Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

Biochemical Analysis Leads to Improved Orthogonal Bioluminescent Tools

Engineered luciferase-luciferin pairs have expanded the number of cellular targets that can be visualized in tandem. While light production relies on selective processing of synthetic luciferins by mutant luciferases, little is known about the origin of selectivity. The development of new and improved pairs requires a better understanding of the structure−function relationship of bioluminescent probes. In this work, we report a biochemical approach to assessing and optimizing two popular bioluminescent pairs: Cashew/d -luc and Pecan/4′-BrLuc. Single mutants derived from Cashew and Pecan revealed key residues for selectivity and thermal stability. Stability was further improved through a rational addition of beneficial residues. In addition to providing increased stability, the known stabilizing mutations surprisingly also improved selectivity. The resultant improved pair of luciferases are >100-fold selective for their respective substrates and highly thermally stable. Collectively, this work highlights the importance of mechanistic insight for improving bioluminescent pairs and provides significantly improved Cashew and Pecan enzymes which should be immediately suitable for multicomponent imaging applications.