高效液相色谱法在脱氧核糖核酸酶解动力学

为了更好地对脱氧核糖核酸酶解动力学过程进行研究,建立脱氧核糖核酸(DNA)酶解液中4种脱氧核苷酸(腺嘌呤脱氧核苷酸(dAMP)、鸟嘌呤脱氧核苷酸(dGMP)、胞嘧啶脱氧核苷酸(dCMP)、胸腺嘧啶脱氧核苷酸(dTMP))的高效液相测定方法,能将酶解液中4种脱氧核苷酸完全分离并准确定量.在此基础上,对DNA酶解的动力学进行初步研究,其反应机理为不存在底物和产物抑制的双底物顺序反应,动力学方程为x=1/bln(1+abt)(其中a=0.372 3ρ0-0.974;b=-0.049 3ρ20+1.115 3ρ0 - 1.110 3),该方程可以很好地描述DNA酶解过程,误差仅为3.31%.     

纳豆激酶发酵液的脱色工艺研究

从11种待选脱色介质中筛选出330(OH)型树脂对纳豆激酶发酵液进行脱色研究.结果表明:(1)330(OH)型树脂对纳豆激酶发酵液中的色素为优吸型吸附,其动力学模型符合扩散方程,其动力学拟和方程为-ln(1-F)=0.0223t+0.0511,R2=0.9978,其中F=Qt/Qe,Qt为脱色时间为t时330(OH)型...     

响应面法优化枯草芽孢杆菌NHS1产芽孢发酵培养

为提高枯草芽孢杆菌(Bacillus subtilis)NHS1菌株发酵液中芽孢含量,采用单因素试验与响应曲面法优化试验相结合的方式,对该菌发酵培养基中的碳源、氮源以及无机盐等组成成分进行了优化。通过单因素试验、Plackett-Burman试验、最陡爬坡试验以及BoxBchnken试验构建响应方程,得到方程为:Y=-3.30+3.853X1+0.0928X2+0.623X3-0.913X1×X1-0.00704X2×X2-0.0433X3×X3-0.0033X1×X2-0.0700X1×X3+0.00167X2×X3。利用该方程预测得到最优培养基:淀粉1.88 g·L~(-1)、Na Cl 6.83 g·L~(-1)、玉米粉5.60 g·L~(-1),酵母粉10g·L~(-1),蛋白胨10 g·L~(-1),牛肉膏15 g·L~(-1),葡萄糖2 g·L~(-1),Mg SO43 g·L~(-1)。利用优化培养基,在36℃、170 r·min~(-1)条件下摇瓶发酵72 h,芽孢数达到2.42×109cfu·m L~(-1),比优化前提高1.5倍。     

涪陵磨盘沟桫椤种群格局的分形特征-信息维数

应用分形理论中的信息维数探讨了涪陵磨盘沟桫椤种群分布格局的分形特征.结果表明桫椤种群的分布格局具有分形特征,信息维数适用于涪陵磨盘沟桫椤种群分布格局分形特征的定量描述.6个不同植物群落中桫椤种群的信息维数大小依次为桫椤+毛叶木姜子(1.584)>桫椤+血桐-火炭木(0.947)>桫椤+盐肤木-腹水草(0.828)>桫椤+黄牛奶树(0.779)>桫椤+黄杞-南川楼梯草(0.635)>桫椤+白栎+异叶榕(0.535),这种差异反映了桫椤在各群落中更新状况的差异.集群型的信息维数比随机型的高,信息维数揭示了桫椤种群格局强度的尺度变化程度和表征了种群个体分布的非均匀性.     

黑土和棕壤对铜的吸附研究

研究了黑土与棕壤对Cu吸附的热力学和动力学特性．结果表明,在实验所采用Cu^2+浓度范围内,黑土和棕壤对CU^2+的吸附量均随着加入Cu^2+浓度的增加而增加,但黑土对cu^2+的吸附固定能力明显高于棕壤．在吸附平衡液Cu^2+浓度为95mg·kg^-时,棕壤对cu^2+的吸附量接近3720mg·kg^-1,黑土对Cu^2+的吸附量高达6076mg·kg^-1,最大CuCl2浓度(400mg·kg^-1)时,黑土和棕壤对Cu^2+的吸附量分别达到6159．0和4736．6mg·kg^-1．两种土壤对Cu^2+的吸财等量线与Freundlich和Temkin方程均有较好的拟合性,可以用Freundlich方程对其吸附行为进行描述．Langmuir方程不适宜描述两种土壤对Cu^2+的等温吸附过程．黑土和棕壤对Cu^2+的吸附均较快,最初2min内就可以达到平衡后吸附量的90％以上,在15-20min左右吸附基本达到平衡．描述黑土和棕壤动力学过程的最优模型为双常数速率方程,其次为一级动力学方程和Elovich方程。     

大黄酸和大黄素的热分析及其动力学研究

本文采用热重法(TG)和差热分析法(DTA)测定了大黄酸和大黄素的DTA,TG-DTG曲线。两者的DTA曲线中皆有两个较为明显的吸热峰,第一个在熔化过程中出现,第二个发生在热分解过程中并伴随有明显的失重现象。TG曲线均有一个失重平台,失重率在90%以上。用TG-DTG法对两者在非等温条件下进行热分解动力学研究,把从TG-DTG曲线中取得的数据和31个不同的方程采用Achar微分法和Madhusudanan-Krishnan-Ni-nan(MKN)积分法对其进行非等温分解动力学研究,得到动力学参数活化能(E和指前因子A)和分解动力学机理及方程。得出结论:大黄酸和大黄素的动力学方程为dα/dt=Aexp(-E/RT)3/2(1-α)4/3[1/(1-α)1/3-1]-1和dα/dt=Aexp(-E/RT)3/2(1-α)2/3[1/(1-α)1/3]-1,其分解等合3D抗理。二者的活化能E(kJ/mol)分别为117.6和86.79,lnA/s-1分别是36.72和27.44。     

核盘菌5-烯醇丙酮酰莽草酸-3-磷酸合酶的酶学性质

核盘菌5-烯醇丙酮酰莽草酸-3-磷酸合酶（EPSP合酶）是AROM多功能酶的活性之一.该酶催化莽草酸磷酸（S3P）和磷酸烯醇式丙酮酸（PEP）产生5-烯醇丙酮酰莽草酸-3-磷酸和无机磷酸的可逆反应,受除草剂草甘膦（N-（膦羧甲基）甘氨酸）抑制.纯化了核盘菌AROM蛋白并对EPSP合酶进行了酶学特征研究.结果显示,该酶反应的最适pH值为7.2,最适温度为30℃.热失活反应活化能是69.62 kJ/mol.底物S3P和PEP浓度分别高于1 mmol/L和2 mmol/L时,对EPSP合酶反应产生抑制作用.用双底物反应恒态动力学Dalziel方程求得的Km(PEP)为140.98 μmol/L,K _m(S3P)为139.58 μmol/L.酶动力学模型遵循顺序反应机制.草甘膦是该酶反应底物PEP的竞争性抑制剂（Ki为0.32 μmol/L）和S3P的非竞争性抑制剂.正向反应受K⁺激活.当［K⁺］增加时,K _m(PEP)随之降低,Km(S3P)不规律变化,而K _i(PEP)随［K⁺］增加而提高.     

