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Type-II Xanthanuria is an genetic disorder associated with diminished serum uric acid levels. Patients with xanthanuria has absence of xanthine oxidase or xanthine dehydrogenase activity, the enzyme that converts hypoxanthine to xanthine and xanthine to uric acid. Deficiency of these enzyme leads to elevated levels of xanthine in urine which further leads to precipitation of xanthine in urine which further helps to formation of renal stones and ultimately leads to chronic kidney disease and end stage renal disease. We report a 23 years old male, who reached ESRD due to Type 2 xanthinuria, which was confirmed by genetic studies, who later successfully underwent renal transplant surgery and currently having normal life with functioning graft. [ABSTRACT FROM AUTHOR]

Byline: Rajiv. Rathod, Prashant. Bansal, Srinivas. Gutta Ureteric stones are usually small and symptomatic. We present a case of a 35-year old female who presented with minimally symptomatic right distal [...]

In the field of digital image processing, Image Contrast Enhancement (ICE) is an emerging research area, which helps to attain the sub-standard quality images. The existing ICE techniques have a few limitations like brightness preservation and contrast limitation. Also, the existing ICE techniques affects from over-enhancement that leads to unrealistic results and halo-like artifacts in the high contrast region. To address these issues, a new contrast enhancement method (Second Order Derivative with Weber’s Law (SODWL)) is developed in this research study. The proposed SODWL method includes two major phases (i) estimate the relationship between different image exposures and identify the appropriate Camera Response Model (CRM), (ii) illumination estimation method is applied to calculate the exposure ratio map. The SODWL method improved the performance of ICE effectively, because the developed approach considers even the small edge pixel values for contrast enhancement. After that, Weber’s law is applied for adjusting the lighting conditions of the input image. The experimental analysis was conducted on the reputed datasets: VV, LIME-data, DICM, and MEF. Finally, the performance of proposed method is evaluated by applying the evaluation metrics such as, Lightness Order Error (LOE), and Visual Information Fidelity (VIF). The experimental result showed that the proposed system improved the VIF value upto 0.4-0.1 and also reduced the LOE value up to 2447-2 as related to the existing systems. [ABSTRACT FROM AUTHOR]

In this paper we describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The FERET database allows us to demonstrate performance on hundreds or thousands of images. The mixture of experts is implemented using the 'divide and conquer' modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the 'gating network' components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ('expert') for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification. Index Terms--Decision trees (DT), ensembles of radial basis functions (ERBF), ethnic classification, face processing, FERET, gating networks, gender classification, mixtures of experts, pose discrimination, radial basis functions (RBF's) kernels, support vector machines (SVMs).