医院网络安全管理方案与措施

随着计算机和互联网技术的飞速发展,医院内外网连接更加紧密,通过双核心网络虚拟化建设、内外网连接安全措施、虚拟局域网划分、网管软件的应用、网管人才和使用人员安全意识培养等一系列安全管理措施的实施,形成了一套安全稳定的医院网络系统,提高了网络的可靠性和安全性,保障了医院信息系统不间断安全运行。     

医院计算机网络信息资源管理的优化策略探析

计算机网络技术在医院信息资源管理中的普遍应用在很大程度上优化医院的工作方式,加强医疗信息的整合能力,提升医院的工作效率,在应用中计算机网络技术发挥重要作用。我们从医院计算机网络信息资源的内涵及特点入手,分析当前医院信息资源管理现状,最后针对医院信息管理现状中出现的具体问题,提出相应的优化策略,以期对以后计算机网络技术在医院信息资源管理中的进一步研究有所裨益。     

医院计算机网络管理系统初步研究

医院的计算机网络是一个较大型的网络系统,是管理各种复杂的医疗信息,为医院管理信息进行处理的中枢,它既要满足较大容量的传输要求,又要满足实时处理的快速响应要求。因而,医院计算机网络的管理是一个十分复杂但却十分重要的工程。它的总体视划的原则是:这个系统必须具有先进性、可靠性、可扩充性及可升级性。     

大型医院信息化网络平台的建设

现代化医院需要一个能够承载生命信息的数字化网络平台,面临大量的设备和众多的应用系统,网络架构的先进性、可扩展性、稳定性、可靠性和安全性是必须的。以瑞金医院信息网络平台建设为例,提出了大型现代综合性医院网络建设的难点和要点,认为网络系统建设可采用模块化、层次化的结构,提高网络的可扩展性和可管理性,减少网络广播的危害性,同时对关键节点采用冗余设备进行备份,对关键应用系统采用QoS、负载均衡技术优化网络总体性能,可加强网络运行的稳定性和安全性。     

分子系统生物学与细胞发生动力学

系统生物学采用系统理论和实验生物技术、计算机数学模型等方法整合研究动态生物系统网络.生物系统的结构理论和生物系统技术,研究基因组——生物体复杂系统与细胞分子网络系统的动态结构发生与进化,分析基因组的逻辑程序和人工设计原理.细胞信号传导、基因调控网络、代谢反应链和基因反馈调控的自组织化人工设计和基因、基因链、基因组人工合成等系统生物工程开发,可用于复杂疾病机理分析、药物分子筛选和转基因表达系统的生物反应器、纳米生物计算机等.     

微生物学与系统生物学发展

系统生物学是研究生物网络系统的科学,从基因调控网络到细胞、组织和生物体网络,阐明生物系统的构成、动力学及控制方式。系统生物学是以假说驱动的研究策略,在综合现有数据的基础上构建系统模型,根据模型提出可以检测的假说,并设计实验以验证假说,根据实验结果再修正模型,不断重复以得到正确的系统模型。在系统生物学的发展中,细菌作为一个很好的模式生物,将起到重要作用。系统生物学的发展不仅依赖于新技术的发明和应用,更有赖于以新的思路去培养新一代的生物学家。     

近存储饱和状态下联想学习记忆的神经网络模型

本文提出了神经网络在近饱和状态下的一种联想学习记忆模型.讨论了该模型的主要特性,对由100个神经元、记忆10个随机图样组成的网络系统给出并分析了计算机模拟结果,讨论了该模型的学习律与传统的Hebb学习律的区别,研究了网络在学习记忆和联想新态时初始噪声Pi和联想噪声Pa对新态恢复行为的影响,总结了在近饱和状态下该模型所具有的优势.     