白头翁皂苷提取方法比较及加压提取传质动力学

采用加热回流、超声辅助、微波辅助以及加压热水等方法提取白头翁总皂苷,比较不同提取方法对白头翁皂苷提取率、抑菌活性、抗氧化活性的影响;同时采用电镜扫描观察提取方法对药材结构的影响;在此基础上,研究加压提取的传质动力学。结果表明:采用加压热水方法提取白头翁皂苷具有提取率高(提取率6.74%,质量分数)、提取物抗氧化、抑菌活性强的特点;电镜扫描表明采用加压热水进行提取时,不破坏药材的细胞结构;加压热水提取的传质过程符合二阶动力学模型,其动力学方程为ρ_t=t[(2.6exp(-331.04/T))~(-1)+t(0.007 8T~2-6.416 9T+1 329.3)~(-1)]~(-1),提取过程活化能Ea为74.376 2 k J/mol。     

广义Emden-Fowler微分方程的振动准则（英文）

本文运用广义Riccati变换和中值定理,讨论了广义Emden-Fowler方程(r(t)|z'(t)|~(α-1)z'(t))'+q(t)|x(σ(t))|~(β-1)x(σ(t))=0的振动性,其中z(t)=x(t)+p(t)x(τ(t)),β≥α0,得到了该方程存在振动解的充分条件,推广和改进了已有结果,并用实例给出了其应用.     

镉在黑土和棕壤中吸附行为比较研究

比较研究了重金属镉在黑土和棕壤中的吸附热力学和动力学行为.结果表明,在实验设定的浓度范围内,黑土和棕壤对Cd²⁺吸附量随溶液中Cd²⁺浓度的增加而增加.黑土对Cd²⁺的吸附固定能力明显强于棕壤.在平衡液浓度为20 mg·kg^-1时,黑土对Cd²⁺的吸附量为1 485.2 mg·kg^-1、棕壤为700.6 mg·kg^-1.两种土壤对Cd²⁺的吸附等温线与Langmuir、Freundlich和Henry方程均有较好的拟合性,而Temkin方程不适合用来描述Cd²⁺在两种土壤中的等温吸附.根据Langmuir的拟合结果,Cd²⁺在黑土和棕壤中的最大吸附量分别可达5 939.3和2 790 mg·kg^-1.黑土较高的吸附能力与其高的有机质含量和粘粒含量有关.黑土和棕壤中Cd²⁺的吸附是一个快速反应过程,2 min内能达到平衡吸附量的90%,15～30 min左右达到吸附平衡.一级动力学方程是拟合Cd²⁺在黑土和棕壤中吸附动力学过程的最优方程,其次为Elovich方程和双常数方程.随着初始浓度的增加,Cd²⁺在土壤中的吸附速率也不断增大.随着吸附量的增大和反应时间的延长,吸附速率不断下降.在较低的初始浓度下,Cd²⁺在黑土中的下降趋势要快于棕壤.     

Age- and sex-dependent alterations in primary somatosensory cortex neuronal calcium network dynamics during locomotion

Over the past 30 years, the calcium (Ca²⁺) hypothesis of brain aging has provided clear evidence that hippocampal neuronal Ca²⁺ dysregulation is a key biomarker of aging. Age-dependent Ca²⁺-mediated changes in intrinsic excitability, synaptic plasticity, and activity have helped identify some of the mechanisms engaged in memory and cognitive decline based on work done mostly at the single-cell level and in the slice preparation. Recently, our lab identified age- and Ca²⁺-related neuronal network dysregulation in the cortex of the anesthetized animal. Still, investigations in the awake animal are needed to test the generalizability of the Ca²⁺ hypothesis of brain aging. Here, we used in vigilo two-photon imaging in ambulating mice, to image GCaMP8f in the primary somatosensory cortex (S1), during ambulation and at rest. We investigated aging- and sex-related changes in neuronal networks in the C56BL/6J mouse. Following imaging, gait behavior was characterized to test for changes in locomotor stability. During ambulation, in both young adult and aged mice, an increase in network connectivity and synchronicity was noted. An age-dependent increase in synchronicity was seen in ambulating aged males only. Additionally, females displayed increases in the number of active neurons, Ca²⁺ transients, and neuronal activity compared to males, particularly during ambulation. These results suggest S1 Ca²⁺ dynamics and network synchronicity are likely contributors of locomotor stability. We believe this work raises awareness of age- and sex-dependent alterations in S1 neuronal networks, perhaps underlying the increase in falls with age.     

