Isolation and purification of BVⅠ-2H from bee venom and analysis of its biological action

The medical use of bee venom for rheumatoid arthritis ( RA ) has a very long tradition. In this study, isolation and purification of polypeptides from bee venom were carried out on sephadex chromatography, heparin sepharose CL-6B chromatography and HPLC. Several fractions were extracted, and their effects on activation of splenocyte and THP-1 cell were studied. The inhibitory fraction was selected for further studies. Finally, BVⅠ-2H that the HPLC elution profiles was a single peak was isolated by C8 column. ESI- MS detection results showed that BVⅠ-2H was a fraction of bee venom, and the molecular weight of the major component was 644.8. BVⅠ-2H could inhibit ConA-induced splenocyte proliferation, IL-1 production and interfere with splenocyte cycle in mice. Moreover, BVⅠ-2H could inhibit PMA-induced TNFα production in THP-1 cells, which was due to its inhibitory effects on TNFα mRNA expression and protein phosphorylation of IκBα. Our studies indicated that BVⅠ-2H was one of the anti-inflammatory components of bee venom.     

基于多目标蜂群算法的数据分类方法

为了保证运算时效的同时,提高复杂数据的分类精度,提出了基于多目标蜂群算法和极限学习机的数据分类算法。该方法以最小的特征个数和最高的分类精度为优化目标,利用改进的多目标蜂群算法对数据的特征个数和分类器参数进行寻优,针对多个有代表性的数据集进行仿真,结果表明所提出方法的有效性。     

人工蜂群算法及其应用

为促进人工蜂群算法理论和应用的发展, 在分析人工蜂群算法的基本原理基础上, 针对算法的不足, 全面地归纳了国内外学者对算法的改进研究, 对算法的蜜源初始化、更新策略的改进、调整策略的改进、适应度函数的选择以及与其他算法的融合进行综述, 提出了更有效的改进策略。同时从多方面综述了人工蜂群算法的应用, 并对人工蜂群算法的发展方向进行了总结和展望。     

中华蜜蜂工蜂上颚腺的超微结构

对中华蜜蜂工蜂上颚腺超微结构的电镜观察,发现上颚腺呈囊状,胞内管和胞外管的管壁由3层结构组成。细胞连接为隔壁桥粘连接。核为卯圆形,腺细胞内含有许多核糖体和粗面内质网,滑面内质网也存在。线粒体和高尔基复合体也被观察到。     

Isolation and purification of BVⅠ-2H from bee venom and analysis of its biological action

The medical use of bee venom for rheumatoid arthritis (RA) has a very long tradition. In this study, isolation and purification of polypeptides from bee venom were carried out on sephadex chromatography, heparin sepharose CL-6B chromatography and HPLC. Several fractions were extracted, and their effects on activation of splenocyte and THP-1 cell were studied. The inhibitory fraction was selected for further studies. Finally, BVⅠ-2H that the HPLC elution profiles was a single peak was isolated by C8 column. ESI- MS detection results showed that BVⅠ-2H was a fraction of bee venom, and the molecular weight of the major component was 644.8. BVⅠ-2H could inhibit ConA-induced splenocyte proliferation, IL-1 production and interfere with splenocyte cycle in mice. Moreover, BVⅠ-2H could inhibit PMA-induced TNFa production in THP-1 cells, which was due to its inhibitory effects on TNFa mRNA expression and protein phosphorylation of IkBa. Our studies indicated that BVⅠ-2H was one of the anti-inflammatory components of bee venom.     

阜阳地区草莓“简、密、蜂”新型栽培模式探讨

研究不同的大棚结构、定植密度及授粉方式对草莓(Fragaria gracilis Losinsk.)产量和品质的影响。结果表明,采用简易半坡式大棚,东西走向,坐北向南可提高光合效率,增强保温性能,使草莓成熟期提前,获得早上市、高产草莓;以12000株/667m2左右的密度定植,待其顶花序果实采收完毕后,及时调整密度,降至6000株/667m2左右,可较大幅度提高草莓产量;采用蜜蜂授粉加人工辅助授粉的方法可获得优质草莓。研究结果为高产优质草莓生产提供了参考。     

基于粒子群与蜂群结合的算法求解含风电场电力系统经济调度问题

提出一种新的混合智能算法解决含阀点效应和系统约束的含风电场的电力系统经济调度问题,将蜂群中的觅食行为与聚群行为引入改进的粒子群,提出改进粒子群一蜂群混合智能算法．在算法上进行优化,大大地提高搜索的能力,从而使结果更优．通过引入交叉策略,对那些速度保持不变的点,重新赋值．以一定的比例选拔最优点,其中选拔出的最优点,不止一个．同时精英策略的采用,有利于加强全局寻优,跳出局部最优,从而使算法得到很大的改善．最后对一个10机系统的算例进行求解,通过与改进的粒子群算法、蜂群算法进行比较,验证了改进的粒子群一蜂群混合智能优化算法在解决含风申．场的申力系统终济调度问题中的有效性与优撼性．     

贝叶斯预测蜂群算法在无线传感器网络优化中的应用

针对无线传感器网络(WSN,wireless sensor network)节点分布不合理,存在较多的监测盲区等不足,提出了利用贝叶斯预测人工蜂群算法(BPABC,Bayesian predictive artificial bee colony algorithm)制定节点分布方案。BPABC算法借鉴贝叶斯预测算法的思想对蜂群算法中各蜜源存在最优解的概率进行预测,并以此为依据指导跟随蜂寻优工作。采用BPABC算法对WSN中的节点分布进行优化,与人工蜂群算法、全局人工蜂群算法制定的优化方案进行比较。结果表明,BPABC在平均覆盖率、最差覆盖率等方面均优于其他两种算法,并且BPABC算法在迭代收敛速度方面也有明显的优势。为了进一步验证改进算法的实用性,采用BPABC制定不同监测区域的WSN节点分布方案。WSN的覆盖率均在97%左右,并且标准差不超过0.005%。由此可见,基于BPABC的WSN节点分布优化方案具有较高的覆盖率、良好的适应性和稳定性。     

基于混沌人工蜂群算法优化的SVM 齿轮故障诊断

为克服支持向量机中模型参数的随意选择对分类性能造成的不利影响, 提出了基于混沌人工蜂群算法的支持向量机(CABC鄄SVM: Chaotic Artificial Bee Colony algorithm of Support Vector Machine)参数优化方法。该方法采用Logistic 混沌映射初始化种群和锦标赛选择策略, 对支持向量机的惩罚因子和核函数参数进行优化时以分类准确率作为适应度函数。通过UCI 标准数据集实验证明, CABC 具有较强的局部和全局搜索能力, 其优化的支持向量机可在很大程度上克服局部极值点, 从而获取更高的分类准确率, 并有效缩短了搜索时间。将该方法应用于实际齿轮故障诊断中, 采用小波相对能量作为特征输入支持向量机, 分类准确率达到99. 4%, 验证了该方法的可行性和有效性。     

求解TSP的离散人工蜂群算法

针对旅行商问题,提出了一种新型的离散人工蜂群算法.根据该优化问题及离散量的特点,对引领蜂、跟随蜂和侦查蜂角色转变机制和搜索策略进行了重新定义.蜂群角色转变基于定义的收益比因子.引领蜂邻域搜索采用2-Opt算子和学习操作来加速算法收敛速度;跟随蜂搜索引入禁忌表来提高算法的局部求精能力;侦查蜂搜索定义了排斥操作来保持种群的多样性,从而较好地平衡了算法的探索及开采能力.实验结果表明,算法能够在较短时间内找到相对满意解,提高了TSP的求解效率.