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| 1 | IL-17 sustains the plasma cell response via p38-mediated Bcl-xL RNA stability in lupus pathogenesis显示文摘Recent studies have demonstrated a central role for plasma cells in the development of autoimmune diseases,such as systemic lupus erythematosus(SLE).Currently,both the phenotypic features and functional regulation of autoreactive plasma cells during SLE pathogenesis remain largely unclear.In this study,we first found that a major subset of IL-17 receptor-expressing plasma cells potently produced anti-dsDNA IgG upon IL-17A(IL-17)stimulation in SLE patients and lupus mice.Using a humanized lupus mouse model,we showed that the transfer of Th17 cell-depleted PBMCs from lupus patients resulted in a significantly reduced plasma cell response and attenuated renal damage in recipient mice compared to the transfer of total SLE PBMCs.Moreover,long-term BrdU incorporation in lupus mice detected highly enriched long-lived BrdU+subsets among IL-17 receptor-expressing plasma cells.Lupus mice deficient in IL-17 or IL-17 receptor C(IL-17RC)exhibited a diminished plasma cell response and reduced autoantibody production with attenuated renal damage,while the adoptive transfer of Th17 cells triggered the plasma cell response and renal damage in IL-17-deficient lupus mice.In reconstituted chimeric mice,IL-17RC deficiency resulted in severely impaired plasma cell generation but showed no obvious effect on germinal center B cells.Further mechanistic studies revealed that IL-17 significantly promoted plasma cell survival via p38-mediated Bcl-xL transcript stabilization.Together,our findings identified a novel function of IL-17 in enhancing plasma cell survival for autoantibody production in lupus pathogenesis,which may provide new therapeutic strategies for the treatment of SLE. | Kongyang Ma Wenhan Du Fan Xiao Man Han Enyu Huang Na Peng Yuan Tang Chong Deng Lixiong Liu Yulan Chen Jingjing Li Shiwen Yuan Qin Huang Xiaoping Hong Dajun Hu Xiaoyan Cai Quan Jiang Dongzhou Liu Liwei Lu | 2021 | Cellular & Molecular Immunology2021，18，7： | 6 |
| 2 | Two-layer networked learning control using self-learning fuzzy control algorithms显示文摘Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control system (NLCS) is proposed in this paper. Its lower layer has a number of local controllers that are operated independently, and its upper layer has a learning agent that communicates with the independent local controllers in the lower layer. To implement such a system, a packet-discard strategy is firstly developed to deal with network-induced delay and data packet loss. A cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent based on a novel radial basis function neural network (RBFNN) is used to update the parameters of fuzzy controllers. A nonlinear heating, ventilation and air-conditioning (HVAC) system is used to demonstrate the feasibility and effectiveness of the proposed system. | Du Dajun Fei Minrui Hu Huosheng Li Lixiong | 2007 | 仪器仪表学报2007，28，12： | 3 |
| 3 | Apogossypolone targets mitochondria and light enhances its anticancer activity by stimulating generation of singlet oxygen and reactive oxygen species显示文摘Apogossypolone (ApoG2), a novel derivative of gossypol, has been shown to be a potent inhibitor of antiapoptotic Bcl-2 family proteins and to have antitumor activity in multiple types of cancer cells. Recent reports suggest that gossypol stimulates the generation of cellular reactive oxygen species (ROS) in leukemia and colorectal carcinoma cells; however, gossypol-mediated cell death in leukemia cells was reported to be ROS-independent. This study was conducted to clarify the effect of ApoG2-induced ROS on mitochondria and cell viability, and to further evaluate its utility as a treatment for nasopharyngeal carcinoma (NPC). We tested the photocytotoxicity of ApoG2 to the poorly differentiated NPC cell line CNE-2 using the ROS-generating TL/10 illumination system. The rapid ApoG2-induced cell death was partially reversed by the