基础生物统计学遗传学家和流行病学家实用指南

任何从事遗传学或流行病学研究的专业人员阅读专业文献时都不可避免地要面对许多统计数据和统计方法，这就要求他们对生物统计学的概念和基本方法能准确地理解。本书两位作者基于科研、     

解Pell方程

Pell方程是比较简单也是最基本的一类丢番图方程，大约2000年前就被数学家研究，并且在近代和现代初等数论教程中它始终是必不可少的内容，但一般地说，讲解的深度都是比较浅的。本书是关于Pen方程的专著，与现已出版的经典的关于丢番图方程及Pen方程的专著相比，无论在取材的范围和论述的深度上都有所超越，它不仅包含了经典结果，而且吸收了散存于专业刊物中的新成果，特别强调了解Pell方程的计算技术以及与推导这些技术相应的理论材料。另外，在初等数论和抽象代数的基础上，比较系统地论述了与Pell方程的研究紧密相关的代数数论基础，还包括Pell方程对密码学的应用，是一本不可多得的好书。     

Suboptimal Auditory Dominance in Audiovisual Integration of Temporal Cues

The present study examined whether audiovisual integration of temporal stimulus features in humans can be predicted by the maximum likelihood estimation(MLE) model which is based on the weighting of unisensory cues by their relative reliabilities.In an audiovisual temporal order judgment paradigm,the reliability of the auditory signal was manipulated by Gaussian volume envelopes,introducing varying degrees of temporal uncertainty.While statistically optimal weighting according to the MLE rule was found in half of the participants,the other half consistently overweighted the auditory signal.The results are discussed in terms of a general auditory bias in time perception,interindividual differences,as well as in terms of the conditions and limits of statistically optimal multisensory integration.     

基于知识的神经计算模糊逻辑方法

人工神经网络（ANNs）作为强大的计算工具，应用于分类、模式识别、函数逼近和生物神经网络建模等领域。人工神经网络具有从实例中学习的程序，它们可以解决那些还不知道算法解的难题。但是，     

电子学固态物理

本书是作者多年来讲授应用、材料及电子物理学研究生课程的讲义。描述了电子学中使用的材料的基础物理，使读者能深入地理解其理论基础和该学科主要的应用领域，对于材料物理性质的详尽综述将会有助于理解电子及光子器件制造中工艺过程。     

转基因与新型农作物

转基因农作物具有多种全新优良品质,其特征是采用生物工程技术将一个物种基因嵌入另一个物种中。现行的基因工程方法主要有以下几种: 1.首先选择所需特性的DNA片断,以便将它导入农作物中。然后将称为细胞质遗传体的螺旋形DNA链从土壤细菌中抽取出来,再把细胞质遗     

决策问题中区间数的模糊偏好顺序

在传统的数学规划中，问题的系数经常被专家们规定为以古典数学推理形式的明确值。但现实中，在不精确和不确定的环境下，专家的知识和代表能力并不能达到精确的结果。本书的主要目的就是研究在不精确环境下各种真实的决策情况。作者采用区间数来刻画数据的不精确性或不确定性。     

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.     

Structure of the Sec13/31 COPII coat cage

Endomembranes of eukaryotic cells are dynamic structures that are in continuous communication through the activity of specialized cellular machineries, such as the coat protein complex II (COPII), which mediates cargo export from the endoplasmic reticulum (ER). COPII consists of the Sar1 GTPase, Sec23 and Sec24 (Sec23/24), where Sec23 is a Sar1-specific GTPase-activating protein and Sec24 functions in cargo selection, and Sec13 and Sec31 (Sec13/31), which has a structural role. Whereas recent results have shown that Sec23/24 and Sec13/31 can self-assemble to form COPII cage-like particles, we now show that Sec13/31 can self-assemble to form minimal cages in the absence of Sec23/24. We present a three-dimensional reconstruction of these Sec13/31 cages at 30 A resolution using cryo-electron microscopy and single particle analysis. These results reveal a novel cuboctahedron geometry with the potential to form a flexible lattice and to generate a diverse range of containers. Our data are consistent with a model for COPII coat complex assembly in which Sec23/24 has a non-structural role as a multivalent ligand localizing the self-assembly of Sec13/31 to form a cage lattice driving ER cargo export.     

Insights from the genome of the biotrophic fungal plant pathogen Ustilago maydis

Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant-microbe interactions. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no 'true' virulence factors had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens.