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The current notion that obesity is a major risk factor for the development of and the mortality associated with a subset of liver cancer is well appreciated. However, detailed mechanistic insights underlying this relationship are lacking. Better understanding of the adipose tissue-secreted miRNAs that play a potential role in defining primary liver cancer development and mediating the obesity-cancer communication offers the potential for new insights into tumor growth and interventions to modulate tumor formation and progression. In this study, we clearly demonstrated that miR-27a is more highly upregulated in cancer, plasma, and adipose samples from obese liver cancer cases, and therefore reasoned that miR-27a excreted from adipose tissue leads to liver cancer development. To address this idea, we prepared miR-27a-overexpressing 3T3-L1 adipocytes and cocultured them with HepG2 liver cancer cells. Our results demonstrated that secretory miR-27a promoted liver cancer cell proliferation through the downregulation of the transcription factor FOXO1 and promoted the G1/S cell cycle transition by decreasing the cell cycle inhibitors p21 and p27 and increasing the cell cycle regulator cyclin D1. These findings improve our understanding of the involvement of miR-27a in obesity-liver cancer communication and might provide a novel putative target for obesity-driven primary liver cancer diagnosis and therapy.

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Abstract:The algorithm of synthetic aperture radar (SAR) for automatic target recognition consists of two stages: feature extraction and classification. The quality of extracted features has significant impacts on the final classification performance. This paper presents a SAR automatic target classification method based on the wavelet-scattering convolution network. By introducing a deep scattering convolution network with complex wavelet filters over spatial and angular variables, robust feature representations can be extracted across various scales and angles without training data. Conventional dimension reduction and a support vector machine classifier are followed to complete the classification task. The proposed method is then tested on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set and achieves an average accuracy of 97.63% on the classification of ten-class targets without data augmentation.

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A quasi-solid-state lithium battery is assembled by plasma sprayed amorphous Li4Ti5O12 (LTO) electrode and ceramic/polymer composite electrolyte with a little liquid electrolyte (10 µL/cm2) to provide the outstanding electrochemical stability and better normal interface contact. Scanning Electron Microscope (SEM), Scanning Transmission Electron Microscopy (STEM), Transmission Electron Microscopy (TEM), and Energy Dispersive Spectrometer (EDS) were used to analyze the structural evolution and performance of plasma sprayed amorphous LTO electrode and ceramic/polymer composite electrolyte before and after electrochemical experiments. By comparing the electrochemical performance of the amorphous LTO electrode and the traditional LTO electrode, the electrochemical behavior of different electrodes is studied. The results show that plasma spraying can prepare an amorphous LTO electrode coating of about 8 µm. After 200 electrochemical cycles, the structure of the electrode evolved, and the inside of the electrode fractured and cracks expanded, because of recrystallization at the interface between the rich fluorine compounds and the amorphous LTO electrode. Similarly, the ceramic/polymer composite electrolyte has undergone structural evolution after 200 test cycles. The electrochemical cycle results show that the cycle stability, capacity retention rate, coulomb efficiency, and internal impedance of amorphous LTO electrode are better than traditional LTO electrode. This innovative and facile quasi-solid-state strategy is aimed to promote the intrinsic safety and stability of working lithium battery, shedding light on the development of next-generation high-performance solid-state lithium batteries.