The effects of N-methyl-D-aspartate (NMDA) on opioid receptor-mediated G protein activation were explored in neuroblastoma X glioma hybrid (NG108-15) cells. Treatment of the cells with NMDA resulted in a remarkable attenuation of [35S]guanosine-5'-O-(3-thio)triphosphate binding stimulated by [D-Pen2,D-Pen5]-enkephalin (DPDPE), a delta-opioid receptor agonist. The effects of NMDA were dose and time dependent with an IC50 value of 5 nM and could be blocked by NMDA receptor antagonists. After NMDA treatment, the DPDPE dose-response curve shifted to the right (EC50 value increased approximately 7-fold, from 6 to 40 nM), and the maximal response induced by DPDPE was reduced by approximately 60%. The effects of NMDA were reversible, and the DPDPE response could recover within 60 min. The functional responses of delta-, mu-, and kappa-opioid receptors in primarily cultured neurons also were attenuated significantly by NMDA treatment. The inhibitory effects of NMDA on opioid receptor-mediated G protein activation could be blocked by coadministration of the protein kinase C (PKC) inhibitors or by elimination of the extracellular Ca2+. Correspondingly, NMDA treatment of NG108 cells significantly elevated cellular PKC activity and stimulated Gialpha2 phosphorylation. Transient transfection into NG108-15 cells of the wild-type Gialpha2 and a mutated Gialpha2 (Ser144Ala) resulted in a 2-fold increase in DPDPE-stimulated G protein activation. The DPDPE responses were greatly inhibited by NMDA treatment in the wild-type Gialpha2-transfected cells but much less affected in the mutant Gialpha2-transfected cells. In summary, NMDA attenuates opioid receptor/G protein coupling, and this process requires activation of PKC. 相似文献
We propose a novel online multiple object tracker taking structure information into account. State-of-the-art multi-object tracking (MOT) approaches commonly focus on discriminative appearance features, while neglect in different levels structure information and the core of data association. Addressing this, we design a new tracker fully exploiting structure information and encoding such information into the cost function of the graph matching model. Firstly, a new measurement is proposed to compare the structure similarity of two graphs whose nodes are equal. With this measurement, we define a complete matching which performs association in high efficiency. Secondly, for incomplete matching scenarios, a structure keeper net (SKnet) is designed to adaptively establish the graph for matching. Finally, we conduct extensive experiments on benchmarks including MOT2015 and MOT17. The results demonstrate the competitiveness and practicability of our tracker.
This paper extends the quadrature method to price exotic options under jump-diffusion models. We compute the transition density of jump-extended models using convolution integrals. Furthermore, a simpler and more efficient lattice grid is introduced to implement the recursion more directly in matrix form. It can be shown that a lot of running time can be saved. At last, we apply the developed approach to the different jump-extended models to demonstrate its universality and provide a detailed comparison for the discrete path-dependent options to demonstrate its advantages in terms of speed and accuracy.
