Injuries due to sharp trauma detected by post-mortem multislice computed tomography (MSCT): a feasibility study

Modern cross-sectional imaging techniques are being increasingly implemented in forensic pathology. In order to assess the practicability of such a method, namely post-mortem multislice computed tomography (MSCT) in cases of fatal cut and stab injuries, 12 corpses underwent such an examination prior to forensic autopsy. The questions regarding detection of foreign bodies, wound channels, skeletal and organ injuries, as well as the cause of death were addressed at MSCT and autopsy. The results of the two techniques revealed that post-mortem MSCT a useful tool in the assessment of such injuries.

Intellectual Property and Traditional Cultural Expressions in a Digital Environment

In the face of increasing globalisation, and a collision between global communication systems and local traditions, this book offers innovative trans-disciplinary analyses of the value of traditional cultural expressions (TCE) and suggests appropriate protection mechanisms for them. It combines approaches from history, philosophy, anthropology, sociology and law, and charts previously untravelled paths for developing new policy tools and legal designs that go beyond conventional copyright models. Its authors extend their reflections to a consideration of the specific features of the digital environment, which, despite enhancing the risks of misappropriation of traditional knowledge and creativity, may equally offer new opportunities for revitalising indigenous peoples' values and provide for the sustainability of TCE.This book will appeal to scholars interested in multidisciplinary analyses of the fragmentation of international law in the field of intellectual property and traditional cultural expressions. It will also be valuable reading for those working on broader governance and human rights issues.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).