Pitfalls in molecular diagnosis in a family with severe factor VII (FVII) deficiency—misdiagnosis by direct sequence analysis using a PCR product

Molecular diagnostic tests are becoming a routine analysis in many laboratories. These modern analyses are widely used in clinical medicine, forensic, genetic and prenatal diagnosis and also in preimplantation genetic diagnosis. The accuracy of analysis is highly dependent on the success achieved in minimising genotyping errors. The pitfalls in molecular diagnostic tests can be due to a simple technique such as the polymerase chain reaction (PCR) used universally. This technique is routinely used for its apparent accuracy, but it is also a well-known source of errors. We report an error introduced during PCR reaction that leads to a wrong sequence result and consequently to a ‘false’ molecular result in a next prenatal diagnosis in a family with severe factor VII (FVII) deficiency. This error was verified using an unsuitable primer design in a rich repetitive sequence of the FVII gene that leads to a false annealing and then to a wrong molecular diagnosis. It is essential to link closely molecular data with clinical and phenotype analysis in order to avoid false-negative or false-positive results, which is of great importance to diagnosis and molecular prevention. Copyright © 2003 John Wiley & Sons, Ltd.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

对大气采样误差的理论研究

环境监测数据是环境保护工作的基础,监测仪器设备的精良及监测方法的科学性则是获得优质数据的保障工具.本文运用数学方法对目前广泛使用的采样仪器进行了采样误差的分析研究,指出了改善仪器和改善监测方法去获取更精确数据的途径.     

环境科学研究中两变量非线性相关时的线性回归方程

以环境科学研究两变量非线性相关的问题为对象，讨论建立线性回归方程的方法。有绝对误差均方和最小的最小二乘法、相对误差最小的新回归法。以简易的方法，建立相关性好、误差小的回归方程。介绍了两变量各种非线性相关时的线性回归方程在环境科学研究中的应用。     

有关《环境保护法》的思考

本文综合分析了以环境保护法为基本法的环境保护法律的体系，试图从环境法律关系和法律责任等方面对环境法进行法律属性的分析，得学我国环境管理体制实质上就是环境行政管理体制的结论，环境行政管理必须在法制的轨道上运行，这样才会从根本上解决环境保护的问题，才会使我们的生存环境更加美好。     

方头柱塞冷挤镦工艺及模具设计

分析了硬铝合金方头柱塞件的冷挤镦工艺 ,设计了方头柱塞冷挤镦件图 ,计算及选用了毛坯形状及尺寸 ,验算了许可变形程度 ,选择了冷成形用的设备 ,拟制了冷挤镦工艺前的毛坯处理并设计了方头柱塞冷挤镦模具结构。     

佳木斯台形变观测对东北地区几次浅震“场兆”异常的探讨

大地震在主破裂前往往行成地壳变形,高应力作用下进入峰值后的变形阶段,反映到地倾斜观测日均值曲线上,表现为固体潮形变趋势异常,这些异常能否指示远距离的地震,近年来不少研究者发现,远场前兆往往出现在对应力、应变变化反应灵敏的特殊构造部位。佳木斯台可能处于这种特殊的构造部位,对东北地区浅源地震短期前兆异常“场兆”的趋势性变化具有一定的意义。     

TVS／TFS去除率在ATAD污泥稳定评价的应用

在ATAD(Autothermal Thermophilic Aerobic Digestion,自升温高温好氧消化)工艺污泥稳定评价中引入TFS．提出“TVS/ TFS去除率”的概念,得出“TVS/TFS去除率”可以代替“VS(g)去除率”而成为一种新的污泥稳定指标的结论;采用数学微分的方法分析了“TVS/TFS去除率”和“VS(g)去除率”的数据误差问题,进泥TS为60g/L,TVS/TS为56％的运行条件下,前者的最大数据误差仅为3．16％,远小于后者的误差最大值15．45％,即引入TFS解决了“VS(g)去除率”数据波动大的问题;TFS的引入还节约了污泥稳定研究所需的时间和工作量。     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.     

Effect of catalyzed diesel particulate filter and its catalyst loading on emission characteristics of a non-road diesel engine

In this study, the effects of a diesel oxidation catalyst (DOC) coupled with a catalyzed diesel particulate filter (CDPF) with different catalyst loadings on the power, fuel consumption, gaseous and particulate emissions from a non-road diesel engine were investigated. Results showed that the after-treatment had a negligible effect on the power and fuel consumption. The reduction effect of the DOC on the CO and hydrocarbon (HC) increased with the engine load. Further reductions occurred coupling with the CDPF. Increasing the catalyst loading resulted in a more significant reduction in the HC emissions than CO emissions. The DOC could increase the NO₂ proportion to 37.9%, and more NO₂ was produced when coupled with the CDPF below 250°C; above 250°C, more NO₂ was consumed. The after-treatment could reduce more than 99% of the particle number (PN) and 98% of the particle mass (PM). Further reductions in the PN and PM occurred with a higher CDPF catalyst loading. The DOC had a better reduction effect on the nucleation particles than the accumulation ones, but the trend reversed with the CDPF. The DOC shifted the particle size distribution (PSD) to larger particles with an accumulation particle proportion increasing from 13% to 20%, and the geometric mean diameter (GMD) increased from 18.2 to 26.0 nm. The trend reversed with the CDPF and the accumulation particle proportion declined to less than 10%. A lower catalyst loading on the CDPF led to a higher proportion of nucleation particles and a smaller GMD.