辣椒细胞质雄性不育系的3种同工酶分析

以辣椒细胞质雄性不育系21A及其同核异质保持系21B为试验材料,比较分析两系雄配子发育过程中酯酶（EST）、谷氨酸脱氢酶（GDH）和苹果酸脱氢酶（MDH）同工酶的表达特征。结果表明：幼叶和花蕾的EST同工酶酶谱在谱带数目和酶带强弱上存在时空表达差异,并且随着雄配子发育的进行,保持系21B从中花蕾至特大花蕾比不育系21A多1条清晰的谱带（EST3e）,其差异表达发生在细胞学上观察到的败育时期之前;在GDH同工酶中,保持系21B从大花蕾至特大花蕾比不育系多6条谱带（GDH,和GDH1／2）,酶谱差异表达时期与细胞学上观察到的败育时期一致;而在MDH同工酶中,不育系21A和保持系21B的幼叶和各级花蕾的酶谱在谱带数目和谱带强弱上均没有明显差异。     

适用于盐生植物的双向电泳样品制备方法

比较了三氯乙酸,丙酮沉淀法（TCA）、三氯乙酸沉淀法（E-TCA）和酚抽法（Phe）3种方法对盐生植物盐角草（Salicornia europaea L．）总蛋白的提取效果。3种方法分别得到579、343和535个蛋白点;TCA和E-TCA法所得图谱均存在严重的横向纹理,Phe法所得图谱则背景干净,基本上没有纹理。说明Phe法不仅能很好地提取盐角草蛋白,而且能有效去除样品中的盐分。对Phe法的提取液进行了改进,所得图谱背景更加清晰,蛋白点数增加。为其他盐生植物以及嗜盐微生物蛋白质的提取提供了重要参考。     

第VI类中间丝蛋白—Nestin

Nestin最早发现于神经上皮干细胞,在肌肉和牙齿组织中也有表达。Nestin在中枢神经系统（central nervous system,CNS）中特异表达于神经前体细胞,其mRNA的减少与神经发育中干细胞的减少相平行,而且在神经系统病变和损伤的组织中有Nestin表达,表明Nestin可以作为研究神经系统发育的一个手段,对神经系统疾病的诊断也有一定的参考价值。     

空间独立成分分析实现fMRI信号的盲分离

独立成分分析（ICA）在功能核磁共振成像（fMRI)技术中的应用是近年来人们关注的一个热点。简要介绍了空间独立成分分析（SICA）的模型和方法,将fMRI信号分析看作是一种盲源分离问题,用快速算法实现fMRI信号的盲源分离。对fMRI信号的研究大多是在假定已知事件相关时间过程曲线的情况下,利用相关性分析得到脑的激活区域。在不清楚有哪几种因素对fMRI信号有贡献、也不清楚其时间过程曲线的情况下,用SICA可以对fMRI信号进行盲源分离,提取不同独立成分得到任务相关成分、头动成分、瞬时任务相关成分、噪声干扰、以及其它产生fMRI信号的多种源信号。     

多通道脑电信号的盲分离

提出一种新的多通道脑电信号盲分离的方法,将小波变换和独立分量分析(independent component analysis,ICA)相结合,利用小波变换的滤噪作用,将混合在原始脑电的部分高频噪声滤除后,再重构原始脑电作为ICA的输入信号,有效地克服了现有ICA算法不能区分噪声的缺陷。实验结果表明,该方法对多通道脑电的盲分离是很有效的。     

