睡眠脑电的分形维数分析

为了研究是否用维数来描述脑功能的复杂程序,我们计算了５例正常人整夜睡眠时不同阶段的ＥＥＧ资料维数。结果显示,随着睡眠深度的增加而维数降低。这一事实和生理概念及其他作者所报道的结果相一致,故我们考虑维数分析在表示脑功能状态上有一定的理论意义。如果我们用简化法来计算维数,此法就有可能实时地应用于临床诊断。     

纤溶酶原激活物抑制因子－1突变体E350G，E351K的构建及性质研究

利用ＰＣＲ扩增和合成突变引物的方法,将ＰＡＬ－１的Ｇｌｕ３５０和Ｇｌｕ３５１分别突变为Ｇｌｙ和Ｌｙｓ,在大肠杆菌中表达并分离纯化突变体ＰＡＬ－１（Ｅ３５０Ｇ,Ｅ３５１Ｋ）,用盐酸胍激活并以ＥＬＩＳＡ法确定它与野生型ｒＰＡＩ－１的相对含量。通过对ｕ－ＰＡ,ｔ－ＰＡ抑制的动力学研究表明,突变体与野生型ｒＰＡＩ－１相比,对ｕ－ＰＡ和ｔ－ＰＡ的抑制活性都有明显下降,由活性态向潜伏态转变的半寿期也由０．８３ｈ缩短为０．５７ｈ。     

氨基PEG化试剂的合成及其修饰能力的测定

分别用氰脲酰氯法及Ｎ－羟基丁二酰亚胺活性酯法合成了蛋白质的氨基ＰＥＧ化试剂ｍＰＥＧｃｃ和ｍＰＥＧ－ＧＳ,并研究了它们对蛋白质的修饰作用。在合成过程中,通过分析反应体系中微量水分的存在对ｍＰＥＧｃｃ合成效率的影响,及溶剂中小分子可活化杂质成分对ｍＰＥＧ－ＧＳ合成产物质量的影响,发现去水剂的存在,可使ｍＰＥＧｃｃ产率提高７倍;二氧六环优于ＤＭＦ,且二氧六环的预处理也很重要。同时,为了测定活化ＰＥＧ修饰     

G蛋白Rap1活化新机制：GEFs研究进展

小分子Ｇ蛋白Ｒａｓ超家庭成员都是通过与鸟苷酸交换因子（ｇｕａｎｉｎｅ ｎｕｃｌｅｏｔｉｄｅ ｅｘｃｈａｎｇｅ ｆａｃｔｏｒｓ,ＧＥＦｓ）结合而被活化,ＧＥＦｓ能增加这类小分子Ｇ蛋白对ＧＴＰ的摄取,并使之形成有活性的ＧＴＰ结合构象。Ｒａｐ１是Ｒａｓ样的小分子Ｇ蛋白。迄今为止,已发现Ｇ３Ｇ、ＣａｌＤＡＧ－ＧＥＦ１、Ｅｐａｃ、ｃＡＭ－ＧＥＦ１、ｃＡＭＰ－ＧＥＦⅡ、ｎＲａｐＧＥＰ、ＧＦＲ可特异的活化Ｒａｐ     

人血管内皮细胞生长因子在毕赤酵母中的高效表达

血管内皮细胞生长因子 (Ｖａｓｃｕｌａｒｅｎｄｏｔｈｅｌｉａｌｇｒｏｗｔｈｆａｃ ｔｏｒ,简称ＶＥＧＦ)是一类多功能细胞因子 ,特异地作用于血管内皮受体ＫＤＲ和Ｆｌｔ 1,促进新生血管形成并增加微血管的渗透性[1] 。ＶＥＧＦ在生理和病理 ,如肿瘤血管发生、伤口愈和、类风湿性关节炎、胚胎发育及冠心病等过程中起着非常重要的作用。天然ＶＥＧＦ是由两条糖蛋白链形成的二聚体。目前发现ＶＥＧＦ至少有 5种亚型 ,根据单体残基数不同分别为ＶＥＧＦ12 1,ＶＥＧＦ14 5,ＶＥＧＦ165,ＶＥＧＦ189和ＶＥＧＦ2 0 6,其中ＶＥＧＦ12 1和ＶＥＧ…     

陕西秦岭自然保护区群GEF项目简介

陕西秦岭自然保护区群ＧＥＦ项目简介陕西秦岭自然保护区群ＧＥＦ（ＧｌｏｂａｌＥｎｖｉｒｏｍｅｎｔＦａｃｉｌｉｔｙ）项目是全球环保基金组织在中国资助自然保护区管理的项目,这在我国是第一次。项目的主要目标是：用先进并创新的办法建设一批示范性、具有全球意义...     

