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Vascular endothelial growth factor (VEGF) is a well-known angiogenic factor that plays a critical role in various physiological and pathological processes. VEGF also contributes to the process of embryo implantation by enhancing embryo development, improving endometrial receptivity, and facilitating the interactions between the developing embryo and the endometrium. There is a correlation between the alteration of VEGF expression and reproductive failure, including recurrent implantation failure (RIF) and recurrent miscarriage (RM). In order to clarify the role of VEGF in embryo implantation, we reviewed recent literature concerning the expression and function of VEGF in the reproductive system around the time of embryo implantation and we provide a summary of the findings reported so far. We also explored the effects and the possible underlying mechanisms of action of VEGF in embryo implantation.

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In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least one attribute. If an object cannot be dominated by any other object, it is a skyline object. The skyline group problem involves finding k-item groups that cannot be dominated by any other k-item group. Existing algorithms designed to find skyline groups can only process static data. However, data changes as a stream with time in many applications, and algorithms should be designed to support skyline group queries on dynamic data. In this paper, we propose new algorithms to find skyline groups over a data stream. We use data structures, namely a hash table, dominance graph, and matrix, to store dominance information and update results incrementally. We conduct experiments on synthetic datasets to evaluate the performance of the proposed algorithms. The experimental results show that our algorithms can efficiently find skyline groups over a data stream.

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