A kinetic theory approach to capturing interneuronal correlation: the feed-forward case

We present an approach for using kinetic theory to capture first and second order statistics of neuronal activity. We coarse grain neuronal networks into populations of neurons and calculate the population average firing rate and output cross-correlation in response to time varying correlated input. We derive coupling equations for the populations based on first and second order statistics of the network connectivity. This coupling scheme is based on the hypothesis that second order statistics of the network connectivity are sufficient to determine second order statistics of neuronal activity. We implement a kinetic theory representation of a simple feed-forward network and demonstrate that the kinetic theory model captures key aspects of the emergence and propagation of correlations in the network, as long as the correlations do not become too strong. By analyzing the correlated activity of feed-forward networks with a variety of connectivity patterns, we provide evidence supporting our hypothesis of the sufficiency of second order connectivity statistics. Action Editor: Carson C. Chow     

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways

Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity. In this paper, we extend previous studies of input selectivity induced by (STDP) for single neurons to the biologically interesting case of a neuronal network with fixed recurrent connections and plastic connections from external pools of input neurons. We use a theoretical framework based on the Poisson neuron model to analytically describe the network dynamics (firing rates and spike-time correlations) and thus the evolution of the synaptic weights. This framework incorporates the time course of the post-synaptic potentials and synaptic delays. Our analysis focuses on the asymptotic states of a network stimulated by two homogeneous pools of “steady” inputs, namely Poisson spike trains which have fixed firing rates and spike-time correlations. The (STDP) model extends rate-based learning in that it can implement, at the same time, both a stabilization of the individual neuron firing rates and a slower weight specialization depending on the input spike-time correlations. When one input pathway has stronger within-pool correlations, the resulting synaptic dynamics induced by (STDP) are shown to be similar to those arising in the case of a purely feed-forward network: the weights from the more correlated inputs are potentiated at the expense of the remaining input connections.     

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity—symmetry breaking

Spike-timing-dependent plasticity (STDP) is believed to structure neuronal networks by slowly changing the strengths (or weights) of the synaptic connections between neurons depending upon their spiking activity, which in turn modifies the neuronal firing dynamics. In this paper, we investigate the change in synaptic weights induced by STDP in a recurrently connected network in which the input weights are plastic but the recurrent weights are fixed. The inputs are divided into two pools with identical constant firing rates and equal within-pool spike-time correlations, but with no between-pool correlations. Our analysis uses the Poisson neuron model in order to predict the evolution of the input synaptic weights and focuses on the asymptotic weight distribution that emerges due to STDP. The learning dynamics induces a symmetry breaking for the individual neurons, namely for sufficiently strong within-pool spike-time correlation each neuron specializes to one of the input pools. We show that the presence of fixed excitatory recurrent connections between neurons induces a group symmetry-breaking effect, in which neurons tend to specialize to the same input pool. Consequently STDP generates a functional structure on the input connections of the network.     

Fractal model of ion-channel kinetics

Markov models with discrete states, such as closed in equilibrium with closed in equilibrium with open have been widely used to model the kinetics of ion channels in the cell membrane. In these models the transition probabilities per unit time (the kinetic rate constants) are independent of the time scale on which they are measured. However, in many physical systems, a property, L, depends on the scale, epsilon, at which it is measured such that L(epsilon) alpha epsilon 1-D where D is the fractal dimension. Such systems are said to be 'fractal'. Based on the assumption that the kinetic rates are given by k(t) alpha t1-D we derive a fractal model of ion-channel kinetics. This fractal model has fewer adjustable parameters, is more consistent with the dynamics of protein conformations, and fits the single-channel recordings from the corneal endothelium better than the discrete-state Markov model.     

