaries of the estuary (Asua, Galindo and Nerbioi-Ibaizabal) every two months during 1999. The digestion procedure was proposed from the conclusions of a fractionated factorial design, and the precision and accuracy of the method was verified using a certified reference sediment (RTC008-050). The results of the analysis were statistically treated by means of principal component analysis and correlation analysis. The principal component analysis of sediment data (32 samples x 9 metals) indicated different patterns of contamination regarding the tributary and sampling station. The two main patterns observed were a steady increment of the metal concentration along all the campaigns in the samples collected in the Galindo River and a seasonal variation in the Nerbioi-Ibaizabal River, with higher metallic content during summertime and lower content during wintertime.

Ecosystem↗
PubMed2001

Modern pollen studies in the territory of Sagalassos (Southwest Turkey) and their use in the interpretation of a Late Holocene pollen diagram.

Modern pollen precipitation studies were carried out on a regional scale in the territory of the classical city of Sagalassos (Southwest Turkey). 57 moss cushions and 7 soil samples from different vegetation types were analysed palynologically. The resulting pollen spectra were grouped into 14 vegetation groups, which were examined using discriminant analysis and principal components analysis. These numerical procedures are applied to display the structure in the data set in a comprehensive way. Some of the modern spectra groups seem to be well-defined, whereas others are difficult to distinguish from each other. After the relationships between the modern pollen spectra had been analysed, the fossil data from a core from the marsh of Gravgaz were added to the data set so that any similarities between fossil and modern spectra could be seen. A joint principal components analysis of the modern and fossil pollen spectra revealed that the pollen spectra from the earliest (ca. 2600-2500 BP; Cal. 830-590 BC till 790-520 BC) and the latest (starting at ca. 1300 BP; Cal. 660-780 AD) pollen assemblage zones have modern counterparts. On the other hand, the pollen spectra from the pollen assemblage zone dominated by Artemisia (ca. 2500-2300 BP; Cal. 790-520 BC till 410-210 BC) and from the pollen assemblage zone that indicated the practice of arboriculture (ca. 2300-1300 BP; Cal. 410-210 BC till 660-780 AD) have no close modern analogues, although individual tabulated pollen percentages revealed some similarities with some modern spectra groups.

Journal Article↗
PubMed2003

The impact of microsatellite instability on the molecular phenotype of colorectal tumors.

Frequent microsatellite instability MSI (MSI-H) occurring in human tumors is characterized by defective DNA mismatch repair and unique clinical features. However, infrequent MSI (MSI-L) has not been attributable to any other defined molecular pathway, and its existence as a biologically distinct category has been challenged. Moreover, the global molecular phenotypes (GMPs) underlying MSI-H, MSI-L, or microsatellite-stable (MSS) tumors have never been evaluated. To evaluate the impact of MSI status on GMP and to determine the importance of MSI relative to other molecular and clinical features, cDNA microarray-derived data from 41 colon cancers were interpreted using principal components analysis. The clinically relevant principal component with the greatest impact on GMP was component 3, which distinguished MSI-H from non-MSI-H (i.e., MSI-L and microsatellite stable) tumors and was designated the MSI-H separator. Notably, MSI-L cancers were also clearly distinguished from non-MSI-L tumors by another principle component, component 10 (the "MSI-L separator"). This second finding validates the existence of MSI-L tumors as a distinct molecular phenotypic category. Thus, both components 3 and 10 reflected different aspects of MSI and helped to establish principal components analysis as a useful tool to identify and characterize distinct biological features of human malignancy.

Aged↗
PubMed1996

Patterns of positive and negative symptoms in first episode schizophrenia.

BACKGROUND: The aim is to examine, in first episodes of schizophrenia, the appropriateness of the simple two-dimensional model of schizophrenia ('negative' and 'positive' dimensions) and more complex variants. METHOD: All patients with a first episode of schizophrenia who, over a two-year period, made contact with any of the public mental health services of the autonomous region of Cantabria in northern Spain were investigated. The psychiatric evaluation included, among other instruments, the Present State Examination (PSE-9), and the scales for the assessment of the 'positive' and 'negative' symptoms of schizophrenia (SAPS and SANS respectively). The dimensionality of the SAPS/SANS item scores and sub-scales was examined throughout the use of principal component analysis. RESULTS: The principal component solution that best fits the data obtained with the initial SANS/SAPS sub-scales reflects the existence of three different ('negative', 'positive', 'disorganisation') factors. The strategy adopted of repeating the analysis after extracting the principal components of the original sub-scales, revealed that although the nature and item composition of the initial 'negative' and 'disorganisation' factors were in general confirmed, the 'positive' dimension presented a more complex structure with at least two 'positive' ('Non-Paranoid' and 'Paranoid') independent factors. CONCLUSION: The psychopathological structure of the early stages of schizophrenia, as evaluated by the SANS/SAPS, is characterised by the presence of four dimensions: two 'positive', one 'negative' and one 'disorganisation'.

