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

In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization...

Modular Brain Networks

Local field potentials (LFPs) provide a wealth of information about synaptic processing in cortical populations but are difficult to interpret. Einevoll and colleagues consider the neural origin of cortical LFPs and discuss LFP modelling and analysis methods that can improve the interpretation of LFP data.

Modelling and analysis of local field potentials for studying the function of cortical circuits

The Kuramoto model in complex networks

Modular networks of delay-coupled and pulse-coupled oscillators are presented, which display both transient (metastable) synchronization dynamics and the formation of a large number of "chimera" states characterized by coexistent synchronized and desynchronized subsystems. We consider networks based on both community and small-world topologies. It is shown through simulation that the metastable behaviour of the system is dependent in all cases on connection delay, and a critical region is found that maximizes indices of both metastability and the prevalence of chimera states. We show dependence of phase coherence in synchronous oscillation on the level and strength of external connectivity between communities, and demonstrate that synchronization dynamics are dependent on the modular structure of the network. The long-term behaviour of the system is considered and the relevance of the model briefly discussed with emphasis on biological and neurobiological systems.

https://www.doc.ic.ac.uk/~mpsha/WildieShanahanChaosPreprint.pdf

Metastability and chimera states in modular delay and pulse-coupled oscillator networks.

A double-talk detector (109) for an acoustic echo canceler (104) of a VoIP terminal (101) converts (216,226) received far-end signals (212) and transmitted near-end signals (222) into the frequency domain and high-pass filters out (218,228) low-frequencies representing noise to obtain a complex reference signal and a complex error signal, respectively It then correlates (230) the complex signals and computes (232) the instantaneous error energy (234) and smoothed (long-term average) error energy (235) of the complex error signal If the convergence value is below 006 (300), the signals are converged and double-talk detection is enabled (306) If the convergence value is above 009 (312), the signals are diverged and double-talk detection is disabled (316) If double-talk detection is not enabled, an error threshold (236) is set (304,318) to the value of the smoothed error energy If double-talk detection is enabled (320), presence of double-talk is indicated (328) if the instantaneous error energy value exceeds (324) the error threshold, and absence of double-talk is indicated (326) if the instantaneous error energy value does not exceed (324) the error threshold

Double-talk detector suitable for a telephone-enabled PC

A network measure called knotty-centrality is defined that quantifies the extent to which a given subset of a graph’s nodes constitutes a densely intra-connected topologically central connective core. Using this measure, the knotty centre of a network is defined as a sub-graph with maximal knotty-centrality. A heuristic algorithm for finding subsets of a network with high knotty-centrality is presented, and this is applied to previously published brain structural connectivity data for the cat and the human, as well as to a number of other networks. The cognitive implications of possessing a connective core with high knotty-centrality are briefly discussed.

/pdf/knotty-centrality-finding-the-connective-core-of-a-complex-19yv9wi3vb.pdf

Knotty-Centrality: Finding the Connective Core of a Complex Network

Apparatus and methods for training of robotic devices. A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. User interface of the remote controller may be reconfigured based on the detected phenotype and/or operational changes.

Adaptive robotic interface apparatus and methods

The role of gamma frequency oscillation in neuronal interaction, and the relationship between oscillation and information transfer between neurons, has been the focus of much recent research. While the biological mechanisms responsible for gamma oscillation and the properties of resulting networks are well studied, the dynamics of changing phase relationships and mechanisms underlying the formation of phase coherence between oscillating neuronal populations are not well understood. To this end we develop a computational model of competitive selection between multiple stimuli, where selection and the transfer of population-encoded information arises from competition between converging stimuli to entrain a target population of neurons. Oscillation is generated by Pyramidal-Interneuronal Network Gamma through the action of recurrent synaptic connections between a locally connected network of excitatory and inhibitory neurons. Competition between stimuli is driven by differences in coherence of oscillation, while transmission of a single selected stimulus is enabled between generating and receiving neurons via Communication-through-Coherence. We explore the effect of varying synaptic parameters on the competitive transmission of stimuli over different neuron models, and identify a continuous region within the parameter space of the recurrent synaptic loop where inhibition-induced oscillation results in entrainment of target neurons. Within this optimal region we find that competition between stimuli of equal coherence results in model output that alternates between representation of the stimuli, in a manner strongly resembling well-known biological phenomena resulting from competitive stimulus selection such as binocular rivalry.

/pdf/establishing-communication-between-neuronal-populations-yfhz7sjb7t.pdf

Mark Wildie

Papers

Metastability and chimera states in modular delay and pulse-coupled oscillator networks.

Double-talk detector suitable for a telephone-enabled PC

Knotty-Centrality: Finding the Connective Core of a Complex Network

Adaptive robotic interface apparatus and methods

Establishing Communication between Neuronal Populations through Competitive Entrainment.