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

A comprehensive review of spatiotemporal pattern formation in systems driven away from equilibrium is presented, with emphasis on comparisons between theory and quantitative experiments. Examples include patterns in hydrodynamic systems such as thermal convection in pure fluids and binary mixtures, Taylor-Couette flow, parametric-wave instabilities, as well as patterns in solidification fronts, nonlinear optics, oscillatory chemical reactions and excitable biological media. The theoretical starting point is usually a set of deterministic equations of motion, typically in the form of nonlinear partial differential equations. These are sometimes supplemented by stochastic terms representing thermal or instrumental noise, but for macroscopic systems and carefully designed experiments the stochastic forces are often negligible. An aim of theory is to describe solutions of the deterministic equations that are likely to be reached starting from typical initial conditions and to persist at long times. A unified description is developed, based on the linear instabilities of a homogeneous state, which leads naturally to a classification of patterns in terms of the characteristic wave vector q0 and frequency ω0 of the instability. Type Is systems (ω0=0, q0≠0) are stationary in time and periodic in space; type IIIo systems (ω0≠0, q0=0) are periodic in time and uniform in space; and type Io systems (ω0≠0, q0≠0) are periodic in both space and time. Near a continuous (or supercritical) instability, the dynamics may be accurately described via "amplitude equations," whose form is universal for each type of instability. The specifics of each system enter only through the nonuniversal coefficients. Far from the instability threshold a different universal description known as the "phase equation" may be derived, but it is restricted to slow distortions of an ideal pattern. For many systems appropriate starting equations are either not known or too complicated to analyze conveniently. It is thus useful to introduce phenomenological order-parameter models, which lead to the correct amplitude equations near threshold, and which may be solved analytically or numerically in the nonlinear regime away from the instability. The above theoretical methods are useful in analyzing "real pattern effects" such as the influence of external boundaries, or the formation and dynamics of defects in ideal structures. An important element in nonequilibrium systems is the appearance of deterministic chaos. A greal deal is known about systems with a small number of degrees of freedom displaying "temporal chaos," where the structure of the phase space can be analyzed in detail. For spatially extended systems with many degrees of freedom, on the other hand, one is dealing with spatiotemporal chaos and appropriate methods of analysis need to be developed. In addition to the general features of nonequilibrium pattern formation discussed above, detailed reviews of theoretical and experimental work on many specific systems are presented. These include Rayleigh-Benard convection in a pure fluid, convection in binary-fluid mixtures, electrohydrodynamic convection in nematic liquid crystals, Taylor-Couette flow between rotating cylinders, parametric surface waves, patterns in certain open flow systems, oscillatory chemical reactions, static and dynamic patterns in biological media, crystallization fronts, and patterns in nonlinear optics. A concluding section summarizes what has and has not been accomplished, and attempts to assess the prospects for the future.

/pdf/pattern-formation-outside-of-equilibrium-1601b9znum.pdf

Pattern formation outside of equilibrium

Images and force measurements taken by an atomic‐force microscope (AFM) depend greatly on the properties of the spring and tip used to probe the sample’s surface. In this article, we describe a simple, nondestructive procedure for measuring the force constant, resonant frequency, and quality factor of an AFM cantilever spring and the effective radius of curvature of an AFM tip. Our procedure uses the AFM itself and does not require additional equipment.

Calibration of atomic‐force microscope tips

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

http://experimentationlab.berkeley.edu/sites/default/files/AFMImages/butt_AFM.pdf

Force measurements with the atomic force microscope: Technique, interpretation and applications

We introduce a rate-equation formalism to study DNA replication kinetics in the presence of defects resulting from DNA damage and find a crossover between two regimes: a normal regime, where the influence of defects is local, and an initiation-limited regime. In the latter, defects have a global impact on replication, whose progress is set by the rate at which origins of replication are activated, or initiated. Normal, healthy cells have defect densities in the normal regime. Our model can explain an observed correlation between interorigin separation and rate of DNA replication.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-003562.pdf

Defects and DNA replication

We review studies based on feedback traps of the fundamental thermodynamic limits for information erasure (generalized Landauer limit). We report the first direct measurement of nonequilibrium system entropy, showing it matches Shannon’s expression. OCIS codes: 350.4855, 110.0180, 000.6850, 140.7010. 1. Feedback traps and their applications to the stochastic thermodynamics of small systems In 2005, Cohen and Moerner developed the Anti-Brownian Electrokinetic (ABEL) trap, a new tool for trapping and manipulating small objects in solution [1]. The idea, to trap small objects by counteracting Brownian motion using a feedback loop, is illustrated in Fig. 1. One measures the position of an object in the trap, for example, by taking a camera image and using image processing to find the object’s coordinates. Then one computes and applies the desired force F to the charged particles via a voltage V . To emphasize the generality of the technique, we adopt the more generic name of feedback trap. In the trap used for the bulk of the experiments reported here, the loop cycle time was 5 ms, and the bead was 1.5 μm in diameter. The silica bead had a density large enough to confine the particle near the bottom of the sample cell while still allowing horizontal diffusion.

Feedback traps and the generalized Landauer limit for erasing information

The traditional proportional-integral-derivative (PID) algorithm for regulation suffers from a tradeoff: placing the sensor near the sample being regulated ensures that its steady-state temperature matches the desired setpoint. However, the propagation delay (lag) between heater and sample can limit the control bandwidth. Moving the sensor closer to the heater reduces the lag and increases the bandwidth but introduces offsets and drifts into the temperature of the sample. Here, we explore the consequences of using two probes—one near the heater, one near the sample—and assigning the integral term to the sample probe and the other terms to the heater probe. The split-PID algorithm can outperform PID control loops based on one sensor.

Note: Split PID control—Two sensors can be better than one

We present a calculation technique for modeling inhomogeneous DNA replication kinetics, where replication factors such as initiation rates or fork speeds can change with both position and time. We can use our model to simulate data sets obtained by molecular combing, a widely used experimental technique for probing replication. We can also infer information about the replication program by fitting our model to experimental data sets and also test the efficacy of planned experiments by fitting our model to simulated data sets. We consider asynchronous data sets and illustrate how a lack of synchrony affects replication profiles. In addition to combing data, our technique is well-adapted to microarray-based studies of replication.

John Bechhoefer

Papers

Defects and DNA replication

Feedback traps and the generalized Landauer limit for erasing information

Note: Split PID control—Two sensors can be better than one

Numerical modeling of inhomogeneous DNA replication kinetics

When geometry is irrelevant for heat diffusion: the transition from lumped-element to field formulations