前列腺专一蛋白因子抑制雄激素受体与其应答元件作用

雄激素受体片段（氨基酸：３５９￣７３２）以及糖皮质激素受体片段（氨基酸：３９６￣５４８）以与ＧＳＴ融合形式在大肠杆菌中分别表达为ＧＳＴ－ＡＲ和ＧＳＴ－ＧＲ两种融合蛋白,它们含有ＤＮＡ结合结构域和一些旁侧氨基酸,凝胶阻滞分析表明能与雄激素／糖皮质激素应答元件（ＡＲＥ／ＧＲＥ）在体外结合。本文报道在大鼠前列腺中发现有抑制ＧＳＴ－ＡＲ以及ＧＳＴ－ＧＲ与ＡＲＥ／ＧＲＥ结合的因子,抑制作用与ＡＲＥ／ＧＲＥ来     

氨基PEG化试剂的合成及其修饰能力的测定

分别用氰脲酰氯法及Ｎ－羟基丁二酰亚胺活性酯法合成了蛋白质的氨基ＰＥＧ化试剂ｍＰＥＧｃｃ和ｍＰＥＧ－ＧＳ,并研究了它们对蛋白质的修饰作用。在合成过程中,通过分析反应体系中微量水分的存在对ｍＰＥＧｃｃ合成效率的影响,及溶剂中小分子可活化杂质成分对ｍＰＥＧ－ＧＳ合成产物质量的影响,发现去水剂的存在,可使ｍＰＥＧｃｅ产率提高７倍;二氧六环优于ＤＭＦ,且二氧六环的预处理也很重要。同时,为了测定活化ＰＥＧ修饰蛋白质的效能,首次以ＢＳＡ为模型蛋白,建立起一种测定活化ＰＥＧ修饰能力的方法,应用此方法能直观而又准确地比较各种方法活化的ＰＥＧ对蛋白质的修饰能力,具有普遍的意义。     

纤溶酶原激活物抑制因子—1突变体E350G,E351K的构建及性质研究

利用ＰＣＲ扩增和合成突变引物的方法,将ＰＡＩ－１的Ｇｌｕ３５０和Ｇｌｕ３５１分别突变为Ｇｌｙ和Ｌｙｓ在大肠杆菌中表达并分离纯化突变体ＰＡＩ－１（Ｅ３５０Ｇ,Ｅ３５１Ｋ）有 酸胍激活并以ＥＬＩＳＡ法确定它与野生型ｒＰＡＩ－１的相对含量,通过对ｕ－ＰＡ抑制的动力学研究表明,突变体与野生型ｒＰＡＩ－１相比,对ｕ－ＰＡ和ｔ－ＰＡ的抑制活性都有明显下降,由活性态向潜伏态转变的半寿期也由０．８３ｈ缩短为０．５     

RFP_（134）对UMR106细胞EGF受体酪氨酸蛋白激酶的调节作用

以大鼠成骨肉瘤细胞（ＵＭＲ１０６）为模型,研究了表皮生长因子（ＥＧＦ）对其受体酪氨酸蛋白激酶（ＴＰＫ）的调节作用。以本实验室从植物中提取纯化的二萜类活性物质（ＲＦＰ１３４）为诱导分化剂,观察了ＲＦＰ１３４对ＵＭＲ１０６细胞ＥＧＦ受体ＴＰＫ的活性和磷酸化作用的影响,并与ＲＡ和ＲＦＰ１３４＋ＲＡ处理细胞做了比较,结果显示ＥＧＦ与其受体结合后能激活ＴＰＫ,使ＴＰＫ活性增加２倍．ＲＦＰ１３４,ＲＡ,ＲＦＰ１３４＋ＲＡ处理细胞后,分别降低ＥＧＦ诱导的受体ＴＰＫ活性５０％,４３％,５５％,降低磷酸化ＴＰＫ含量５５％,３６％,５３％。从结果中发现无ＥＧＦ刺激的细胞也具有受体ＴＰＫ磷酸化作用,用ＲＦＰ１３４,ＲＡ,ＲＦＰ１３４＋ＲＡ处理细胞,分别降低受体磷酸化ＴＰＫ含量５９％,４０％,５７％,而且我们发现用ＥＧＦ诱导的细胞受体ＴＰＫ含量高于无ＥＧＦ作用的细胞．提示ＵＭＲ１０６细胞本身可能具有受体ＴＰＫ活性,能够引起细胞受体自动磷酸化,ＥＧＦ刺激后ＴＰＫ的磷酸化作用增强,可见ＲＦＰ１３４对ＥＧＦ诱导的ＴＰＫ磷酸化和无ＥＧＦ诱导的受体自动磷酸化都具有明显的抑制作用,（并强于ＲＡ）这可能与在第二信使水平上阻抑ＰＴＰＫ活性密切相关     

