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Background: The role and underlying mechanism of liver macrophages and their derived miR-155-5p in hepatic lymphangiogenesis in liver fibrosis remain unclear. Here, we investigated the mechanism by which macrophages and miR-155-5p were involved in lymphangiogenesis during liver fibrosis and cirrhosis. Methods: In vivo, hepatic lymphatic vessel expansion was evaluated; the liver macrophage subsets, proportion of peripherally-derived macrophages and expressions of CCL25, MCP-1, VAP-1 and MAdCAM-1 were documented; and miR-155-5p in the peripheral blood and liver was detected. In vitro, macrophages with miR-155-5p overexpression and inhibition were used to clarify the effect of miR-155-5p on regulation of macrophage polarization and the possible signalling pathway. Results: Hepatic lymphangiogenesis was observed in mice with liver fibrosis and cirrhosis challenged with carbon tetrachloride (CCl4). In the liver, the number of M1 macrophages was associated with lymphangiogenesis and the degree of fibrosis. The liver recruitment of peripherally-derived macrophages occurred during liver fibrosis. The levels of miR-155-5p in the liver and peripheral blood gradually increased with aggravation of liver fibrosis. In vitro, SOCS1, a target of miR-155-5p, regulated macrophage polarization into the M1 phenotype through the JAK1/STAT1 pathway. Conclusion: MiR-155-5p-SOCS1/JAK1/STAT1 pathway participates in hepatic lymphangiogenesis in mice with liver fibrosis and cirrhosis induced by CCl4 by regulating the polarization of macrophages into the M1 phenotype. [ABSTRACT FROM AUTHOR]

Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning. [ABSTRACT FROM AUTHOR]