Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli

Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons.     

Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings

Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.     

四种不同穗型小麦的穗部性状分析

为了通过研究不同长相穗型小麦穗部特性,以达到提高产量和挖掘穗部潜力的目的。本文对长方型、棍棒型、椭圆型和纺锤型四种不同长相穗型小麦品种的穗部性状特性进行了分析。结果表明不同穗型小麦在小花数、穗粒数、粒重等方面均表现出先升后降的二次曲线趋势,但不同穗型小麦表现趋势不同,长方型和椭圆型小麦在穗粒数、穗重、各粒位粒重等方面均优于棍棒型和纺锤型小麦,棍棒型小麦由于其特殊的穗型结构,在小穗密度、粒重等方面表现独特,纺锤型小麦在各方面表现均较差。结果表明,在小麦生产用种和品种选育上,穗型的选择至关重要。长方型小麦有提高小麦产量的潜力。     

Spike Triggered Hormone Secretion in Vasopressin Cells; a Model Investigation of Mechanism and Heterogeneous Population Function

Vasopressin neurons generate distinctive phasic patterned spike activity in response to elevated extracellular osmotic pressure. These spikes are generated in the cell body and are conducted down the axon to the axonal terminals where they trigger Ca²⁺ entry and subsequent exocytosis of hormone-containing vesicles and secretion of vasopressin. This mechanism is highly non-linear, subject to both frequency facilitation and fatigue, such that the rate of secretion depends on both the rate and patterning of the spike activity. Here we used computational modelling to investigate this relationship and how it shapes the overall response of the neuronal population. We generated a concise single compartment model of the secretion mechanism, fitted to experimentally observed profiles of facilitation and fatigue, and based on representations of the hypothesised underlying mechanisms. These mechanisms include spike broadening, Ca²⁺ channel inactivation, a Ca²⁺ sensitive K⁺ current, and releasable and reserve pools of vesicles. We coupled the secretion model to an existing integrate-and-fire based spiking model in order to study the secretion response to increasing synaptic input, and compared phasic and non-phasic spiking models to assess the functional value of the phasic spiking pattern. The secretory response of individual phasic cells is very non-linear, but the response of a heterogeneous population of phasic cells shows a much more linear response to increasing input, matching the linear response we observe experimentally, though in this respect, phasic cells have no apparent advantage over non-phasic cells. Another challenge for the cells is maintaining this linear response during chronic stimulation, and we show that the activity-dependent fatigue mechanism has a potentially useful function in helping to maintain secretion despite depletion of stores. Without this mechanism, secretion in response to a steady stimulus declines as the stored content declines.     

猪源冠状病毒TGEV中国分离株纤突蛋白生物信息学分析

以猪源冠状病毒(Porcine coronavirus) TGEV 纤突蛋白基因序列为基础,利用生物软件对TGEV中国分离株纤突蛋白进行N-糖基化位点、跨膜螺旋、进化树、二级结构及抗原位点的预测和分析.结果显示所有TGEV毒株可分为3个基因型,4株中国分离株分别归属于以上3个基因型,属于Ⅰ型的中国株TSX在跨膜螺旋的氨基酸及二级结构上与其他中国株有明显的差异,而同属于Ⅲ型中国毒株SC-Y和TH- 98在跨膜螺旋的氨基酸及二级结构上非常接近,而所有中国分离株抗原表位没有明显差别,表明TGEV在中国没有显著的变异.     

Controlled Payload Release by Magnetic Field Triggered Neural Stem Cell Destruction for Malignant Glioma Treatment

Stem cells have recently garnered attention as drug and particle carriers to sites of tumors, due to their natural ability to track to the site of interest. Specifically, neural stem cells (NSCs) have demonstrated to be a promising candidate for delivering therapeutics to malignant glioma, a primary brain tumor that is not curable by current treatments, and inevitably fatal. In this article, we demonstrate that NSCs are able to internalize 2 μm magnetic discs (SD), without affecting the health of the cells. The SD can then be remotely triggered in an applied 1 T rotating magnetic field to deliver a payload. Furthermore, we use this NSC-SD delivery system to deliver the SD themselves as a therapeutic agent to mechanically destroy glioma cells. NSCs were incubated with the SD overnight before treatment with a 1T rotating magnetic field to trigger the SD release. The potential timed release effects of the magnetic particles were tested with migration assays, confocal microscopy and immunohistochemistry for apoptosis. After the magnetic field triggered SD release, glioma cells were added and allowed to internalize the particles. Once internalized, another dose of the magnetic field treatment was administered to trigger mechanically induced apoptotic cell death of the glioma cells by the rotating SD. We are able to determine that NSC-SD and magnetic field treatment can achieve over 50% glioma cell death when loaded at 50 SD/cell, making this a promising therapeutic for the treatment of glioma.     

