Predicting COVID-19 using lioness optimization algorithm and graph convolution network.

In this paper, a graph convolution network prediction model based on the lioness optimization algorithm (LsOA-GCN) is proposed to predict the cumulative number of confirmed COVID-19 cases in 17 regions of Hubei Province from March 23 to March 29, 2020, according to the transmission characteristics of COVID-19. On the one hand, Spearman correlation analysis with delay days and LsOA are used to capture the dynamic changes of feature information to obtain the temporal features. On the other hand, the graph convolutional network is used to capture the topological structure of the city network, so as to obtain spatial information and finally realize the prediction task. Then, we evaluate this model through performance evaluation indicators and statistical test methods and compare the results of LsOA-GCN with 10 representative prediction methods in the current epidemic prediction study. The experimental results show that the LsOA-GCN prediction model is significantly better than other prediction methods in all indicators and can successfully capture spatio-temporal information from feature data, thereby achieving accurate prediction of epidemic trends in different regions of Hubei Province.

Specific activation of embryonic IFNAR1 and endometrial IFNAR2 induced by embryonic IFNτ directs normal uterine fate for bovine early implantation.

Interferon-tau (IFNτ), as an antiluteolytic factor secreted by trophoderm during the pregnancy of ruminants, actually functions by activating the IFNτ receptor 1 (IFNAR1) and IFNτ receptor 2 (IFNAR2). However, it has not been clearly understood how IFNτ-IFNAR cascade regulation processes between the embryo and uterine epithelial cells in ruminants. In this study, we found the expression and location of IFNτ in the bovine blastocysts from different production sources. IFNτ, IFNAR1 and IFNAR2 were all located in the trophoblast cells of the blastocyst. However, the fluorescence intensity of IFNAR1 was consistent with that of IFNτ. Antagonizing the expressions of IFNAR1 and IFNAR2 in embryos and co-culture with endometrial epithelium cells (EECs) reduced the expressions of Integrin αv ß3, WNT7A, and ISG15 in EECs. Knocking out IFNAR1 and IFNAR2 reduce the expressions of Integrin αv ß3 and WNT7A in EECs, the deletion of IFNAR2 gene has a greater impact than that of IFNAR1 gene. IFNAR1-/IFNAR2+ and IFNAR1+/IFNAR2- EECs were co-cultured with IVF embryos, the expression of Integrin αv ß3 was inhibited, and the inhibition of IFNAR1+/IFNAR2- was much stronger, and the expression of WNT7A was not inhibited. The expressions of Integrin αv ß3 and WNT7A did not change significantly after IFNAR1-/IFNAR2+ and IFNAR1+/IFNAR2- co-culture with PA embryos. All of these results strongly suggest that specific activation of embryonic IFNAR1 and endometrial IFNAR2 induced by embryonic IFNτ directs normal uterine preparation for bovine early implantation.