Identification and functional assessment of novel gene sets towards better understanding of dysplasia associated oral carcinogenesis

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

The NmrA-like family domain containing 1 pseudogene Loc344887 is amplified in gallbladder cancer and promotes epithelial-mesenchymal transition

The expression pattern and biological role of long non-coding RNA (lncRNA) in cancer has been reported to be involved in many cancers. Here, we report the expression and biological role of a newly discovered lncRNA NmrA-like family domain containing 1 pseudogene (Loc344887) in gallbladder cancer (GBC). In this study, we found that the expression of Loc344887 was significantly elevated in GBC tissues and cell lines when compared with matched normal tissues and normal epithelial bile duct cell line, respectively. High Loc344887 was associated with larger tumor size. Loc344887 was upregulated significantly after ectopic expression of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) in GBC cells. Downregulation of Loc344887 in GBC cells suppressed cell proliferation, blocked cells in G0/S phase, and decreased the migration and invasion cell numbers. In addition, downregulation of Loc344887 decreased the expression of transcription factor Twist, mesenchymal marker Vimentin, and N-cadherin and increased the expression of epithelial maker E-cadherin, which could prompt a mesenchymal-to-epithelial transition phenotype. These results demonstrated that Loc344887 might contribute to cell proliferation and epithelial-to-mesenchymal transition process in GBC, which might be a potential therapeutic target.