Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

多媒体在微生物教学中的应用

多媒体计算机技术教学是一种新型的教学方式之一。多媒体教学克服了传统的微生物教学可视化差、形象不够直观等缺点。但全课堂多媒体教学则会扼杀教师教学中的即兴创造,成为典型的电脑“满堂灌”。所以多媒体课件的制作应符合教学规律,片面强调声形兼备,会导致许多环节与课堂脱节。教学中应目的明确、化难为易,探索其固有教学规律,努力做到教学相长。     

A CLIQUE algorithm using DNA computing techniques based on closed-circle DNA sequences

DNA computing has been applied in broad fields such as graph theory, finite state problems, and combinatorial problem. DNA computing approaches are more suitable used to solve many combinatorial problems because of the vast parallelism and high-density storage. The CLIQUE algorithm is one of the gird-based clustering techniques for spatial data. It is the combinatorial problem of the density cells. Therefore we utilize DNA computing using the closed-circle DNA sequences to execute the CLIQUE algorithm for the two-dimensional data. In our study, the process of clustering becomes a parallel bio-chemical reaction and the DNA sequences representing the marked cells can be combined to form a closed-circle DNA sequences. This strategy is a new application of DNA computing. Although the strategy is only for the two-dimensional data, it provides a new idea to consider the grids to be vertexes in a graph and transform the search problem into a combinatorial problem.     

BDCGrid:基于Globus的生物信息高性能计算网格系统

随着分子生物信息数据量高速增长,生物信息学面临着大规模、高通量、密集型计算的巨大挑战。为有效利用计算机资源,缩短高通量生物信息计算程序执行时间,我们基于Globus Toolkit网格中间件,实现了一个支持高通量生物数据计算的网格系统(Biological Data Computing Grid,简称BDCGrid)。BDCGrid计算网格系统模型可以有效整合中小型生物信息学实验室计算机资源,大大缩短高通量生物信息计算程序执行时间,为相关研究人员利用现有计算机资源处理大规模、高通量生物信息计算任务提供一种新的途径。     

Cellular Adaptation Relies on Regulatory Proteins Having Episodic Memory

The ability to memorize changes in the environment is present at all biological levels, from social groups and individuals, down to single cells. Trans-generational memory is embedded subcellularly through genetic and epigenetic mechanisms. Evidence that cells process and remember features of the immediate environment using protein sensors is reviewed. It is argued that this mnemonic ability is encapsulated within the protein conformational space and lasts throughout its lifetime, which can overlap with the lifespan of the organism. Means to determine diachronic changes in protein activity are presented.     

Bio-Imitation of Mexican Migration Routes to the USA with Slime Mould on 3D Terrains

Plasmodium of Physarum polycephalum (P. polycephalum) is a large single cell visible by an unaided eye. It shows sophisticated behavioural traits in foraging for nutrients and developing an optimal transport network of protoplasmic tubes spanning sources of nutrients. When placed in an environment with distributed sources of nutrients the cell ‘computes’ an optimal graph spanning the nutrients by growing a network of protoplasmic tubes. P. polycephalum imitates development of man-made transport networks of a country when configuration of nutrients represents major urban areas. We employed this feature of the slime mould to imitate mexican migration to USA. The Mexican migration to USA is the World's largest migration system. We bio-physically imitated the migration using slime mould P. polycephalum. In laboratory experiments with 3D Nylon terrains of USA we imitated development of migratory routes from Mexico-USA border to ten urban areas with high concentration of Mexican migrants. From results of laboratory experiments we extracted topologies of migratory routes, and highlighted a role of elevations in shaping the human movement networks.     

Converting differential-equation models of biological systems to membrane computing

This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand–receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.     

Generation of initial molecular dynamics configurations in arbitrary geometries and in parallel

A computational pre-processing tool for generating initial configurations of molecules for molecular dynamics simulations in geometries described by a mesh of unstructured arbitrary polyhedra is described. The mesh is divided into separate zones and each can be filled with a single crystal lattice of atoms. Each zone is filled by creating an expanding cube of crystal unit cells, initiated from an anchor point for the lattice. Each unit cell places the appropriate atoms for the user-specified crystal structure and orientation. The cube expands until the entire zone is filled with the lattice; zones with concave and disconnected volumes may be filled. When the mesh is spatially decomposed into portions for distributed parallel processing, each portion may be filled independently, meaning that the entire molecular system never needs to fit onto a single processor, allowing very large systems to be created. The computational time required to fill a zone with molecules scales linearly with the number of cells in the zone for a fixed number of molecules, and better than linearly with the number of molecules for a fixed number of mesh cells. Our tool, molConfig, has been implemented in the open source C++ code OpenFOAM.     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.