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

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application.

Artificial neural networks: a tutorial

Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

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Mapping the Structural Core of Human Cerebral Cortex

Several means for improving the performance and training of neural networks for classification are proposed Crossvalidation is used as a tool for optimizing network parameters and architecture It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks >

/pdf/neural-network-ensembles-4ujxrg94mn.pdf

Neural network ensembles

This paper gives the 2010 self-consistent set of values of the basic constants and conversion factors of physics and chemistry recommended by the Committee on Data for Science and Technology (CODATA) for international use. The 2010 adjustment takes into account the data considered in the 2006 adjustment as well as the data that became available from 1 January 2007, after the closing date of that adjustment, until 31 December 2010, the closing date of the new adjustment. Further, it describes in detail the adjustment of the values of the constants, including the selection of the final set of input data based on the results of least-squares analyses. The 2010 set replaces the previously recommended 2006 CODATA set and may also be found on the World Wide Web at physics.nist.gov/constants.

/pdf/codata-recommended-values-of-the-fundamental-physical-4bsdant71c.pdf

CODATA Recommended Values of the Fundamental Physical Constants: 2006

Are some ways of measuring scientific quality better than others? Sune Lehmann, Andrew D. Jackson and Benny E. Lautrup analyse the reliability of commonly used methods for comparing citation records. Citation analysis can loom large in a scientist's career. In this issue Sune Lehmann, Andrew Jackson and Benny Lautrup compare commonly used measures of author quality. The mean number of citations per paper emerges as a better indicator than the more complex Hirsch index; a third method, the number of papers published per year, measures industry rather than ability. Careful citation analyses are useful, but Lehmann et al. caution that institutions often place too much faith in decisions reached by algorithm, use poor methodology or rely on inferior data sets.

Measures for measures

Recent developments in the comparison between theory and experiments in quantum electrodynamics

We derive analytic expressions for infinite products of random 2 x 2 matrices. The determinant of the target matrix is log-normally distributed, whereas the remainder is a surprisingly complicated function of a parameter characterizing the norm of the matrix and a parameter characterizing its skewness. The distribution may have importance as an uncommitted prior in statistical image analysis.

Benny Lautrup

Papers

Measures for measures

Recent developments in the comparison between theory and experiments in quantum electrodynamics

Products of random matrices.

Static quantities in Weinberg's model of weak and electromagnetic interactions

On High Order Estimates in QED