DNA damage strength modulates a bimodal switch of p53 dynamics for cell-fate control

Background: The p53 pathway is differentially activated in response to distinct DNA damage, leading to alternative phenotypic outcomes in mammalian cells. Recent evidence suggests that p53 expression dynamics play an important role in the differential regulation of cell fate, but questions remain as to how p53 dynamics and the subsequent cellular response are modulated by variable DNA damage. Results: We identified a novel, bimodal switch of p53 dynamics modulated by DNA-damage strength that is crucial for cell-fate control. After low DNA damage, p53 underwent periodic pulsing and cells entered cell-cycle arrest. After high DNA damage, p53 underwent a strong monotonic increase and cells activated apoptosis. We found that the damage dose-dependent bimodal switch was due to differential Mdm2 upregulation, which controlled the alternative cell fates mainly by modulating the induction level and pro-apoptotic activities of p53. Conclusions: Our findings not only uncover a new mode of regulation for p53 dynamics and cell fate, but also suggest that p53 oscillation may function as a suppressor, maintaining a low level of p53 induction and pro-apoptotic activities so as to render cell-cycle arrest that allows damage repair.BiologySCI(E)10ARTICLEnull1

A Temporal Pool Learning Algorithm Based on Location Awareness

Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms