People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.
Because of the simplicity of cells, the key to building biological computing systems may lie in constructing distributed systems based on cell–cell communication. Guided by a mathematical model, in this study we designed,simulated, and constructed a genetic double-branch structure in the bacterium Escherichia coli. This genetic double-branch structure is composed of a control cell and two reporter cells.The control cell can activate different reporter cells according to the input. Two quorum-sensing signal molecules, 3OC12-HSL and C4-HSL, form the wires between the control cell and the reporter cells. This study is a step toward scalable biological computation, and it may have many potential applications in biocomputing, biosensing, and biotherapy.
