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To further explore the metabolism of Lycium barbarum polysaccharides, the cell wall microstructure of Lycium barbarum fruits in different growth periods was observed by transmission electron microscopy, and the cell wall polysaccharide content and its degrading enzyme activity were extracted by chemical method. The changes of polysaccharide components in cell wall of Lycium barbarum fruits in different growth periods were analyzed by microcopolymer Raman spectroscopy. The correlation between the content of polysaccharide in cell wall and the activity of degrading enzyme was analyzed. The results showed that the changes of cell wall structure mainly occurred during 28~35 d, with the cell wall becoming significantly thinner, pectin degrading, and the cell wall structure gradually collapsing. Cell wall material (CWM) content increased first and then decreased, and water-soluble pectin (WSP) content increased significantly (P

Optical synthetic aperture with homogeneous circular sub-mirrors greatly improves the spatial resolution of space telescopes; however, the discrete and sparse characteristics of the sub-mirrors reduce the mid-frequency modulation transfer function (MTF), resulting in blurred images being obtained. In this paper, a method combining variational physics-informed with deep learning is presented, which shows blind image restoration without complex priors. The constraint effect of traditional maximum a posterior (MAP) framework is removed by variational inference framework, which is embedded into Variational Physics-informed Network (VPIN) to optimize neural network training. Residual dense blocks (RDBs) construction is contributed to image feature extraction. Networks with SSIM-corrected loss functions can be trained at the feature level to help with convergence. When SNR = 30 dB, the PSNR of Golay-6 remote sensing test set increases from 20.16 dB to 23.90 dB, SSIM is from 0.610 to 0.842, and MS-SSIM is from 0.930 to 0.955.

Myocardial hypertrophy is an independent risk factor for various serious cardiovascular diseases.Epigenetic mechanisms such as DNA methylation, histone modification, and RNA methylation are involved in the pathogenesis and development of cardiac hypertrophy.To understanding the role of epigenetic regulation on cardiac hypertrophy contributes to to development of an effective protocol and provide scientific basis for the prevention and early treatment of cardiac hypertrophy.

Medicago ruthenica, a wild legume forage widely distributed in the Eurasian steppe, demonstrates high genetic and phenotypic variation. M. ruthenica with a purely yellow flower (YFM), differing from the general phenotype of M. ruthenica with a purple flower (PFM), was recently discovered. The similar characteristics of YFM with Medicago falcata have led to conflicting opinions on its taxonomy using traditional morphological methods. The lack of chemotaxonomy information about M. ruthenica species and the unclear flower coloration mechanisms have hampered their study. Here, we investigated M. ruthenica using targeted metabolomics based on the chemotaxonomy method and elaborated the floral coloration mechanisms using transcriptomics. The identified flavonoids were the same types, but there were different contents in YFM and PFM, especially the contents of cyanidin-3-O-glucoside (C3G), an anthocyanin that causes the purple-reddish color of flowers. The over-accumulation of C3G in PFM was 1,770 times more than YFM. Nineteen anthocyanin-related genes were downregulated in YFM compared with their expression in PFM. Thus, YFM could be defined as a variety of M. ruthenica rather than a different species. The loss of purple flower coloration in YFM was attributed to the downregulation of these genes, resulting in reduced C3G accumulation. The taxonomic characteristics and molecular and physiological characteristics of this species will contribute to further research on other species with similar external morphologies.

Image dehazing is a traditional task, yet it still presents arduous problems, especially in the removal of haze from the texture and edge information of an image. The state-of-the-art dehazing methods may result in the loss of some visual informative details and a decrease in visual quality. To improve dehazing quality, a novel dehazing model is proposed, based on a fractional derivative and data-driven regularization terms. In this model, the contrast constrained adaptive histogram equalization method is used as the data fidelity item; the fractional derivative is applied to avoid over-enhancement and noise amplification; and the proposed data-driven regularization terms are adopted to extract the local and non-local features of an image. Then, to solve the proposed model, half-quadratic splitting is used. Moreover, a dual-stream network based on Convolutional Neural Network (CNN) and Transformer is introduced to structure the data-driven regularization. Further, to estimate the atmospheric light, an atmospheric light model based on the fractional derivative and the atmospheric veil is proposed. Extensive experiments display the effectiveness of the proposed method, which surpasses the state-of-the-art methods for most synthetic and real-world images, quantitatively and qualitatively.

Under the new power system reform, the scale of electricity trading market in China grows rapidly, burdening power companies with more uncertainty risk relate to market trading. This paper constructs a risk warning model for electricity trading, and take market price fluctuation as the signal of market trading risk to carry out risk warning.