A Time-Course Proteomic Analysis of Rice Triggered by Plant Activator BTH

Benzothiadiazole (BTH) is an artificial inducer of systemic acquired resistance. Due to rice being an important food crop and model plant, we investigated its response to BTH using label-free proteomics technology coupled with bioinformatics. Protein expression levels were verified using the multi-reaction monitoring mode and semi-quantitative RT-PCR. BTH treatment can up- or down-regulate many proteins produced by the rice host at all four periods, with the numbers of proteins being 6/24, 9/10, 14/10, and 8/20, respectively. Compared with mock treatments (phosphate buffered saline with 0.1 % dimethylsulfoxide and 0.5 % Tween-20), some proteins related to plant resistance were only detected after BTH treatments, such as ascorbate peroxidase (POD) 3, chitinase A, thioredoxin-dependent POD 2, beta-1,3-glucanase 2, POD superfamily protein, major facilitator superfamily (MFS) protein, copper/zinc-superoxide dismutase (SOD) 1, pathogenesis-related protein (PR) 1. Other proteins showing up-regulation after BHT treatment included PR-5, glyceraldehyde-3-phosphate dehydrogenase C, plasma-membrane associated cation-binding protein 1, and oxidoreductase family proteins. These results indicated that BTH was involved with inducing rice resistance. Some up-regulated proteins were also involved in other metabolic processes. The activity and expression level of POD, phenylalanine ammonia-lyase (PAL), and SOD, lipoxygenase (LOX), beta-1,3-glucanases, and chitinases were determined using the enzyme activity assay and semi-quantitative RT-PCR. These results indicated that BTH can enhance the activity of beta-1,3-glucanases, LOX, PAL, and POD. BTH can also induce up-regulation of the copper/zinc-SOD, ascorbate POD, glutathione POD 1, Chitinase, and LOX1 genes.     

SARS-CoV S蛋白受体结合区的重组表达及免疫原性分析

为了表达SARS-CoV的S蛋白的受体结合区并对其免疫原性进行分析,用PCR方法扩增S蛋白的受体结合区基因片段,克隆至原核表达质粒pET-32a+并在大肠杆菌中表达,应用Western-blot鉴定表达的目的蛋白,而后以该蛋白作为诊断抗原包被酶联板来检测20份SARS病人血清和28份健康人血清,结果原核表达的S蛋白能够和所用的SARS病人血清反应.这提示表达的S重组蛋白具有良好的抗原性.将变性纯化的重组蛋白和复性蛋白分别皮下免疫小鼠,第三次免疫一周后收集抗血清,用ELISA测定抗体和同时测定中和抗体活性.用变性的抗原免疫的小鼠血清均无中和活性;而用复性的蛋白免疫的小鼠产生了中和抗体.实验表明,S蛋白受体结合区无线性中和表位,中和抗体的产生是由构象表位诱导的.提示该蛋白有可能应用于亚单位疫苗的研究.     

严重急性呼吸综合征(SARS)冠状病毒S蛋白的鉴定与分析

利用293细胞对SARS病人样品进行病毒扩增,培养上清中病毒颗粒经过纯化后,利用蛋白质组学技术,对纯化得到的SARS冠状病毒颗粒蛋白进行初步分离与鉴定。其中质谱分析结果最终表明,分子量约150kD的蛋白质的氨基酸序列与SARS—CoV基因组所预测S蛋白质序列高度吻合,从而首次从蛋白质水平对SARS冠状病毒S蛋白的氨基酸序列进行了证实。     

SARS-CoV S蛋白受体结合区的重组表达及免疫原性分析

为了表达SARS-CoV的S蛋白的受体结合区并对其免疫原性进行分析,用PCR方法扩增S蛋白的受体结合区基因片段,克隆至原核表达质粒pET-F32a＋并在大肠杆菌中表达,应用Western—blot鉴定表达的目的蛋白,而后以该蛋白作为诊断抗原包被酶联卡反来检测20份SARS病人血清和28份健康人血清,结果原核表达的S蛋白能够和所用的SARS病人血清反应。这提示表达的S重组蛋白具有良好的抗原性。将变性纯化的重组蛋白和复性蛋白分别皮下免疫小鼠,第三次免疫一周后收集抗血清,用ELISA测定抗体和同时测定中和抗体活性。用变性的抗原免疫的小鼠血清均无中和活性;而用复性的蛋白免疫的小鼠产生了中和抗体。实验表明,S蛋白受体结合区无线性中和表位,中和抗体的产生是由构象表位诱导的。提示该蛋白有可能应用于亚单位疫苗的研究。     

