浅议高校校园网的安全问题

从校园网的特点出发,针对当前存在的主要安全威胁以及内部安全威胁的表现形式,分析了校园网中存在的各种安全问题,并提出了以多种技术手段和管理手段加强校园网安全的应对策略。     

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.     

Reliability of pattern separation by the cerebellar mossy fiber — granule cell system

As a simple example of a neuronal network in which synaptic connectivity among neurons is probabilistic, Marr's model for the granular layer of cat cerebellar cortex is examined. The mean and variance are computed for the fraction of granule cells activated, and for the extent of pattern separation by granule cells, for various mossy fiber inputs and various values of connectivity and electrical parameters of the network structure. Results suggest different functions for the network, and different optimal ranges for its parameters, depending on whether Golgi cells are present or absent. The model network does not perform the functions originally prescribed for it with high reliability.     

The thiamine pyrophosphatase technique as an indicator of the morphology of the Golgi apparatus in the neurons

Summary Detailed studies have been made on the morphology of the TPPase-positive material in various components of the cerebellum. The various types of nerve fibers in various layers of the cerebellum and the glomeruli are free of any TPPase-positive Golgi material. The stellate and basket cells have a thick plate-like Golgi complex. The Purkinje cells showed either a Golgi network or discrete masses of TPPase-positive material of various sizes and shapes. The Bergman glial cells showed a delicate Golgi network. The Golgi type II cells showed a thick or thin TPPase-positive network. The granule cells showed only discrete vesicles, granules, and comma-shaped masses found at any one pole of each cell. The cells of the deep cerebellar nuclei also showed a network. The results of this study are compared with similar studies made on the spinal cord, olfactory bulb, Ammon's horn, fascia dentata, cerebral cortex, and ganglion cells.     

Network-Based Study Reveals Potential Infection Pathways of Hepatitis-C Leading to Various Diseases

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.     

Discovering functions and revealing mechanisms at molecular level from biological networks

With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein-protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools.     

Markovian neural networks

The neural network that efficiently and nearly optimally solves difficult optimization problems is defined. The convergence proof for the Markovian neural network that asynchronously updates its neurons' states is also presented. The comparison of the performance of the Markovian neural network with various combinatorial optimization methods in two domains is described. The Markovian neural network is shown to be an efficient tool for solving optimization problems.     

Mechanisms of respiratory rhythm generation.

In mammals, a three-phasic respiratory rhythm is generated by a network of various types of neurons in the lower brainstem. The cellular mechanisms of rhythmogenesis involve cooperative interactions between synaptic processes and specific membrane properties. The network seems to be driven by extrinsic sources in mature animals, whereas in the immature network pacemaker neurons might be involved.     

Reduction-oxidation network for flexible adjustment of cellular metabolism in photoautotrophic cells

Photosynthesis generates the energy carriers NADPH and ATP to be consumed in assimilatory processes. Continuous energy conversion and optimal use of the available light energy are only guaranteed when all reduction-oxidation (redox) processes are tightly controlled. A robust network links metabolism with regulation and signalling. Information on the redox situation is generated and transferred by various redox components that are parts of this network. Any imbalance in the network is sensed, and the information is transmitted in order to elicit a response at the various levels of regulation and in the different cellular compartments. Redox information within the chloroplast is derived from intersystem electron transport, the ferredoxin-NADP oxidoreductase (FNR)/NADPH branch of the redox network, the thioredoxin branch and from reactive oxygen species (ROS), resulting in a high diversity of responses that are able to adjust photosynthesis, as well as poising and antioxidant systems accordingly in each specific situation. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) represents a central step in CO(2) reduction and in carbohydrate oxidation involving both forms of energy, namely NAD(P)H and ATP, with its various isoforms that are located in plastids, cytosol and nucleus. GAPDH is used as an example to demonstrate complexity, flexibility and robustness of the regulatory redox network in plants.     

On the contours of apparent motion: A new perspective on visual space-time

Previous results on the perception of motion indicate that perceived motion paths cannot be explained solely in terms of simple feature-specific analyzers. This is particularly true of apparent (phi) motion. In this paper we develop a dynamic network, with simple filtering and summation properties, which can predict the geometric paths of apparent motion in various spatio-temporal configurations. The network assumptions predict a non-Euclidean metric for the visual space-time of motion perception and we consider the implications of such distortions for various visual displays, including illusions.     

