The role of p38 MAP kinase and c-Jun N-terminal protein kinase signaling in the differentiation and apoptosis of immortalized neural stem cells

The two distinct members of the mitogen-activated protein (MAP) kinase family c-Jun N-terminal protein kinase (JNK) and p38 MAP kinase, play an important role in central nervous system (CNS) development and differentiation. However, their role and functions are not completely understood in CNS. To facilitate in vitro study, we have established an immortal stem cell line using SV40 from fetal rat embryonic day 17. In these cells, MAP kinase inhibitors (SP600125, SB202190, and PD98059) were treated for 1, 24, 48, and 72 h to examine the roles of protein kinases. Early inhibition of JNK did not alter phenotypic or morphological changes of immortalized cells, however overexpression of Bax and decrease of phosphorylated AKT was observed. The prolonged inhibition of JNK induced polyploidization of immortalized cells, and resulted in differentiation and inhibition of cell proliferation. Moreover, JNK and p38 MAP kinase but not ERK1/2 was activated, and p21, p53, and Bax were overexpressed by prolonged inhibition of JNK.

These results indicate that JNK and p38 MAP kinase could play dual roles on cell survival and apoptosis. Furthermore, this established cell line could facilitate study of the role of JNK and p38 MAP kinase on CNS development or differentiation/apoptosis.     

一种复合神经元网络结构的并行动力学及快速学习算法

提出两种功能互相不同的神经细胞组成的复合神经元网络(CNN)模型;导出一种特殊结构的CNN的并行动力学;而且证明了它的稳定性。在这些结果基础上,得到快速的假逆矩阵学习算法。计算机仿真试验证实学习算法与动力学稳定性的正确性,并表现出良好的容错性能与存储容量。     

二元神经网络模型概周期解的存在性

本文研究了两个神经元的神经网络模型y1(t)=-α1y1(t) f(y2(t-T1) -Г2)) J1(t),y2(t)=-α2y2(t) g(y1(t-T2)-Г1)) J2(t)概周期解的存在性,获得存在概周期解的充分条件。     

对比不同类型干细胞对于缺血性脑卒中治疗的研究进展

成人中枢神经系统存在着一定量的神经干细胞,其具有两大关键能力;自我更新和多向分化潜能。缺血性脑卒中是一种由于由脑血流的缺失或减少引起的脑动脉闭塞,进而导致脑组织梗死的脑血管疾病。虽然对于脑损伤的药物治疗已经取得了一定的成果,但目前以干细胞为基础的治疗方法仍成为了研究热点。无论是内源性神经干细胞还是外源性神经干细胞移植均可在脑损伤后向远端损伤区迁移并分化成新的神经细胞,从而在中枢神经系统疾病尤其是脑梗死后进行组织修复和功能恢复。因此在这篇综述中,我们主要探讨不同类型的干细胞对脑梗死介导的脑损伤的应用潜能,对比不同类型干细胞对缺血性脑卒中的治疗优缺点。     

自组织特征人工神经网络在庞泉沟自然保护区植物群落分类中的应用

人工神经网络是较新的数学分析工具,其中的自组织特征映射网络（SOFM）具有较强的聚类功能。应用SOFM网络对庞泉沟自然保护区植物群落进行了分类研究。在讨论了SOFM网络的数学原理、聚类方法和步骤的前提下,分类过程在MATLAB（6.5）神经网络工具箱（NNTool）中编程实现。结果将89个样方分为13个植物群落类型。分类结果符合植被实际,生态意义明确,表明SOFM网络可以很好地反映植物群落的生态关系,是非常有效的植物群落数量分类方法。     

Neural progenitor genes. Germinal zone expression and analysis of genetic overlap in stem cell populations

The identification of the genes regulating neural progenitor cell (NPC) functions is of great importance to developmental neuroscience and neural repair. Previously, we combined genetic subtraction and microarray analysis to identify genes enriched in neural progenitor cultures. Here, we apply a strategy to further stratify the neural progenitor genes. In situ hybridization demonstrates expression in the central nervous system germinal zones of 54 clones so identified, making them highly relevant for study in brain and neural progenitor development. Using microarray analysis we find 73 genes enriched in three neural stem cell (NSC)-containing populations generated under different conditions. We use the custom microarray to identify 38 "stemness" genes, with enriched expression in the three NSC conditions and present in both embryonic stem cells and hematopoietic stem cells. However, comparison of expression profiles from these stem cell populations indicates that while there is shared gene expression, the amount of genetic overlap is no more than what would be expected by chance, indicating that different stem cells have largely different gene expression patterns. Taken together, these studies identify many genes not previously associated with neural progenitor cell biology and also provide a rational scheme for stratification of microarray data for functional analysis.     

Portrait of visual cortical circuits for generating neural oscillation dynamics

The mouse primary visual cortex (V1) has emerged as a classical system to study neural circuit mechanisms underlying visual function and plasticity. A variety of efferent-afferent neuronal connections exists within the V1 and between the V1 and higher visual cortical areas or thalamic nuclei, indicating that the V1 system is more than a mere receiver in information processing. Sensory representations in the V1 are dynamically correlated with neural activity oscillations that are distributed across different cortical layers in an input-dependent manner. Circuits consisting of excitatory pyramidal cells (PCs) and inhibitory interneurons (INs) are the basis for generating neural oscillations. In general, INs are clustered with their adjacent PCs to form specific microcircuits that gate or filter the neural information. The interaction between these two cell populations has to be coordinated within a local circuit in order to preserve neural coding schemes and maintain excitation–inhibition (E–I) balance. Phasic alternations of the E–I balance can dynamically regulate temporal rhythms of neural oscillation. Accumulating experimental evidence suggests that the two major sub-types of INs, parvalbumin-expressing (PV⁺) cells and somatostatin-expressing (SOM⁺) INs, are active in controlling slow and fast oscillations, respectively, in the mouse V1. The review summarizes recent experimental findings on elucidating cellular or circuitry mechanisms for the generation of neural oscillations with distinct rhythms in either developing or matured mouse V1, mainly focusing on visual relaying circuits and distinct local inhibitory circuits.     

基于最小二乘支持向量机的农田水汽通量建模

支持向量机是在统计学习理论基础上发展起来的一种新型的机器学习方法,已在模式识别、非线性建模等领域中得到了应用．本文将最小二乘支持向量机方法应用于农田水汽通量的建模中,并同前馈反向传播神经网络的建模性能进行了比较．结果表明,最小二乘支持向量机方法具有可调参数少、学习速度较快等优点,具有更好的推广能力,以更高的精度建立农田水汽通量模型．模型的敏感性分析进一步显示,用最小二乘支持向量机方法建立的农田水汽通量模型是合理可行的．