Statistical Analysis of Membrane Potential Fluctuations: Relation with Presynaptic Spike Train

In a study of integration at the single neuron level, the relationships between the postsynaptic membrane potential and the presynaptic spike train were analyzed. Fluctuations in membrane potential of neurons in the visceral ganglion of Aplysia were measured and described by histograms. The histogram estimates the probability density function of the membrane potential. Comparisons were made among histograms when there was no synaptic input, and when there was a single input in which variations were made in the PSP (postsynaptic potential) sign, i.e. excitatory or inhibitory, and arrival statistics, e.g. slow or fast, regular, Poisson-like, or patterned. This was examined in cells where the membrane potential was constant and in cells in which there was spontaneous pacemaker activity. The form of the histogram depended on whether the neuron was spontaneously quiescent or a pacemaker, or whether it received presynaptic input and, if it did, on the sign and temporal characteristics of such input. From such histograms the mean firing rate of output spike trains can be predicted; additional information of a temporal nature is required, however, to predict features of the interval structure of the output train. Suggestions are made concerning the way the nervous system might utilize the information summarized in the membrane potential histogram.     

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.     

Information-Theoretic Analysis of Neural Coding

We describe an approach to analyzing single- and multiunit (ensemble) discharge patterns based on information-theoretic distance measures and on empirical theories derived from work in universal signal processing. In this approach, we quantify the difference between response patterns, whether time-varying or not, using information-theoretic distance measures. We apply these techniques to single- and multiple-unit processing of sound amplitude and sound location. These examples illustrate that neurons can simultaneously represent at least two kinds of information with different levels of fidelity. The fidelity can persist through a transient and a subsequent steady-state response, indicating that it is possible for an evolving neural code to represent information with constant fidelity.     

Phase Resetting Curves Allow for Simple and Accurate Prediction of Robust N:1 Phase Locking for Strongly Coupled Neural Oscillators

Existence and stability criteria for harmonic locking modes were derived for two reciprocally pulse coupled oscillators based on their first and second order phase resetting curves. Our theoretical methods are general in the sense that no assumptions about the strength of coupling, type of synaptic coupling, and model are made. These methods were then tested using two reciprocally inhibitory Wang and Buzsáki model neurons. The existence of bands of 2:1, 3:1, 4:1, and 5:1 phase locking in the relative frequency parameter space was predicted correctly, as was the phase of the slow neuron's spike within the cycle of the fast neuron in which it occurred. For weak coupling the bands are very narrow, but strong coupling broadens the bands. The predictions of the pulse coupled method agreed with weak coupling methods in the weak coupling regime, but extended predictability into the strong coupling regime. We show that our prediction method generalizes to pairs of neural oscillators coupled through excitatory synapses, and to networks of multiple oscillatory neurons. The main limitation of the method is the central assumption that the effect of each input dies out before the next input is received.     

犬冠状病毒大熊猫株纤突蛋白全基因的克隆与序列分析

在国内首次对犬冠状病毒大熊猫野毒株(CCV DXMV)纤突蛋白基因进行了克隆和序列测定.该基因全长4362bp,编码1453个氨基酸,N端前18个氨基酸为推测的信号肽序列,后1435个氨基酸构成成熟蛋白.与GenBank中已发表的11个CCV毒株S基因相比,S基因核苷酸序列同源性在40.2%～99.5%之间;推导的氨基酸序列同源性在15.9%～99.0%之间.DXMV株S基因变异区主要集中在该基因前1/2处,其中350-370、439-478、1718-1818三个区域碱基变异较大,而1060-1700区却十分保守.基于S全基因及其蛋白的聚类分析表明,DXMV株与K378、NVSL和US patent株亲源关系最近.推导的DXMV株S蛋白氨基酸序列潜在的N-联糖基化位点与CCV强毒V54相同,为34个,比Insavc-1弱毒多一个;其中第566-568位糖基化位点为多数强毒拥有而弱毒没有的.另外,DXMV株S蛋白疏水性及抗原表位与其它毒株有一定的差异,这些差异对DXMV株致病性和免疫原性等影响尚待进一步的研究.     

Autonomous Synchronization of Chemically Coupled Synthetic Oscillators

Synthetic biology has recently provided functional single-cell oscillators. With a few exceptions, however, synchronization across a population has not been achieved yet. In particular, designing a cell coupling mechanism to achieve autonomous synchronization is not straightforward since there are usually several different design alternatives. Here, we propose a method to mathematically predict autonomous synchronization properties, and to identify the network structure with the best performance, thus increasing the feasibility for a successful implementation in vivo.     

Spike timing precision of neuronal circuits

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.