一种改进的近似熵——样品熵及其在颞叶癫痫患者脑电信号分析中的应用

采用了近似熵(approximately entropy,ApEn)和它的改进算法,即样品熵(sample entropy,SampEn)分析了8位颞叶癫痫患者和10位健康人员的短程脑电信号。在计算过程中使用了两种滑动窗口和5个不同的过滤标准r。结果显示颞叶癫痫患者组脑电信号的熵值显著低于健康组,而且患者癫痫病灶所在的脑半球的复杂度远远小于非癫痫病灶的脑半球。小的滑动窗口能更多地反映与癫痫发作相关的细节。对于1秒的滑动窗口,过滤标准r不能小于时间序列标准差的0．15％;而对于4秒的滑动窗口,则过滤标准r不能小于时间序列标准差的10％。研究结果表明,在短程脑电信号的非线性分析中,样品熵是一种比近似熵更为可靠的非线性分析方法。颞叶癫痫患者脑电信号的熵值低于健康人员,这可能表明脑电活动的非线性程度的降低是由于神经信号在大脑内的传递受到了阻碍或者损坏,使得神经信号成了相对孤立的信息源。     

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease

The complexity change of brain activity in Alzheimer’s disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.     

Estimating the mutual information of an EEG-based Brain-Computer Interface.

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.     

The dynamics of EEG entropy

The scaling properties of human EEG have so far been analyzed predominantly in the framework of detrended fluctuation analysis (DFA). In particular, these studies suggested the existence of power-law correlations in EEG. In DFA, EEG time series are tacitly assumed to be made up of fluctuations, whose scaling behavior reflects neurophysiologically important information and polynomial trends. Even though these trends are physiologically irrelevant, they must be eliminated (detrended) to reliably estimate such measures as Hurst exponent or fractal dimension. Here, we employ the diffusion entropy method to study the scaling behavior of EEG. Unlike DFA, this method does not rely on the assumption of trends superposed on EEG fluctuations. We find that the growth of diffusion entropy of EEG increments of awake subjects with closed eyes is arrested only after approximately 0.5 s. We demonstrate that the salient features of diffusion entropy dynamics of EEG, such as the existence of short-term scaling, asymptotic saturation, and alpha wave modulation, may be faithfully reproduced using a dissipative, first-order, stochastic differential equation—an extension of the Langevin equation. The structure of such a model is utterly different from the “noise+trend” paradigm of DFA. Consequently, we argue that the existence of scaling properties for EEG dynamics is an open question that necessitates further studies.     

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.     

脑电图复杂度分析中的粗粒化问题 I.过分粗粒化和三种复杂度的比较

在对生物医学信号时间序列进行复杂度分析时,往往需要首先对原始信号进行粗粒化预处理。这种预处理有可能会造成丢失原始信号中所蕴含的信息,甚至在某些情况下根本改变原信号的动力学性质。本文提出了克服这一问题的一些途径,通过对若干种复杂度定义的比较研究,建议采用近似熵和我们所定义的Ｃ０复杂度作为复杂性度量。并以脑电的复杂性分析为例作了说明。     

Local Properties of Vigilance States: EMD Analysis of EEG Signals during Sleep-Waking States of Freely Moving Rats

