The plant hormone auxin influences a variety of developmental and physiological processes. But the mechanism of its action is quite unclear. In order to identify and analyze the expression of auxin responsive genes, a...The plant hormone auxin influences a variety of developmental and physiological processes. But the mechanism of its action is quite unclear. In order to identify and analyze the expression of auxin responsive genes, a cDNA array approach was used to screen for genes with altered expression from Arabidopsis suspension culture after IAA treatment and was identified 50 differentially expressed genes from 13824 cDNA clones. These genes were related to signal transduction, stress responses, senescence, photosynthesis, protein biosynthesis and transportation. The results provide the molecular evidence that auxin influences a variety of physiological processes and pave a way for further investigation of the mechanism of auxin action. Furthermore, we found that the expression of a ClpC (regulation subunit of Clp protease) was repressed by exogenous auxin, but increased in dark-induced senescing leaves. This suggests that ClpC may be a senescence-associated gene and can be regulated by auxin.展开更多
In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improv...In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.展开更多
基金This work was supported by the National Natural Science Foundation of China (Grant Nos. 30070074, 39889003 and 39525017).
文摘The plant hormone auxin influences a variety of developmental and physiological processes. But the mechanism of its action is quite unclear. In order to identify and analyze the expression of auxin responsive genes, a cDNA array approach was used to screen for genes with altered expression from Arabidopsis suspension culture after IAA treatment and was identified 50 differentially expressed genes from 13824 cDNA clones. These genes were related to signal transduction, stress responses, senescence, photosynthesis, protein biosynthesis and transportation. The results provide the molecular evidence that auxin influences a variety of physiological processes and pave a way for further investigation of the mechanism of auxin action. Furthermore, we found that the expression of a ClpC (regulation subunit of Clp protease) was repressed by exogenous auxin, but increased in dark-induced senescing leaves. This suggests that ClpC may be a senescence-associated gene and can be regulated by auxin.
文摘In the fifth generation(5G)wireless system,a closed-loop power control(CLPC)scheme based on deep Q learning network(DQN)is introduced to intelligently adjust the transmit power of the base station(BS),which can improve the user equipment(UE)received signal to interference plus noise ratio(SINR)to a target threshold range.However,the selected power control(PC)action in DQN is not accurately matched the fluctuations of the wireless environment.Since the experience replay characteristic of the conventional DQN scheme leads to a possibility of insufficient training in the target deep neural network(DNN).As a result,the Q-value of the sub-optimal PC action exceed the optimal one.To solve this problem,we propose the improved DQN scheme.In the proposed scheme,we add an additional DNN to the conventional DQN,and set a shorter training interval to speed up the training of the DNN in order to fully train it.Finally,the proposed scheme can ensure that the Q value of the optimal action remains maximum.After multiple episodes of training,the proposed scheme can generate more accurate PC actions to match the fluctuations of the wireless environment.As a result,the UE received SINR can achieve the target threshold range faster and keep more stable.The simulation results prove that the proposed scheme outperforms the conventional schemes.
