844 resultados para principal components analysis (PCA) algorithm


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Many cell types have no known functional attributes. In the bladder and prostate, basal epithelial and stromal cells appear similar in cytomorphology and share several cell surface markers. Their total gene expression (transcriptome) should provide a clear measure of the extent to which they are alike functionally. Since urologic stromal cells are known to mediate organ-specific tissue formation, these cells in cancers might exhibit aberrant gene expression affecting their function. For transcriptomes, cluster designation (CD) antigens have been identified for cell sorting. The sorted cell populations can be analyzed by DNA microarrays. Various bladder cell types have unique complements of CD molecules. CD9(+) urothelial, CD104(+) basal and CD13(+) stromal cells of the lamina propria were therefore analyzed, as were CD9(+) cancer and CD13(+) cancer-associated stromal cells. The transcriptome datasets were compared by principal components analysis for relatedness between cell types; those with similarity in gene expression indicated similar function. Although bladder and prostate basal cells shared CD markers such as CD104, CD44 and CD49f, they differed in overall gene expression. Basal cells also lacked stem cell gene expression. The bladder luminal and stromal transcriptomes were distinct from their prostate counterparts. In bladder cancer, not only the urothelial but also the stromal cells showed gene expression alteration. The cancer process in both might thus involve defective stromal signaling. These cell-type transcriptomes provide a means to monitor in vitro models in which various CD-isolated cell types can be combined to study bladder differentiation and bladder tumor development based on cell-cell interaction.

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Background: Early progressive nonfluent aphasia (PNFA) may be difficult to differentiate from semantic dementia (SD) in a nonspecialist setting. There are descriptions of the clinical and neuropsychological profiles of patients with PNFA and SD but few systematic comparisons. Method: We compared the performance of groups with SD (n = 27) and PNFA (n = 16) with comparable ages, education, disease duration, and severity of dementia as measured by the Clinical Dementia Rating Scale on a comprehensive neuropsychological battery. Principal components analysis and intergroup comparisons were used. Results: A 5-factor solution accounted for 78.4% of the total variance with good separation of neuropsychological variables. As expected, both groups were anomic with preserved visuospatial function and mental speed. Patients with SD had lower scores on comprehension-based semantic tests and better performance on verbal working memory and phonological processing tasks. The opposite pattern was found in the PNFA group. Conclusions: Neuropsychological tests that examine verbal and nonverbal semantic associations, verbal working memory, and phonological processing are the most helpful for distinguishing between PNFA and SD.

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Terrestrial amphibians may dehydrate when exposed to low humidity, representing an important factor affecting spatial distribution and community composition. In this study we investigated whether rates of dehydration and rehydration are able to explain the spatial distribution of an anuran community in a Restinga environment at the northern coast of the State of Bahia, Brazil, represented by 11 species distributed in 27 sample units. The environmental data set containing 20 variables was reduced to a few synthetic axes by principal component analysis (PCA). Physiological variables measured were rates of dehydration, rehydration from water, and rehydration from a neutral substrate. Multiple regression analyses were used to test the null hypothesis of no association between the environmental data set (synthetic axes of PCA) and each axis representative of a physiological variable, which was rejected (P < 0.001). Of 15 possible partial regressions only rehydration rate from neutral substrate vs. PC1. and PC2, rehydration rate from water vs. PC1, and dehydration rate vs. PC2 were significant. Our analysis was influenced by a gradient between two different groups of sample units: a beach area with high density of bromeliads and an environment without bodies of water with low density of bromeliads. Species of very specific natural history and morphological characters occur in these environments: Phyllodytes melanomystax and Scinax auratus, species frequently occurring in terrestrial bromeliads, and Ischnocnema paulodutrai, common along the northern coast of Bahia and usually found in forest remnants within environments with low number of bodies of water. In dry environments species with lower rates of dehydration were dominant, whereas species showing greater rates of dehydration were found predominantly in microhabitats with greater moisture or abundance of bodies of water.

