Long-term live-cell imaging technology has emerged in the study of cell culture and development, and it is expected to elucidate the differentiation or reprogramming morphology of cells and the dynamic process of interaction between cells. There are some advantages to this technique: it is noninvasive, high-throughput, low-cost, and it can help researchers explore phenomena that are otherwise difficult to observe. Many challenges arise in the real-time process, for example, low-quality micrographs are often obtained due to unavoidable human factors or technical factors in the long-term experimental period. Moreover, some core dynamics in the developmental process are rare and fleeting in imaging observation and difficult to recapture again. Therefore, this study proposes a deep learning method for microscope cell image enhancement to reconstruct sharp images. We combine generative adversarial nets and various loss functions to make blurry images sharp again, which is much more convenient for researchers to carry out further analysis. This technology can not only make up the blurry images of critical moments of the development process through image enhancement but also allows long-term live-cell imaging to find a balance between imaging speed and image quality. Furthermore, the scalability of this technology makes the methods perform well in fluorescence image enhancement. Finally, the method is tested in long-term live-cell imaging of human-induced pluripotent stem cell-derived cardiomyocyte differentiation experiments, and it can greatly improve the image space resolution ratio.

In urban vehicular ad hoc networks (VANETs), the intersection-based routing scheme has represented its greater applicability and better efficiency to adapt to high and constrained mobility. How to make an accurate decision for street selection is a challenging issue due to the rapid topology changes in VANETs. In this paper, we propose a microscopic mechanism based on intersection records (MMIR) in which the intersection vehicle nodes maintain and update a records table with every passing vehicle's individual information. By analyzing and processing these entries, we evaluate these vehicles' current positions so as to compute the connectivity probability or estimated delivery delay for all candidate streets to support street selection. In contrast to the statistical and macroscopic information for the common condition, we firstly make use of the individual and microscopic data to enhance the accuracy of estimated results. Furthermore, according to the quantity and the running interval, we classify vehicles into two categories: individual and queue vehicles, in order to effectively decrease the complexity of position estimation. Lastly, since there are no dedicated control packets generated in MMIR, the network overhead is low. The simulation results show that the proposed MMIR outperforms existing approaches of street selection in terms of the accuracy of computed connectivity probability and estimated delay.

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