基于“HY-LM”的生态廊道与生态节点综合识别研究

传统的生态网络或生态安全格局构建研究中，多基于最小累积阻力模型（MCR）提取最小费用路径作为生态廊道，并人工判别生态节点，这种方式缺少对生态过程中能量流、信息流等交换过程的考虑，导致生态网络在结构、功能上存在一定的缺失。以福建省上杭县为例，以上杭县森林公园、湿地保护区、自然保护区等为主要生态源地，从陆生动物迁移特征出发选取土地覆被类型、距道路距离、地形起伏度、坡度、距水域距离、NDVI植被指数等因素为阻力因子，利用熵权法获得权重加权叠加生成综合阻力面，运用水文分析原理Hydrology （HY）和Linkage Mapper工具箱（LM）中电路理论等方法综合识别生态廊道和生态节点，运用重力模型对生态廊道重要性进行评价并对生态廊道和生态节点划分等级。研究结果如下：（1）基于LM方法共提取187条生态廊道，生态夹点52个，生态障碍点55个，基于HY方法共提取生态廊道240条，生态节点133个；（2） LM和HY提取的生态廊道和生态节点进行叠加，共提取生态廊道197条，辐射道30条，生态节点283个；（3）运用重力模型提取关键生态廊道103条（含辐射道30条），一般生态廊道124条，同时判别关键生态节点97个，一般生态节点186个，关键生态廊道和关键生态节点主要集中在高阻力和较高阻力值集中的区域，关键生态节点多分布在生态源地周围；（4）对关键生态廊道、关键生态节点缓冲区所在区域土地覆被类型构成进行分析，森林、耕地和草地等土地覆被类型占比具有绝对优势，并从生态连通性和生境质量角度针对各土地覆被类型提出了优化及生态建设策略。研究结果可为区域生态网络安全格局构建、国土空间规划与生态系统修复等研究提供参考，同时也为生物多样性保护与生态文明建设提供科学依据。     

寨卡病毒NS1的结构研究进展及其应用

寨卡病毒感染与小头畸形和神经系统并发症紧密相关,甚至可能损伤男性生殖系统,引起了全球性的关注,研究其结构和致病机制以及开发有效的诊断治疗方法成为当务之急。寨卡病毒的非结构蛋白NS1是病毒与宿主相互作用的重要蛋白,在病毒复制、发病机制及免疫逃逸中起着关键作用。本文总结了寨卡病毒NS1的空间精细结构,并将其与其它黄病毒NS1进行比较。本文也分析了寨卡病毒基于NS1的致病机理,总结了NS1在疾病诊断中的应用。     

JAK2/STAT3信号通路在运动预适应抗心肌细胞凋亡中的作用

目的：探讨Janus激酶2-信号转导子和转录激活子3（JAK2/STAT3）信号通路在运动预适应（EP）抗心肌细胞凋亡中的作用及其机制。方法：健康雄性SD大鼠80只,随机分为对照组（C组）、力竭组（EE组）、运动预适应组（EP组）、运动预适应+AG490组（EP+AG组）（n=20）。连续3 d的间歇跑台运动建立EP动物模型,力竭运动致大鼠运动性心肌损伤。采用TUNEL法检测心肌细胞凋亡改变、Western blot法检测心脏Caspase-3定量表达的变化,免疫组织化学法和Western blot法显示心脏p-JAK2和p-STAT3定位和定量表达的变化。结果：与C组相比,EE组心肌细胞凋亡、心脏Caspase-3、p-JAK2和p-STAT3的表达均显著升高;与EE组相比,EP组心肌细胞凋亡和心脏Caspase-3表达明显降低,而心脏p-JAK2和p-STAT3表达显著升高;与EP组相比,EP+AG组心肌细胞凋亡和心脏Caspase-3表达均显著升高,而心脏p-JAK2和p-STAT3表达明显降低。结论：EP可诱导心脏磷酸化JAK2和STAT3表达增加,减少心脏Caspase-3的表达,抑制心肌细胞凋亡,提示JAK2/STAT3信号通路参与了EP抗心肌细胞凋亡的作用。     