Adolescent↗
PubMed2005

Environmentally stratified sampling design for the development of Great Lakes environmental indicators.

Understanding the relationship between human disturbance and ecological response is essential to the process of indicator development. For large-scale observational studies, sites should be selected across gradients of anthropogenic stress, but such gradients are often unknown for apopulation of sites prior to site selection. Stress data available from public sources can be used in a geographic information system (GIS) to partially characterize environmental conditions for large geographic areas without visiting the sites. We divided the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then summarized over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute sites across the range of environmental variation in our GIS data. This design has broad applicability when the goal is to develop ecological indicators using observational data from large-scale surveys.

Animals↗
PubMed2005

Discrimination of Francisella tularensis subspecies using surface enhanced laser desorption ionization mass spectrometry and multivariate data analysis.

Francisella tularensis causes the zoonotic disease tularemia, and is considered a potential bioterrorist agent due to its extremely low infection dose and potential for airborne transmission. Presently, F. tularensis is divided into four subspecies; tularensis, holarctica, mediasiatica and novicida. Phenotypic discrimination of the closely related subspecies with traditional methods is difficult and tedious. Furthermore, the results may be vague and they often need to be complemented with virulence tests in animals. Here, we have used surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to discriminate between the four subspecies of F. tularensis. The method is based on the differential binding of protein subsets to chemically modified surfaces. Bacterial thermolysates were added to anionic, cationic, and copper ion-loaded immobilized metal affinity SELDI chip surfaces. After binding, washing, and SELDI-TOF-MS different protein profiles were obtained. The spectra generated from the different surfaces were then used to characterize each bacterial strain. The results showed that the method was reproducible, with an average intensity variation of 21%, and that the mass precision was good (300-450 ppm). Moreover, in subsequent cluster analysis and principal component analysis (PCA) data for the analyzed Francisella strains grouped according to the recognized subspecies. Partial least squares-discriminant analysis (PLS-DA) of the protein profiles also identified proteins that differed between the strains. Thus, the protein profiling approach based on SELDI-TOF-MS holds great promise for rapid high-resolution phenotypic identification of bacteria.

Animals↗
PubMed2003

Principal component regression analysis with SPSS.

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Regression Analysis↗
PubMedDate not supplied

Population genetic structure of Carchesium polypinum (Ciliophora: Peritrichia) in four Chinese lakes inferred from ISSR fingerprinting: high diversity but low differentiation.

Although the peritrichous ciliate Carchesium polypinum is common in freshwater, its population genetic structure is largely unknown. We used inter-simple sequence repeat (ISSR) fingerprinting to analyze the genetic structure of 48 different isolates of the species from four lakes in Wuhan, central China. Using eight polymorphic primers, 81 discernible DNA fragments were detected, among which 76 (93.83%) were polymorphic, indicating high genetic diversity at the isolate level. Further, Nei's gene diversity (h) and Shannon's Information index (I) between the different isolates both revealed a remarkable genetic diversity, higher than previously indicated by their morphology. At the same time, substantial gene flow was found. So the main factors responsible for the high level of diversity within populations are probably due to conjugation (sexual reproduction) and wide distribution of swarmers. Analysis of molecular variance (AMOVA) showed that there was low genetic differentiation among the four populations probably due to common ancestry and flooding events. The cluster analysis and principal component analysis (PCA) suggested that genotypes isolated from the same lake displayed a higher genetic similarity than those from different lakes. Both analyses separated C. polypinum isolates into subgroups according to the geographical locations. However, there is only a weak positive correlation between the genetic distance and geographical distance, suggesting a minor effect of geographical distance on the distribution of genetic diversity between populations of C. polypinum at the local level. In conclusion, our studies clearly demonstrated that a single morphospecies may harbor high levels of genetic diversity, and that the degree of resolution offered by morphology as a marker for measuring distribution patterns of genetically distinct entities is too low.

Animals↗
PubMed1985

Genetic markers of an aboriginal Taiwanese population.

A group of Taiwan aborigines, the Toroko, was typed for 21 classical genetic loci. This is part of an ongoing program aimed at a comprehensive study of Taiwan aborigines. In this first paper a short summary of historical, archeological, and anthropological data in the literature is made, and results of the present survey are compared with older results from other aborigine tribes. An analysis of other neighboring populations from southeast Asia has also been carried out in order to give a preliminary answer to the question of origin of Taiwanese aborigines. Fifteen populations were studied for 13 loci by tree analysis, principal components, and isolation by distance. Tree analysis and principal component analysis gave results in fairly good agreement and indicate three major population clusters: a northeast cluster (Ainu, Korea, Japan, and Ryukyu); a southeast cluster (south China, Thailand, Vietnam, Philippines, Taiwan, and Toroko); and a third cluster including Malaya and Borneo. The positions of Polynesia, Micronesia, and Melanesia are somewhat peripheral. Analysis of the tree shows some potential cases of convergence, perhaps owing to admixture, and of divergence. The analysis of isolation by distance shows that geographic propinquity is a reasonably good predictor of general similarity in this area.

