Correlation between concentrations of chlorophyll-a and satellite derived climatic factors in the Persian Gulf.

Monthly mean satellite derived Chl-a, aerosols, wind, SST, PAR, and turbidity datasets were used to investigate the possible factors regulating phytoplankton variability in the Persian Gulf. The spatial correlation analysis revealed two distinct regions of SST and PAR, and a relatively uniform spatial correlation pattern of the other parameters. The cross correlation between aeolian dusts and Chl-a was significantly positive with 1-3 months offset. The pattern of spatial correlation between Chl-a and SST was positive in the shallow regions without time lag, and was negative with time offset of 3-5 months in deeper regions. The cross correlation between Chl-a and north-ward winds were positive with time lags of 1-3 months. Vertical mixing under the influence of north-ward winds in the deeper region, availability of light and nutrients in the shallow regions, and dust fertilizations over the whole area were suggested to be the major controlling factors regulating phytoplankton growth.

Kernel sparse representation based model for skin lesions segmentation and classification.

BACKGROUND AND OBJECTIVES: Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images. METHODS: Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a kernel-based dictionary and a linear classifier. We also present an adaptive K-SVD algorithm for kernel dictionary and classifier learning. RESULTS: We test our approach for both segmentation and classification tasks. The evaluation results on both dermoscopic and digital datasets demonstrate our approach to be competitive as compared to the available state-of-the-art methods, with the advantage of not needing any pre-processing. CONCLUSIONS: Our method is insensitive to noise and image conditions and can be used effectively for challenging skin lesions. Our approach is so extensive to be adapted to various medical image segmentations.

Use of physiologically based kinetic (PBK) modeling to study interindividual human variation and species differences in plasma concentrations of quercetin and its metabolites.

Biological activities of flavonoids in vivo ultimately depend on the systemic bioavailability of the aglycones and their metabolites. We aimed to develop physiologically based kinetic (PBK) models to predict plasma concentrations of the flavonoid quercetin and its metabolites in individual human subjects and to define species differences compared with male rat. The human models were developed based on in vitro metabolic parameters derived from incubations with pooled and 20 individual human tissue fractions and by fitting kinetic parameters to available in vivo data. The outcomes obtained were compared to a previously developed model for quercetin and its metabolites formation in male rat. Quercetin-3'-O-glucuronide was predicted to be the major circulating metabolite in 19 out of 20 individuals, while in male rat di- and tri-conjugates of quercetin containing a glucuronic acid, sulfate and/or methyl moieties are the major metabolites. Significant species differences occur in major circulating metabolites of quercetin suggesting that rat is not an adequate model to study effects of quercetin in man. The defined PBK models can be used to guide the experimental design of in vitro experiments with flavonoids, especially to better take into account the relevance of metabolism and the contribution of metabolites to the biological activity in humans.