浅谈计算机网络的应用分析

随着科学技术的快速发展,计算机技术和互联网技术在全球得到了普及,计算机网络已经伸入每个人的生活当中,成为人们生活必不可缺少的内容。网络的应用得到了人们的广泛关注,网络信息资源的共享给人类社会的发展带来的巨大的冲击,在逐渐的改变着人们的生活和生产方式。本文对计算机网络应用的基础做一研究,提出如何使用强大的网络信息系统为人们的生活提供快捷的服务,方便人们的生产生活。人们可以通过网络了解所需的信息,与世界各地自由的交流与沟通,传播自己优秀的文化,做到足不出户而知天下。     

医院计算机化医疗设备的维护管理

现代医疗设备大都采用了计算机化设计,分析了目前医院拥有的计算机现状、医疗设备的分类和管理。最后,提出了维护管理措施。     

基于整合医学搭建无边界医院的探索与实践

为贯彻落实“基层首诊、双向转诊、急慢分治、上下联动”的分级诊疗要求,太和医院基于整合医学理念搭建了远程无边界医院。以太和医院为区域中心,借助“互联网+”,上联国际、国内知名医院,下联县、乡、村四级网络医院,跨学科、跨区域实现了对医院整合和人员整合,构建综合诊疗大平台。这不仅促进了医院内涵建设,还为我院医联体发展添砖加瓦,在创新实践中取得了良好效果,其应用经验值得积极推广。

     

Network modeling and analysis of lumbar muscle surface EMG signals during flexion–extension in individuals with and without low back pain

In this paper, we propose modeling the activity coordination network between lumbar muscles using surface electromyography (sEMG) signals and performing the network analysis to compare the lumbar muscle coordination patterns between patients with low back pain (LBP) and healthy control subjects. Ten healthy subjects and eleven LBP patients were asked to perform flexion–extension task, and the sEMG signals were recorded. Both the subject-level and the group-level PC_fdr algorithms are applied to learn the sEMG coordination networks with the error-rate being controlled. The network features are further characterized in terms of network symmetry, global efficiency, clustering coefficient and graph modules. The results indicate that the networks representing the normal group are much closer to the order networks and clearly exhibit globally symmetric patterns between the left and right sEMG channels. While the coordination activities between sEMG channels for the patient group are more likely to cluster locally and the group network shows the loss of global symmetric patterns. As a complementary tool to the physical and anatomical analysis, the proposed network analysis approach allows the visualization of the muscle coordination activities and the extraction of more informative features from the sEMG data for low back pain studies.     

The growth and form of tunnelling networks in ants

Many biological networks grow under strong spatial constraints, where the large-scale structure emerges from the extension, the branching and intersection of growing parts of the network. One example is provided by ant tunnelling networks, which represent the most common nest architecture in ants. Our goal was to understand how these network structures emerge from the tunnel growth dynamics. We used a standardized two-dimensional set-up shaped as a disk and studied the characteristics of tunnel growth in terms of initiation, propagation and termination of new digging sites and found that they can be described with simple probabilistic laws. We show that a model based on these simple laws and for which parameters were measured from the sand disks experiments can account for the emergence of several topological properties that were observed in experimental networks. In particular, the model accurately reproduced an allometric relation between the number of edges and the number of nodes, as well as an invariance of the node degree distribution. The model was then used to make predictions about the resulting networks' topology when the geometry of the sand substrate was shaped as a square. Experiments aimed at testing the model's predictions showed that the predictions were indeed validated. Both in the model and in the experiments, there was a similar trend for the node degree distribution tail to be steeper in the square sand patch than in the disk sand patch, while other characteristics such as the meshedness (i.e. how densely the network is internally connected) remained constant. Because network growth based on branching/fusion events is widespread in biological systems, this general model might provide useful insights for the study of other systems and, more generally, the evolution of spatial networks in biological systems.     

