Many forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades. These models have been widely applied to various problem domains, especially in dealing with forecasting probl...Many forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades. These models have been widely applied to various problem domains, especially in dealing with forecasting problems in which historical data are linguistic values. In this paper, we present a new fuzzy time series forecasting model, which uses the historical data as the universe of discourse and uses the K-means clustering algorithm to cluster the universe of discourse, then adjust the clusters into intervals. The proposed method is applied for forecasting University enrollment of Alabama. It is shown that the proposed model achieves a significant improvement in forecasting accuracy as compared to other fuzzy time series forecasting models.展开更多
Considering the recent developments in deep learning, it has become increasingly important to verify what methods are valid for the prediction of multivariate time-series data. In this study, we propose a novel method...Considering the recent developments in deep learning, it has become increasingly important to verify what methods are valid for the prediction of multivariate time-series data. In this study, we propose a novel method of time-series prediction employing multiple deep learners combined with a Bayesian network where training data is divided into clusters using K-means clustering. We decided how many clusters are the best for K-means with the Bayesian information criteria. Depending on each cluster, the multiple deep learners are trained. We used three types of deep learners: deep neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). A naive Bayes classifier is used to determine which deep learner is in charge of predicting a particular time-series. Our proposed method will be applied to a set of financial time-series data, the Nikkei Average Stock price, to assess the accuracy of the predictions made. Compared with the conventional method of employing a single deep learner to acquire all the data, it is demonstrated by our proposed method that F-value and accuracy are improved.展开更多
目的探讨METTL14通过调控巨噬细胞分化抑制宫颈癌病理性发展及相关机制。方法检测宫颈癌病变样本METTL14 m RNA和蛋白,以及IL-6、iNOS、Arg-1和CD206表达变化。PMA诱导THP-1细胞转化为巨噬细胞,慢病毒过表达或抑制METTL14表达,检测IL-6...目的探讨METTL14通过调控巨噬细胞分化抑制宫颈癌病理性发展及相关机制。方法检测宫颈癌病变样本METTL14 m RNA和蛋白,以及IL-6、iNOS、Arg-1和CD206表达变化。PMA诱导THP-1细胞转化为巨噬细胞,慢病毒过表达或抑制METTL14表达,检测IL-6、iNO、Arg-1和CD206表达变化以及PI3K/AKT/GSK3β/β-catenin信号通路相关蛋白表达情况。随后加入PI3K/AKT/GSK3β/β-catenin信号通路激动剂和抑制剂,检测过表达或抑制METTL14后,巨噬细胞IL-6、iNO、Arg-1和CD206表达变化,并取其上清制成条件培养基,孵育Hela细胞,检测细胞凋亡和增殖情况。结果1)宫颈癌病变组织中METTL14 mRNA和蛋白表达降低(P<0.05),巨噬细胞M1型标志物IL-6和iNOS表达明显降低(P<0.05),而M2型标志物Arg-1和CD206表达明显升高(P<0.05)。2)巨噬细胞过表达METTL14后,IL-6和iNOS表达明显升高(P<0.05),而Arg-1和CD206表达明显降低(P<0.05),M1/M2比例升高;抑制METTL14表达后,M1型标志物降低(P<0.05),M2型标志物升高(P<0.05),M1/M2比例降低。3)巨噬细胞中转染OE-METTL14慢病毒组PI3K/AKT/GSK3β/β-catenin信号通路被抑制(P<0.05);加入PI3K/AKT激动剂后,M1型标志物降低而M2型标记物升高(P<0.05),M1/M2比例降低;OE-METTL14可逆转此趋势。Sh-METTL14慢病毒组PI3K/AKT/GSK3β/β-catenin信号通路被激活(P<0.05),加入PI3K/AKT抑制剂后,M1型标志物升高而M2型标记物降低(P<0.05),M1/M2比例升高;Sh-METTL14可逆转此趋势。4)取转染OE-METTL14慢病毒后的巨噬细胞上清培养Hela细胞,可见细胞凋亡明显增多(P<0.05),增殖明显减少(P<0.05)。Sh-METTL14组的Hela细胞则表现出细胞凋亡减少(P<0.05),增殖增多(P<0.05)。结论METTL14通过PI3K/AKT/GSK3β/β-catenin信号通路调控巨噬细胞分化可能有促进宫颈癌细胞凋亡,抑制增殖的作用。展开更多
文摘Many forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades. These models have been widely applied to various problem domains, especially in dealing with forecasting problems in which historical data are linguistic values. In this paper, we present a new fuzzy time series forecasting model, which uses the historical data as the universe of discourse and uses the K-means clustering algorithm to cluster the universe of discourse, then adjust the clusters into intervals. The proposed method is applied for forecasting University enrollment of Alabama. It is shown that the proposed model achieves a significant improvement in forecasting accuracy as compared to other fuzzy time series forecasting models.
