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

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

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

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

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Statistics for Spatial Data.

Raymond W.Y. Kao

Neuroscience has had access to high-resolution recordings of large-scale cortical activity and structure for decades, but still lacks a generally adopted basis to analyze and interrelate results from different individuals and experiments. Here it is argued that the natural oscillatory modes of the cortex—cortical eigenmodes—provide a physically preferred framework for systematic comparisons across experimental conditions and imaging modalities. In this framework, eigenmodes are analogous to notes of a musical instrument, while commonly used statistical patterns parallel frequently played chords. This intuitive perspective avoids problems that often arise in neuroimaging analyses, and connects to underlying mechanisms of brain activity. We envisage this approach will lead to novel insights into whole-brain function, both in existing and prospective datasets, and facilitate a unification of empirical findings across presently disparate analysis paradigms and measurement modalities.

/pdf/the-music-of-the-hemispheres-cortical-eigenmodes-as-a-2n5kxizq.pdf

The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns

The problem of finding a compact natural representation of brain dynamics and connectivity is addressed using an expansion in terms of physical spatial eigenmodes and their frequency resonances. It is demonstrated that this discrete expansion via the system transfer function enables linear and nonlinear dynamics to be analyzed in compact form in terms of natural dynamic ``atoms,'' each of which is a frequency resonance of an eigenmode. Because these modal resonances are determined by the system dynamics, not the investigator, they are privileged over widely used phenomenological patterns, and obviate the need for artificial discretizations and thresholding in coordinate space. It is shown that modal resonances participate as nodes of a discrete spectral network, are noninteracting in the linear regime, but are linked nonlinearly by wave-wave coalescence and decay processes. The modal resonance formulation is shown to be capable of speeding numerical calculations of strongly nonlinear interactions. Recent work in brain dynamics, especially based on neural field theory (NFT) approaches, allows eigenmodes and their resonances to be estimated from data without assuming a specific brain model. This means that dynamic equations can be inferred using system identification methods from control theory, rather than being assumed, and resonances can be interpreted as control-systems data filters. The results link brain activity and connectivity with control-systems functions such as prediction and attention via gain control and can also be linked to specific NFT predictions if desired, thereby providing a convenient bridge between physiologically based theories and experiment. Amplitudes of modes and resonances can also be tracked to provide a more direct and temporally localized representation of the dynamics than correlations and covariances, which are widely used in the field. By synthesizing many different lines of research, this work provides a way to link quantitative electrophysiological and imaging measurements, connectivity, brain dynamics, and function. This underlines the need to move between coordinate and spectral representations as required. Moreover, standard theoretical-physics approaches and mathematical methods can be used in place of ad hoc statistical measures such as those based on graph theory of artificially discretized and decimated networks, which are highly prone to selection effects and artifacts.

Discrete spectral eigenmode-resonance network of brain dynamics and connectivity.

The effects of phase bunching on the collisionless dissipation of nonlinear wave fields is explored, with emphasis on situations relevant to strong turbulence applications. It is argued that in a homogeneous, steady-state plasma, there is no preferred phase of the electric field experienced by particles as they enter a wave packet. However, an initially phase-uniform ensemble of particles will generally be phase-bunched after interacting with a wave packet. This can lead to a dramatically intensified interaction with subsequent packets encountered by the particles. Numerical calculations reveal that the local wave dissipation can increase by orders of magnitude if the transiting particles have been phase-bunched prior to entering a wave packet. The wave particle interactions, called transit-time dissipation, comprise Landau damping and a nonresonant type of damping. The nonresonant damping causes a redistribution of field energy within a wave packet. This effect is particularly strong in phase-bunched systems. These results may force modifications to previous treatments of strong turbulence which have assumed isotropy and homogeneity, and employed standard Landau damping.

Effects of phase-bunching in strongly turbulent plasmas

The role of physical and geometrical constraints in determining structure of brain networks is outlined. It is shown that requirements imposed by dynamics, stability, and network geometry strongly constrain possible networks to structures that strongly resemble those found in real brains.

Peter A. Robinson

Papers

Raymond W.Y. Kao

The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns

Discrete spectral eigenmode-resonance network of brain dynamics and connectivity.

Effects of phase-bunching in strongly turbulent plasmas

Structure, Stability, Dynamics, and Geometry in Brain Networks