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

In the course of development, sleep, or mental disorders, certain neurons in the brain display spontaneous spike-burst activity. The synaptic plasticity evoked by such activity is here studied in the presence of spike-timing-dependent plasticity (STDP). In two chemically coupled bursting model neurons, the spike-burst activity can translate the STDP related to pre- and postsynaptic spike activity into burst-timing-dependent plasticity (BTDP), based on the timing of bursts of pre- and postsynaptic neurons. The resulting BTDP exhibits exponential decays with the same time scales as those of STDP. In weakly coupled bursting neuron networks, the synaptic modification driven by the spike-burst activity obeys a power-law distribution. The model can also produce a power-law distribution of synaptic weights. Here, the considered bursting behavior is made of stereotypical groups of spikes, and bursting is evenly spaced by long intervals.

Synaptic modifications driven by spike-timing-dependent plasticity in weakly coupled bursting neurons.

Erratum: Weak Synchronization of Chaotic Coupled Map Lattices [Phys. Rev. Lett. 81, 3639 (1998)]

Describing a complex system is in many ways a problem akin to identifying an object, in that it involves defining boundaries, constituent parts and their relationships by the use of grouping laws. Here we propose a novel method which extends the use of complex networks theory to a generalized class of non-Gestaltic systems, taking the form of collections of isolated, possibly heterogeneous, scalars, e.g. sets of biomedical tests. The ability of the method to unveil relevant information is illustrated for the case of gene expression in the response to osmotic stress of {\it Arabidopsis thaliana}. The most important genes turn out to be the nodes with highest centrality in appropriately reconstructed networks. The method allows predicting a set of 15 genes whose relationship with such stress was previously unknown in the literature. The validity of such predictions is demonstrated by means of a target experiment, in which the predicted genes are one by one artificially induced, and the growth of the corresponding phenotypes turns out to feature statistically significant differences when compared to that of the wild-type.

Parenclitic networks' representation of data sets

We apply a new strategy to isolate cortical rhythms as deterministic signals and discuss several related topics. Extraction of the relevant signal from the original data is achieved by a novel use of the localized ltering action of the wavelet transform. Alternative nonlinear data analysis tools are used to test the validity of the procedure. In the case of neuromagnetic signals measured on the occipital cortex of a normal subject, we demonstrate the existence of a deterministic activity within the -band (8{12 Hz). The study of the brain bioelectrical activities in terms of deterministic nonlinear dynamics is quite recent (1)-(3). Perspectives of clinical use of the nonlinear tools range from an augmented predictive classication of Alzheimer's desease (4) to the possibility of controlling epileptic activity (5). However, the nonstationarity of the brain states plus the poor Signal-to-Noise Ratio (SNR) due to the large amount of brain processes active at the same time have forbidden so far a generally accepted procedure to estimate suitable indicators (2), (6), (7). Whence the need for a suitably tailored lter able to extract the activity under study to avoid contamination between noise and bioelectrical activities while maintaining a sucient number of data to perform indicator calculations. The reason for this communication is twofold. We apply for the rst time a novel ltering technique based on the local features of the wavelet transformation (8) and, second, we propose a procedure for brain signal processing, making use of available nonlinear tests. The procedure here suggested estimates the presence of determinism in a magnetoencephalo- gram using the newly developed test (9), then, whenever allowed, it calculates the correlation dimensions (CD) (10) and tests the validity of the calculation. We will show that, while the measured signal contains deterministic activity, it is not possible to reasonably estimate the CD, for which we nd, however, an upper bound of 7.

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Discrimination of deterministic dynamics in the spontaneous activity of the human brain cortex

Many papers have been published recently about the characterization of time-dependent processes through techniques using wavelet approach. Our work takes into account a particular class of time-dependent processes in nonlinear realm. We want to characterize chaotic dynamics from the standpoint of its unstable periodicities. For this aim we introduce a new technique able to stabilize such unstable orbits. We illustrate this technique both from the theoretical and the experimental standpoint. As a further step, we want to deal with the problem of detecting and removing noise from chaotic dynamics. In this paper, firstly, we show how our technique is able to distinguish with very high sensitivity between a purely chaotic dynamics and a chaotic dynamics with noise even though the noise percentage is very low (of the order of 1 percent only Secondly, we apply our technique to remove noise from this dynamics. Finally, we compare both from the theoretical and experimental standpoint our technique with the well known wavelet technique. This work is a part of 'Skynnet' international project supported by the Italian National Institute for Nuclear Physics (INFN) and partially devoted to the application of new chaotic techniques instantiated in neural architectures for compressing, storing and transmitting information to earth from satellites.

Stefano Boccaletti

Papers

Synaptic modifications driven by spike-timing-dependent plasticity in weakly coupled bursting neurons.

Erratum: Weak Synchronization of Chaotic Coupled Map Lattices [Phys. Rev. Lett. 81, 3639 (1998)]

Parenclitic networks' representation of data sets

Discrimination of deterministic dynamics in the spontaneous activity of the human brain cortex

Mutually recursive method to detect and remove noise in chaotic dynamics