Eph受体家族和Ephrin-A5在NSCLCs中上调表达及其临床意义

采用荧光实时定量PCR的方法在48个非小细胞肺癌组织以及配对的正常肺组织中检测Eph/ephrin 基因的mRNA水平.发现肺癌组织中EphB4, EphB3 和 ephrin-A5的表达水平与配对的正常肺组织相比分别上调了60%,81%和65%,并具有显著的统计学差异. 而且,统计学分析发现ephrin-A5的表达水平与肿瘤分型、转移、性别、吸烟以及诊断年龄等临床病理学参数之间存在显著的相关性.此外,EphA2的表达水平与肿瘤大小、家族遗传史以及诊断年龄之间也存在显著的相关性.总之,这些结果提示EphB4, EphA2, EphB3 和ephrin-A5 在肺癌的发生和发展过程中可能以一种单独或者协同的形式发挥作用.这些蛋白有可能成为研究和诊断肺癌的重要靶向基因.     

遗传算法模式理论研究

给出了多种交叉方式遗传算法的模式定理及相关的证明.该定理避免了遗传算法模式理论的不足,使模式理论更加准确、严格.     

咪唑季铵盐棉纤维制备及其对DOSO_3~-吸附性能研究

本文用20%的NaOH溶液对棉纤维的预处理、醚化、接枝合成工艺进行了优化;在不同条件下,研究了咪唑季铵盐棉纤维对DOSO~-_3的吸附性能.结果表明,用NaOH预处理棉纤维后,在适当的条件下醚化棉纤维素,再用氢氧化钠作催化剂,按3 g环氧基纤维素醚与10 mL甲基咪唑配比接枝,得到白色味唑季铵盐纤维素.用咪唑季铵盐棉纤维对DOSO~-_3进行吸附,吸附率可达91.8%,吸附效果令人满意.     

反周期函数(0,P(I))三角插值

通过引进积分算子P(I),研究了反周期函数插值及反周期函数的2-周期(0,P(I))插值,得到了解存在的条件,并给出对应条件下解的显式.     

序列模式挖掘的两种典型算法及比较

序列模式挖掘是数据挖掘的重要分支,GSP算法与PSP算法是序列模式挖掘中的两种典型算法。本文介绍了这两种算法并对其进行了分析与比较。     

A NEW DESCRIPA NEW DESCRIPTOR OF AMINO ACIDS BASED ON THE THREEDIMENSIONAL VECTOR OF ATOMIC INTERACTION FIELD

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) IS A VERY IMPORTANT TOPIC FOR MANY SCIENTIFIC FIELDS AND CAN BE TERMED AS QUANTITATIVE SEQUENCE ACTIVITY MODELS(QSAM)[1] WHEN REFERRED TO THE RESEARCH ON RELATION- SHIPS BETWEEN STRUCTURES AND ACTIVITIES OF BIOLOGICAL MOLECULES, SUCH AS PROTEINS, AND NUCLEIC ACIDS. HOW- EVER, IT IS VERY DIFFICULT TO ESTABLISH A QSAM MODEL,…     

多维函数全局寻优的团队进步算法

通过模仿团队进步需要的学习、探索行为和成员更新规则,提出了一种新颖的双群体演化算法,称为团队进步算法(TPA).算法将一个团队的成员分为精英和普通组,建立了两组的学习样板,定义了学习和探索运算,并合理设定了成员更新规则.两组成员在搜索过程中出现了明显分工,使算法兼备了全局搜索、局部搜索和定向搜索的能力.数值试验结果验证了新算法具有实现简单、全局寻优成功率高、收敛快、计算量少、坚韧性强和参数选择相对容易等特性,对解决优化应用问题具有较大的价值.     

IGF-PI3K-Akt信号通路在生理性心肌肥大中的作用研究进展

心肌肥大是心肌细胞对多种刺激因素的复杂反应。心肌肥大可以分为三种:心肌的正常生长、运动诱导生理性心肌肥大和病理性心肌肥大。近年来,研究表明心肌的正常生长以及运动诱导的心肌生理性肥大,主要是通过IGF-PI3K-Akt信号通路进行信号转导。本文就IGF-PI3K-Akt信号转导通路的结构特点、调节过程作一综述。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

三维光盘存储系统中双光头同步控制的频域分析与辨识

详细介绍了双光头同步控制原理.对采集的伺服模块聚焦误差跟踪信号及共焦模块读写光头的聚焦线圈驱动信号进行了频域分析,并以此两路信号作为系统输入输出,分别对三种参数模型进行参数优化.根据3种模型的频率响应及阶跃响应,选择双光头同步控制过程的参数模型为[450]阶的ARX模型.实测曲线与模型仿真结果符合度(fit)为78.0259%.