Summary The reform of power market has presented new challenges to short‐term load forecasting (STLF), and the accuracy of forecast results is of great significance to the orderly and efficient operation of the market. To improve the accuracy of load forecasting, an STLF method based on deep neural network (DNN) with sample weights is proposed. By filtering samples and assigning corresponding weights to different training samples, this method effectively improves the forecasting accuracy of the DNN model. Two important steps of this method are as follows: (a) calculating the distance between the samples and the forecast day to measure the similarity between them, and the training samples are selected based on the distance; (b) after selecting training samples, corresponding weights can be assigned to training samples according to the distance between the training samples and the forecast day. This allows DNN to focus on the key samples. Finally, we carried out simulation analysis by using the actual load data of Guangdong Power Grid in January 2017. The results show that the proposed method can effectively improve the accuracy and reliability of load forecasting.

Malignant pleural effusion (MPE) is a common complication of lung cancer. Accumulating evidence has suggested that circular RNAs (circRNAs) play important roles in oncogenesis and progression of cancer. However, the expression pattern of circRNAs in MPE remains largely unknown and awaits investigation. The study was designed to elucidate the potential roles of differentially expressed circRNAs in MPE. Herein, we detected a total of 1350 differentially expressed circRNAs and 1727 differentially expressed mRNAs in lung adenocarcinoma-associated malignant pleural effusion (LA-MPE) compared with tuberculous pleural effusion (TPE) by Clariom D Human Microarray. Among the top 5 up-regulated circRNAs (hsa_circ_0067705, hsa_circ_0025542, hsa_circ_0072793, hsa_-circ_0084927, and hsa_circ_0085386), four were verified significantly up-regulated in LA-MPE by qRT-PCR and hsa_circ_0085386 had an increasing trend. CircRNA-miRNA-mRNA network for the top 5 upregulated circRNAs was constructed and pathway analysis indicated that the enriched mRNA targets involved in PI3K-Akt signaling pathway, Axon guidance, Regulation of actin cytoskeleton and Rap1 signaling pathway were potentially regulated by these aberrantly expressed circRNAs. We generated specific circRNA profiles in LA-MPE for the first time. And analysis of circRNA regulatory network could provide evidence that circRNAs are important in MPE development because they participate in cancer-related pathways by sequestering miRNAs. Our findings suggested that aberrantly expressed circRNAs may be involved in the development of LA-MPE. ARTICLE HISTORY

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