Humans↗
PubMed2005

A new image representation algorithm inspired by image submodality models, redundancy reduction, and learning in biological vision.

We develop a new biologically motivated algorithm for representing natural images using successive projections into complementary subspaces. An image is first projected into an edge subspace spanned using an ICA basis adapted to natural images which captures the sharp features of an image like edges and curves. The residual image obtained after extraction of the sharp image features is approximated using a mixture of probabilistic principal component analyzers (MPPCA) model. The model is consistent with cellular, functional, information theoretic, and learning paradigms in visual pathway modeling. We demonstrate the efficiency of our model for representing different attributes of natural images like color and luminance. We compare the performance of our model in terms of quality of representation against commonly used basis, like the discrete cosine transform (DCT), independent component analysis (ICA), and principal components analysis (PCA), based on their entropies. Chrominance and luminance components of images are represented using codes having lower entropy than DCT, ICA, or PCA for similar visual quality. The model attains considerable simplification for learning from images by using a sparse independent code for representing edges and explicitly evaluating probabilities in the residual subspace.

Algorithms↗
PubMed2006

Multivariate statistical analysis of metal concentrations in teeth of residents of Silesian region, southern Poland.

Concentrations of 11 metals (Cd, Cr, Cu, Fe, Mn, Pb, Zn, Na, K, Ca, and Mg) were assayed in the teeth of two populations of the Silesian region. The first group (n = 83) comprised the residents of Katowice-Szopienice, a town located in the center of the Upper Silesian industrial region, in close proximity to a Pb plant, whereas the other was a group of residents of the agricultural community Strumień (n = 44). The concentrations of all the trace metals were found to be higher in the teeth of Katowice-Szopienice residents. The methods of cluster analysis distinguished two clusters of metals in the teeth: essential (Ca, Na, Mg, Zn, and K) and trace (Cd, Mn, Cr, Cu, Fe, and Pb) elements. Euclidean distances reflected the differences in concentrations between the two groups. The results obtained were analyzed using principal component analysis. Four principal factors accounted for 68.1% of the total variance for Katowice-Szopienice residents and for 80.1% for Strumień residents. The first factor showed high contributions of Cu and Cr, elements present in humans as a result of diet, and also Mn for Katowice-Szopienice residents because of environmental contamination. The second factor was characterized by large Zn and Cd fractions and, for Katowice-Szopienice residents, Pb and K as well. The diversified exposure of both populations was reflected by the differences in Euclidean distances and contribution of particular elements with respect of principal components.

Adolescent↗
PubMed2002

Classification of fermentation performance by multivariate analysis based on mean hypothesis testing.

Multivariate analysis, such as principal component analysis and artificial autoassociative neural networks, is currently extensively applied to feature capturing, physiological state recognition, fault detection and bioprocess control. However, it is not clear which process variable should be selected as an important input for multivariate analysis to analyze physiological conditions and/or bioprocess performance a priori. An efficacious method to select more informative process variables from the repository of historical data is highly desired. In this study, we focused on a premodeling step. Mean hypothesis testing (MHT) was used to select appropriate variables for multivariate analysis. Fermentation data sets were classified into two classes "good" and "bad" according to the MHT results. The results showed that selecting discriminating process variables from the historical database by MHT enhanced the overall effectiveness of multivariate analysis prior to principal component analysis and artificial autoassociative neural network model creation.

Journal Article↗
PubMed2001

Deciphering the genetic architecture of a multivariate phenotype.

A heritable multivariate quantitative phenotype comprises several correlated component phenotypes that are usually pleiotropically controlled by a set of major loci and environmental factors. One approach to decipher the genetic architecture of a multivariate phenotype, in particular to map the underlying loci, is to reduce the dimensionality of the data by means of a data reduction technique, such as principal component analysis. The extracted principal components are then analyzed in conjunction with marker data to map the underlying loci. We have examined the efficiency of this approach with and without taking into account the correlation structure of the multivariate phenotype when extracting principal components. We have assumed that genome-wide scan data on sibpairs are available for low-density (widely spaced) and high-density markers. Using extensive simulations, based on three models of the multivariate phenotype, we have shown that although ignoring the correlation structure of the multivariate phenotype does not have any serious impact on the efficiency of mapping the underlying trait loci in wide marker intervals, there is a significant adverse effect of this practice for fine-mapping. We, therefore, recommend that the correlation structure of the multivariate phenotype be carefully examined to decide on the strategy of extracting principal components for deciphering the genetic architecture of the multivariate phenotype.

