Differential Contribution of L-, N-, and P/Q-type Calcium Channels to [Ca2+]i Changes Evoked by Kainate in Hippocampal Neurons

Abstract We investigated the contribution of L-, N- and P/Q-type Ca2+ channels to the [Ca2+]i changes, evoked by kainate, in the cell bodies of hippocampal neurons, using a pharmacological approach and Ca2+ imaging. Selective Ca2+ channel blockers, namely nitrendipine, ?-Conotoxin GVIA (?-GVIA) and ?-Agatoxin IVA (?-AgaIVA) were used. The [Ca2+]i changes evoked by kainate presented a high variability, and were abolished by NBQX, a AMPA/kainate receptor antagonist, but the N-methyl-d-aspartate (NMDA) receptor antagonist, D-AP5, was without effect. Each Ca2+ channel blocker caused differential inhibitory effects on [Ca2+]i responses evoked by kainate. We grouped the neurons for each blocker in three subpopulations: (1) neurons with responses below 60% of the control; (2) neurons with responses between 60% and 90% of the control, and (3) neurons with responses above 90% of the control. The inhibition caused by nitrendipine was higher than the inhibition caused by ?-GVIA or ?-AgaIVA. Thus, in the presence of nitrendipine, the percentage of cells with responses below 60% of the control was 41%, whereas in the case of ?-GVIA or ?-AgaIVA the values were 9 or 17%, respectively. The results indicate that hippocampal neurons differ in what concerns their L-, N- and P/Q- type Ca2+ channels activated by stimulation of the AMPA/kainate receptors

Robust optimization for resource-constrained project scheduling with uncertain activity durations

The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible scenarios. Our modeling techniques stem from robust optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxationbased heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics