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4篇 您的检索式:作者名="Gan IS"
    题名 作者 年代 出处 被引量
1Function of apoptosis and expression of the proteins Bcl-2,p53 and C-myc in the development of gastric cancer显示文摘INTRODUCTIONIn China ,the incidence and mortality of gastric cancer rank the second among all cancers. Recent development of cancer [1-20].The aim of this study was investigat the insight of apoptosis and bcl-2, p53 and C-myc protein expression in the development of gastric cancer .An Gao Xu Shao Guang Li Ji Hong Liu Ai Hua Gan Research Laboratory of Digestive Disease,Huizhou Central People’s Hospital,Huizhou 516001,Guangdong Province,ChinaDr.An Gao Xu graduated from Guangdong Medical College in 1984.He is an associate physician-in-chief,specializing in the research and treatment of gastrointestinal and liver tumors.He has published 24 papers and 1 book. 2001World Journal of Gastroenterology2001,7,3:91
2Determination ofsulfur environments in borosilicate waste glasses using X-rayabsorption near-edge spectroscopy显示文摘DA McKeown IS Muller H Gan 2004Journal of Non-Crystalline Solids2004,333,1:1
3CYP3A4 induction by xenobiotics: biochemistry, experimental methods and impact on drug discovery and devdopment显示文摘Luo G Guenthner T Gan IS 2004Curr Drug Metab2004,5,6:1
4Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding显示文摘Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning,our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, whereλ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.Yuhang Li Tianyi Gan Bijie Bai Cagatay Isıl Mona Jarrahi Aydogan Ozcan 2023Advanced Photonics2023,5,4:0
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