A neural network model for texture discrimination

A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.     

Logical networks and probability

The logical network of a Pitts-McCulloch type is developed further with the specific addition of a motivational system which acts as a differential reinforcer to the network. A consideration is given to the various ways that probabilities enter the logical net transforming it into a probability net, and lastly some notes are added on the possibility of including the strategies of Theory of Games in the logical network.     

A Bionic Neural Network for Fish-Robot Locomotion

A bionic neural network for fish-robot locomotion is presented. The bionic neural network inspired from fish neural net- work consists of one high level controller and one chain of central pattern generators （CPGs）. Each CPG contains a nonlinear neural Zhang oscillator which shows properties similar to sine-cosine model. Simulation re, suits show that the bionic neural network presents a good performance in controlling the fish-robot to execute various motions such as startup, stop, forward swimming, backward swimming, turn right and turn left.     

A killer whale social network is vulnerable to targeted removals

Individuals play various roles in maintaining social integrity of mammalian populations. However, many models developed for managing wildlife resources assume that all individuals are equal. Killer whales are social animals that rely on relationships within and among family groups for survival. In the northeastern Pacific, fish-eating, 'resident' killer whale populations are composed of matrilines from which offspring do not disperse. We analysed the influence of various individuals' age, sex and matrilineal affiliation on their position in a social network. Here, we show that some matrilines appeared to play more central roles than others in the network. Furthermore, juvenile whales, especially females, appeared to play a central role in maintaining network cohesion. These two key findings were supported subsequently by simulating removal of different individuals. The network was robust to random removals; however, simulations that mimicked historic live-captures from the northeastern Pacific were likely to break the network graph into isolated groups. This finding raises concern regarding targeted takes, such as live-capture or drive fisheries, of matrilineal cetaceans.     

Synchrony measures for biological neural networks

 Synchronous firing of a population of neurons has been observed in many experimental preparations; in addition, various mathematical neural network models have been shown, analytically or numerically, to contain stable synchronous solutions. In order to assess the level of synchrony of a particular network over some time interval, quantitative measures of synchrony are needed. We develop here various synchrony measures which utilize only the spike times of the neurons; these measures are applicable in both experimental situations and in computer models. Using a mathematical model of the CA3 region of the hippocampus, we evaluate these synchrony measures and compare them with pictorial representations of network activity. We illustrate how synchrony is lost and synchrony measures change as heterogeneity amongst cells increases. Theoretical expected values of the synchrony measures for different categories of network solutions are derived and compared with results of simulations. Received: 6 June 1994/Accepted in revised form: 13 January 1995     

Phenotypic characterization of <Emphasis Type="Italic">Corynebacterium glutamicum</Emphasis> using elementary modes towards synthesis of amino acids

Elementary flux mode (EFM) analysis is a powerful tool to represent the metabolic network structure and can be further utilized for flux analysis. The method enables characterization and quantification of feasible phenotypes in microbes. EFM analysis was employed to characterize the phenotype of Corynebacterium glutamicum to yield various amino acids. The metabolic network of C. glutamicum yielded 62 elementary modes by incorporating the accumulation of amino acids namely, lysine, alanine, valine, glutamine and glutamate. The analysis also allowed us to compute the maximum theoretical yield for the synthesis of various amino acids. These 62 elementary modes were further used to obtain optimal phenotypic space towards accumulation of biomass and lysine. The study indicated that the optimal solution space from 62 elementary modes forms a super space which incorporates various mutants including lysine producing strain of C. glutamicum. The analysis was also extended to obtain sensitivity of the network to variation in the stoichiometry of NADP in the definition of biomass.     

Exploring biological network structure with clustered random networks

Background  

Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics.     

全细胞网络重建与细胞工厂设计

在各种组学及其相应的网络研究相对成熟的基础上,集成各组学网络的细胞整合型网络或全细胞网络将大大提高对生物表型的预测能力,并成为代谢工程决策的有力武器.本文在阐述了细胞工厂设计中应该考虑细胞整合网络之后,综述了细胞整合网络的重建、分析、设计方法方面的有关问题,并进一步就研究细胞整合网络涉及的数据库、软件平台、并行计算几方面的作用作了介绍.     

A Three-Dimensional Computational Model of Collagen Network Mechanics

Extracellular matrix (ECM) strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model) and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned). We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions.