改进的遗传算法在医用非线性曲线模型拟合中的应用

本文提出医用非线性曲线模型拟合改进的遗传算法,以指数曲线模型为例,选择了一组标样进行了模拟,结果表明,改进的遗传算法性能良好,可望成为各类医用非线性曲线拟合的有效手段。     

利用Excel的VBA语言自动分析大量共聚焦线扫描图像数据的方法

目的：Microsoft Excel的内置控制语言是VBA(visual basic for application)。它可以极大地增强Excel的数据处理能力。本文通过一个简单的例子说明如何利用VBA自动分析大量共聚焦线扫描图像数据并图示分析结果。方法与结果：文中首先描述了取自共聚焦线扫描图像的实验数据的结构及处理要求。然后具体说明宏程序(用VBA编写)的录制、修改和使用的详细方法。宏程序代码很接近自然语言,较好理解,而且在大多数情况下可通过“录制宏”功能自动生成,把编程的工作减至最少。结论：与手工使用Excel一步步进行数据处理相比,使用Excel中的VBA处理数据可少花时间、少犯错误、减少大量单调重复的劳动．．这些可极大地提高数据处理效率,使研究者可把更多的时间用于数据处理方案的设计和完善上。特别在处理量大而复杂的实验数据时更需要如此。这样,数据中蕴含的有用信息才能更好地被有效而准确地提取出来并加以显示。     

运用改进单纯形法拟合Logistic曲线的研究

Ｌｏｇｉｓｔｉｃ方程是研究有限空间内种群增长规律的重要工具之一本文运用改进单纯形法最优拟合Ｌｏｇｉｓｔｉｃ曲线,结果表明改进单纯形法具有较强的拟合非线性方程的能力,对生物实验及生态、生理学中诸多非线性曲线的参数估计具有普遍意义．     

三次设计结合模矢法拟合Logistic曲线的研究

Ｌｏｇｉｓｔｉｃ曲线是种群动态的一个经典模型,针对传统拟合方法存在的缺陷和三次设计的优点,以及拟合过程是离差平方和最小──可看作是运筹学中无约束极值问题──的优化原则,本文提出新的方法－三次设计结合模大法的组合方法来拟合,结果表明该法能取长补短,拟合效果最佳,因此在一般非线性模型参数求解中具有普遍意义．     

Inhibition of Glutamine Synthetase Activity by Phosphinothricin Results in Disappearance of the Peak M2 of the Chlorophyll Fluorescence Induction Curve

The treatment of green algae Chlorococcum lobatum with the herbicide BASTA containing phosphinothricin lead to a significant decrease in the level of peak M2 of the chlorophyll fluorescence induction curve. This agrees with the suggestion that glutamine synthetase activity affects this region of the induction curve. This revised version was published online in June 2006 with corrections to the Cover Date.     

Inhibition of Glutamine Synthetase Activity by Phosphinothricin Results in Disappearance of the Peak M2 of the Chlorophyll Fluorescence Induction Curve

The treatment of green algae Chlorococcum lobatum with the herbicide BASTA containing phosphinothricin lead to a significant decrease in the level of peak M2 of the chlorophyll fluorescence induction curve. This agrees with the suggestion that glutamine synthetase activity affects this region of the induction curve.     

Plant Growth Analysis: a Program for the Fitting of Lengthy Series of Data by the Method of B-splines*

A method is presented for fitting curves to lengthy and/or complicatedseries of data on plant growth. A computer program which derivesthe usual plant growth analytical quantities, and their errors,from these fitted curves is also described and offered for circulation.The fitted curves are splined cubic polynomial exponentials.Examples of their application are given, employing both realand artificial data. In any set of data the number of splines,and the position of the knots joining them, may be determinedeither by objective statistical tests or by the experimenterhimself, who thus retains a considerable degree of control overthe process but can call on the assistance of the program ifrequired. The value of this method is considered in relationto other curve-fitting approaches to plant growth analysis andit is concluded that, provided sufficient primary data are available,the method is free from many of the problems which beset earlierwork in this field, and also provides new possibilities of itsown. growth curves, approximating functions, empirical models, regression analysis, growth analysis     

Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart’s predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.     

Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation

Recovery rate is essential to the estimation of the portfolio’s loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody’s. However, it has a fatal defect that it can’t fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody’s new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.     

Guidelines for the Fitting of Anomalous Diffusion Mean Square Displacement Graphs from Single Particle Tracking Experiments

Single particle tracking is an essential tool in the study of complex systems and biophysics and it is commonly analyzed by the time-averaged mean square displacement (MSD) of the diffusive trajectories. However, past work has shown that MSDs are susceptible to significant errors and biases, preventing the comparison and assessment of experimental studies. Here, we attempt to extract practical guidelines for the estimation of anomalous time averaged MSDs through the simulation of multiple scenarios with fractional Brownian motion as a representative of a large class of fractional ergodic processes. We extract the precision and accuracy of the fitted MSD for various anomalous exponents and measurement errors with respect to measurement length and maximum time lags. Based on the calculated precision maps, we present guidelines to improve accuracy in single particle studies. Importantly, we find that in some experimental conditions, the time averaged MSD should not be used as an estimator.     

Mechanical Signaling on the Single Protein Level Studied Using Steered Molecular Dynamics

Efficient communication between the cell and its external environment is of the utmost importance to the function of multicellular organisms. While signaling events can be generally characterized as information exchange by means of controlled energy conversion, research efforts have hitherto mainly been concerned with mechanisms involving chemical and electrical energy transfer. Here, we review recent computational efforts addressing the function of mechanical force in signal transduction. Specifically, we focus on the role of steered molecular dynamics (SMD) simulations in providing details at the atomic level on a group of protein domains, which play a fundamental role in signal exchange by responding properly to mechanical strain. We start by giving a brief introduction to the SMD technique and general properties of mechanically stable protein folds, followed by specific examples illustrating three general regimes of signal transfer utilizing mechanical energy: purely mechanical, mechanical to chemical, and chemical to mechanical. Whenever possible the physiological importance of the example at hand is stressed to highlight the diversity of the processes in which mechanical signaling plays a key role. We also provide an overview of future challenges and perspectives for this rapidly developing field.