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

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

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Statistical mechanics of complex networks

Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

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

Complex networks: Structure and dynamics

https://www.tau.ac.il/%7Eklafter1/258.pdf

The random walk's guide to anomalous diffusion: a fractional dynamics approach

The band structure of crystalline methane has been calculated in a linear-combination-of-molecular-orbitals approximation. In this molecular tight-binding method the Bloch sums are built up of Hartree-Fock orbitals of the free molecules. Interactions between the states of different molecules are calculated directly from molecular integrals without reducing the exchange interaction to an exchange potential. The approximations involved are shown to be well justified for a crystal made of closed-shell units. The band structure is obtained by solving secular equation in 400 points along the relevant symmetry lines. The calculated bands are relatively narrow, less than 2.5 eV wide, around the molecular levels. The band-to-band gap in solid methane is 24.5 eV. This value has the same order of magnitude as in solid rare gases.

Electron Band Structure of Solid Methane: Ab Initio Calculations

The large scale distribution of galaxies in the universe displays a complex pattern of clusters, super-clusters, filaments and voids with sizes limited only by the boundaries of the available samples. A quantitative statistical characterization of these structures shows that galaxy distribution is inhomogeneous in these samples, being characterized by large-amplitude fluctuations of large spatial extension. Over a large range of scales, both the average conditional density and its variance show a nontrivial scaling behavior: at small scales, r<20 Mpc/h, the average (conditional) density scales as 1/r. At larger scales, the density depends only weakly (logarithmically) on the system size and density fluctuations follow the Gumbel distribution of extreme value statistics. These complex behaviors are different from what is expected in a homogeneous distribution with Gaussian fluctuations. The observed density inhomogeneities pose a fundamental challenge to the standard picture of cosmology but it also represent an important opportunity which points to new directions with respect to many cosmological puzzles. Indeed, the fact that matter distribution is not uniform, in the limited range of scales sampled by observations, rises the question of understanding how inhomogeneities affect the large-scale dynamics of the universe. We discuss several attempts which try to model inhomogeneities in cosmology, considering their effects with respect to the role and abundance of dark energy and dark matter.

The complex universe: recent observations and theoretical challenges

Many properties of the superconducting state of high-Tc materials have been interpreted within the Eliashberg framework with a very large coupling (λ ≥ 3). Here we point out that the inclusion of non-adiabatic corrections acts as an effective coupling for various properties that can, therefore, be reproduced with a much smaller coupling (λ ≈ 1) within the generalized theory. This can provide a new perspective for the analysis of superconductivity data with moderate couplings but within a theory that goes beyond Migdal's theorem.

https://iopscience.iop.org/article/10.1209/0295-5075/28/5/010/pdf

Superconductivity beyond Migdal's Theorem and High-Tc Phenomenology

Low-Temperature Specific-Heat Anomaly of a One-Dimensional Ionic Conductor

Superionic conductors are characterized by an electrical conductivity comparable to that of a liquid electrolyte 1. Classically the approach in discussing the d.c. conductivity σ(0) has been to treat it in terms of defect concentration, defect mobility and their respective activation energies 2.

Luciano Pietronero

Papers

Electron Band Structure of Solid Methane: Ab Initio Calculations

The complex universe: recent observations and theoretical challenges

Superconductivity beyond Migdal's Theorem and High-Tc Phenomenology

Low-Temperature Specific-Heat Anomaly of a One-Dimensional Ionic Conductor

Lattice Dynamics and Ionic Motion in Superionic Conductors