Chromosome Mapping↗
PubMed2006

Chemotaxonomy of Portuguese Ulex: quinolizidine alkaloids as taxonomical markers.

Six species of Portuguese Ulex L. in a total of nineteen populations were studied by GC-EIMS as to their content in quinolizidine alkaloids. Sparteine, beta-isosparteine, jussiaeiine A, N-methylcytisine, cytisine, 5,6-dehydrolupanine, rhombifoline, lupanine, jussiaeiine B, N-formylcytisine, N-acetylcytisine, anagyrine, jussiaeiine C, jussiaeiine D, pohakuline, baptifoline, and epibaptifoline were detected. Analysis of the chromatograms showed that the chemical profile of all species was mainly composed of N-methylcytisine, cytisine, anagyrine, and jussiaeiines A, B, C and D. Therefore a quantification study of these alkaloids in all the populations studied was done by GC. These data were then submitted to cluster analysis and principal component analysis, which allowed the definition of five chemotypes and the recognition of hybrids. N-methylcytisine, cytisine, and jussiaeiines A, C and D are recognized as markers of this genus in Portugal.

Alkaloids↗
PubMed2006

Multivariate data analysis as a fast tool in evaluation of solid state phenomena.

A thorough understanding of solid state properties is of growing importance. It is often necessary to apply multiple techniques offering complementary information to fully understand the solid state behavior of a given compound and the relations between various polymorphic forms. The vast amount of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets analyzed covered dehydration phenomena of a set of hydrates followed by variable temperature X-ray powder diffractometry and Raman spectroscopy and the crystallization of amorphous lactose monitored by Raman spectroscopy. Identification of different transitional states upon the dehydration enabled the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information.

Anti-Bacterial Agents↗
PubMed2005

Differentiating lifting technique between those who develop low back pain and those who do not.

BACKGROUND: No research to date has been able to discriminate differences in lifting technique for healthy individuals who eventually develop low back pain compared to those that do not while employed in a manual materials handling industry. The purpose of this study was to demonstrate the ability of principal component analysis to identify differences in lifting technique. METHODS: Principal component analysis was applied to sixteen kinematic and kinetic waveforms describing the two-dimensional motion of the trunk and load. The principal component scores for each variable were used as the dependent measures in a one-way ANOVA to determine group differences. FINDINGS: Significant group differences (P<0.05) were found for five of the principal component scores capturing associated kinematic waveform patterns related to the control and placement of the box on the shelf, and associated kinetic waveform patterns related to the relative timing of extension moment generation in the sacral and thoracic regions. A related waveform pattern for trunk compression was also found. INTERPRETATION: Due to the coordinated movements involved in tasks such as lifting, differences among clinical populations have been difficult to demonstrate empirically. We were able to identify different characteristics in lifting kinematics and kinetics prior to the development of low back pain. Principal component analysis was able to identify important biomechanical differences where traditional analyses failed. This is the first study to identify such lifting differences prior to the development of low back pain.

Diagnosis, Computer-Assisted↗
PubMed2006

The scent of urine spots of male mice, Mus musculus: Changes in chemical composition over time.

A dominant male mouse scent-marks his territory very frequently by emitting small urinary spots. The urine spots release in the air a variety of odorants that transmit different information to other mice, especially those concerning the time of deposition. To investigate this effect, small spots of urine of a dominant male mouse were left to freely release the odorants in the air for time intervals ranging from 0 min to 24 h prior to sampling. Thereupon, the odorants remaining in the spot were sampled at diffusion equilibrium (45 degrees C) in a small vial by means of headspace solid-phase microextraction followed by gas chromatography coupled to flame ionisation detection and mass spectrometry. Thirteen odorants were consistently found. Nine odorants were identified and four were matched. The rate of release of each odorant was characteristic and was described using principal component analysis. A first principal component was based on nine early odorants that showed a decreasing release over time. The odorants were 2,4-dehydro-exo-brevicomin, an unknown with 78% matching to 4-acetonilcycloheptanone, linalool, 2,4-dimethyl-phenol, 4-ethylphenol, indole, 2-butyl-1-octanol, an unknown with 83% matching to 2-ethyl-1-decanol, and 2,4-bis-(1,1-dimethylethyl)phenol. A second principal component, based on two unknowns with 73% matching to yohimban-17-one and 71% matching to the 2-methyl-3-hydroxy-2,4,4-trimethyl ester of propanoic acid, had an irregular release after deposition. A third principal component of late odorants, based on 2-sec-butyl-4,5-dihydrothiazole and 6,10-dimethyl-5,9-undecaden-2-one, had a peak of release at about 22 min. In conclusion, the release of the odorants in the headspace of a urine spot may code and transmit information on the deposition time.

Animals↗