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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

Using an exact functional renormalization group (RG), we study wetting transitions in d = 1 + 1 dimensions. This RG acts in the function space of the interaction potentials for two interfaces. The RG flow exhibits i) a whole line of fixed points and ii), a separatrix which represents an analytic continuation of the fixed-point line. Numerical and analytical work shows that the line of fixed points almost forms a closed loop in function space. The separatrix, on the other hand, describes a subregime where the wetting transitions are first-order but have unusual scaling properties. A generalization of the RG indicates that these unusual transitions are replaced by usual first-order transitions in d ≠ 1 + 1.

Exact Functional Renormalization Group for Wetting Transitions in 1 + 1 Dimensions

Simple concepts from statistical physics are discussed to describe the transduction of chemical energy of a fuel to mechanical work on the molecular level. Such approaches can characterize general physical features of motor proteins that generate forces in the cell cytoskeleton. In integrated cellular systems such as cilia and hair bundles, cytoskeletal filaments and motors form complex structures and interact in large numbers. In such systems the interplay of filaments and motors can lead to emergent dynamic behaviors such as oscillating collective modes or to wave-like patterns. We discuss general aspects of such dynamic states and relate them to the dynamics of cytoskeletal structures in cells.

Statistical physics of active processes in cells

Sequential segmentation of the body axis is fundamental to vertebrate embryonic patterning. This relies on the segmentation clock, a multi-cellular oscillating genetic-network, which mainifests as tissue-level kinematic waves of gene expression that arrest at the position of each new segment. How this hallmark wave pattern is generated is an open question. We compare cellular-resolution oscillatory patterns in the embryo to those generated cell-autonomously in culture without extrinsic signals. We find striking similarity, albeit with greater variability in the timing of clock arrest in culture. Our simple physical description of a clock controlled by a noisy cell-intrinsic timer captures these dynamics. We propose the segmentation clock integrates an intrinsic, timer-driven oscillatory program, which underlies the waves and arrest, with extrinsic cues regulating the intrinsic timer’s duration and precision. One-sentence Summary Segmentation clock and wavefront activities underlying tissue-level wave patterns are cell-autonomous properties in the PSM.

/pdf/cell-autonomous-generation-of-the-wave-pattern-within-the-1xghnd3gxu.pdf

Cell-autonomous generation of the wave pattern within the vertebrate segmentation clock

The levels of nuclear protein Lamin A/C are crucial for nuclear mechanotransduction. Lamin A/C levels are known to scale with tissue stiffness and extracellular matrix levels in mesenchymal tissues. But in epithelial tissues, where cells lack a strong interaction with the extracellular matrix, it is unclear how Lamin A/C is regulated. Here, we show in epithelial tissues that Lamin A/C levels scale with apico-basal cell compression, independent of tissue stiffness. Using genetic perturbations in Drosophila epithelial tissues, we show that apico-basal cell compression regulates the levels of Lamin A/C by deforming the nucleus. Further, in mammalian epithelial cells, we show that nuclear deformation regulates Lamin A/C levels by modulating the levels of phosphorylation of Lamin A/C at Serine 22, a target for Lamin A/C degradation. Taken together, our results reveal a mechanism of Lamin A/C regulation which could provide key insights for understanding nuclear mechanotransduction in epithelial tissues.

/pdf/apico-basal-cell-compression-regulates-lamin-a-c-levels-in-3iikmqxs3d.pdf

Apico-basal cell compression regulates Lamin A/C levels in epithelial tissues.

Hair cells perform the mechanoelectrical transduction of sound signals in the auditory and vestibular systems of vertebrates. The part of the hair cell essential for this transduction is the so-called hair bundle. In vitro experiments on hair cells from the sacculus of the American bullfrog have shown that the hair bundle comprises active elements capable of producing periodic deflections like a relaxation oscillator. Recently, a continuous nonlinear stochastic model of the hair bundle motion [Nadrowski et al., Proc. Natl. Acad. Sci. U.S.A. 101, 12195 (2004)] has been shown to reproduce the experimental data in stochastic simulations faithfully. Here, we demonstrate that a binary filtering of the hair bundle's deflection (experimental data and continuous hair bundle model) does not change significantly the spectral statistics of the spontaneous as well as the periodically driven hair bundle motion. We map the continuous hair bundle model to the FitzHugh-Nagumo model of neural excitability and discuss the bifurcations between different regimes of the system in terms of the latter model. Linearizing the nullclines and assuming perfect time-scale separation between the variables we can map the FitzHugh-Nagumo system to a simple two-state model in which each of the states corresponds to the two possible values of the binary-filtered hair bundle trajectory. For the two-state model, analytical expressions for the power spectrum and the susceptibility can be calculated [Lindner and Schimansky-Geier, Phys. Rev. E 61, 6103 (2000)] and show the same features as seen in the experimental data as well as in simulations of the continuous hair bundle model.

/pdf/two-state-approach-to-stochastic-hair-bundle-dynamics-1lu5g1aixa.pdf

Frank Jülicher

Papers

Exact Functional Renormalization Group for Wetting Transitions in 1 + 1 Dimensions

Statistical physics of active processes in cells

Cell-autonomous generation of the wave pattern within the vertebrate segmentation clock

Apico-basal cell compression regulates Lamin A/C levels in epithelial tissues.

Two-state approach to stochastic hair bundle dynamics.