基于DNA微阵列数据分析的分级Bayes模型

如何分离出少量区别不同组织类型的特异性基因是DNA微阵列数据分析中的主要问题,特别是构建恰当的统计模型来刻画这些不同组织类型的DNA表达形式尤为重要.为此,基于基因DNA微阵列数据的特点,我们假定对数变换后的微阵列数据服从混合正态分布.我们采用分级Bayesian先验刻画不同基因的相关性,利用分级Bayesian方法构建模型,给出了刻画不同组织基因表达的差异的一个标准,用MCMC迭代计算该标准.模拟计算表明我们的模型具有较好的识别能力.     

4种分子立体结构表示式之间的转换方法

楔形透视式、锯架透视式、纽曼投影式和费歇尔投影式是4种表述分子立体结构的工具,它们之间的相互转换是整个立体化学的基础知识。但这个思维过程因对空间想象力有一定的要求,初学者普遍感到难以理解和掌握这些方法,是有机化学教学中值得关注的难点之一。本文以图示的方式揭示了4种立体结构表示式中任意2种之间相互转换的操作技巧,以帮助学生理解和掌握这部分知识,并用以解决实际问题。     

Parametric Mulholland-type inequalities

By means of the weight functions and the idea of introducing parameters, a discrete Mulholland-type inequality with the general homogeneous kernel and the equivalent form are given. The equivalent statements of the best possible constant factor related to some parameters, the operator expressions and some particular examples are considered.     

On an extended Hardy-Hilbert""s inequality in the whole plane

By introducing independent parameters and applying the weight coefficients, we give an extended Hardy-Hilbert"s inequality in the whole plane with a best possible constant factor. Furthermore, the equivalent forms, a few particular cases and the operator expressions are considered.     

Supramolecular Mobility in Polyrotaxanes Exploits Biomedical Functions

The mobility of cyclodextrins (CDs) threaded onto a linear polymeric chain and the dethreading of the CDs from the chain are the most fascinating features seen in polyrotaxanes. These structural characteristics are very promising to their possible applications in biomedical use. Enhancing multivalent interaction between ligand-receptor systems by using ligand-polyrotaxane conjugates is one of the excellent properties related to the CD mobility. Gene delivery using cytocleavable polyrotaxanes is more practical but highly crucial in drug delivery. Such a supramolecular approach using CD-containing polyrotaxanes is extensively expected to exploit a new paradigm of advanced biomaterials for future medicines.     

基于三维数字图像相关方法的面部表情变形测量研究

目前关于人脸面部表情的相关研究正在逐渐应用到各领域,而其研究大多为基于数据库的定性分析。本文将三维数字图像相关方法用于人脸面部表情研究。首先,针对人脸面部图像的具体特点,在深入研究方法原理的基础上,提出以参数优化和消除刚体位移来提高实验测量精度。在此基础上,对普通表情和微表情状态下面部肌肉变形进行精确的计算和定量的研究,分析特定表情的形成原因。实验采用自传式回忆的方法唤起被测试者的基本情绪,随之进行模仿来诱发面部普通表情;采用指导性表达抑制的方法诱发被测试者的微表情。通过对特定表情状态下的肌肉运动进行全场和局部的计算,获得精确的三维位移场和位移矢量场。结果表明,面部微表情状态与普通表情状态的运动规律基本一致,只是位移幅度存在显著差异。实验测量结果与实际情况基本吻合,符合常规认知。不同的是本文将特定表情状态下面部肌肉的运动规律从以往的感性认知上升到精确计算和定量分析的水平,为面部表情的自动识别及形成机理的深入研究提供了良好的基础。     

Laplacian regularized low-rank representation for cancer samples clustering

Cancer samples clustering based on biomolecular data has been becoming an important tool for cancer classification. The recognition of cancer types is of great importance for cancer treatment. In this paper, in order to improve the accuracy of cancer recognition, we propose to use Laplacian regularized Low-Rank Representation (LLRR) to cluster the cancer samples based on genomic data. In LLRR method, the high-dimensional genomic data are approximately treated as samples extracted from a combination of several low-rank subspaces. The purpose of LLRR method is to seek the lowest-rank representation matrix based on a dictionary. Because a Laplacian regularization based on manifold is introduced into LLRR, compared to the Low-Rank Representation (LRR) method, besides capturing the global geometric structure, LLRR can capture the intrinsic local structure of high-dimensional observation data well. And what is more, in LLRR, the original data themselves are selected as a dictionary, so the lowest-rank representation is actually a similar expression between the samples. Therefore, corresponding to the low-rank representation matrix, the samples with high similarity are considered to come from the same subspace and are grouped into a class. The experiment results on real genomic data illustrate that LLRR method, compared with LRR and MLLRR, is more robust to noise and has a better ability to learn the inherent subspace structure of data, and achieves remarkable performance in the clustering of cancer samples.     

Amino Acid–Substituted Dextran‐Based Non‐Viral Vectors for Gene Delivery

To form bio‐inspired non‐viral vectors for DNA delivery, the polysaccharide dextran is allowed to react with Boc‐amino protected amino acids glycine, β‐alanine, and L‐lysine activated with 1,1’‐carbonyldiimidazole and subsequent dextran ester deprotection. A library of such dextran esters is made available to investigate the relationship between polymer structure, complex formation, stability, toxicity, and transfection. Only dextran esters of β‐alanine and L‐lysine are able to efficiently interact with DNA as shown by dye exclusion assays, to form nanosized complexes (70–110 nm) with positive zeta potential. With increasing substitution degree and complex charge ratios, the L‐lysine esters accomplish more effective binding and protection of DNA against enzymatic degradation than β‐alanine esters. However, luciferase reporter gene assays reveal higher transfection for β‐alanine than for L‐lysine esters due to a more effective DNA release and better suited buffing area of the amino groups triggering the endosomal release. Conclusively, β‐alanine‐substituted dextran derivatives may serve as promising non‐viral vectors.