1,4-Diazabicyclo[2.2.2]octane (DABCO) as a useful catalyst in organic synthesis

1,4-diazabicyclo[2.2.2]octane (DABCO) has been used in many organic preparations as a good solid catalyst. DABCO has received considerable attention as an inexpensive, eco-friendly, high reactive, easy to handle and non-toxic base catalyst for various organic transformations, affording the corresponding products in excellent yields with high selectivity. In this review, some applications of this catalyst in organic reactions were discussed

Challenges for identifying the neural mechanisms that support spatial navigation: the impact of spatial scale.

Spatial navigation is a fascinating behavior that is essential for our everyday lives. It involves nearly all sensory systems, it requires numerous parallel computations, and it engages multiple memory systems. One of the key problems in this field pertains to the question of reference frames: spatial information such as direction or distance can be coded egocentrically-relative to an observer-or allocentrically-in a reference frame independent of the observer. While many studies have associated striatal and parietal circuits with egocentric coding and entorhinal/hippocampal circuits with allocentric coding, this strict dissociation is not in line with a growing body of experimental data. In this review, we discuss some of the problems that can arise when studying the neural mechanisms that are presumed to support different spatial reference frames. We argue that the scale of space in which a navigation task takes place plays a crucial role in determining the processes that are being recruited. This has important implications, particularly for the inferences that can be made from animal studies in small scale space about the neural mechanisms supporting human spatial navigation in large (environmental) spaces. Furthermore, we argue that many of the commonly used tasks to study spatial navigation and the underlying neuronal mechanisms involve different types of reference frames, which can complicate the interpretation of neurophysiological data

Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites (Extended Abstract)

In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding proteins start, the so-called translation initiation sites (TIS). This can be modeled as a classification problem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to successfully incorporate biological prior knowledge by engineering an appropriate kernel function