antioxidant N-acetyl-L-cysteine (NAC), but the ApoG2-induced reduction of mitochondrial membrane potential (MMP) was not reversed by NAC. In the presence of TL/10 illumination, ApoG2 generated massive amounts of singlet oxygen and was more effective in inhibiting cell growth than in the absence of illumination. We also determined the influence of light on the anti-proliferative activity of ApoG2 using a CNE-2-xenograft mouse model. ApoG2 under TL/10 illumination healed tumor wounds and suppressed tumor growth more effectively than ApoG2 treatment alone. These results indicate that the ApoG2-induced CNE-2 cell death is partly ROS-dependent. ApoG2 may be used with photodynamic therapy (PDT) to treat NPC. | Zhe-Yu Hu Jing Wang Gang Cheng Xiao-Feng Zhu Peng Huang Dajun Yang Yi-Xin Zeng | 2011 | Chinese Journal of Cancer2011，30，1： | 2 |
| 4 | T1 locus in cotton is the candidate gene affecting lint percentage, fiber quality and spiny bollworm (Earias spp.) resistance显示文摘 | Qun Wan Zhengsheng Zhang Meichun Hu Li Chen Dajun Liu Xiao Chen Wei Wang Jing Zheng | 2007 | Euphytica (-)2007，，1： | 1 |
| 5 | A social computing method for energy safety显示文摘Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,available methods for evaluating the impacts of cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even with limited data availability.To extend this risk-neutral model,we incorporate conditional value at risk as an essential risk measure,capturing the uncertainty and diverse impact scenarios arising from social computing factors.The empirical results affirm that the KDR method,which is enriched with social computing considerations,produces resilient,economically efficient solutions for managing the impacts of cyberattacks on a CPES.By integrating social computing principles into the optimization framework,it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs,ultimately improving the overall resilience and effectiveness of the system’s response measures. | Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Daniel Dajun Zeng Da Xie Yichen Shen Jiangfeng Li Tianyi Luo | 2024 | Journal of Safety Science and Resilience2024，5，1： | 0 |
| 6 | Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis显示文摘Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA. | LIANG GuiZhao CHEN ZeCong YANG ShanBin MEI Hu ZHOU Yuan YANG Li ZHOU Peng YANG ShengXi SHU Mao LIAO ChunYang WU ShiRong LI GenRong HE Liu GAO JianKun Gan MengYu LI DeJing CHEN GuoPing WANG GuiXue LONG Sha JING JuHua ZHENG XiaoLin ZENG Hui ZHANG QiaoXia ZHANG MengJun YANG Qi TIAN FeiFei TONG JianBo WANG JiaoNa LIU YongHong LI Bo QIU LiangJia CAI ShaoXi ZHAO Na YANG Yan SU XiaLi SONG Jian CHEN MeiXia ZHANG XueJiao SUN JiaYing LI JingWei CHEN GuoHua CHEN Gang DENG Jie PENG ChuanYou ZHU WanPing XU LuoNan WU YuQuan LIAO LiMin LI Zhi LI Jun LU DaJun SU QinLiang HUANG ZhengHu ZHOU Ping LI ZhiLiang | 2008 | Science China Chemistry2008，51，2： | 0 |
| 7 | Fair hierarchical clustering of substations based on Gini coefficient显示文摘For the load modeling of a large power grid,the large number of substations covered by it must be segregated into several categories and,thereafter,a load model built for each type.To address the problem of skewed clustering tree in the classical hierarchical clustering method used for categorizing substations,a fair hierarchical clustering method is proposed in this paper.First,the fairness index is defined based on the Gini coefficient.Thereafter,a hierarchical clustering method is proposed based on the fairness index.Finally,the clustering results are evaluated using the contour coefficient and the t-SNE two-dimensional plane map.The substations clustering example of a real large power grid considered in this paper illustrates that the proposed fair hierarchical clustering method can effectively address the problem of the skewed clustering tree with high accuracy. | Dajun Si Wenyue Hu Zilin Deng Yanhui Xu | 2021 | Global Energy Interconnection2021，4，6： | 0 |
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