The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems

In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries in international and domestic economies and show that these networks are significantly nested. This means that the less filled rows and columns of these networks'' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that their nestedness remains constant over time and that it is sustained by both, a bias for industries that deviate from the networks'' nestedness to disappear, and a bias for the industries that are missing according to nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.     

The Topology of a Discussion: The #Occupy Case

Introduction

We analyse a large sample of the Twitter activity that developed around the social movement ''Occupy Wall Street'', to study the complex interactions between the human communication activity and the semantic content of a debate.

Methods

We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the ''semantic network'', whose nodes are hashtags, and the ''users interest network'', whose nodes are users. In the first instance, we find out that discussion topics (#hashtags) present a high structural heterogeneity, with a relevant role played by the semantic hubs that are responsible to guarantee the continuity of the debate. In the users’ case, the self-organisation process of users’ activity, leads to the emergence of two classes of communicators: the ''professionals'' and the ''amateurs''.

Results

Both the networks present a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when certain sets of topics are censored and/or accounts are removed.

Conclusions

By analysing the characteristics of the dynamical networks we can distinguish three phases of the discussion about the movement. Each phase corresponds to a specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of prototypical #hashtags in the discussion.     

Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

Based on large-scale human mobility data collected in San Francisco and Boston, the morning peak urban rail transit (URT) ODs (origin-destination matrix) were estimated and the most vulnerable URT segments, those capable of causing the largest service interruptions, were identified. In both URT networks, a few highly vulnerable segments were observed. For this small group of vital segments, the impact of failure must be carefully evaluated. A bipartite URT usage network was developed and used to determine the inherent connections between urban rail transits and their passengers'' travel demands. Although passengers'' origins and destinations were easy to locate for a large number of URT segments, a few show very complicated spatial distributions. Based on the bipartite URT usage network, a new layer of the understanding of a URT segment''s vulnerability can be achieved by taking the difficulty of addressing the failure of a given segment into account. Two proof-of-concept cases are described here: Possible transfer of passenger flow to the road network is here predicted in the cases of failures of two representative URT segments in San Francisco.     

Limited Urban Growth: London's Street Network Dynamics since the 18th Century

We investigate the growth dynamics of Greater London defined by the administrative boundary of the Greater London Authority, based on the evolution of its street network during the last two centuries. This is done by employing a unique dataset, consisting of the planar graph representation of nine time slices of Greater London''s road network spanning 224 years, from 1786 to 2010. Within this time-frame, we address the concept of the metropolitan area or city in physical terms, in that urban evolution reveals observable transitions in the distribution of relevant geometrical properties. Given that London has a hard boundary enforced by its long standing green belt, we show that its street network dynamics can be described as a fractal space-filling phenomena up to a capacitated limit, whence its growth can be predicted with a striking level of accuracy. This observation is confirmed by the analytical calculation of key topological properties of the planar graph, such as the topological growth of the network and its average connectivity. This study thus represents an example of a strong violation of Gibrat''s law. In particular, we are able to show analytically how London evolves from a more loop-like structure, typical of planned cities, toward a more tree-like structure, typical of self-organized cities. These observations are relevant to the discourse on sustainable urban planning with respect to the control of urban sprawl in many large cities which have developed under the conditions of spatial constraints imposed by green belts and hard urban boundaries.     

Network-Driven Reputation in Online Scientific Communities

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method''s best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.     

农工共生型生态产业园的构建——以郑州经开区为例

生态产业园已成为我国循环经济建设和可持续发展的重要载体之一,但我国的生态产业园明显存在着产业结构趋同、区域特色不鲜明等问题。提出了农工共生型生态产业园的概念,这是一类以工业园区为核心,集园区周边各类型农业相关企业、合作组织、农民、乡镇及农田为一体的一种"社会-经济-自然"复合区域产业共生体系。农工共生型生态产业园可以实现核心园区与周边腹地的协同发展,可以提升区域整体的生态效率。给出了农工共生型生态产业园的发展模式、建设意义及设计原则。并以农业大省河南省中唯一的国家级经济技术开发区为研究案例,结合当地区域特色及资源优势,构建了由3条产品链及一条服务链所组成的农工共生型产业生态系统。农工共生型生态产业园是产业共生理论在区域层面的具体实现,是复合生态系统理论在产业园层次的创新性应用。     

WHO in crisis.

Media attention has been focused on the leadership of the World Health Organisation, rather than on the real factors that limit WHO''s effectiveness. These factors relate to the organisation''s structure and also to its current priorities, methods, and management. This article examines the objectives and strategy of WHO in view of financial constraints and donor countries'' demands; WHO''s stated goal of integrated primary health care; staff morale; and the growing dislocation between the regions and headquarters.     

Statistically validated networks in bipartite complex systems

Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved.     

A Network Based Method for Analysis of lncRNA-Disease Associations and Prediction of lncRNAs Implicated in Diseases

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer''s disease, pancreatic cancer, and gastric cancer were verified by other independent studies.     

Features of the Correlation Structure of Price Indices

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network''s structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies.     

Marine Protected Area Networks: Assessing Whether the Whole Is Greater than the Sum of Its Parts

Anthropogenic impacts are increasingly affecting the world''s oceans. Networks of marine protected areas (MPAs) provide an option for increasing the ecological and economic benefits often provided by single MPAs. It is vital to empirically assess the effects of MPA networks and to prioritize the monitoring data necessary to explain those effects. We summarize the types of MPA networks based on their intended management outcomes and illustrate a framework for evaluating whether a connectivity network is providing an outcome greater than the sum of individual MPA effects. We use an analysis of an MPA network in Hawai''i to compare networked MPAs to non-networked MPAs to demonstrate results consistent with a network effect. We assert that planning processes for MPA networks should identify their intended outcomes while also employing coupled field monitoring-simulation modeling approaches, a powerful way to prioritize the most relevant monitoring data for empirically assessing MPA network performance.     

北京城市蝴蝶蜜源植物网络特征及重要蜜源植物识别

物种间相互作用网络研究能为物种多样性的保护、城市生态系统稳定性的维持提供指导。基于群落水平的城市蝴蝶蜜源植物互作网络的研究较少,对城市蝴蝶蜜源网络结构缺乏深入认识。研究在国内城市生态系统中构建蝴蝶蜜源网络,并探讨不同类型植物对网络特征的影响。2020年6-9月,在北京26个城市公园中记录访花蝴蝶和蜜源植物物种及互作频次,采用交互多样性（ID）、交互均匀性（IE）、专业化程度（H2''）定量化生态网络结构特征,采用Kruskal-Wallis秩和检验和变差分解分析不同生长型、起源、栽培方式的植物类型对网络结构的影响差异。采用物种的伙伴多样性（PD）和专业化程度（d''）识别重要蜜源植物。研究结果表明：（1）北京城市公园中22种蝴蝶与81种开花植物的交互作用,形成趋于泛化的生态网络结构;（2）不同生长型及不同起源的植物-蝴蝶网络的交互多样性及专业化程度有显著差异,草本及乡土植物对丰富网络中交互多样性和支持专业性更高的蝴蝶物种具重要作用,而植物的栽培方式对蜜源网络结构影响较小;（3）伙伴多样性高且专业化程度高的植物可被视为重要蜜源植物。基于蝴蝶多样性保护的目标,在城市生态系统中,绿色空间应注重构建乡土草本植物群落,优先选择重要蜜源植物。我们的发现印证了蝴蝶-蜜源植物生态网络方法作为联结生态研究和城市绿地实践管理的有效工具,能为城市生态系统中生物多样性保护提供科学策略,具有重要意义。     

Promoting Cold-Start Items in Recommender Systems

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users'' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.     

中国植物分类及标本采集史简述

植物分类学是一门古老的科学,其发展也受到了社会历史变革的深刻影响。该文通过对我国植物分类学研究和发展史进行简要回顾,根据人们对植物分类学科的认识程度以及社会重大历史事件等,将我国植物分类的发展史大体分为原始、古代、近代和现代4个阶段,并对各阶段进行了简要说明。植物标本是植物分类研究最基本和最重要的凭证材料,因此我国的植物标本采集史也见证了植物采集人员在我国植物分类学研究发展的历史过程中所付出的血汗、泪水甚至生命。植物资源的保护和种质资源的收集和保存正日益成为我国生态文明建设的主要内容,在新时代生物多样性保护的背景下,植物分类学和植物分类研究人员将会发挥更重要的作用,为我国的生物多样性保护提供更加有力的支撑。     

Epidemiologically Optimal Static Networks from Temporal Network Data

One of network epidemiology''s central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets.     

Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree–herbivore networks

Nestedness and modularity are measures of ecological networks whose causative effects are little understood. We analyzed antagonistic plant–herbivore bipartite networks using common gardens in two contrasting environments comprised of aspen trees with differing evolutionary histories of defence against herbivores. These networks were tightly connected owing to a high level of specialization of arthropod herbivores that spend a large proportion of the life cycle on aspen. The gardens were separated by ten degrees of latitude with resultant differences in abiotic conditions. We evaluated network metrics and reported similar connectance between gardens but greater numbers of links per species in the northern common garden. Interaction matrices revealed clear nestedness, indicating subsetting of the bipartite interactions into specialist divisions, in both the environmental and evolutionary aspen groups, although nestedness values were only significant in the northern garden. Variation in plant vulnerability, measured as the frequency of herbivore specialization in the aspen population, was significantly partitioned by environment (common garden) but not by evolutionary origin of the aspens. Significant values of modularity were observed in all network matrices. Trait-matching indicated that growth traits, leaf morphology, and phenolic metabolites affected modular structure in both the garden and evolutionary groups, whereas extra-floral nectaries had little influence. Further examination of module configuration revealed that plant vulnerability explained considerable variance in web structure. The contrasting conditions between the two gardens resulted in bottom-up effects of the environment, which most strongly influenced the overall network architecture, however, the aspen groups with dissimilar evolutionary history also showed contrasting degrees of nestedness and modularity. Our research therefore shows that, while evolution does affect the structure of aspen–herbivore bipartite networks, the role of environmental variations is a dominant constraint.     

我国原核微生物分类学七十年北大核心

2022年和2023年分别是中国微生物学会成立70周年和《微生物学报》创刊70周年,我国原核微生物分类学研究从白手起家、跟踪模仿到逐步走上国际舞台,从跟跑、并跑到整体处于国际先进行列,部分领域领跑国际同行,并为国际同行提供微生物数据服务,也已走过了70个岁月。这些成绩的取得是我国原核微生物分类领域学者几代人努力的结果,是与国家发展、民族复兴同频共振的结果。特别是近30年,我国原核微生物分类无论从理论、方法创新还是新物种发现方面都取得了令人瞩目的成绩,在国际上的地位和影响力日益提高,已经逐渐成为国际原核系统分类领域的主导力量。本文本着尊重历史、客观求实的态度梳理了70年来我国在细菌和古菌分类学及相关领域的发展脉络和取得的成绩,并结合该领域最新发展方向进行了展望。