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https://www.selleckchem.com/products/ly364947.html Clathrin is a highly evolutionarily conserved protein, which can affect membrane cleavage and membrane release of vesicles. The absence of clathrin in the cellular system affects a variety of human diseases. Effective recognition of clathrin plays an important role in the development of drugs to treat related diseases. In recent years, deep learning has been widely applied in the field of bioinformatics because of its high efficiency and accuracy. In this study, we propose a deep learning framework, DeepCLA, which combines two different network structures, including a convolutional neural network and a bidirectional long short-term memory network to identify clathrin. The investigation of different deep network architectures demonstrates that the prediction performance of a hybrid depth network model is better than that of a single depth network. On the independent test dataset, DeepCLA outperforms the state-of-the-art methods. It suggests that DeepCLA is an effective approach for clathrin prediction and can provide more instructive guidance for further experimental investigation of clathrin. Moreover, the source code and training data of DeepCLA are provided at https//github.com/ZhangZhang89/DeepCLA.We report plasmon-free polymeric nanowrinkled substrates for surface-enhanced Raman spectroscopy (SERS). Our simple, rapid, and cost-effective fabrication method involves depositing a poly(ethylene glycol)diacrylate (PEGDA) prepolymer solution droplet on a fully polymerized, flat PEGDA substrate, followed by drying the droplet at room conditions and plasma treatment, which polymerizes the deposited layer. The thin polymer layer buckles under axial stress during plasma treatment due to its different mechanical properties from the underlying soft substrate, creating hierarchical wrinkled patterns. We demonstrate the variation of the wrinkling wavelength with the drying polymer molecular weight and concentration (direct re

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