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

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

Certain subsystems of nonlinear, chaotic systems can be made to synchronize by linking them with common signals. The criterion for this is the sign of the sub-Lyapunov exponents. We apply these ideas to a real set of synchronizing chaotic circuits.

Synchronization in chaotic systems

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

We consider the stochastic Ginzburg-Landau equation in a bounded domain. We assume the stochastic forcing acts only on high spatial frequencies. The low-lying frequencies are then only connected to this forcing through the non-linear (cubic) term of the Ginzburg-Landau equation. Under these assumptions, we show that the stochastic PDE has a unique invariant measure. The techniques of proof combine a controllability argument for the low-lying frequencies with an infinite dimensional version of the Malliavin calculus to show positivity and regularity of the invariant measure. This then implies the uniqueness of that measure.

Uniqueness of the Invariant Measure for a Stochastic PDE Driven by Degenerate Noise

Continuous physical systems, such as electromagnetic fields or fluids, are described dynamically by partial differential equations, or field theories. Thus they have infinitely many degrees of freedom; accordingly, it came as a surprise at the end of the seventies that such systems can exhibit low dimensional chaos which is characteristic of systems with few degrees of freedom. In the meanwhile this miracle has been fully understood. By keeping a fluid in a box which is not too large compared to some typical macroscopic scale (like the size of a convection roll) one can maintain spatial coherence but the system will become temporally chaotic. Only a few spatial modes get appreciably excited, and their amplitudes define a low-dimensional “phase-space” in which the chaotic dynamics takes place.

Spatio-Temporal Chaos

Estimates of the fractal dimension $D$ of the set of galaxies in the universe, based on ever improving data sets, tend to settle on $D\approx 2$. This result raised a raging debate due to its glaring contradiction with astrophysical models that expect a homogeneous universe. A recent mathematical result indicates that there is no contradiction, since measurements of the dimension of the {\em visible} subset of galaxies is bounded from above by D=2 even if the true dimension is anything between D=2 and D=3. We demonstrate this result in the context of a simple fractal model, and explain how to proceed in order to find a better estimate of the true dimension of the set of galaxies.

On the Fractal Dimension of the Visible Universe

It has been recognized in the last few years that dynamical systems with few degrees of freedom can play an important role in the description of some physical systems which behave in a chaotic way. The rather simple one dimensional dynamical systems can also be used to test some ideas about higher dimensional systems. The question of the statistical description of chaotic motions was already raised several decades ago ([U]) and after the discovery of the ergodic theorems it became obvious that an important notion is that of invariant measure. For a given dynamical system, there are in general many invariant measures, and one is led to the problem of choosing the relevant one (if any).

Measures Invariant under Mappings of the Unit Interval

We discuss various questions which arise when one considers the central projection of three dimensional fractal sets (galaxy catalogs) onto the celestial globe. The issues are related to how fractal such projections look. First we show that the lacunarity in the projection can be arbitrarily small. Further characteristics of the projected set---in particular scaling---depend sensitively on how the apparent sizes of galaxies are taken into account. Finally, we discuss the influence of opacity of galaxies. Combining these ideas, seemingly contradictory statements about lacunarity and apparent projections can be reconciled.

Jean-Pierre Eckmann

Papers

Uniqueness of the Invariant Measure for a Stochastic PDE Driven by Degenerate Noise

Spatio-Temporal Chaos

On the Fractal Dimension of the Visible Universe

Measures Invariant under Mappings of the Unit Interval

Angular Projection of Fractal Sets