脊髓损伤机制研究的生物信息学分析

用生物信息学方法筛选参与脊髓损伤(spinal cord injury, SCI)发展过程的关键分子和通路,可为脊髓损伤发展机制的研究提供指导。从GEO数据库下载基因芯片数据,并将数据集中的样本分为脊髓损伤组(SCI组)和正常组(normal组)。应用R语言处理来自不同数据集样本间的批次效应,同时对基因芯片的表达数据进行标准化处理,并通过PCA分析监测标准化处理后数据的质量。应用R语言中的limma包分析标准化后的基因表达矩阵,以得到差异基因。将差异基因导入DAVID数据库进行GO (gene ontology)分析,并通过KEGG数据库进行通路分析。然后应用STRING数据库构建PPI网络,并通过Cytoscape中的cytoHubba插件分析得到10个hub基因。最后应用箱式图监测hub基因在不同样本中的表达,并用GeneCards数据库查询hub基因的功能。此外,为了补充差异基因筛选的不足,通过R语言对基因表达矩阵进行了GSEA (gene set enrichment analysis)分析。结果显示:TYROBP、ITGB2、PTPRC和FCER1G等基因在脊髓损伤发展过程中发挥重要的作用;细胞外基质的炎症反应、葡糖醛酸基转移酶活性的变化和星形胶质细胞的迁移等与脊髓损伤的发展机制关系密切; TNF信号通路、NF-κB信号通路和p53信号通路在脊髓损伤的发展机制中发挥重要的作用。这些关键的分子和通路在脊髓损伤中的作用值得我们进行更深入的探讨。     

高粱抗丝黑穗病基因的RAPD初步分析

目的：对高粱抗丝黑穗病的基因进行分析。方法：以高粱2381恢复系（抗病）、矮四恢复系（感病）、7050B保持系（抗病）、TX622B保持系（感病）为材料,采用CTAB提取DNA的方法提取高粱基因组DNA,然后应用RAPD分子标记技术对DNA进行多态性扩增。结果：初步建立了高粱丝黑穗病的RAPD反应体系;从RAPD反应中所用的48个随机引物中筛选出28个适宜引物,其余20个引物没有扩增出谱带,被淘汰;共扩增出114条谱带,其中引物OPM-05300和OPM-13450扩增出了差异谱带,分别命名为OPM-05300和OPM-13450。结论：在该反应体系下找到了高粱抗丝黑穗病抗感品种间基因组差异的两条差异谱带。     

A Novel Algorithm for Independent Component Analysis with Reference and Methods for Its Applications

This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.     

诱发电位的多通道时频相干提取算法

多通道时频域相干成分提取算法是针对低信噪比的宽频带信号提取问题提出的。它采用多通道同步观测,在各通道的观测数据中信号成分具有较高的相干性,而噪声的相干性较低,因此根据其相干性的高低差别即可将信号与噪声分离,提取有效信号。为实现信号与噪声的分离,首先应用小波包分解将信号在时频域展开,然后通过计算相干系数确定信号的时频分布,最终通过小波包重构将信号从噪声中分离出来。这一算法不需要信号的任何先验知识,收敛快,可以有效地提取宽频带信号,极大地提高信号的信噪比,对非重复性信号具有良好的捕捉能力．应用此算法成功地实现了视觉诱发电位的单次提取。     

A fast fixed-point algorithm for independent component analysis of complex valued signals

Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. Also, the local consistency of the estimator given by the algorithm is proved.     

Comparison between principal component analysis and independent component analysis in electroencephalograms modelling

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal principal components. Independent Component Analysis (ICA) is a technique of array processing and data analysis, aiming at recovering unobserved signals or 'sources' from observed mixtures, exploiting only the assumption of mutual independence between the signals. The separation of the sources by ICA has great potential in applications such as the separation of sound signals (like voices mixed in simultaneous multiple records, for example), in telecommunication or in the treatment of medical signals. However, ICA is not yet often used by statisticians. In this paper, we shall present ICA in a statistical framework and compare this method with PCA for electroencephalograms (EEG) analysis.We shall see that ICA provides a more useful data representation than PCA, for instance, for the representation of a particular characteristic of the EEG named event-related potential (ERP).     

