다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

Complex brain networks: graph theoretical analysis of structural and functional systems

A method for the transmission of information from a transmitter (1) to a receiver (3) is described, in which the transmitter and the receiver each comprise a dynamic system [X ˙1=f(X1, µ); X ˙3=f (X3, µ) ], said two systems being able to be synchronized with each other; a signal (y 3) relating to one of the variables of one of said systems is exchanged between said two dynamic systems; and the information to be transmitted is coded in the form of a disturbance in one of the variables of the system of the transmitter. Characteristically the transmitter comprises: a first and a second dynamic system [X ˙1=f (X1, CL) ; X ˙2=f (X2 r CL)] ] described by identical equations; the receiver (3) transmits to the transmitter (1) a signal (y 3) relating to one of the variables of the dynamic system of the receiver, which signal replaces the corresponding variable (y 2) of the second dynamic system of the transmitter in order to synchronize the second dynamic system of the transmitter with the dynamic system of the receiver; the transmitter (1) sends to the receiver (3) a signal (U(t)) which is a function of the difference between two corresponding variables (x1 (t), x2 (t)) of the first and the second dynamic system of the transmitter; the signal (y 3) which the receiver sends to the transmitter (1) is interrupted for interruption time intervals (To) greater than a decorrelation time of the dynamic systems of the transmitter and the receiver.

Method for the adaptive synchronization of chaotic systems and data transmission system using said method

We extend the concept of eigenvector centrality to multiplex networks, and introduce several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities. In addition, we rigorously show that, under reasonable conditions, such centrality measures exist and are unique. Computer experiments and simulations demonstrate that the proposed measures provide substantially different results when applied to the same multiplex structure, and highlight the non-trivial relationships between the different measures of centrality introduced.

Centrality of nodes in multiplex networks

In the ever-increasing availability of massive data sets describing complex systems, i.e. systems composed of a plethora of elements interacting in a non-linear way, complex networks have emerged as powerful tools for characterizing these structures of interactions in a mathematical way. In this contribution, we explore how different Data Mining techniques can be adapted to improve such characterization. Specifically, we here describe novel techniques for optimizing network representations of different data sets; automatize the extraction of relevant topological metrics, and using such metrics toward the synthesis of high-level knowledge. The validity and usefulness of such approach is demonstrated through the analysis of medical data sets describing groups of control subjects and patients. Finally, the application of these techniques to other social and technological problems is discussed.

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Analysis of Complex Data by Means of Complex Networks

Based on an automatic feedback adjustment of an additional parameter of a dynamical system, we propose a strategy for controlling periodic orbits of desired periods in chaotic dynamics and tracking them toward the set of unstable periodic orbits embedded within the original chaotic attractor. The method does not require information on the system to be controlled, nor on any reference states for the targets, and it overcomes some of the difficulties encountered by other techniques. Assessments of the method's effectiveness and robustness are given by means of the application of the technique to the stabilization of unstable periodic orbits in both discrete- and continuous-time systems.

Stefano Boccaletti

Papers

Method for the adaptive synchronization of chaotic systems and data transmission system using said method

Centrality of nodes in multiplex networks

Analysis of Complex Data by Means of Complex Networks

Automatic control and tracking of periodic orbits in chaotic systems.

Stability and pattern formation in networks of dynamical systems