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Environmental conditions favor the predominance of dense populations of cyanobacteria in reservoirs in northeastern Brazil. The aim of this study was to understand cyanobacterial population dynamics in the rainy and dry seasons at two depths in the Arcoverde reservoir. Microalgae and cyanobacteria samples were collected during 24 hours with intervals of 4 hours (nycthemeral) at sub-surface and 10 m using a van Dorn bottle and a determined biomass. Physical and chemical variables were obtained and the data were analyzed using the principal component analysis (PCA). No nycthemeral variations in the taxonomic composition or distribution of the populations of cyanobacteria were found between the different times of day in either the rainy or dry season. In both seasons, the greatest biomass of the phytoplankton community was made up of cyanobacteria at two depths and all times of the day. Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju was dominant at all times of the day on both the surface and at the bottom. In the rainy season, the differences in cyanobacterial biomass between the surface and bottom were less significant than in the dry season. The differences in cyanobacterial biomass between surface and bottom were less pronounced than those found in the dry season. We concluded that a) physical variables better explain the alterations of species in the phytoplankton community in an environment dominated by cyanobacteria throughout the year; b) seasonal climatic factors associated to periods of stratification and de-stratification are important for alterations in the community and variations in biomass and, c) the turbidity caused by rainfall favored the emergence and establishment of other cyanobacteria, especially Planktothrix agardhii (Gomont) Anagnostidis & Komarek.

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Advances in genotyping technologies have contributed to a better understanding of human population genetic structure and improved the analysis of association studies. To analyze patterns of human genetic variation in Brazil, we used SNP data from 1129 individuals - 138 from the urban population of Sao Paulo, Brazil, and 991 from 11 populations of the HapMap Project. Principal components analysis was performed on the SNPs common to these populations, to identify the composition and the number of SNPs needed to capture the genetic variation of them. Both admixture and local ancestry inference were performed in individuals of the Brazilian sample. Individuals from the Brazilian sample fell between Europeans, Mexicans, and Africans. Brazilians are suggested to have the highest internal genetic variation of sampled populations. Our results indicate, as expected, that the Brazilian sample analyzed descend from Amerindians, African, and/or European ancestors, but intermarriage between individuals of different ethnic origin had an important role in generating the broad genetic variation observed in the present-day population. The data support the notion that the Brazilian population, due to its high degree of admixture, can provide a valuable resource for strategies aiming at using admixture as a tool for mapping complex traits in humans. European Journal of Human Genetics (2012) 20, 111-116; doi:10.1038/ejhg.2011.144; published online 24 August 2011

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2-Methylisoborneol (MIB) and geosmin (GSM) are sub products from algae decomposition and, depending on their concentration, can be toxic: otherwise, they give unpleasant taste and odor to water. For water treatment companies it is important to constantly monitor their presence in the distributed water and avoid further costumer complaints. Lower-cost and easy-to-read instrumentation would be very promising in this regard. In this study, we evaluate the potentiality of an electronic tongue (ET) system based on non-specific polymeric sensors and impedance measurements in monitoring MIB and GSM in water samples. Principal component analysis (PCA) applied to the generated data matrix indicated that this ET was capable to perform with remarkable reproducibility the discrimination of these two contaminants in either distilled or tap water, in concentrations as low as 25 ng L-1. Nonetheless, this analysis methodology was rather qualitative and laborious, and the outputs it provided were greatly subjective. Also, data analysis based on PCA severely restricts automation of the measuring system or its use by non-specialized operators. To circumvent these drawbacks, a fuzzy controller was designed to quantitatively perform sample classification while providing outputs in simpler data charts. For instance, the ET along with the referred fuzzy controller performed with a 100% hit rate the quantification of MIB and GSM samples in distilled and tap water. The hit rate could be read directly from the plot. The lower cost of these polymeric sensors allied to the especial features of the fuzzy controller (easiness on programming and numerical outputs) provided initial requirements for developing an automated ET system to monitor odorant species in water production and distribution. (C) 2012 Elsevier B.V. All rights reserved.