文摘Considering the recent developments in deep learning, it has become increasingly important to verify what methods are valid for the prediction of multivariate time-series data. In this study, we propose a novel method of time-series prediction employing multiple deep learners combined with a Bayesian network where training data is divided into clusters using K-means clustering. We decided how many clusters are the best for K-means with the Bayesian information criteria. Depending on each cluster, the multiple deep learners are trained. We used three types of deep learners: deep neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). A naive Bayes classifier is used to determine which deep learner is in charge of predicting a particular time-series. Our proposed method will be applied to a set of financial time-series data, the Nikkei Average Stock price, to assess the accuracy of the predictions made. Compared with the conventional method of employing a single deep learner to acquire all the data, it is demonstrated by our proposed method that F-value and accuracy are improved.
文摘目的探讨METTL14通过调控巨噬细胞分化抑制宫颈癌病理性发展及相关机制。方法检测宫颈癌病变样本METTL14 m RNA和蛋白,以及IL-6、iNOS、Arg-1和CD206表达变化。PMA诱导THP-1细胞转化为巨噬细胞,慢病毒过表达或抑制METTL14表达,检测IL-6、iNO、Arg-1和CD206表达变化以及PI3K/AKT/GSK3β/β-catenin信号通路相关蛋白表达情况。随后加入PI3K/AKT/GSK3β/β-catenin信号通路激动剂和抑制剂,检测过表达或抑制METTL14后,巨噬细胞IL-6、iNO、Arg-1和CD206表达变化,并取其上清制成条件培养基,孵育Hela细胞,检测细胞凋亡和增殖情况。结果1)宫颈癌病变组织中METTL14 mRNA和蛋白表达降低(P<0.05),巨噬细胞M1型标志物IL-6和iNOS表达明显降低(P<0.05),而M2型标志物Arg-1和CD206表达明显升高(P<0.05)。2)巨噬细胞过表达METTL14后,IL-6和iNOS表达明显升高(P<0.05),而Arg-1和CD206表达明显降低(P<0.05),M1/M2比例升高;抑制METTL14表达后,M1型标志物降低(P<0.05),M2型标志物升高(P<0.05),M1/M2比例降低。3)巨噬细胞中转染OE-METTL14慢病毒组PI3K/AKT/GSK3β/β-catenin信号通路被抑制(P<0.05);加入PI3K/AKT激动剂后,M1型标志物降低而M2型标记物升高(P<0.05),M1/M2比例降低;OE-METTL14可逆转此趋势。Sh-METTL14慢病毒组PI3K/AKT/GSK3β/β-catenin信号通路被激活(P<0.05),加入PI3K/AKT抑制剂后,M1型标志物升高而M2型标记物降低(P<0.05),M1/M2比例升高;Sh-METTL14可逆转此趋势。4)取转染OE-METTL14慢病毒后的巨噬细胞上清培养Hela细胞,可见细胞凋亡明显增多(P<0.05),增殖明显减少(P<0.05)。Sh-METTL14组的Hela细胞则表现出细胞凋亡减少(P<0.05),增殖增多(P<0.05)。结论METTL14通过PI3K/AKT/GSK3β/β-catenin信号通路调控巨噬细胞分化可能有促进宫颈癌细胞凋亡,抑制增殖的作用。
基金supported by the National Natural Science Foundation of China (No.51975330)Key Research and Development Program of Shandong Province,China (No.2021ZLGX01)Project of Colleges and Universities Innovation Team of Jinan City,China (No.2021GXRC030)。
