抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

We have developed a simple and highly efficient method to disrupt chromosomal genes in Escherichia coli in which PCR primers provide the homology to the targeted gene(s). In this procedure, recombination requires the phage lambda Red recombinase, which is synthesized under the control of an inducible promoter on an easily curable, low copy number plasmid. To demonstrate the utility of this approach, we generated PCR products by using primers with 36- to 50-nt extensions that are homologous to regions adjacent to the gene to be inactivated and template plasmids carrying antibiotic resistance genes that are flanked by FRT (FLP recognition target) sites. By using the respective PCR products, we made 13 different disruptions of chromosomal genes. Mutants of the arcB, cyaA, lacZYA, ompR-envZ, phnR, pstB, pstCA, pstS, pstSCAB-phoU, recA, and torSTRCAD genes or operons were isolated as antibiotic-resistant colonies after the introduction into bacteria carrying a Red expression plasmid of synthetic (PCR-generated) DNA. The resistance genes were then eliminated by using a helper plasmid encoding the FLP recombinase which is also easily curable. This procedure should be widely useful, especially in genome analysis of E. coli and other bacteria because the procedure can be done in wild-type cells.

/pdf/one-step-inactivation-of-chromosomal-genes-in-escherichia-25avsrekpc.pdf

One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

On the nonlinear dielectric properties of biological systems: Saccharomyces cerevisiae

Several modified particle swarm optimizers are proposed in this paper. In DVPSO, a distribution vector is used in the update of velocity. This vector is adjusted automatically according to the distribution of particles in each dimension. In COPSO, the probabilistic use of a 'crossing over' update is introduced to escape from local minima. The landscape adaptive particle swarm optimizer (LAPSO) combines these two schemes with the aim of achieving more robust and efficient search. Empirical performance comparisons between these new modified PSO methods, and also the inertia weight PSO (IFPSO), the constriction factor PSO (CFPSO) and a covariance matrix adaptation evolution strategy (CMAES) are presented on several benchmark problems. All the experimental results show that LAPSO is an efficient method to escape from convergence to local optima and approaches the global optimum rapidly on the problems used.

http://dbkgroup.org/Papers/jin_adaptivePSO_published.pdf

The landscape adaptive particle swarm optimizer

The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

/pdf/event-based-text-mining-for-biology-and-functional-genomics-gpgjtaxzpl.pdf

Event-based text mining for biology and functional genomics

1.

A system consisting of an array of cylindrical, polytopic membrane proteins (or protein complexes) possessed of a permanent dipole moment and immersed in a closed, spherical phospholipid bilayer sheet is considered. It is assumed that rotation of the protein (complex) in a plane normal to the membrane, if occurring, is restricted by viscous drag alone. Lateral diffusion is assumed either to be free and random or to be partially constrained by barriers of an unspecified nature.




2.

The dielectric relaxation times calculated for membrane protein rotation in a suspension of vesicles of the above type are much longer than those observed with globular proteins in aqueous solution, and fall in the mid-to-high audio-frequency range.




3.

If the long range lateral diffusion of (charged) membrane protein complexes is essentially unrestricted, as in the “fluid mosaic” membrane model, dielectric relaxation times for lateral motions will lie, except in the case of the very smallest vesicles, in the sub-audio (ELF) range.




4.

If, in contrast, the lateral diffusion of membrane protein complexes is partially restricted by “barriers” or “long-range” interactions (of unspecified nature), significant dielectric dispersions may be expected in both audio- and radio-frequency ranges, the critical (characteristic) frequencies depending upon the average distance moved before a barrier is encountered.




5.

Similar analyses are given for rotational and translational motions of phospholipids.




6.

At very low frequencies, a dispersion due to vesicle orientation might in principle also be observed; the dielectrically observable extent of this rotation will depend, inter alia, upon the charge mobility and disposition of the membrane protein complexes, as well as, of course, on the viscosity of the aqueous phase.




7.

The role of electroosmotic interactions between double layer ions (and water dipoles) and proteins raised above the membrane surface is considered. In some cases, it seems likely that such interactions serve to raise the dielectric increment, relative to that which might otherwise have been expected, of dispersions due to protein motions in membranes. Depending upon the tortuosity of the ion-relaxation pathways, such a relaxation mechanism might lead to almost any characteristic frequency, and, even in the absence of protein/lipid motions, would cause dielectric spectra to be much broader than one might expect from a simple, macroscopic treatment.

/pdf/on-the-dielectrically-observable-consequences-of-the-2gjtlr801f.pdf

On the dielectrically observable consequences of the diffusional motions of lipids and proteins in membranes

Background: When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles. Materials and Methods: We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification. Results: We show that while all cocktails and all methods gave good accuracy of predictions (G94%), careful selection of both the stains and the analysis method could improve this figure (to G99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model. Conclusions: Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis. Cytometry 35:162‐168, 1999. r 1999 Wiley-Liss, Inc.

/pdf/variable-selection-and-multivariate-methods-for-the-46ju2t75yz.pdf

Douglas B. Kell

Papers

On the nonlinear dielectric properties of biological systems: Saccharomyces cerevisiae

The landscape adaptive particle swarm optimizer

Event-based text mining for biology and functional genomics

On the dielectrically observable consequences of the diffusional motions of lipids and proteins in membranes

Variable selection and multivariate methods for the identification of microorganisms by flow cytometry.