Analysis of Spatial and Temporal Distribution of Purinergic P2 Receptors in the Mouse Hippocampus

ATP and other nucleotides are important glio-/neurotransmitters in the central nervous system. They bind to purinergic P2X and P2Y receptors that are ubiquitously expressed in various brain regions modulating various physiological and pathophysiological processes. P2X receptors are ligand-gated ion channels mediating excitatory postsynaptic responses whereas P2Y receptors are G protein-coupled receptors mediating slow synaptic transmission. A variety of P2X and P2Y subtypes with distinct neuroanatomical localization provide the basis for a high diversity in their function. There is increasing evidence that P2 receptor signaling plays a prominent role in learning and memory and thus, in hippocampal neuronal plasticity. Learning and memory are time-of-day-dependent. Moreover, extracellular ATP shows a diurnal rhythm in rodents. However, it is not known whether P2 receptors have a temporal variation in the hippocampus. This study provides a detailed systematic analysis on spatial and temporal distribution of P2 in the mouse hippocampus. We found distinct spatial and temporal distribution patterns of the P2 receptors in different hippocampal layers. The temporal distribution of P2 receptors can be segregated into two large time domains, the early to mid-day and the mid to late night. This study provides an important basis for understanding dynamic P2 purinergic signaling in the hippocampal glia/neuronal network.     

Towards an Intelligent Tutoring System Architecture that Supports Remedial Tutoring

For successful teaching to take place an intelligenttutoring system has to be able to cope with anystudent errors that may occur during a tutoringinteraction. Remedial tutoring is increasingly viewedas a central part of the overall tutoring process, andrecent research calls for adaptive remedial tutoring. This paper discusses the issues of remedial tutoringthat have been proposed or implemented to supportefficient remedial tutoring. These issues serve touncover any underlying principles of remediation thatgovern remedial tutoring with intelligent tutoringsystems. In order to incorporate these principles ofremediation into intelligent tutoring systemsdevelopment processes this paper continues with thedevelopment of a model that can be employed in thedevelopment of an intelligent tutoring system that iscapable of offering remedial tutoring according tothese principles. This model is a formalisation ofremedial interventions with intelligent tutoringsystems. To demonstrate how the model can be employed indeveloping an intelligent tutoring system, INTUITION,the implementation of an existing business simulationgame, has been developed. This paper concludes with anillustration of how the model for remedial operationsprovides for remedial tutoring within INTUITION. Theevaluation of INTUITION shows that the model forremedial operations is a useful method for providingefficient remedial tutoring.