Ancestral inference from DNA could serve as an important adjunct for both standard and future human identity testing procedures. However, current STR methods for the inference of ancestral affiliation have inherent statistical and technical limitations. In an effort to identify biallelic markers that can be used to infer ancestral affiliation from DNA, we screened 211 SNPs in the human pigmentation and xenobiotic metabolism genes. Allele frequencies of 56 SNPs (most from pigmentation genes) were dramatically different between groups of unrelated individuals of Asian, African, and European descent, and both observed and simulated log likelihood ratios revealed that the markers were of exceptional value for ancestral inference. Log likelihood ratios of the multilocus estimates of biological ancestry (EAE/EBA) ranged from 7 to 10, which are on par with the best of the STR batteries yet described. A linear classification method was developed for incorporating these SNPs into a classifier model that was 99, 98, and 100% accurate for identifying individuals of European, African, and Asian descent, respectively. The methods and markers we describe are therefore an important first step for the development of a practical multiplex test for the inference of ancestry in a forensics setting.

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Subvalvular aneurysms are the least common type of left ventricular (LV) aneurysms and can be fatal. Subaortic LV aneurysms are much rarer than submitral LV aneurysms and mostly reported in infancy. They can be congenital or acquired secondary to infections, cardiac surgery or trauma. Here, we report a unique presentation of a large, idiopathic subaortic aneurysm in an adult masquerading as an acute coronary syndrome. Diagnosis was made with the help of a CT aortography. Aneurysm was surgically resected with good results. This case highlights the clinical presentation and management of subaortic aneurysms, an important differential for congenital aortic malformations.

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Road Extraction from satellite imagery is of fundamental importance in the context of spatial data capturing and updating for GIS applications. As fully automatic method for feature extraction is difficult due to the increasing complexity of objects. This paper proposes a semiautomatic road extraction methodology from high resolution satellite imagery using active contour model (Snakes). First the image is preprocessed using relaxed median filter. In the next step the user inputs initial seed points on the road to be extracted. Then the road segment is extracted using active contour model. The method is tested using high resolution satellite imagery and the results are presented in the paper.