Interaction network of vascular epiphytes and trees in a subtropical forest

The commensalistic interaction between vascular epiphytes and host trees is a type of biotic interaction that has been recently analysed with a network approach. This approach is useful to describe the network structure with metrics such as nestedness, specialization and interaction evenness, which can be compared with other vascular epiphyte-host tree networks from different forests of the world. However, in several cases these comparisons showed different and inconsistent patterns between these networks, and their possible ecological and evolutionary determinants have been scarcely studied. In this study, the interactions between vascular epiphytes and host trees of a subtropical forest of sierra de San Javier (Tucuman, Argentina) were analysed with a network approach. We calculated metrics to characterize the network and we analysed factors such as the abundance of species, tree size, tree bark texture, and tree wood density in order to predict interaction frequencies and network structure. The interaction network analysed exhibited a nested structure, an even distribution of interactions, and low specialization, properties shared with other obligated vascular epiphyte-host tree networks with a different assemblage structure. Interaction frequencies were predicted by the abundance of species, tree size and tree bark texture. Species abundance and tree size also predicted nestedness. Abundance indicated that abundant species interact more frequently; and tree size was an important predictor, since larger-diameter trees hosted more vascular epiphyte species than small-diameter trees. This is one of the first studies analyzing interactions between vascular epiphytes and host trees using a network approach in a subtropical forest, and taking the whole vascular epiphyte assemblage of the sampled community into account.     

Network medicine

To more effectively target complex diseases like cancer, diabetes and schizophrenia, we may need to rethink our strategies for drug development and the selection of molecular targets for pharmacological treatments. Here, we discuss the potential use of protein signaling networks as the targets for new therapeutic intervention. We argue that by targeting the architecture of aberrant signaling networks associated with cancer and other diseases new therapeutic strategies can be implemented. Transforming medicine into a network driven endeavour will require quantitative measurements of cell signaling processes; we will describe how this may be performed and combined with new algorithms to predict the trajectories taken by a cellular system either in time or through disease states. We term this approach, network medicine.     

用于识别心电图的BP网络系统

基于ＢＰ神经网络算法和心电图（ＥＣＧ）识别的原理,作者系统地探讨了ＢＰ网络用于心电图识别的方法并设计了一个用ＢＰ网络识别三种类型（ＥＣＧ）一正常、下壁心肌心梗塞和前间壁心肌梗搴系统。实验结果较好。     

Design principles for robust oscillatory behavior

Oscillatory responses are ubiquitous in regulatory networks of living organisms, a fact that has led to extensive efforts to study and replicate the circuits involved. However, to date, design principles that underlie the robustness of natural oscillators are not completely known. Here we study a three-component enzymatic network model in order to determine the topological requirements for robust oscillation. First, by simulating every possible topological arrangement and varying their parameter values, we demonstrate that robust oscillators can be obtained by augmenting the number of both negative feedback loops and positive autoregulations while maintaining an appropriate balance of positive and negative interactions. We then identify network motifs, whose presence in more complex topologies is a necessary condition for obtaining oscillatory responses. Finally, we pinpoint a series of simple architectural patterns that progressively render more robust oscillators. Together, these findings can help in the design of more reliable synthetic biomolecular networks and may also have implications in the understanding of other oscillatory systems.

Electronic supplementary material

The online version of this article (doi:10.1007/s11693-015-9178-6) contains supplementary material, which is available to authorized users.     

The smallest of all worlds: pollination networks

A pollination network may be either 2-mode, describing trophic and reproductive interactions between communities of flowering plants and pollinator species within a well-defined habitat, or 1-mode, describing interactions between either plants or pollinators. In a 1-mode pollinator network, two pollinator species are linked to each other if they both visit the same plant species, and vice versa for plants. Properties of 2-mode networks and their derived 1-mode networks are highly correlated and so are properties of 1-mode pollinator and 1-mode plant networks. Most network properties are scale-dependent, i.e. they are dependent upon network size. Pollination networks have the strongest small-world properties of any networks yet studied, i.e. all species are close to each other (short average path length) and species are highly clustered. Species in pollination networks are much more densely linked than species in traditional food webs, i.e. they have a higher density of links, a shorter distance between species, and species are more clustered.     

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.     

人代谢网络巨强连通体的复杂网络分析

采用复杂网络理论分析了人代谢网络的巨强连通体。通过对高质量人代谢网络数据的整理,获取了该网络巨强连通体中的所有代谢反应。用代谢物图表示这些反应形成了一个网络,它包含了256个节点和648条连线。选用模拟退火算法对该网络进行社团结构分析,发现得到的模块具备重要的生物学功能。通过多种不同的中心化方法,分析了该网络的关键节点,并讨论了它们的生物学和理疗意义。