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CHEMICAL PROFILE COMPARISON OF SUGARCANE SPIRITS FROM THE SAME WINE DISTILLED IN ALEMBICS AND COLUMNS. Six wines were distilled in two different distillation apparatus (alembic and column) producing 24 distillates (6 for each alembic fraction - head, heart and tail; 6 column distillates). The chemical composition of distillates from the same wine was determined using chromatographic techniques. Analytical data were subjected to Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) allowing discrimination of four clusters according to chemical profiles. Both distillation processes influenced the sugarcane spirits chemical quality since two types of distillates with different quantitative chemical profiles were produced after the elimination of fermentation step influence.

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Locomotor capacity is often considered an excellent measure of whole animal performance because it requires the integrated functioning of many morphological, physiological (and biochemical) traits. However, because studies tend to focus on either structural or functional suits of traits, we know little on whether and how morphological and physiological traits coevolve to produce adequate locomotor capacities. Hence, we investigate the evolutionary relationships between morphological and physiological parameters related to exercise physiology, using tropidurine lizards as a model. We employ a phylogenetic principal component analysis (PCA) to identify variable clusters (factors) related to morphology, energetic metabolism and muscle metabolism, and then analyze the relationships between these clusters and measures of locomotor performance, using two models (star and hierarchical phylogenies). Our data indicate that sprint performance is enhanced by simultaneous evolutionary tendencies affecting relative limb and tail size and physiological traits. Specifically, the high absolute sprint speeds exhibited by tropidurines from the sand dunes are explained by longer limbs, feet and tails and an increased proportion of glycolytic fibers in the leg muscle, contrasting with their lower capacity for overall oxidative metabolism [principal component (PC1)]. However, when sprint speeds are corrected for body size, performance correlates with a cluster (PC3) composed by moderate loads for activity metabolic rate and body size. The simultaneous measurement of morphological and physiological parameters is a powerful tool for exploring patterns of coadaptation and proposing morphophysiological associations that are not directly predictable from theory. This approach may trigger novel directions for investigating the evolution of form and function, particularly in the context of organismal performance.

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Gunshot residues (GSR) can be used in forensic evaluations to obtain information about the type of gun and ammunition used in a crime. In this work, we present our efforts to develop a promising new method to discriminate the type of gun [four different guns were used: two handguns (0.38 revolver and 0.380 pistol) and two long-barrelled guns (12-calibre pump-action shotgun and 0.38 repeating rifle)] and ammunition (five different types: normal, semi-jacketed, full-jacketed, green, and 3T) used by a suspect. The proposed approach is based on information obtained from cyclic voltammograms recorded in solutions containing GSR collected from the hands of the shooters, using a gold microelectrode; the information was further analysed by non-supervised pattern-recognition methods [(Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA)]. In all cases (gun and ammunition discrimination), good separation among different samples in the score plots and dendrograms was achieved. (C) 2012 Elsevier B.V. All rights reserved.

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Fractal theory presents a large number of applications to image and signal analysis. Although the fractal dimension can be used as an image object descriptor, a multiscale approach, such as multiscale fractal dimension (MFD), increases the amount of information extracted from an object. MFD provides a curve which describes object complexity along the scale. However, this curve presents much redundant information, which could be discarded without loss in performance. Thus, it is necessary the use of a descriptor technique to analyze this curve and also to reduce the dimensionality of these data by selecting its meaningful descriptors. This paper shows a comparative study among different techniques for MFD descriptors generation. It compares the use of well-known and state-of-the-art descriptors, such as Fourier, Wavelet, Polynomial Approximation (PA), Functional Data Analysis (FDA), Principal Component Analysis (PCA), Symbolic Aggregate Approximation (SAX), kernel PCA, Independent Component Analysis (ICA), geometrical and statistical features. The descriptors are evaluated in a classification experiment using Linear Discriminant Analysis over the descriptors computed from MFD curves from two data sets: generic shapes and rotated fish contours. Results indicate that PCA, FDA, PA and Wavelet Approximation provide the best MFD descriptors for recognition and classification tasks. (C) 2012 Elsevier B.V. All rights reserved.

