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Ankyrin repeat (ANK) C3HC4-type RING finger (RF) genes comprise a large family in plants and play important roles in various physiological processes of plant life. In this study, we identified 187 ANK C3HC4-type RF proteins from 29 species with complete genomes and named the ANK C3HC4-type RF proteins the XB3-like proteins because they are structurally related to the rice (Oryza sativa) XB3. A phylogenetic relationship analysis suggested that the XB3-like genes originated from ferns, and the encoded proteins fell into 3 major groups. Among these groups, we found that the spacing between the metal ligand position 6 and 7, and the conserved residues, which was in addition to the metal ligand amino acids, in the C3HC4-type RF were different. Using a wide range of protein structural analyses, protein models were established, and all XB3-like proteins were found to contain two to seven ANKs and a C3HC4-type RF. The microarray data for the XB3-like genes of Arabidopsis, Oryza sative, Zea mays and Glycine max revealed that the expression of XB3-like genes was in different tissues and during different life stages. The preferential expression of XB3-like genes in specified tissues and the response to phytohormone and abiotic stress treatments of Arabidopsis and Zea mays not only confirmed the microarray analysis data but also demonstrated that the XB3-like proteins play roles in plant growth and development as well as in stress responses. Our data provide a very useful reference for the identification and functional analysis of members of this gene family and also provide a new method for the genome-wide analysis of gene families.

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The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term memory-convolutional neural network (BiLSTM-CNN) is proposed for short-term wind power forecasting. First, the grey correlation analysis is utilized to select the inputs for forecasting model; Then, the proposed hybrid model extracts multi-dimension features of inputs to predict the wind power from the temporal-spatial perspective, where the Bi-LSTM model is utilized to mine the bidirectional temporal characteristics while the convolution and pooling operations of CNN are utilized to extract the spatial characteristics from multiple input time series. Lastly, a case study is conducted to verify the superiority of the proposed model. Other deep learning models (Bi-LSTM, LSTM, CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are also simulated to conduct comparison from three aspects. The results show that the BiLSTM-CNN model has the best accuracy with the lowest RMSE of 2.5492, MSE of 6.4984, MAE of 1.7344 and highest R2 of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature.