工业园区循环经济关联与生态效率评价——以福建省蛟洋循环经济示范园区为例

工业园区因物质能量的高度集聚,生产-消费过程的网络关联,以及区域示范带动效应,在循环经济发展中受到持续关注。本研究以福建省蛟洋循环经济示范园区为例,应用物质流、生态网络和生态效率等测度分析方法,综合评估了2012—2016年间园区循环经济的网络关联和生态效率。研究结果表明:(1)园区物质流结构单一,关联度和稳定度明显较弱;(2)园区网络的韧性不足,关键节点的级联效应显著,尤其对中下游企业的影响程度大;(3)生态效率指数分析发现,园区关键节点企业在循环经济效益方面带动能力不足,影响园区循环经济的前景。为此,从园区网络关联稳定度和企业生态经济效率优化提升的角度,探讨园区未来的循环经济发展策略。可为处于起步阶段的专一化工业园区探索可行的循环经济模式提供借鉴。     

生态工业园共生网络的脆弱性

工业生态系统的稳定可持续运行是生态工业园区实现环境效益、经济效益和社会效益的重要保障,对生态工业共生网络脆弱性分析是工业生态学领域值得探讨的重要问题。运用复杂网络理论,从网络拓扑结构出发,论证该工业园具有小世界性和无标度性,为网络脆弱性分析奠定基础;通过攻击负载最大节点,利用网络效率和最大连通子图对网络的脆弱性进行分析,从而衡量节点失效对整个网络造成的破坏性。指出复杂网络在生态工业园共生网络中进一步研究的方向。     

生物分子网络弹性研究进展

弹性是生物分子网络重要且基础的属性之一,一方面弹性赋予生物分子网络抵抗内部噪声与环境干扰并维持其自身基本功能的能力,另一方面,弹性为网络状态的恢复制造了阻力。生物分子网络弹性研究试图回答如下3个问题：a. 生物分子网络弹性的产生机理是什么？b. 弹性影响下生物分子网络的状态如何发生转移？c. 如何预测生物网络状态转换临界点,以防止系统向不理想的状态演化？因此,研究生物分子网络弹性有助于理解生物系统内部运作机理,同时对诸如疾病发生临界点预测、生物系统状态逆转等临床应用具有重要的指导意义。鉴于此,本文主要针对以上生物分子网络弹性领域的3个热点研究问题,在研究方法和生物学应用上进行了系统地综述,并对未来生物分子网络弹性的研究方向进行了展望。     

生态产业园的复合生态效率及评价指标体系

生态产业园的建设在国内取得了丰富的成果,已成为我国循环经济建设和可持续发展的重要载体之一。与国外不同,我国的生态产业园最终将演化为生态城市,其功能也从工业生产功能发展为融生产、生活和生态为一体的复合功能。生态效率是评价一个研究对象投入产出效益的量化工具,将生态效率的理念融入生态产业园,从园区作为区域可持续发展的载体、作为生态城市演化的一个特定阶段的视角,提出了复合生态效率的概念,构建了园区复合生态效率评价指标体系,并以郑州经济技术开发区为例,说明了复合生态效率指标在产业园中的应用。     

基于社会网络分析法的生态工业园典型案例研究

以丹麦卡伦堡和广西贵港生态工业园为例,应用社会网络分析法分析典型生态工业园的组织结构和网络结构。用簇系数、平均最短距离、传递性以及核心-边缘结构来衡量其稳定性,分析其不同结构特征。卡伦堡生态工业园的簇系数为0.715,远高于贵港生态工业园的0.246,说明其聚合性较好;从最短距离长度来看,卡伦堡和贵港分别为2.110和2.236,复杂性弱;从传递性来看,两者传递性皆低于25%,节点之间联系性不够强;从核心-边缘结构来看,卡伦堡各节点之间的异质性(0.057)高于贵港(0.005),可见贵港参与主体的中心性较为均衡,而卡伦堡差异性较大。两者都以一个产业或者企业集团为网络核心;网络内其他节点联系不够紧密,长链条联结较少;网络复杂性弱。     

基于复杂网络理论的代谢网络结构研究进展

后基因组时代研究的一个重要内容就是在系统生物学的基础上对多种分子和基因相互作用网络进行分析,理解生物系统是如何从单个构造模块的基础上组织起来的。近几年,复杂网络理论得到了迅速发展,其理论方法在生物网络的研究中得到了广泛应用。一些学者运用该理论方法研究了大量有关代谢网络的结构组成以及网络中子集团的层次组成关系,并获得了一些有意义的结果。这些结果对生物功能的预测具有一定的指导作用。对近几年来有关这方面的进展作简要综述。     

生态工业园区生态系统理论及调控机制

随着工业生态学理论的发展 ,尤其是循环经济理念的广泛推广 ,生态工业园区建设在全球范围蓬勃开展起来。然而 ,由于对生态工业园区生态系统结构、功能及其优化调控机制认识的不足 ,导致目前生态工业园区建设走入了单纯构建闭环链以实现废物循环利用的误区。本研究旨在剖析生态工业园区生态系统的特点 ,建立其生态系统概念模型 ,并阐释其优化调控机理。为此 ,作者首先深入分析了生态工业园区生态系统的特点 ,阐释了技术流和知识流在该类生态系统功能中的重要性 ,在此基础上提出了基于社会子系统、环境子系统、经济子系统和资源子系统的四维一体生态工业园区复合生态系统结构框架 ,并给出了系统功能协调度、调节费用及其系统效益之间的理论变化曲线。其次 ,作者又从生态系统功能流的角度分析了其物质流、能量流、信息流、价值流、技术流和知识流 ,并给出了各功能流的表征指标和衡算方法。另外 ,从系统优化调控的角度提出了基于绿色招商和绿色供应链管理的生态工业园区生态系统控制管理体系。最后 ,以苏州工业园区为例 ,对其生态系统做了优化研究     

Hierarchical networks of food exchange in the black garden ant Lasius niger

In most eusocial insects, the division of labor results in relatively few individuals foraging for the entire colony. Thus, the survival of the colony depends on its efficiency in meeting the nutritional needs of all its members. Here, we characterize the network topology of a eusocial insect to understand the role and centrality of each caste in this network during the process of food dissemination. We constructed trophallaxis networks from 34 food-exchange experiments in black garden ants (Lasius niger). We tested the influence of brood and colony size on (i) global indices at the network level (i.e., efficiency, resilience, centralization, and modularity) and (ii) individual values (i.e., degree, strength, betweenness, and the clustering coefficient). Network resilience, the ratio between global efficiency and centralization, was stable with colony size but increased in the presence of broods, presumably in response to the nutritional needs of larvae. Individual metrics highlighted the major role of foragers in food dissemination. In addition, a hierarchical clustering analysis suggested that some domestics acted as intermediaries between foragers and other domestics. Networks appeared to be hierarchical rather than random or centralized exclusively around foragers. Finally, our results suggested that networks emerging from social insect interactions can improve group performance and thus colony fitness.     

Framework for Applying a Complex Adaptive System Approach to Model the Operation of Eco‐Industrial Parks

Models of eco‐industrial parks (EIPs) might help us transform our production systems by fostering the emergence of sustainable EIPs since such models have the potential to support the decision‐making processes of cooperative companies that participate and to decrease operational uncertainties. In this article, a conceptual framework for modeling the operation of EIPs is presented. The framework is underpinned by complex adaptive systems theory, industrial ecology, and an analysis of the experiences of existing EIPs. The proposed framework draws on the observed strengths of two types of industrial symbiosis models—planned eco‐industrial parks (PEIPs) and EIPs that developed through self‐organizing symbiosis (SOS)—as well as their observed weaknesses and the features of complex adaptive systems. From this analysis, five key properties to be modeled are deduced: functionality, reliability, life span, theoretical knowledge, and adaptability. It is proposed that the properties of functionality and theoretical knowledge are determined by the goals of the EIP and its member companies, while the property of adaptability is determined by the understanding that the companies in an EIP have of the environment surrounding the EIP, while the properties of reliability and life span are determined by the internal and external relationships of the companies that make up an EIP.     

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.     

Boolean network analysis of a neurotransmitter signaling pathway

BACKGROUND: A Boolean network is a simple computational model that may provide insight into the overall behavior of genetic networks and is represented by variables with two possible states (on/off), of the individual nodes/genes of the network. In this study, a Boolean network model has been used to simulate a molecular pathway between two neurotransmitter receptor, dopamine and glutamate receptor, systems in order to understand the consequence of using logic gate rules between nodes, which have two possible states (active and inactive). RESULTS: The dynamical properties of this Boolean network model of the biochemical pathway shows that, the pathway is stable and that, deletion/knockout of certain biologically important nodes cause significant perturbation to this network. The analysis clearly shows that in addition to the expected components dopamine and dopamine receptor 2 (DRD2), Ca(2+) ions play a critical role in maintaining stability of the pathway. CONCLUSION: So this method may be useful for the identification of potential genetic targets, whose loss of function in biochemical pathways may be responsible for disease onset. The molecular pathway considered in this study has been implicated with a complex disorder like schizophrenia, which has a complex multifactorial etiology.     

Intrinsic properties of Boolean dynamics in complex networks

We study intrinsic properties of attractor in Boolean dynamics of complex networks with scale-free topology, comparing with those of the so-called Kauffman's random Boolean networks. We numerically study both frozen and relevant nodes in each attractor in the dynamics of relatively small networks (20?N?200). We investigate numerically robustness of an attractor to a perturbation. An attractor with cycle length of ?_c in a network of size N consists of ?_c states in the state space of 2^N states; each attractor has the arrangement of N nodes, where the cycle of attractor sweeps ?_c states. We define a perturbation as a flip of the state on a single node in the attractor state at a given time step. We show that the rate between unfrozen and relevant nodes in the dynamics of a complex network with scale-free topology is larger than that in Kauffman's random Boolean network model. Furthermore, we find that in a complex scale-free network with fluctuation of the in-degree number, attractors are more sensitive to a state flip for a highly connected node (i.e. input-hub node) than to that for a less connected node. By some numerical examples, we show that the number of relevant nodes increases, when an input-hub node is coincident with and/or connected with an output-hub node (i.e. a node with large output-degree) one another.     

Development and validation of hub genes for lymph node metastasis in patients with prostate cancer

Lymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis. We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co‐expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein‐protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well‐distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa.     

Network theory and metapopulation persistence: incorporating node self‐connections

Network analysis is gaining increasing importance in conservation planning. However, which network metrics are the best predictors of metapopulation persistence is still unresolved. Here, we identify a critical limitation of graph theory‐derived network metrics that have been proposed for this purpose: their omission of node self‐connections. We resolve this by presenting modifications of existing network metrics, and developing entirely new metrics, that account for node self‐connections. Then, we illustrate the performance of these new and modified metrics with an age‐structured metapopulation model for a real‐world marine reserve network case study, and we evaluate the robustness of our findings by systematically varying particular features of that network. Our new and modified metrics predict metapopulation persistence much better than existing metrics do, even when self‐connections are weak. Existing metrics become good predictors of persistence only when self‐connections are entirely absent, an unrealistic scenario in the overwhelming majority of metapopulation applications. Our study provides a set of novel tools that can substantially enhance the extent to which network metrics can be employed to understand, and manage for, metapopulation persistence.     

油菜基因流网络的结构特性与小团体分析

为从系统角度掌握转基因油菜（Brassica campestris）基因流规律,基于常规油菜与十字花科植物杂交的有关文献,构建了油菜基因流网络拓扑图实例并分析其网络结构特性。研究结果表明,该网络节点度服从幂律分布,具有无标度特性。从随机攻击和恶性攻击两个方面对标准结构熵和网络效率2个指标的网络稳健性进行分析,结果显示在随机移除不到10%的顶点时网络表现较好的鲁棒性,但在受到选择性移除10%的顶点时,网络具有极弱的抗攻击性。基于软件UCINET对网络进行了小团体和结构同型性分析,可将网络中298个节点22类十字花科植物划分为2个小团体和5类结构角色,其中甘蓝型油菜在基因流网络小团体中具有关键性作用。这些研究结果可为揭示转基因油菜基因流规律提供新思路,同时也可对转基因油菜商业化种植采取合理的农业生产栽培管理措施提供参考。     

A network theory of mental disorders

In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self‐sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.     

On the Complexity of Life Cycle Inventory Networks: Role of Life Cycle Processes with Network Analysis

Determining the relevance and importance of a technosphere process or a cluster of processes in relation to the rest of the industrial network can provide insights into the sustainability of supply chains: those that need to be optimized or controlled/safeguarded. Network analysis (NA) can offer a broad framework of indicators to tackle this problem. In this article, we present a detailed analysis of a life cycle inventory (LCI) model from an NA perspective. Specifically, the network is represented as a directed graph and the “emergy” numeraire is used as the weight associated with the arcs of the network. The case study of a technological system for drinking water production is presented. We investigate the topological and structural characteristics of the network representation of this system and compare properties of its weighted and unweighted network, as well as the importance of nodes (i.e., life cycle unit processes). By identifying a number of advantages and limitations linked to the modeling complexity of such emergy‐LCI networks, we classify the LCI technosphere network of our case study as a complex network belonging to the scale‐free network family. The salient feature of this network family is represented by the presence of “hubs”: nodes that connect with many other nodes. Hub failures may imply relevant changes, decreases, or even breaks in the connectedness with other smaller hubs and nodes of the network. Hence, by identifying node centralities, we can rank and interpret the relevance of each node for its special role in the life cycle network.     

海湾生态韧性的陆海统筹测度与治理研究——基于厦门湾系统性视角

海岸带长期承受陆海生态风险的交互扰动,基于陆海统筹的系统性治理是提升其生态韧性的重要途径。海岸带生态管治条块分割的问题突出,生态系统具有相对独立性的海湾,作为陆海统筹的实施单元,为海岸带生态韧性测度与规划提供了新视角。在梳理海湾生态要素构成的基础上,分析陆海生态灾害诱因及其作用方式,进而厘清海湾生态系统的韧性响应机制。在陆海统筹目标下构建了海湾生态韧性测评指标体系,并以厦门湾为空间单元展开实例研究,采用相关模型剖析厦门湾陆域-海域生态韧性的耦合协调特征及演进驱动因子;聚焦厦门湾生态韧性薄弱之处,从构筑生态安全格局、分级整治韧性空间、联动管控韧性要素、建构跨界协同机制4个维度提出陆海统筹治理策略,以促进厦门湾生态系统可持续发展。该研究的理论建构及实践总结可为其他海湾单元生态文明建设提供参考。     

成渝城市群区域生态与城镇发展双网络格局分析及时空演变

协调生态网络格局与城镇网络格局是城市可持续发展的重要课题。基于2000、2010、2020年成渝城市群3期土地利用数据,应用形态空间格局分析(MSPA)、景观格局指数、景观连通性、最小累计阻力(MCR)模型等方法构建研究区生态源地与生态廊道、经济社会源地与交通廊道形成的区域生态网络格局与城镇发展网络格局,分析节点、廊道、源地、网络等“点、线、面、网”4类空间要素特征与演变趋势,探讨区域双网络格局空间关系与干扰时空变化情况。结果表明：(1)2000—2020年,研究区生态源地与生态廊道受到不同程度城镇化影响,两者总面积分别减少2660.26 km²、1169.9 km²,经济社会源地与交通廊道总面积则分别增长4324.19 km²、2392.14 km²;(2)以重庆中部、四川西南部为核心的高密度区生态关键点在2000—2020年间重心呈现先由东至西南迁移,后又向东及东北回移的趋势,数量减少了311,同时2020年生态干扰点数量达到2000年的3倍;(3)2000—2020年交通廊道对生态源地的干扰不...     

谷氨酸棒状杆菌集成细胞网络的构建与结构分析

谷氨酸棒状杆菌是一种重要的传统工业微生物,其基因组学和分子遗传操作工具的快速发展使得谷氨酸棒状杆菌具备了作为新型细胞工厂的潜力。但是,相对于大肠杆菌等模式生物,对于棒杆菌的代谢调控研究较少,特别是目前还缺乏谷氨酸棒状杆菌集成细胞网络的研究,这一现状阻碍了谷氨酸棒状杆菌的系统生物学研究和大规模菌种理性设计优化。文中综合应用公共数据库、文献数据库资源,首次构建了谷氨酸棒状杆菌的集成细胞网络,包含1 384个反应,1 276个代谢物,88个调节子,999对转录调控关系。其转录调控可分为5层,代谢网络呈现出清晰的bow-tie结构。文中还以赖氨酸的生物合成为例,提出了一种提取代谢调控子网络的新方法,这对氨基酸等产品高产生物机制的研究和工程菌株的重新设计具有指导意义。