携带Herceptin与MICA胞外结构域融合基因的重组腺病毒的构建及鉴定

通过构建能表达抗体Herceptin与MICA胞外结构域的融合蛋白的重组腺病毒,为进一步联合NK细胞的治疗研究奠定基础.采用Overlap-PCR的方法获得Herceptin与MICA胞外结构域的融合基因,并将融合基因连接到载体pDC339上.接着再利用酶切及PCR方法鉴定载体及重组病毒基因的正确性,并通过ELISA和Westem Blot检测融合蛋白的表达情况,最后经间接免疫荧光分析(IFA)检测融合蛋白的结合特性.构建的载体酶切后经电泳鉴定证实构建成功,重组病毒DNA的PCR显示病毒携带了Herceptin的轻链基因、重链基因及MICA胞外结构域的基因;ELISA和Western Blot证实重组腺病毒成功地表达出了目的融合蛋白;IFA实验表明融合蛋白能与SK-OV-3细胞表面的Her-2受体特异性地结合.最后成功地构建了能表达具有正常结合特性的抗体Herceptin与MICA胞外结构域的融合蛋白的重组腺病毒,为之后与NK细胞的联合治疗研究奠定基础.     

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model’s structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.     

AFM observations of silane coupling agent layers having a vinyl group deposited on a mica surface

The topography of silane layers deposited on an inorganic surface was observed using atomic force microscopy. For this purpose, cleaved mica plates were treated with some silane coupling agents at various conditions. Silanes having a vinyl group as the organic functional group with mono-, di- and trialkoxy structures were used. Four different solvents for silane solutions, 2-propanol, 2-propanol/water mixture, water and toluene, were used. The pH of the aqueous solution was controlled. As a result, the suitable solvent and pH were identified in order to obtain a smooth silane layer. The solubility of silane in the solution, the wettability of silane onto the inorganic surface and the depression of the self-condensation of silane molecules in the solution were found to be important parameters for this purpose.     

基于自适应多向独立成分分析的间歇过程监控的研究

针对间歇过程批次与批次之间,操作条件缓慢变化的特性,提出一种基于自适应多向独立成分分析(MICA)的监控算法。该方法首先用MICA法建模,然后在历史数据集中加入新的正常批次并剔除最早批次,逐渐更新模型,同时引入遗忘因子,提高对新过程特性的适应性。青霉素发酵过程的仿真结果表明,自适应MICA比MICA更准确地描述过程行为,并有效减少检测故障时的误报。     

传感器网络基站与节点接口的设计与实现

传感器网络是集传感技术,无线通信技术,微处理器技术于一体的新兴技术.传感器网络的基站是网络的输出端和控制台,可以用PC机或笔记本电脑实现.MICA系统是当今得到广泛认可的传感器网络实验系统.以MICA系统为基础,探讨传感器网络基站与节点接口的设计和实现.     

Application of multiway ICA for on-line process monitoring of a sequencing batch reactor

Multiway principal component analysis has been shown to be a powerful monitoring tool in many industrial batch processes. However, it has the shortcomings that all batch lengths should be equal, the measurement variables must be normally distributed and that future values of the current batch must be estimated to allow on-line monitoring. In this work, it is shown that multiway independent component analysis (MICA) can be used to overcome these drawbacks and obtain better monitoring performance. The on-line MICA monitoring of batch processes is based on a new unfolding method and independent component analysis (ICA). ICA provides better monitoring performance than PCA in cases with non-Gaussian data because it is not based on the assumption that the latent variables are normally distributed. The MICA algorithm does not require any estimation of future batch values and can also be applied to non-equal batch length data sets. This article describes the application of on-line MICA monitoring of a sequencing batch reactor (SBR). It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of disturbance sources with non-Gaussian characteristics. The SBR poses an interesting challenge from the point of process monitoring characterized by non-stationary, batchwise, multiscale, and non-Gaussian characteristics. The results of the bench-scale SBR monitoring clearly showed the power and advantages of MICA monitoring in comparison to conventional monitoring methods.     

Multivariate monitoring for time-derivative non-Gaussian batch process

This research is an application of process monitoring on a pilot-scale sequencing batch reactor (SBR) using a batchwise multiway independent component analysis method (MICA) for denoising effect, which can extract meaningful hidden information from non-Gaussian data. Three-way batch data of SBR are unfolded batch wise, and then a multivariate monitoring method is used to capture the non-Gaussian and nonlinear characteristics of normal batches. It is successfully applied to an 80 L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. In the monitoring result, multiway principal component analysis (MPCA) can detect the abnormal batches with a false alarm rate of 47.5%, whereas MICA charts show less false alarm rate of 4.5%. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the MICA monitoring technique showed lower false alarm rate and physically meaningful robust monitoring results.     

考虑储能和需求侧响应的微网光伏消纳能力研究

在光伏发电迅速发展的情况下,分布式光伏的消纳成为人们关注的热点。构建了含有分布式光伏、储能系统以及需求侧响应的光伏微网系统。以光伏发电消纳量最大为首要目标,同时考虑微网系统的用电成本,并加入功率平衡、微网可靠性、储能系统功率上下限、需求侧响应以及光伏出力界限等约束条件。为了对比分析储能和需求侧响应对微网光伏消纳的影响,设立不考虑储能系统和需求侧响应、只考虑储能系统、只考虑需求侧响应、同时考虑储能系统和需求侧响应4种情景,使用改进帝国竞争算法解决约束条件较多的问题。通过对比发现,借助储能系统和需求侧响应能够有效地提高微网中分布式光伏的消纳率,同时也保证了微网用电的经济性。     

基于MICA-OCSVM的间歇过程故障监测

针对多向独立成分分析(multi-way independent component analysis,MICA)需要假设过程变量服从非高斯分布的要求,以及MICA基于马氏距离构造的监控统计量会导致故障检测率降低的问题,研究了一种将多向独立成分分析与单类支持向量机(one-class support vector machines,OCSVM)相结合的MICA-OCSVM监测方法.首先采用MICA提取间歇过程所有批次的独立成分;然后分别对每个时刻的所有批次的独立成分进行OCSVM建模,利用确定的决策超平面构造非线性的监控统计量;最后计算所有建模数据的监控统计量,并利用核密度估计确定相应的控制限.将该方法应用到青霉素发酵过程仿真平台,实验结果表明:该方法相比于传统的MICA故障监测方法,无需考虑过程变量服从何种分布,能够有效利用独立成分的结构信息,故障的误报率、漏报率明显降低.     

Space and Angular Dependence of Interface Currents in the Multiband-CCCP Method

The present paper discusses the effect on accuracy of eigenvalue by the degree of discretization at the cell boundary within the framework of the multiband-CCCP, a combination of the multiband method and the CCCP method. A study on sensitivity of discretization has been done concerning the dependence of surface flux distribution in space and angular domain, or interface currents. It is found that appropriate discretization with more than five for both of segments and sectors is required to accurately calculate effective cross sections and eigenvalue in the multiband-CCCP method. However, in practice, discretization schemes with one segment and several sectors can be employed with some biases on eigenvalue.

The distribution of surface flux at the boundary of a typical PWR-MOX cell has been studied. It is found that discontinuity and strong angular dependence in the distribution of surface flux on the off-centered segments have large influence in the multiband-CCCP method. An improved scheme of angular discretization has been examined for the feasibility to retain calculation accuracy with less degree of discretization in angular domain.