Asymmetric PCR method in generation of HBV ssDNA for pyrosequencing

Objective To explore the optimal primer ratio and concentration of asymmetric polymerase chain reaction (A-PCR) in producing hepatitis B virus (HBV) single-stranded DNA (ssDNA) for pyrosequencing. Methods A-PCR was carried out to generate HBV ssDNA with forward to reverse primers of different ratios (50 : 1, 100 : 1) and concentrations (13. 0 pmol/25μL and 0.14 pmol/25μL, 19. 5 pmol/25μL and 0. 21 pmol/25μL), and the product yield and quality were compared respectively. Results The forward to reverse primer ratio of 50 : 1 provided better yield and concentration of 19. 5 pmol/25μL and 0. 21 pmol//25μL generated a clearer band. Conclusion A simple and feasible method to produce HBV ssDNA for pyrosequencing in batch is established.     

Cloning and sequencing of a DNA fragment encoding N37 apoptotic peptide derived from p53

Objective It was reported that p53 apoptotic peptide (N37) could inhibit p73 gene through being bound with iASPP, which could induce tumor cell apoptosis. To further explore the function of N37, we constructed the cloning plasmid of DNA fragment encoding p53 (N37) apoptotic peptide by using DNA synthesis and molecular biology methods. Methods According to human p53 sequence from the GenBank database, the primer of p53(N37) gene was designed using Primer V7.0 software. The DNA fragment encoding p53 (N37) apoptotic peptide was amplified by using self-complementation polymerase chain reaction (PCR) method and cloned into the pGEM-T Easy vector. The constructed plasmid was confirmed by endonuclease analysis and sequencing. Results The insertion of objective DNA fragment was confirmed by plasmid DNA enzyme spectrum analysis, p53 (N37) gene was successfully synthesized chemically in vitro. The sequencing result of positive clone was completely identical to the human p53(N37) sequence in GenBank using BLAST software (http://www. ncbi. him. nih. gov/cgi-bin /BLASTn). Conclusion The cloning of DNA fragment encoding p53(N37) apoptotic peptide was constructed by using DNA synthesis and pGEM-T Easy cloning methods. With the constructed plasmid, we could further investigate the function of N37 peptide.     

吉林省高速公路网规划建设项目实施序列优化

本文结合吉林省交通厅项目“吉林省高速公路网规划”,依据规划期公路发展的总目标和大布局,确定优化指标体系,使优化指标能全面反映公路网的全网经济性、交通功能性和社会功能性,同时,指标的口径对各公路网一致。运用本文提出的方法,对吉林省高速公路网规划建设项目进行为期5年的建设项目实施序列优化设计。     

基于自回归移动平均模型的梁桥损伤识别

桥梁结构的损伤识别是当前桥梁工程内十分活跃的研究领域,有着广泛的应用背景,与此相关的理论和技术也在不断的发展和成熟。基于自回归移动平均模型的研究方法以往都应用于结构的状态预测或系统识别,将其用于桥梁结构的损伤识别中还处于发展和完善阶段。拟结合桥梁结构的自身特点,对该方法在桥梁结构损伤识别中的应用做进一步的研究。     

SOx、NOx、PM10浓度的时间序列模型及其残差T2控制图

利用统计学软件,对内蒙古包头市2012年1月1日~2012年12月31日市环境监测站的SOx、NOx、PM10日浓度数据,进行了时间序列分析,并建立了时间序列模型(ARIMA)。然后对模型进行识别、估计,并对估计后的残差进行检验,经检验后的SOx、NOx、PM10浓度的残差值均为白噪声序列,则计算出相应的SOx、NOx、PM10日浓度的残差值。由于残差值满足传统的统计过程质量控制理论中的统计量彼此独立的基本假设,因此我们绘制出多元自相关过程的残差T2控制图。实例应用表明,该方法是合理科学的。     

车载ATP测试系统设计

介绍一种基于开放的标准功能接口的车载自动防护系统测试方案,并通过测试序列的实例说明该系统在CTCS-2环境下支持的应用对象。     

基于BP神经网络的基坑变形预测

基坑工程施工中,需要根据现场实际情况、周围环境、建筑安全等级等对变形进行严格控制。通过对基坑实测变形数据进行整理和分析,对未来变形量作出预测,保证基坑安全。结合BP神经网络的高度非线性映射能力,提出了一种基于BP神经网络的基坑变形时间序列预测方法。在基坑开挖过程中,采取滚动预测的方法,不断利用前期已有实测数据建模预测后期变形量,以实现信息化施工和动态控制。实例分析表明,BP神经网络模型具有较高的预测精度,并能获得满意的预测结果。     

钢筋混凝土框架桥优化设计的序列线性规划法

本文介绍了应用具有运动极限的序列线性规划方法对钢筋混凝土框架桥进行截面优化设计的方法。文中着重研究了优化设计变量的选择、各种构件的钢筋和混凝土应力约束方程的建立，以及结构近似重分分析的方法，还提出了减少计算机时，提高计算精度和保证收敛的措施，获得了对钢筋混凝土框架桥进行整体优化的一个有效方法。     

FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS

Fault management is crucial to provide quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in management information base, the algorithm is compatible with the existing simple network management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identifiers are acquired from the network simulation. A k-nearest neighbor classifier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, Which prove the accuracy of the algorithm. The identification errors are discussed to illustrate that the novel fault identification algorithm is adaptive in the fault scenarios with network traffic change.