The IFN regulatory factor (IRF) family encodes transcription factors that play important roles in immune defense, stress response, reproduction, development, and carcinogenesis. Although the origin of the IRF family has been dated back to multicellular organisms, invertebrate IRFs differ from vertebrate IRFs in genomic structure and gene synteny, and little is known about their functions. Through comparison of multiple amphioxus genomes, in this study we suggested that amphioxus contains nine IRF members, whose orthologs are supposed to be shared among three amphioxus species. As the orthologs to the vertebrate IRF1 and IRF4 subgroups, Branchiostoma belcheri tsingtauense (bbt)IRF1 and bbtIRF8 bind the IFN-stimulated response element (ISRE) and were upregulated when amphioxus intestinal cells were stimulated with poly(I:C). As amphioxus-specific IRFs, both bbtIRF3 and bbtIRF7 bind ISRE. When activated, they can be phosphorylated by bbtTBK1 and then translocate into nucleus for target gene transcription. As transcriptional repressors, bbtIRF2 and bbtIRF4 can inhibit the transcriptional activities of bbtIRF1, 3, 7, and 8 by competing for the binding of ISRE. Interestingly, amphioxus IRF2, IRF8, and Rel were identified as target genes of bbtIRF1, bbtIRF7, and bbtIRF3, respectively, suggesting a dynamic feedback regulation among amphioxus IRF and NF-κB. Collectively, to our knowledge we present for the first time an archaic IRF signaling framework in a basal chordate, shedding new insights into the origin and evolution of vertebrate IFN-based antiviral networks.
Due to the complexity and similarity of plant leaves, it is very important to study an effective leaf-feature extraction method to improve the recognition rate of plant leaves. We study five multiscale triangle representations: the triangle unsigned area representation (TUA), the triangle vertex angle representation (TVA) and three new representations, which we define as the gray average (TGA), the gray standard deviation (TGSD) and the side length integral (TSLI) on the triangle. In this method the curvature features of the contour, the texture features and the shape area feature are extracted to provide a multiscale leaf-feature description, and a new adaptive KNN for optimization method is proposed to improve the retrieval rate of leaf datasets. Experiments show that compared with the state-of-the-art methods, our method has higher accuracy on the Swedish and Flavia plant leaf datasets, which are respectively 99.35% and 99.43% with 84.76% Mean Average Precision (MAP) value and has comparable results on MPEG-7, kimia99 and kimia216 datasets. When our method is combined with KNN for optimization, the retrieval rate of the above datasets has been significantly improved, especially MAP on the Flavia dataset increases to 94.48%.INDEX TERMS Plant leaf recognition, multi-scale leaf-feature description, multi-scale triangle representation, adaptive KNN for optimization.
Forecasting of oil price is an important area of energy market research. Based on the idea of decomposition-reconstruction-integration, this paper built a new multiscale combined forecasting model with the methods of empirical mode decomposition (EMD), artificial neural network (ANN), support vector machine (SVM), and time series methods. While building the model, we proposed a new idea to use run length judgment method to reconstruct the component sequences. Then this model was applied to analyze the fluctuation and trend of international oil price. Oil price series was decomposed and reconstructed into high frequency, medium frequency, low frequency, and trend sequences. Different features of fluctuation can be explained by irregular factors, season factors, major events, and long-term trend. Empirical analysis showed that the multiscale combined model obtained the best forecasting result compared with single models including ARIMA, Elman, SVM, and GARCH and combined models including ARIMA-SVM model and EMD-SVM-SVM method.
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