Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network.

Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition, fuzzy C-means clustering and bidirectional gated recurrent unit. Firstly, the original water quality data is decomposed into several subseries by empirical wavelet transform, and then, the decomposed subseries are recombined by fuzzy C-means clustering. Next, for each clustered series, bidirectional gated recurrent unit is applied to develop prediction model. Finally, the forecast result is obtained by the summation of the predictions for the subseries. The proposed forecast model is evaluated by the water quality data of Poyang Lake, China. Results show that the proposed forecast model provides highly accurate forecast result for all of the six water quality data: the average of MAPE of the forecast results for the six water quality datasets is 4.59% for 7 day ahead prediction. Furthermore, our model shows better forecast performance than the other models. Particularly, compared with the single BiGRU model, MAPE decreased by 32.86% in average. Results demonstrate that the proposed forecast model can be used effectively for water quality forecasting.

Expression of Yes-associated protein 1 and its clinical significance in ovarian serous cystadenocarcinoma.

Yes-associated protein 1 (YAP1) is a key transcriptional regulator in the Hippo signaling pathway that plays a critical role in the development and progression of several types of malignancies, including ovarian cancer. Herein, we investigated the expression of YAP1 and its clinical significance in a large population of patients with ovarian serous cystadenocarcinoma (OSC), which is the most common form of epithelial ovarian neoplasm, using the TCGA database. Surprisingly, cross-cancer mRNA expression and alterations in YAP1 were higher in OSC than in those of other types of cancers in the TCGA database. YAP1 mRNA expression was significantly higher in OSC compared with normal ovarian samples, and was higher in stages III and IV, than stages I and II. The level of YAP1 protein, which is mainly localized to the nucleus, was also higher in stage IV as compared with stages I, II and III. However, the protein level of pYAP1, which is inactive and is localized to the cytoplasm, was not significantly different between stages. The ratio of pYAP/YAP, which shows higher activity at a low ratio, was lower in stage III than in stages I and II. High YAP and low pYAP levels were significantly correlated with a poor prognosis in patients with OSC. The mRNA and protein expression of YAP1 were significantly increased in the proliferative subtype as compared to the differentiated, immunoreactive and mesenchymal subtypes. According to bioinformatics analysis, YAP1 is most highly correlated with the cell cycle. TGF-ß signaling and WNT signaling were significantly increased in the high YAP1 group according to gene set enrichment analysis. Taken together, our results suggest that not only high YAP1 expression but also its subcellular distribution may be associated with poor overall survival in patients with OSC.