Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo

Many lines of evidence suggest that memory in the mammalian brain is stored with distinct spatiotemporal patterns. Despite recent progresses in identifying neuronal populations involved in memory coding, the synapse-level mechanism is still poorly understood. Computational models and electrophysiological data have shown that functional clustering of synapses along dendritic branches leads to nonlinear summation of synaptic inputs and greatly expands the computing power of a neural network. However, whether neighbouring synapses are involved in encoding similar memory and how task-specific cortical networks develop during learning remain elusive. Using transcranial two-photon microscopy, we followed apical dendrites of layer 5 pyramidal neurons in the motor cortex while mice practised novel forelimb skills. Here we show that a third of new dendritic spines (postsynaptic structures of most excitatory synapses) formed during the acquisition phase of learning emerge in clusters, and that most such clusters are neighbouring spine pairs. These clustered new spines are more likely to persist throughout prolonged learning sessions, and even long after training stops, than non-clustered counterparts. Moreover, formation of new spine clusters requires repetition of the same motor task, and the emergence of succedent new spine(s) accompanies the strengthening of the first new spine in the cluster. We also show that under control conditions new spines appear to avoid existing stable spines, rather than being uniformly added along dendrites. However, succedent new spines in clusters overcome such a spatial constraint and form in close vicinity to neighbouring stable spines. Our findings suggest that clustering of new synapses along dendrites is induced by repetitive activation of the cortical circuitry during learning, providing a structural basis for spatial coding of motor memory in the mammalian brain.     

时态GIS中动态网络建模及实现方法

介绍了动态网络以及组成动态网络的主要单元——动态连接对象的数学描述,分析了动态连接对象的演化特征,将动态连接对象的演化分为生命演化、几何特征演化和拓扑关系演化3种类型,其中生命演化可以支持表达对象演化的继承关系。定义了新增、分裂继承、合并继承、消亡、分裂、合并、形变、相遇和分离9个演化事件。在此基础上,建立了以动态连接对象为中心的动态网络数据结构,使得动态网络的数据操作与维护相对容易实现。设计了基于本文模型的面向动态连接对象的基本操作以及动态网络数据的完整性检验规则。     

一种基于模型概率修正的IMM目标跟踪算法

在IMM算法的基础上,提出了一种新的机动目标跟踪FIMM算法.该算法使用中值滤波对IMM算法模型更新概率进行平滑,再对其利用模糊推理系统进行实时修正,将修正后的概率作为最后输出的权值,让有用模型概率增大,减少了模型之间的竞争,提高跟踪精度.仿真实验表明,提出的FIMM算法显著提高了IMM算法的跟踪性能.     

Benjamin-Bona-Mahony方程的平均隐式差分格式

本文对Benjamin-Bona-Mahony(BBM)方程的初边值问题进行了数值研究,提出了一个平均隐式差分格式,讨论了差分解的存在唯一性.然后利用离散泛函分析方法分析了该格式的二阶收敛性与稳定性,然后利用数值实验进行了验证.     

一维非线性波动方程的有限元分析

对一维非线性波动方程建立了全离散有限元格式,证明了解的存在唯一性,给出了有限元解的误差估计.     

一个有理差分方程解的存在性和稳定性

作者考虑了一个分母含有二次项的有理差分方程.应用线性化方程理论,作者证明了解的存在性和稳定性,并在一定条件下,证明了该方程所有的正解都收敛到唯一的正平衡点.所得结果证明了Sedaghat提出的一个猜想是正确的.     

地铁通风空调系统的逐时优化节能控制策略

针对地铁通风空调系统的负荷特点,提出节能控制目标逐时系统性能系数HSCOP及逐时优化策略.以某新建地铁车站为例,模拟分析了该优化控制策略及其节能效果.结果表明,与普通方案相比,采用所提出的逐时优化节能控制方案可使夏季典型日能耗降低9.4%,节能效果显著.     

金免疫层析法快速测定甲胎蛋白的定量系统研究

采用光电反射式的光路,通过对纳米金层析试条光电信号的信息处理提取特征参量,并采用支持向量机建立纳米金层析试条特征参量与甲胎蛋白浓度之间的拟合模型,建立金免疫层析试条甲胎蛋白定量方法.金免疫层析试条定量系统与化学发光法对比试验数据结果相关系数r=0.967,P<0.000 1,两种检测方法线性相关,测定数据变异系数(CV)小于6%.实验结果表明该金免疫层析定量系统适用于甲胎蛋白的快速、准确、定量测定,具有一定的临床诊断价值.     

鄂尔多斯盆地富县地区中生界热演化史探讨

目的对富县地区中生界热演化史进行探讨,为该区油气勘探及资源评价提供依据。方法统计测温资料和地化资料,采用古地温恢复法、声波时差剥蚀厚度法及盆地模拟法,恢复最大古地温梯度、烃源岩热演化史及生烃史。结果该区三叠纪进入快速沉降阶段,早侏罗世—早白垩世进入缓慢沉降阶段,早白垩世晚期以来进入后期抬升阶段,剥蚀厚度1 100～1 200 m。结论富县地区长7段烃源岩在中侏罗世174 Ma,进入低成熟阶段;早白垩世136 Ma,进入成熟阶段;早白垩世晚期96 Ma,埋深达到最大,Ro=0.85%,地温梯度为4.8℃/100 m;早白垩世晚期96 Ma以来,生烃作用减弱。富县地区长7烃源岩主生烃期为早白垩世。