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Abstract Background Oral squamous cell carcinoma (OSCC) is a frequent neoplasm, which is usually aggressive and has unpredictable biological behavior and unfavorable prognosis. The comprehension of the molecular basis of this variability should lead to the development of targeted therapies as well as to improvements in specificity and sensitivity of diagnosis. Results Samples of primary OSCCs and their corresponding surgical margins were obtained from male patients during surgery and their gene expression profiles were screened using whole-genome microarray technology. Hierarchical clustering and Principal Components Analysis were used for data visualization and One-way Analysis of Variance was used to identify differentially expressed genes. Samples clustered mostly according to disease subsite, suggesting molecular heterogeneity within tumor stages. In order to corroborate our results, two publicly available datasets of microarray experiments were assessed. We found significant molecular differences between OSCC anatomic subsites concerning groups of genes presently or potentially important for drug development, including mRNA processing, cytoskeleton organization and biogenesis, metabolic process, cell cycle and apoptosis. Conclusion Our results corroborate literature data on molecular heterogeneity of OSCCs. Differences between disease subsites and among samples belonging to the same TNM class highlight the importance of gene expression-based classification and challenge the development of targeted therapies.

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Six wines were distilled in two different distillation apparatus (alembic and column) producing 24 distillates (6 for each alembic fraction - head, heart and tail; 6 column distillates). The chemical composition of distillates from the same wine was determined using chromatographic techniques. Analytical data were subjected to Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) allowing discrimination of four clusters according to chemical profiles. Both distillation processes influenced the sugarcane spirits chemical quality since two types of distillates with different quantitative chemical profiles were produced after the elimination of fermentation step influence.

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The purpose of this study was to assess the composition of the rainwater in Araraquara City, Brazil, a region strongly influenced by pre-harvest burning of sugar cane crops. Chemical and mineralogical variables were measured in rainwater collected during the harvest, dry period of 2009 and the non-harvest, wet period of 2010. Ca2+ and NH4+ were responsible for 55% of cations and NO3- for 45% of anions in rainwater. Al and Fe along with K were the most abundant among trace elements in both soluble and insoluble fractions. High volume weighted mean concentration (VWM) for most of the analyzed species were observed in the harvest, dry period, mainly due to agricultural activities and meteorological conditions. The chemistry of the Araraquara rainwater and principal component analysis (PCA) quantification clearly indicate the concurrence of a diversity of sources from natural to anthropogenic especially related to agricultural activities.

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Uca populations have an important functional and structural role in many estuarine ecosystems. These crabs exhibit distinct physiological tolerance to salinity gradients, which may partially explain their heterogeneous distribution. In order to investigate the population structure and distribution of Uca spp. in a tropical estuary, we sampled Uca crabs in replicated 0.75 m2 quadrats at six muddy plain areas during monthly intervals between July and November 2012 in spring tidal conditions. Environmental factors including water temperature, salinity, sediment total organic matter, chlorophyll-a, and granulometry were analyzed. We sampled a total of 2919 individuals distributed in three Uca species (U. uruguayensis, U. thayeri and U. maracoani), from which U. uruguayensis was dominant. The density and biomass of individuals were spatially and temporally heterogeneous. During October and November we found higher Uca spp. densities (71.3 ± 47.3 to 77.6 ± 44,5 ind. 0.75 m-²) and biomass (1.8 ± 1.1 to 2.1 ± 1.0 g 0.75 m-2 AFDW) if compared to the previous months, density (July 55,5± 44,1 August 52,5± 34,9 and September 47,7 ± 25,6 ind. 0,75m-²) and biomass in others months (July 1,0± 0,94 August 1,1 ± 0,72 and September 1,3±0,93 g 0.75 m-2 AFDW ). The same pattern was found for other variables, such as salinity (32 and 34), organic matter (30 and 67%) and chlorophyll-a (89 and 46 μg g-1). In two study areas we found this pattern which suggests that higher Uca productivity and food availability are related. A principal component analysis (PCA) suggests that salinity and granulometry (silt) can influence (60% correspondence) the distribution of U. maracoani. For U. uruguayensis and U. thayeri the PCA suggests chlorophyll-a was important, which is a good indicator for labile organic matter. Our study suggests that the population structure and distribution of Uca species may be regulated by food availability, supporting their utility as biological models for ecosystem monitoring.

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Facial expression recognition is one of the most challenging research areas in the image recognition ¯eld and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human–machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future