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| 1 | Polysaccharides from Angelica sinensis alleviate neuronal cell injury caused by oxidative stress显示文摘Angelica sinensis has antioxidative and neuroprotective effects. In the present study, we aimed to determine the neuroprotective effect of polysaccharides isolated from Angelica sinensis. In a preliminary experiment, Angelica sinensis polysaccharides not only protected PC12 neuronal cells from H 2 O 2-induced cytotoxicity, but also reduced apoptosis and intracellular reactive oxygen species levels, and increased the mitochondrial membrane potential induced by H 2 O 2 treatment. In a rat model of local cerebral ischemia, we further demonstrated that Angelica sinensis polysaccharides enhanced the antioxidant activity in cerebral cortical neurons, increased the number of microvessels, and improved blood flow after ischemia. Our findings highlight the protective role of polysaccharides isolated from Angelica sinensis against nerve cell injury and impairment caused by oxidative stress. | Tao Lei Haifeng Li Zhen Fang Junbin Lin Shanshan Wang Lingyun Xiao Fan Yang Xin Liu Junjian Zhang Zebo Huang Weijing Liao | 2014 | Neural Regeneration Research2014，9，3： | 17 |
| 2 | Comparison of nonhuman primates identified the suitable model for COVID-19显示文摘Identification of a suitable nonhuman primate(NHP)model of COVID-19 remains challenging.Here,we characterized severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection in three NHP species:Old World monkeys Macaca mulatta(M.mulatta)and Macaca fascicularis(M.fascicularis)and New World monkey Callithrix jacchus(C.jacchus).Infected M.mulatta and M.fascicularis showed abnormal chest radiographs,an increased body temperature and a decreased body weight.Viral genomes were detected in swab and blood samples from all animals.Viral load was detected in the pulmonary tissues of M.mulatta and M.fascicularis but not C.jacchus.Furthermore,among the three animal species,M.mulatta showed the strongest response to SARS-CoV-2,including increased inflammatory cytokine expression and pathological changes in the pulmonary tissues.Collectively,these data revealed the different susceptibilities of Old World and New World monkeys to SARS-CoV-2 and identified M.mulatta as the most suitable for modeling COVID-19. | Shuaiyao Lu Yuan Zhao Wenhai Yu Yun Yang Jiahong Gao Junbin Wang Dexuan Kuang Mengli Yang Jing Yang Chunxia Ma Jingwen Xu Xingli Qian Haiyan Li Siwen Zhao Jingmei Li Haixuan Wang Haiting Long Jingxian Zhou Fangyu Luo Kaiyun Ding Daoju Wu Yong Zhang Yinliang Dong Yuqin Liu Yinqiu Zheng Xiaochen Lin Li Jiao Huanying Zheng Qing Dai Qiangming Sun Yunzhang Hu Changwen Ke Hongqi Liu Xiaozhong Peng | 2020 | Signal Transduction and Targeted Therapy2020，5，1： | 5 |
| 3 | An Improved Polar Codes-Based Key Reconciliation for Practical Quantum Key Distribution显示文摘Key reconciliation is important for practical Quantum key distribution(QKD) systems since it corrects the error bits in a key string by sacrificing some key bits. Therefore, its performance directly affects the secret key rate of a practical QKD system. Although key reconciliation scheme based on polar codes can achieve a high coding efficiency, the high frame error rate causes discarding key strings and decreases the secret key rate. In this paper, we fist analyze the limitation of successive cancellation decoding of polar codes, and then we propose an improved key reconciliation scheme using polar codes with successive cancellation list decoding and optimized coding structures, which can decrease the frame error probability, resulting in a higher secret key rate. Numerical results show that the proposed scheme can achieve a 12.8% higher secret key rate than the previous polar codes-based scheme with a code length of 216 bits and a quantum bit error rate of 2%. Besides, the proposed scheme is robust and it can extract secret key bits even when the quantum bit error rate reaches 10.2% with a code length of 2^(20) bits and a coding efficiency of 90.6%. | YAN Shiling WANG Jindong FANG Junbin JIANG Lin WANG Xuan | 2018 | Chinese Journal of Electronics2018，27，2： | 2 |
| 4 | Checking key integrity efficiently for high-speed quantum key distribution using combinatorial group testing显示文摘 | Junbin Fang Zoe Lin Jiang S.M. Yiu Lucas C.K. Hui | 2010 | Optics Communications2010，，1： | 1 |
| 5 | Analysis of DNA methylation related to rice adult plant resistance to bacterial blight based on methylation-sensitive AFLP (MSAP) analysis显示文摘 | Sha Aihua Lin Xinhua Huang Junbin | 2005 | Molecular Genetics and Genomics2005，273，6： | 1 |
| 6 | Diffusionmodels for time-series applications: a survey显示文摘Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions. | Lequan LIN Zhengkun LI Ruikun LI Xuliang LI Junbin GAO | 2024 | Frontiers of Information Technology & Electronic Engineering2024，25，1： | 0 |
| 7 | Deciphering controversial results of cell proliferation on TiO2 nanotubes using machine learning显示文摘With the rapid development of biomedical sciences,contradictory results on the relationships between biological responses and material properties emerge continuously,adding to the challenge of interpreting the incomprehensible interfacial process.In the present paper,we use cell proliferation on titanium dioxide nanotubes(TNTs)as a case study and apply machine learning methodologies to decipher contradictory results in the literature.The gradient boosting decision tree model demonstrates that cell density has a higher impact on cell proliferation than other obtainable experimental features in most publications.Together with the variation of other essential features,the controversy of cell proliferation trends on various TNTs is understandable.By traversing all combinational experimental features and the corresponding forecast using an exhausted grid search strategy,we find that adjusting cell density and sterilization methods can simultaneously induce opposite cell proliferation trends on various TNTs diameter,which is further validated by experiments.This case study reveals that machine learning is a burgeoning tool in deciphering controversial results in biomedical researches,opening up an avenue to explore the structure-property relationships of biomaterials. | Ziao Shen Si Wang Zhenyu Shen Yufei Tang Junbin Xu Changjian Lin Xun Chen Qiaoling Huang | 2021 | Regenerative Biomaterials2021，8，4： | 0 |
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