Oscillations and synchrony in a cortical neural network

In this paper, the oscillations and synchronization status of two different network connectivity patterns based on Izhikevich model are studied. One of the connectivity patterns is a randomly connected neuronal network, the other one is a small-world neuronal network. This Izhikevich model is a simple model which can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Detailed investigations reveal that by varying some key parameters, such as the connection weights of neurons, the external current injection, the noise of intensity and the neuron number, this neuronal network will exhibit various collective behaviors in randomly coupled neuronal network. In addition, we show that by changing the number of nearest neighbor and connection probability in small-world topology can also affect the collective dynamics of neuronal activity. These results may be instructive in understanding the collective dynamics of mammalian cortex.     

Dynamic Finite Size Effects in Spiking Neural Networks

We investigate the dynamics of a deterministic finite-sized network of synaptically coupled spiking neurons and present a formalism for computing the network statistics in a perturbative expansion. The small parameter for the expansion is the inverse number of neurons in the network. The network dynamics are fully characterized by a neuron population density that obeys a conservation law analogous to the Klimontovich equation in the kinetic theory of plasmas. The Klimontovich equation does not possess well-behaved solutions but can be recast in terms of a coupled system of well-behaved moment equations, known as a moment hierarchy. The moment hierarchy is impossible to solve but in the mean field limit of an infinite number of neurons, it reduces to a single well-behaved conservation law for the mean neuron density. For a large but finite system, the moment hierarchy can be truncated perturbatively with the inverse system size as a small parameter but the resulting set of reduced moment equations that are still very difficult to solve. However, the entire moment hierarchy can also be re-expressed in terms of a functional probability distribution of the neuron density. The moments can then be computed perturbatively using methods from statistical field theory. Here we derive the complete mean field theory and the lowest order second moment corrections for physiologically relevant quantities. Although we focus on finite-size corrections, our method can be used to compute perturbative expansions in any parameter.     

Neuronal expression of enhanced green fluorescent protein directed by 5' flanking sequences of the rat aldolase C gene in transgenic mice

The rat aldolase C gene encodes a glycolytic enzyme strongly expressed in adult brain. We previously reported that a combination of distal and proximal 5' flanking sequences, the A+C+0.8 kilobase (kb) pairs fragments, ensured high brain-specific expression in vivo (Skala et al. 1998). We show here that the expression pattern conferred by these sequences, when placed in front of the chloramphenicol acetyltransferase (CAT) or the enhanced green fluorescent protein (EGFP) reporter genes in transgenic mice, is similar to the distribution of the endogenous mRNA and protein. Double immunostaining for neuronal or glial cell-specific markers and for the EGFP protein indicates that the A+C+0.8 kb genomic sequences from the rat aldolase C gene direct a predominant expression in neuronal cells of adult brain.     

Traveling waves and pulses in a one-dimensional network of excitable integrate-and-fire neurons

 We study the existence and stability of traveling waves and pulses in a one-dimensional network of integrate-and-fire neurons with synaptic coupling. This provides a simple model of excitable neural tissue. We first derive a self-consistency condition for the existence of traveling waves, which generates a dispersion relation between velocity and wavelength. We use this to investigate how wave-propagation depends on various parameters that characterize neuronal interactions such as synaptic and axonal delays, and the passive membrane properties of dendritic cables. We also establish that excitable networks support the propagation of solitary pulses in the long-wavelength limit. We then derive a general condition for the (local) asymptotic stability of traveling waves in terms of the characteristic equation of the linearized firing time map, which takes the form of an integro-difference equation of infinite order. We use this to analyze the stability of solitary pulses in the long-wavelength limit. Solitary wave solutions are shown to come in pairs with the faster (slower) solution stable (unstable) in the case of zero axonal delays; for non-zero delays and fast synapses the stable wave can itself destabilize via a Hopf bifurcation. Received: 27 October 1998