Robust Framework for PET Image Reconstruction Incorporating System and Measurement Uncertainties

In Positron Emission Tomography (PET), an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts

Synthetic biology approaches in drug discovery and pharmaceutical biotechnology

Synthetic biology is the attempt to apply the concepts of engineering to biological systems with the aim to create organisms with new emergent properties. These organisms might have desirable novel biosynthetic capabilities, act as biosensors or help us to understand the intricacies of living systems. This approach has the potential to assist the discovery and production of pharmaceutical compounds at various stages. New sources of bioactive compounds can be created in the form of genetically encoded small molecule libraries. The recombination of individual parts has been employed to design proteins that act as biosensors, which could be used to identify and quantify molecules of interest. New biosynthetic pathways may be designed by stitching together enzymes with desired activities, and genetic code expansion can be used to introduce new functionalities into peptides and proteins to increase their chemical scope and biological stability. This review aims to give an insight into recently developed individual components and modules that might serve as parts in a synthetic biology approach to pharmaceutical biotechnology

Motivational profiles for secondary school physical education and its relationship to the adoption of a physically active lifestyle among university students

The promotion of an active lifestyle is one of the central aims of physical education (PE). The present study aimed at investigating the relation between students’ motivation for PE and activity levels using self-determination theory as a guiding framework. A retrospective design was used involving 2617 university students, of which 878 (33.5 percent) were male (age 20.6 1.9). Validated questionnaires were used to measure motivation for PE, self-reported concurrent (activity levels at secondary school) and delayed transfer (activity levels at university). A combination of hierarchical and non-hierarchical cluster analyses was used to generate motivational profiles for PE. Differences in activity levels were analysed using MANCOVAS. The results revealed that students with more optimal (i.e. more autonomous) motivational profiles reported more transfer and reported being more active at secondary school and in early adulthood. Implementing strategies that enhance self-determined motivation might result in more students’ adopting an active lifestyle