a b s t r a c tAs a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique.
The arms race between plants and viruses never ceases. Chinese cabbage, an important type of Brassica vegetable crop, is vulnerable to plant virus infection, especially to Turnip mosaic virus (TuMV). To better examine the molecular mechanisms behind the virus infection, we conducted the correlation analysis of RNA-Seq and quantitative iTRAQ-LC-MS/MS in TuMV-infected and in healthy Chinese cabbage leaves. There were 757 differentially expressed genes and 75 differentially expressed proteins that were screened in Chinese cabbage plants infected with TuMV. These genes were enriched in many pathways, and among them, the plant hormone signal transduction, plant-pathogen interaction, and protein processing in the endoplasmic reticulum pathways were suggested to be closely related pathways. The correlation analysis between RNA-Seq and quantitative iTRAQ-LC-MS/MS was then further explored. Finally, we obtained a preliminary network of several candidate genes associated with TuMV infection, and we found that they mainly belonged to calcium signaling pathways, heat shock proteins, WRKY transcription factors, and non-specific lipid transfer proteins. These results may lead to a better understanding of antiviral mechanisms and of disease-resistant breeding.
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