Understanding the inherent dynamics of the EEG associated to sleep-waking can provide insights into its basic neural regulation. By characterizing the local properties of the EEG using power spectrum, empirical mode decomposition (EMD) and Hilbert-spectral analysis, we can examine the dynamics over a range of time-scales. We analyzed rat EEG during wake, NREMS and REMS using these methods. The average instantaneous phase, power spectral density (PSD) of intrinsic mode functions (IMFs) and the energy content in various frequency bands show characteristic changes in each of the vigilance states. The 2nd and 7th IMFs show changes in PSD for wake and REMS, suggesting that those modes may carry wake- and REMS-associated cognitive, conscious and behavior-specific information of an individual even though the EEG may appear similar. The energy content in θ₂ (6Hz-9Hz) band of the 1st IMF for REMS is larger than that of wake. The decrease in the phase function of IMFs from wake to REMS to NREMS indicates decrease of the mean frequency in these states, respectively. The rate of information processing in waking state is more in the time scale described by the first three IMFs than in REMS state. However, for IMF5-IMF7, the rate is more for REMS than that for wake. We obtained Hilbert-Huang spectral entropy, which is a suitable measure of information processing in each of these state-specific EEG. It is possible to evaluate the complex dynamics of the EEG in each of the vigilance states by applying measures based on EMD and Hilbert-transform. Our results suggest that the EMD based nonlinear measures of the EEG can provide useful estimates of the information possessed by various oscillations associated with the vigilance states. Further, the EMD-based spectral measures may have implications in understanding anatamo-physiological correlates of sleep-waking behavior and clinical diagnosis of sleep-pathology.     

EEG power spectrum analysis for monitoring depth of anaesthesia during experimental surgery

The first attempts to introduce computerized power spectrum analysis of the electroencephalogram (EEG) as an intraoperative anaesthesia monitoring device started approximately 30 years ago. Since that time, the effects of various anaesthetic agents, sedative and analgesic drugs on the EEG pattern have been addressed in numerous studies in human patients and different animal species. These studies revealed dose-dependent changes in the EEG power spectrum for many intravenous and volatile anaesthetics. Moreover, EEG responses evoked by surgical stimuli during relative light levels of surgical anaesthesia have been classified as 'arousal' and 'paradoxical arousal' reaction, previously referred to as 'desynchronization' and 'synchronization', respectively. Contrasting reports on the correlation between quantitative EEG (QEEG) variables derived from power spectrum analysis (i.e. spectral edge frequency, median frequency) and simultaneously recorded clinical signs such as movement and haemodynamic responses, however, limited the routine use of intraoperative EEG monitoring. In addition, the appearance of EEG burst suppression pattern and isoelectricity at clinically relevant concentrations/doses of newer general anaesthetics (i.e. isoflurane, sevoflurane, propofol) may have weakened the dose-related EEG changes previously reported. Despite these findings, the EEG power spectrum analysis may still provide valuable information during intraoperative monitoring in the individual subject. The information obtained from EEG power spectrum analysis may be further supplemented by newer EEG indices such as bispectral index and approximate entropy or other neurophysiological monitors including auditory evoked potentials or somatosensory evoked potentials.     

基于小波包熵的运动意识任务分类研究

提出了以小波包熵作为脑电特征向量的左右手运动意识任务分类方法,对被测试者想象左右手运动时的脑电小波包熵动态变化情况及分析窗口长度的选择进行了研究.结果表明,小波包熵能很好地反映左右手运动想象的脑电特征变化,用线性判别式算法对脑电特征进行识别,分类正确率达到92.14%.由于小波包熵的计算比较简单,稳定性好,识别率高,为大脑运动意识任务的分类提供了新思路.     

Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model

Driver fatigue is increasingly a contributing factor for traffic accidents, so an effective method to automatically detect driver fatigue is urgently needed. In this study, in order to catch the main characteristics of the EEG signals, four types of entropies (based on the EEG signal of a single channel) were calculated as the feature sets, including sample entropy, fuzzy entropy, approximate entropy and spectral entropy. All feature sets were used as the input of a gradient boosting decision tree (GBDT), a fast and highly accurate boosting ensemble method. The output of GBDT determined whether a driver was in a fatigue state or not based on their EEG signals. Three state-of-the-art classifiers, k-nearest neighbor, support vector machine and neural network were also employed. To assess our method, several experiments including parameter setting and classification performance comparison were performed on 22 subjects. The results indicated that it is possible to use only one EEG channel to detect a driver fatigue state. The average highest recognition rate in this work was up to 94.0%, which could meet the needs of daily applications. Our GBDT-based method may assist in the detection of driver fatigue.