Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

电子产品研制阶段可靠性增长试验研究

结合工程实际经验,深入讨论了可靠性增长过程及实现途径,在保持试验条件和改进过程不变的条件下,实施了对具体型号电子产品的可靠性增长试验,达到了预期的可靠性增长目标,并且利用可靠性增长试验的数学模型(AMSAA模型)来评估产品的可靠性增长,对开展可靠性增长与可靠性增长试验工作具有重要的实际意义.     

Implicit Motor Learning in Parkinson's Disease.

Objective: This study examined whether disruption of performance is moderated in Parkinson's disease (PD) patients who acquire their motor behaviors in an implicit manner. Method: Twenty-seven patients with PD learned a hammering task in errorless (implicit) or errorful (explicit) conditions and were tested for robustness of motor performance under a secondary task load, which required them to continuously count backward as they performed the hammering task. Results: Patients in the errorless (implicit) motor learning condition exhibited robustness to secondary task loading, whereas patients in the errorful (explicit) motor learning condition did not. Conclusions: Implicit motor learning techniques should be considered by PD rehabilitation specialists in cases in which existing disruption to movements is exacerbated by conscious control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Electronic properties of magnetically doped nanotubes

Effect of doping of carbon nanotubes by magnetic transition metal atoms has been considered in this paper. In the case of semiconducting tubes, it was found that the system has zero magnetization, whereas in metallic tubes the valence electrons of the tube screen the magnetization of the dopants: the coupling to the tube is usually antiferromagnetic (except for Cr).     

聚硅烷的电子特性及非线性光学材料

综述了聚硅烷的电子结构、光吸收特性。重点介绍了聚硅烷在非线性光学材料中的研究与应用。     

NMDA Receptor Modulation of Incidental Learning in Pavlovian Context Conditioning.

Rats exposed to a footshock show conditional fear when reexposed to the shock context. Immediate presentation of shock after placement in the context significantly reduces this fear. Preexposure to the context in the absence of shock, coupled with a minimum preshock interval during training, overcomes this immediate shock deficit. Because rats learn about the context during preexposure and express that learning after being reinforced, the context preexposure effect is an aversive analogue of latent learning. The authors examined the effect of the N-methyl-D-aspartate (NMDA) receptor antagonist D,L-2-amino-5-phosphovalerate (APV) on the facilitatory effect of context preexposure. Rats were preexposed to a chamber after APV administration. The next day they were placed in the same chamber without drug and received shock 35 s later. APV blocked the facilitatory effect of preexposure. Therefore NMDA receptors are important for contextual latent learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

电力电子的绿色变换技术与绿色电源的发展

电力电子技术应用日益广泛,同时也带来严重的谐波污染和电磁干扰。本文介绍了电力电子在电磁兼容和谐波抑制方面的绿色变换技术。同时还就绿色电源产业的形成和绿色电源产品要求作了简要说明。     

基于支持向量机的多类分类研究

现今流行的分类方法的重要基础是传统的统计学，前提是要有足够的样本，当样本数目有限时容易出现过学习的问题，导致分类效果不理想。引入支持向量机方法，它基于统计学习理论，采用了结构风险最小化原则代替经验风险最小化原则，较好的解决了小样本学习的问题；又由于采用了核函数思想，把非线性空间的问题转换到线性空间，降低了算法的复杂度。对其相关内容包括优化算法及多类分类问题的解决进行了研究，最后用一个实例说明了该方法的可行性和有效性。     

一种基于PWM的电压输出DAC电路设计

对实际应用中的脉宽调制(PWM)波形的频谱进行了理论分析．指出通过一个低通滤波器可以把PWM调制的数模转换信号解调出来．实现从PWM到DAC的转换。论文还对转换误差产生的因素进行了分析，指出了减少误差的方法，论文给出了两种从PWM到0～5V电压输出的电路实现方法，第2种电路具有很高的转换精度。     

基于SOI的双极场效应晶体管

在分析了双极型晶体管和场效应晶体管各自的特点和不足后，介绍了一种既具有双极型晶体管较大电流容量和功率输出，又具有场效应晶体管高输入阻抗的电子器件——双极MOS场效应晶体管(BJMOSFET)，同时指出体硅BJMOSFET的阳极扩散区与衬底之间存在较大的漏电流，可产生较大的寄生效应。提出了一种新型固体电子器件——基于SOI的BJMOSFET，分析了其工作原理j与体硅BJMOSFET比较，由于SOI技术完整的介质隔离避免了体硅器件中存在的大部分寄生效应，使基于SOI的BJMOSFET在体效应、热载流子效应、寄生电容、短沟道效应和闩锁效应等方面具有更优良的特性。