Ultrafiltration‐LC–MS combined with semi‐preparative HPLC for the simultaneous screening and isolation of lactate dehydrogenase inhibitors from Belamcanda chinensis

Stroke represents the fourth leading cause of death in the USA and the second leading cause of death worldwide. Lactate dehydrogenase inhibitors are widely used in the treatment of ischemic stroke and natural products are considered a promising source of novel lactate dehydrogenase inhibitors. In this study, we used PC12 cells to determine the protective effect of extracts from the herb Belamcanda chinensis following toxic challenge. Using ultrafiltration high‐performance liquid chromatography coupled with photo‐diode array detection and electrospray ionization mass spectrometry, we screened and identified isoflavonoids from Belamcanda chinensis extracts. Semi‐preparative high‐performance liquid chromatography was then applied to separate and isolate the active constituents. Using these methods, we identified six major compounds in Belamcanda chinensis as lactate dehydrogenase inhibitors: tectoridin, iristectorin A, iridin, tectorigenin, irigenin, and irisflorentin, which were then isolated to >92% purity. This is the first report that Belamcanda chinensis extracts contain potent lactate dehydrogenase inhibitors. Our results demonstrate that the systematic isolation of bioactive components from Belamcanda chinensis guided by ultrafiltration high‐performance liquid chromatography coupled with photo‐diode array detection and electrospray ionization mass spectrometry represents a feasible and efficient technique that could be extended for the identification and isolation of other enzyme inhibitors.     

改进人工蜂群算法优化ELM分类模型

针对极限学习机(Extreme Learning Machine,ELM)参数优化问题,提出改进人工蜂群算法(Improvement Artificial bee colony, IABC)优化ELM分类模型。算法采用解更新策略池代替固定不变的更新策略,将邻域搜索自适应化;优化侦察蜂搜索方式,利用Kent映射产生均匀性更优的初始随机数序列。在分类数据集中,将IABC-ELM分类模型同ELM、PSO-ELM分类模型进行对比实验。实验中,IABC-ELM模型取得了最佳的分类结果,得到了最低的输出权重范数。结果表明,IABC-ELM模型分类效果显著优于对比模型,证实了IABC算法优化ELM分类模型的有效性和优越性。     

基于模糊多核学习的改进支持向量机算法研究

针对传统SVM对噪声点和孤立点敏感的问题,以及不能解决样本特征规模大、含有异构信息、在特征空间中分布不平坦的问题,将模糊隶属度融入多核学习中,提出了一种模糊多核学习的方法。通过实验验证了模糊多核学习比传统SVM、模糊支持向量机以及多核学习具有更好的分类效果,从而验证了所提方法能够有效的克服传统SVM对噪声点敏感以及数据分布不平坦的问题。     

Behavior of a functional in learning theory

Let H be a Hilbert space, A ∈ L(H), y ∈ , and y ∉ R(A). We study the behavior of the distance square between y and A(B _τ), defined as a functional F(τ), as the radius τ of the ball B _τ of H tends to ∞. This problem is important in estimating the approximation error in learning theory. Our main result is to estimate the asymptotic behavior of F(τ) without the compactness assumption on the operator A. We also consider the Peetre K-functional and its convergence rates.      

集成电路生产中成品率的有效管理

本文分析了在集成电路制造过程中,使用的基础材料、设备、工艺技术所产生的污染粒子,随机缺陷对IC产品成品率的影响。并主要介绍了从经典学习曲线概念中推导出来的成品率提高模型已被用于追踪产品成品率的提高,该模型建立在能优先迅速解决影响成品率诸多问题的基础上。实际成品率与模型化成品率的比较充分证明了成品率提高模型的实际意义。     

Controllability of Linear Systems in Non Reflexive Banch Spaces

§ 1.Introduction　 Let Xand Ube Banch spaces,S(t) a C0 semi-group in X,A the infinitesimal gen-erator of S(t) ,and B∈L(U,X) .Consider the systemΣP:x(t) =Ax(t) + Bu(t) ,　 t≥ 0 ,(1 .1 )where u∈ Lploc([0 ,∞ ) ,U) ,1≤p<∞ .　 The problem of controllability of linear systems in Banch spaces has been studied bymany authors.In [1 ] ,the weak and strong reachability and controllability of infinite di-mensional linear systems were investigated.In[2 ,3 ,4 ] ,the controllability of infi…     

有监督主成分回归法在近红外光谱定量分析中的应用研究

介绍了运用有监督主成分回归法建立近红外光谱定量分析模型的原理和方法.利用该方法先进行近红外光谱定量分析建模的波长信息选择,达到降低光谱数据维数的目的,然后建立数学模型,并用其分析预测集样品.文中以66个小麦样品为实验材料,随机选择其中40个样品建立小麦样品中蛋白质含量的近红外光谱定量分析模型,首先优选出4个波长点:4 632,4 636,5 994,5 997 cm-1,利用这4个波长点处光谱信息建立主成分回归模型预测26个样品的蛋白质含量,其结果与凯氏定氮法分析结果的相关系数为0.991,平均相对误差为1.5%.该方法从大量光谱数据中筛选出最重要的部分波长信息,实现了"少而精"的波长点选择,对建立抗共线性信息干扰的光谱定量分析模型,同时对指导专用近红外分析仪器设计中波长点的选择等方面都有一定的意义.     

Fluorescence characteristics of Dy/CdS nanocrystals doped silica xerogel

Cadmium sulfide semiconductor nanocrystals along with dysprosium ions were incorporated in silica xerogels through sol-gel route. The fluorescence properties were analyzed for samples annealed at different temperatures. The incorporation of the CdS semiconductors as nanocrystallites was confirmed from the HRTEM measurements. The fluorescence intensities were compared for Dy³⁺, CdS and Dy³⁺/CdS-doped silica xerogels. Enhanced blue emission was observed for codoped samples annealed up to 100 °C.     

A bicriteria parallel machine scheduling with a learning effect of setup and removal times

In studies on scheduling problems, generally setup times and removal times of jobs have been neglected or by including those into processing times. However, in some production systems, setup times and removal times are very important such that they should be considered independent from processing times. Since, in general jobs are done according to automatic machine processes in production systems processing times do not differ according to process sequence. But, since human factor becomes influential when setup times and removal times are taken into consideration, setup times will be decreasing by repeating setup processes frequently. This fact is defined with learning effect in scheduling literature. In this study, a bicriteria m-identical parallel machines scheduling problem with a learning effect of setup times and removal times is considered. The objective function of the problem is minimization of the weighted sum of total completion time and total tardiness. A mathematical programming model is developed for the problem which belongs to NP-hard class. Results of computational tests show that the proposed model is effective in solving problems with up to 15 jobs and five machines. We also proposed three heuristic approaches for solving large jobs problems. According to the best of our knowledge, no work exists on the minimization of the weighted sum of total completion time and total tardiness with a learning effect of setup times and removal times.     

Learning effect and deteriorating jobs in the single machine scheduling problems

This paper studies the single machine scheduling problems with learning effect and deteriorating jobs simultaneously. In this model, the processing times of jobs are defined as functions of their starting times and positions in a sequence. It is shown that even with the introduction of learning effect and deteriorating jobs to job processing times, the makespan, the total completion time and the sum of the k$k$th power of completion times minimization problems remain polynomially solvable, respectively. But for the following objective functions: the total weighted completion time and the maximum lateness, this paper proves that the shortest weighted processing time first (WSPT) rule and the earliest due-date first (EDD) rule can construct the optimal sequence under some special cases, respectively.