利用EXCEL快速进行毒力测定中的致死中量计算和卡方检验

根据机率值分析法和最小二乘法的原理 ,在Windows 98 Me 2 0 0 0 XP系统中 ,利用EXCEL软件编制了 2个杀虫剂毒力测定中计算有关毒力回归方程、LD50 、相关系数、LD50 的 95 %置信限、标准误以及卡平方检验等计算程序模板。计算时只需要输入试验浓度 (或剂量 )、试虫数和试虫死亡数 ,即可快速、简便、准确计算毒力测定的结果 ,并进行卡平方值的计算和适合性判断。     

A combined method for determining inhibition type, kinetic parameters, and inhibition coefficients for aerobic cometabolism of 1,1,1-trichloroethane by a butane-grown mixed culture.

A combined method for determining inhibition type, kinetic parameters, and inhibition coefficients is developed and presented. The method was validated by applying it to data obtained from batch kinetics of the aerobic cometabolism of 1,1,1-trichloroethane (1,1,1-TCA) by a butane-grown mixed culture. The maximum degradation rates (k(max)) and half-saturation coefficients (K(s)) were independently determined in single compound tests, and compared with those obtained from inhibition tests. The inhibition type was determined using direct linear plots at various substrate and inhibitor concentrations. Kinetic parameters (k(max) and K(s)) and inhibition coefficients (K(ic) and K(iu)) were determined by nonlinear least squares regression (NLSR) fits of the inhibition model determined from the direct linear plots. Initial guesses of the kinetic parameters for NLSR were determined from linearized inhibition equations that were derived from the correlations between apparent maximum degradation rates (k(app)(max)) and/or the apparent half-saturation coefficient (K(app)(s)) and the k(max), K(s), and inhibitor concentration (I(L)) for each inhibition equation. Two different inhibition types were indicated from the direct linear plots: competitive inhibition of 1,1,1-TCA on butane degradation, and mixed inhibition of 1,1,1-TCA transformation by butane. Good agreement was achieved between independently measured k(max) and K(s) values and those obtained from both NLSR and the linearized inhibition equations. The initial guesses of all the kinetic parameters determined from linear plots were in the range of the values estimated from NLSR analysis. Overall the results show that use of the direct linear plot method to identify the inhibition type, coupled with initial guesses from linearized plots for NLSR analysis, results in an accurate method for determining inhibition types and coefficients. Detailed studies with pure cultures and purified enzymes are needed to further demonstrate the utility of this method.     

Statistical Analyses of Twins Birth Data from a Nigerian Sample

In this paper, both the linear logistic model and its analogous linear model (weighted least squares) are fitted to twin birth data from a Nigerian sample. The logits of the observed perinatal mortality rates are fitted against the birthweight of breech infants for both first born and second born twins. Results show that a quadratic response model fits the data very well, and that the WLS procedure gives a better fit. The results further show that breech births necessarily result in 7.7% and 11.2% mortality rates respectively for first born and second born twins. The results of a log-linear model analysis on the 2×2×4 contingency table formed by the factors, ‘mode of birth’, and twins with the response variable ‘Apgar Scores’-an index of morbidity-also show that both factors play significant roles in explaining the variation of the response variable. It was found that the most important factor effecting the ability of a twin infant to survive as measured by the Apgar scores is the variable ‘mode of birth’ (vertex or breech). In other words, a vertex first twin infant has the best survival chance.     

Optimal Split-plot Designs for a Mixed Model

Exact tests are given, for the usual hypotheses on split-plot models with random blocks and fixed treatment effects, considering different numbers of blocks for each level of whole-plot treatment and assuming normally distributed observations. U- and D-optimal designs are considered with respect to the tests of main effects and interactions as well as to estimation of parameters.     

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.