A comparative study on multiscale fractal dimension descriptors

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.

Global gene expression profiling of oral cavity cancers suggests molecular heterogeneity within anatomic subsites

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.

Comparação do perfil químico entre cachaças de um mesmo vinho destiladas em alambiques e em colunas

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.

Rainwater major and trace element contents in Southeastern Brazil: an assessment of a sugar cane region in dry and wet period

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.

Population structure and distribution of UCA ( Brachyura: Ocypodidae) influenced by abiotic factors in a tropical estuary

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.

Brain activity and medical diagnosis: an EEG study

Abstract Background Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis Results The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making. Conclusions PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.