一种独立分量分析的迭代算法和实验结果

介绍盲信源分离中一种独立分量分析方法,基于信息论原理,给出了一个衡量输出分量统计独立的目标函数。最优化该目标函数,得出一种用于独立分量分析的迭代算法。相对于其他大多数独立分量分析方法来说,该算法的优点在于迭代过程中不需要计算信号的高阶统计量,收敛速度快。通过脑电信号和其他信号的计算机仿真和实验结果表明了算法的有效性。     

非酒精性脂肪肝大鼠脂质代谢及病理变化的动态观察

目的通过高脂饮食建立NAFLD大鼠模型,连续监测4～16周模型动物肝功能、脂质代谢、胰岛素抵抗及肝细胞凋亡在NAFLD进展过程中的变化情况及相互关系,为该模型在脂肪肝发病机制、脂肪肝治疗药物评价等方面的应用提供参考依据。方法 SD大鼠50只,除正常对照组外,其余动物饲喂高脂饲料,分别检测4,8,12,16周大鼠血清GLU、CHO、TG、HDL、LDL、GPT、GOT及胰岛素水平,肝脏组织切片进行病理学及细胞凋亡观察,进一步分析大鼠肝功能、脂质代谢、胰岛素抵抗及肝细胞凋亡对肝组织病理改变的影响。结果模型组大鼠4周后就出现肝功能损伤,脂质代谢紊乱、胰岛素抵抗,肝细胞凋亡8 W后明显增加,肝细胞脂变及炎症为肝组织病理变化的主要特征,且造模时间越长,病变程度越严重。结论经过高脂饲料的喂养,SD大鼠在4～16周内可形成病变程度逐步加重的NAFLD模型,肝功能损伤,脂质代谢紊乱及肝细胞凋亡是引起非酒精性脂肪肝中脂肪变性和炎症的重要因素,该模型可应用于脂肪肝治疗药物评价等方面。     

新的独立成分分析算法实现功能磁共振成像信号的盲分离

采用独立成分分析(independent component analysis,ICA)的一种新的牛顿型算法来提取功能磁共振成像(functional magnetic rasonance imaging,fMRI)信号中的各种独立成分(包括与实验设计相关的成分以及各种噪声)。与fastICA相比,该算法减少了运算量,提高了运算速度,而且能够很好地分离出各个独立成分。结果表明该算法是一种有效的fMRI信号分析手段。     

A general probabilistic model for group independent component analysis and its estimation methods

Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a prespecified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multisubject spatiotemporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood (ML) method is used for estimating this general group ICA model. We propose two expectation-maximization (EM) algorithms to obtain the ML estimates. The first method is an exact EM algorithm, which provides an exact E-step and an explicit noniterative M-step. The second method is a variational approximation EM algorithm, which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.     

The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention

Selective attention can be focused either volitionally, by top-down signals derived from task demands, or automatically, by bottom-up signals from salient stimuli. Because the brain mechanisms that underlie these two attention processes are poorly understood, we recorded local field potentials (LFPs) from primary visual cortical areas of cats as they performed stimulus-driven and anticipatory discrimination tasks. Consistent with our previous observations, in both tasks, we found enhanced beta activity, which we have postulated may serve as an attention carrier. We characterized the functional organization of task-related beta activity by (i) cortical responses (EPs) evoked by electrical stimulation of the optic chiasm and (ii) intracortical LFP correlations. During the anticipatory task, peripheral stimulation that was preceded by high-amplitude beta oscillations evoked large-amplitude EPs compared with EPs that followed low-amplitude beta. In contrast, during the stimulus-driven task, cortical EPs preceded by high-amplitude beta oscillations were, on average, smaller than those preceded by low-amplitude beta. Analysis of the correlations between the different recording sites revealed that beta activation maps were heterogeneous during the bottom-up task and homogeneous for the top-down task. We conclude that bottom-up attention activates cortical visual areas in a mosaic-like pattern, whereas top-down attentional modulation results in spatially homogeneous excitation.