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

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.

https://repository.library.northeastern.edu/files/neu:331357/fulltext.pdf

Epidemic Spreading in Scale-Free Networks

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Preface 1. Introduction 2. Driven lattice gases: simulations 3. Driven lattice gases: theory 4. Lattice gases with reaction 5. Catalysis models 6. The contact process 7. Models of disorder 8. Conflicting dynamics 9. Particle reaction models Bibliography Index.

Nonequilibrium Phase Transitions in Lattice Models

https://ergodic.ugr.es/jmarro/papers/82%20lmk%20am30.pdf

Dynamical scaling of structure function in quenched binary alloys

The time evolution of the structure function and of the cluster (or grain) distribution following quenching in a model binary alloy with a small concentration of minority atoms is obtained from computer simulations. The structure function $\overline{S}(k,t)$ obeys a simple scaling relation, $\overline{S}(k,t)={K}^{\ensuremath{-}3}F(\frac{k}{K})$ with $K(t)\ensuremath{\propto}{t}^{\ensuremath{-}a}$, $a\ensuremath{\cong}0.25$, during the latter and larger part of the evolution. During the same period, the mean cluster size grows approximately linearly with time.

Computer Simulation of the Time Evolution of a Quenched Model Alloy in the Nucleation Region

Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated? To answer this long-standing question, we define the ensemble of correlated networks and obtain the associated Shannon entropy. Maximum entropy can correspond to either assortative (correlated) or disassortative (anticorrelated) configurations, but in the case of highly heterogeneous, scale-free networks a certain disassortativity is predicted---offering a parsimonious explanation for the question above. Our approach provides a neutral model from which, in the absence of further knowledge regarding network evolution, one can obtain the expected value of correlations. When empirical observations deviate from the neutral predictions---as happens for social networks---one can then infer that there are specific correlating mechanisms at work.

/pdf/entropic-origin-of-disassortativity-in-complex-networks-3zwfnevrem.pdf

Joaquín Marro

Papers

Nonequilibrium Phase Transitions in Lattice Models

Nonequilibrium Phase Transitions in Lattice Models

Dynamical scaling of structure function in quenched binary alloys

Computer Simulation of the Time Evolution of a Quenched Model Alloy in the Nucleation Region

Entropic origin of disassortativity in complex networks.