Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods. (C) 2011 Elsevier Ltd. All rights reserved.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

Abstract Background Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics. Results The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis. Conclusions These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.

Chemical evaluation and thermal behavior of Brazil nut oil obtained by different extraction processes

Brazil nut richness has been widely studied aiming at finding transformation models that increase its yield, keep its quality, reduce costs, and increase its productivity. The objective of this study is to evaluate the spectroscopic profile and the chemical and thermal behavior of Brazil nut oil obtained by different extraction processes. Lipid soxhlet extractions with petroleum ether and hexane, and using hydraulic pressing and supercritical carbon dioxide (CO2) were performed. The physicochemical analyses showed an acidity index with significant differences between the samples obtained by hydraulic pressing and supercritical CO2. The peroxide index presented significant difference for the oil obtained by hydraulic pressing compared to that obtained by the other extraction processes. The fatty acid profiles showed a relevant presence of the omega series (w-3, w-6, and w-9) with significant differences for the extraction processes evaluated. The results of the thermogravimetric and differential analyses did not indicate changes in mass loss and presented predominantly exothermic behavior. The spectroscopic profiles indicate that the extraction processes do not change the spectral profile of the oils. (c) 2011 Elsevier Ltd. All rights reserved.

Multivariate analyses of UV-Vis absorption spectral data from cachaca wood extracts: a model to classify aged Brazilian cachacas according to the wood species used

Multivariate analyses of UV-Vis spectral data from cachaca wood extracts provide a simple and robust model to classify aged Brazilian cachacas according to the wood species used in the maturation barrels. The model is based on inspection of 93 extracts of oak and different Brazilian wood species by a non-aged cachaca used as an extraction solvent. Application of PCA (Principal Components Analysis) and HCA (Hierarchical Cluster Analysis) leads to identification of 6 clusters of cachaca wood extracts (amburana, amendoim, balsamo, castanheira, jatoba, and oak). LDA (Linear Discriminant Analysis) affords classification of 10 different wood species used in the cachaca extracts (amburana, amendoim, balsamo, cabreuva-parda, canela-sassafras, castanheira, jatoba, jequitiba-rosa, louro-canela, and oak) with an accuracy ranging from 80% (amendoim and castanheira) to 100% (balsamo and jequitiba-rosa). The methodology provides a low-cost alternative to methods based on liquid chromatography and mass spectrometry to classify cachacas aged in barrels that are composed of different wood species.

Millennial-scale climate cycles in Permian-Carboniferous rhythmites: Permanent feature throughout geologic time?

Two late Paleozoic glacial rhythmite successions from the Itarare Group (Parana Basin, Brazil) were examined for paleoclimate variations. Paleomagnetic (characteristic remanent magnetization, ChRM) and magnetic susceptibility (K(z)) measurements taken from the rhythmites are interpreted as paleoclimatic proxies. Ratios of low-frequency components in the K(z) variations suggest Milankovitch periodicities; this leads to recognition of other, millennial-scale variations reminiscent of abrupt climate changes during late Quaternary time, and are suggestive of Bond cycles and the 2.4 k.y. solar cycle. We infer from these patterns that millennial-scale climate change is not restricted to the Quaternary Period, and that millennial forcing mechanisms may have been prevalent throughout geologic time.

Uric acid biorhythm, a feature of long-term variation in a clinical laboratory database

Background: The biorhythm of serum uric acid was evaluated in a large sample of a clinical laboratory database by spectral analysis and the influence of the gender and age on uric acid variability. Methods: Serum uric acid values were extracted from a large database of a clinical laboratory from May 2000 to August 2006. Outlier values were excluded from the analysis and the remaining data (n = 73,925) were grouped by gender and age ranges. Rhythm components were obtained by the Lomb Scargle method and Cosinor analysis. Results: Serum uric acid was higher in men than in women older than 13 years (p<0.05). Compared with 0-12 year group, uric acid increased in men but not in women older than 13 years (p<0.05). Circannual (12 months) and transyear (17 months) rhythm components were detected, but they were significant only in adult individuals (>26 years, p<0.05). Cosinor analysis showed that midline estimating statistic of rhythm (MESOR) values were higher in men (range: 353-368 mu mol/L) than in women (range: 240-278 mu mol/L; p<0.05), independent of the age and rhythm component. The extent of predictable change within a cycle, approximated by the double amplitude, represented up to 20% of the corresponding MESOR. Conclusions: Serum uric acid biorhythm is dependent on gender and age and it may have relevant influence on preanalytical variability of clinical laboratory results.

Physical and chemical characterization of the pulp of different varieties of avocado targeting oil extraction potential

The aim of this study was to evaluate the physicochemical properties of avocado pulp of four different varieties (Avocado, Guatemala, Dickinson, and Butter pear) and to identify which has the greatest potential for oil extraction. Fresh avocado pulp was characterized by moisture, protein, fat, ash, carbohydrates and energy contents were determined. The carotenoids and chlorophyll contents were determined by the organic solvent extraction method. The results showed significant differences in the composition of the fruit when varieties are compared. However, the striking feature in all varieties is high lipid content; Avocado and Dickinson are the most suitable varieties for oil extraction, taking into account moisture content and the levels of lipids in the pulp. Moreover, it could be said that the variety Dickinson is the most affected by the parameters evaluated in terms of overall quality. Chlorophyll and carotenoids, fat-soluble pigments, showed a negative correlation with respect to lipids since it could be related to its function in the fruit. The varieties Avocado and Dickinson are an alternative to oil extraction having great commercial potential to be exploited thus avoiding waste and increasing farmers income.