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| 1 | ERK kinase phosphorylates and destabilizes the tumor suppressor FBW7 in pancreatic cancer显示文摘 | Shunrong Ji Yi Qin Si Shi Xiangyuan Liu Hongli HU Hu Zhou Jing Gao Bo Zhang Wenyan Xu Jiang Liu Dingkong Liang Liang Liu Chen Liu Jiang Long Haijun Zhou Paul J Chiao Jin Xu Quanxing Ni Daming Gao Xianjun Yu | 2015 | Cell Research2015，25，5： | 19 |
| 2 | Electrical properties of sulfur-implanted cubic boron nitride thin films显示文摘Cubic boron nitride(c-BN)thin films were deposited on Si substrates by applying ion beam assisted deposition and then doped by S ion implantation.To produce a uniform depth profile of S ions in c-BN films,the implantation was carried out for the multiple energies.A slight degradation of c-BN crystallinity resulted from ion implantation can be recovered by thermal annealing,keeping the cubic phase content as high as 92%.The resistance reduces from 1010X for the as-deposited c-BN film to 108X after an S implantation of 5 9 1014ions cm-2and annealing at 1,173 K,suggesting an electrical doping effect of S dopant.The electrical resistance of the S-doped c-BN thin film decreases with increasing temperature,indicating semiconductor characteristics.The activation energy of S dopant is estimated to be 0.28±0.01 eV from the temperature dependence of resistance. | Xingwang Zhang Zhigang Yin Faitong Si Hongli Gao Xin Liu Xiulan Zhang | 2014 | Chinese Science Bulletin2014，59，12： | 2 |
| 3 | Development of prophylactic recombinant HPV58-attenuated Shigella live vector vaccine and evaluation of its protective efficacy and immunogenicity in the guinea pig keratoconjunctivitis model显示文摘开发预防 recombinant 稀释 HPV58L1 的 Shigella 实时向量疫苗并且在畿尼猪 keratoconjunctivitis 模型,评估它的保护的功效和 immunogenicity HPV58L1 基因被克隆进向量 pUCmt,然后 subcloned 进自杀向量 pCVD442。recombinant plasmid pCVD442-HPV58L1 被过滤器交配与助手 plasmid PRK2013 介绍进稀释 Shigella (sf301:virG ) 。积极殖民地被聚合酶链反应收获并且证实。有 60 kDa 的分子的重量的 HPV58L1 蛋白质的表示被西方的污点证实。感兴趣的蛋白质到的能力进像病毒的粒子自我装配被传播电子显微镜,和鼠科的红血球红血球凝聚试金识别。畿尼猪 keratoconjunctivitis 模型被用来评估疫苗的保护的功效和 immunogenicity。动物实验证明没有 keratoconjunctivitis 在使免疫的组(稀释 HPV58 的 Shigella ) 发生了,并且 anti-HPV58L1-IgG 和 -IgA 的浆液层次显然被增加 P < 0.05 ) ,但是 anti-sf301 LPS-IgG 稍微就增加了(P > 0.05 ) 。连接酶的 immunosorbent 点试金证明 HPV58L1 特定的 IgA-antibody-secreting 房间(ASC ) 和怒气和淋巴节点的 IgG-ASC 显然也被增加(P < 0.01 ) 。在这研究, recombinant 稀释 HPV58L1 的 Shigella 实时向量疫苗成功地被构造,并且它能在使免疫的动物导致强壮的体液的有免疫力的回答,并且导致保护的抗体生产。 | Wensheng Li Hongli Liu Xiaofeng Yang Jin Zheng Yili Wang Lusheng Si | 2009 | Acta Biochimica et Biophysica Sinica2009，41，2： | 1 |
| 4 | Quantification of perfluorinated compounds in atmospheric particulate shows potential connection with environmental event显示文摘A method of quantification of perfluorinated compounds(PFCs)from atmospheric particulate matter(APM)is described.A single step pretreatment method,selective pressurized liquid extraction(SPLE),was developed to reduce the high matrix background and avoid contamination from commonly used multiple sample pretreatment steps.An effective sorbent was selected to purify the PFCs during SPLE,followed by liquid chromatography-tandem mass spectrometry(LC–MS/MS),for quantification of PFCs.Conditions affecting the SPLE efficiency,including temperature,static extraction time,and number of extraction cycles used,were studied.The optimum conditions were found to be 120℃,10 min,and 3 cycles,respectively.LC-MS/MS method was developed to obtain the optimal sensitivity specific to PFCs.The method detection limits(MDLs)were 0.006 to 0.48 ng/g for the PFCs studied and the linear response range was from 0.1 to 100 ng/g.To ensure accurate values were obtained,each step of the experiment was evaluated and controlled to prevent contamination.The optimized method was tested by performing spiking experiments in natural particulate matter matrices and good rates of recovery and reproducibility were obtained for all target compounds.Finally,the method was successfully used to measure 16 PFCs in the APM samples collected in Beijing over five years from 2015 to 2019.It is observed that some PFCs follow the trend of total PFC changes,and can be attributed to the environment influencing events and policy enforcement,while others don't seem to change as much with time of the year or from year to year. | Hua Tang Ying Wang Shengling Si Hongli Li David Da Yong Chen | 2024 | Journal of Environmental Sciences2024，，2： | 0 |
| 5 | Predict the evolution of mechanical property of Al-Li alloys in a marine environment显示文摘The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials. | Wei Li Lin Xiang Guang Wu Hongli Si Jinyan Chen Yiming Jin Yan Su Jianquan Tao Chunyang Huang | 2024 | Defence Technology(防务技术)2024，31，1： | 0 |
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