Distribution of phosphorylated Elk-1 in rat brain after Y-maze active avoidance training in a temporal manner

BACKGROUND： Elk-1 mRNA distributes extensively in the neurons of mice, rat and human brains, and the Elk-1 expression may be correlated with the synaptic plasticity, learning and memory. OBJECTIVE： To observe the distribution of phosphorylated Elk-1 （pEIk-1） in whole brain of rats received Y-maze active avoidance training and the changes of pEIk-1 expression at different time points after training. DESIGN ： A randomized controlled study SETTING ： Research Room of Neurobiology, the Second Affiliated Hospital of Southern Medical University MATERIALS ： Fifty-five male clean-degree SD rats of 3-4 months old, weighing 200-250 g, were provided by the Experimental Animal Center of Southem Medical University. The rabbit anti-monoclonal pEIk-1 antibody was purchased from Cell Signal Transduction Company, and ABC kit from Vector Company. METHODS ： The experiments were carried out in the Research Room of Neurobiology, Second Affiliated Hospital of Southern Medical University from September 2004 to February 2005. ① Grouping： The rats were randomly divided into training group （n = 25）, sham-training group （n = 25） and normal control group （n = 5）, and the training and sham-training groups were observed at 0, 1, 3, 6 and 24 hours after training, which represented the five phases in the process of leaming and memory. ② Y-maze training： The rats were preconditioned in the electrical Y-maze apparatus, 20 minutes a day for 3 days continuously, and training began from the 4^th day. In the training group, the rats were trained with the combination of light and electddty. Each rat repeated for 60 times in each training, and the correct times were recorded, those correct for less than 25 times were taken as unqualified, and excluded from the training group, and supplemented by other rats in time. In the sham-training group, there was no fixed correlation between the application of light and electricity. The rats in the normal contrel group were given not any training. ③Detection of pEIk-1 expression： The rats were anesthetized after Y-maze training, brain tissue was removed to prepare coronal freezing sections, and the pEIk-1 expression was detected with routine ABC method. MATN OUTCOME MEASURES： ① Distribution of pEIk-1 immuno-positive neurons in whole brain of rats in the normal control group. ②Comparison of the expression of pEIk-1 immuno-positive neurons in whole brain at different time points after training between the training group and sham-training group. RESULTS ： All the 55 rats were involved in result analysis. ③ Distribution of pEIk-1 immuno-positive neurons in the whole brain of rats in the normal control group： Strong expressions of pEIk-1 immuno-positive neurons were observed in prefrontal lobe, granular layer of olfactory bulbs, Purkinje cell layer and granular layer of cerebellum, whole stdate cortex, temporal cortex, pre-pyriform cortex, hypothalamic supraoptic nucleus, hypothalamic paraventricular nucleus and periventricular nucleus, thalamic paraventricular nucleus, pronucleus and postnucleus of amygdala cortex, central nucleus of amygdala, medial amygdaloid nucleus, entorhinal cortex, hippocampal dentate gyros, CA1-4 regions, caudate-putamen, material division, brain stem spinal nucleus of trigeminal nerve, and superior olivary nucleus, and those in hippocampal dentate gyrus and CA1 region were the strongest.② Distribution of pEIk-1 immuno-positive neurons in the whole brain of rats at different time points after training in the training group and sham-training group： In the training group, the expressions were obviously enhanced in caudate-putamen of striatum, material division, most cortexes, hippocampal dentate gyrus, hippocampal CA regions, nucleus amygdalae, thalamic paraventricular nucleus, Purkinje cell layer of cerebellum, entorhinal cortex, hypothalamic supraoptic nucleus, hypothalamic paraventricular nucleus, and periventricular nucleus at 0 hour after training, and the enhancement lasted for 6 hours at least, and those at 24 hours were decreased to normal. In the sham-training group, obvious enhanced expressions of pEIk-1 immuno-positive neurons could be observed in most cortexes, nucleus amygdalae, entorhinal cortex, hypothalamic supraoptic nucleus, hypothalamic paraventdoular nucleus and periventricular nucleus, brain stem spinal nucleus of trigeminal nerve, Purkinje cell layer and granular layer of cerebellum at O, 1, 3 and 6 hours, and decreased to normal after 24 hours. The expressions in material division, caudate-putamen of striatum, hippocampus were not obviously enhanced as compared with those in the normal control group, but significantly different from those in the training group （0, 1, 3, 6 hours after training, material division： F= 0.576, 0.023, 0.116, 8.873, P〈 0.01; caudate-putamen： F= 0.157, 0.427, 0.030, 0.001, P〈 0.01; hippocampus： F= 6.716, 2.405, 14.137, 1.416, P 〈 0.05-0.01 ）. CONCLUSION： The expression of activated pEIk-1 can be detected in the learning related brain areas under normal status, and the perk-1 expression in the brain areas dynamically changed in a time-dependent manner after Y-maze training, and it is indicated that pEIk-1 is involved in the learning and memory process in Y-maze related brain areas.

Active Collision Avoidance System Design Based on Model Predictive Control with Varying Sampling Time

In active collision avoidance,the trajectory tracking controller determines the deviation from the reference path and the vehicle stability.The main objective of this study was to reduce the tracking error and improve the tracking performance in collision avoidance.Unlike the previously proposed model predictive control(MPC)strategies with constant sampling time,an improved MPC controller with varying sampling time based on the hierarchical control framework was proposed in this paper.Compared with the original MPC tracking controller,the improved MPC controller demonstrated better adaptive capability for the varying road adhesion coefficients and vehicle speed on a curved road.The simulation results revealed that the hierarchical control framework generated an optimal trajectory for collision avoidance in real-time by minimizing the potential field energy.