人类乙型肝炎样病毒核心蛋白中第7位疏水性氨基酸重复肚段区域的突变可以抑制病毒核衣壳的组装

人类乙型肝炎病毒的核衣壳由核心蛋白的二聚体所组成．但是,核心蛋白亚单位与亚单位之间相互作用的机制至今尚不清楚．研究发现,在人类乙型肝炎样病毒──土拨鼠肝炎病毒（WHV）核心蛋白的氨基端,存在着4个保守的疏水氨基酸残基（氨基酸位置101～102）．它们分别是亮氨酸101,亮氨酸108,缬氨酸115和苯丙氨酸122．这4个疏水氨基酸残基以每隔6个氨基酸残基而重复出现1次．它们被称为“第7位疏水性氨基酸重复肽段（hhr）”．由于蛋白质中的疏水键往往在蛋白质的相互作用中起重要作用,因此就在培养细胞系统中研究WHV核心蛋白的hhr区域在…     

人类乙型肝炎样病毒Pre—S1包膜蛋白的一个特定区域为乙肝病毒形成所必需

土拨鼠肝炎病毒（WHV）是一种哺乳类动物的肝炎病毒．这种病毒在结构和抗原怕与人类乙型肝炎病毒（HBV）非常相似．以往的研究报告指出,在一种称为鸭乙型肝炎病毒（DHBV）的Pre－S包膜蛋白中,含有一个特定区域．这一区域由天冬氨酸－天冬氨酸－脯氨酸－亮氨酸－亮氨酸（DDPLL）5个氨基酸残基所组成．已发现,这一区域在像DHBV这一类禽类乙肝病毒的病毒装配和分泌时所必需的（LenhoffR,SummersJ．JVirol,1994,68：4565～4571）．在WHV的Pre－S包胰蛋白中第201个氨基酸到第205个氨基酸所包含的顺序,甘氨酸－天冬氨酸－脯氨…     

An ultra-sparse code underlies the generation of neural sequences in a songbird

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the 'grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.     

Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

Digital circuits such as the flip-flop use feedback to achieve multistability and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multistable and graded. We propose that the neocortex combines digital selection of an active set of neurons with analogue response by dynamically varying the positive feedback inherent in its recurrent connections. Strong positive feedback causes differential instabilities that drive the selection of a set of active neurons under the constraints embedded in the synaptic weights. Once selected, the active neurons generate weaker, stable feedback that provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimulus selection, gain modulation and spatiotemporal pattern generation in the neocortex.