6-磷酸山梨醇脱氢酶(gutD)基因克隆、测序和表达

本研究根据6-磷酸山梨醇脱氢酶基因(gutD)两端序列设计引物，以假单胞杆菌(Pseudonmonas XM)的总DNA为模板通过PCR的方法克隆gutD基因并进行序列分析，将克隆得到的gutD基因与原核表达载体pBV220连接并转化大肠杆菌(Escherichia coli)JM101后检测其蛋白表达及菌株生长耐盐性。     

A cDNA microarray technique applied for analysis of global gene expression profiles in tributyltin-exposed ascidians

To analyze global gene expressions, we constructed a cDNA microarray from a basal chordate, the ascidian Ciona intestinalis. Ciona is a cosmopolitan species and a genomic analysis of Ciona revealed that ascidians had approximately 15,500 protein-coding genes. Our "Ciona intestinalis cDNA chip version 1 (Ci cDNA chip ver. 1)" has arrayed 13,400 unique Ciona cDNAs. To establish a detection system for gene expression profiles in wild ascidians using a cDNA microarray, we analyzed gene expressions in the whole body of Ciona adults after exposure to 100 nM tributyltin (TBT) for 24 h. In our preliminary array data using Ci cDNA chip ver. 1, we found more than 200 genes that showed strong differential expressions. These genes encoded proteins that were concerned with stress response, detoxification, oxidoreduction reaction, biosynthesis, and catabolism. This, the first large cDNA microarray of this animal, should facilitate analyses of global gene expressions following exposure to TBT.     

沉降监测多项式回归分析与神经网络预测

使用多项式和切比雪夫(Tchebyshev)多项式分别对沉降监测数据进行回归分析以预测未来沉降值,其中切比雪夫多项式的外推效果较好;应用前向BP神经网络对两种不同的单因子输入模式进行非线性函数逼近,并进行了不同采样步长的比较,实例表明将时间点作为网络的输入对沉降进行预测效果较好。     

3-D rheologic model of earthquake preparation (I)—Displacement field

Introduction The rock medium shows mainly elastic character under low-temperature, low-pressure and external force for short duration, yet it shows rheologic property under high-temperature, high-pressure and long-time external force. It is reasonable that the rapid earthquake generating process lasted for tens seconds is analyzed by the elastic theory. As for the earthquake generating process for hundreds or thousand years, nevertheless, the rheologic property of rock must be taken into accou…     

A unified formulation of the piecewise exact method for inelastic seismic demand analysis including the P‐delta effect

The non‐linear analysis of single‐degree‐of‐freedom (SDOF) systems provides the essential background information for both strength‐based design and displacement‐based evaluation/design methodologies through the development of the inelastic response spectra. The recursive solution procedure called the piecewise exact method, which is efficiently used for the response analysis of linear SDOF systems, is re‐formulated in this paper in a unified format to analyse the non‐linear SDOF systems with multi‐linear hysteresis models. The unified formulation is also capable of handling the P‐delta effect, which generally involves the negative post‐yield stiffness of the hysteresis loops. The attractiveness of the method lies in the fact that it provides the exact solution when the loading time history is composed of piecewise linear segments, a condition that is perfectly satisfied for the earthquake excitation. Based on simple recursive relationships given for positive, negative and zero effective stiffnesses, the unified form of the piecewise exact method proves to be an extremely powerful and probably the best tool for the SDOF inelastic time‐history and response spectrum analysis including the P‐delta effect. A number of examples are presented to demonstrate the implementation of the method. Copyright © 2003 John Wiley & Sons, Ltd.     

JRC修正直边法的数学表达

JRC是反映爬坡角力学效应的岩体结构面表面形态等效描述指标。本文在爬坡角力学效应机理研究基础上, 阐述了JRC直边法的物理意义；通过岩体结构面表面起伏幅度与金属表面粗糙度幅度对比分析, 肯定了直边法的合理性；考虑JRC的尺寸效应, 推导JRC修正直边法的数学表达式, 编译计算机常用程序。最后, 实例检验了JRC修正直边法数学表达式的合理性和可用性。     

土水特征曲线的通用数学模型研究

土水特征曲线对于研究非饱和土的物理力学特性至关重要。根据土水特征曲线可以确定非饱和土的强度、体应变和渗透系数 ,甚至可以确定地下水面以上水份分布。由于土体物理力学特性的差异 ,导致描述其土水特征曲线的数学模型也各不相同。因此 ,建立土水特征曲线的通用数学表达式 ,显得尤为必要。本文对土水特征曲线数学模型进行了研究 ,依据这些模型的数学表达式形式 ,将其划分为 4种类型。分别由这 4种类型的数学模型推导出具有统一表达式形式的土水特征曲线通用数学模型 ,并运用陕北高原黄土土水特征曲线试验数据对通用数学模型进行了研究     

Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Thermal impact of typical high‐density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and cool‐water streams. This is the first study of its kind that provides simple easy‐to‐use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009–2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90–0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.     

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.