Finding Influential Spreaders from Human Activity beyond Network Location

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.     

Prefrontal cortex lesions impair object-spatial integration

How and where object and spatial information are perceptually integrated in the brain is a central question in visual cognition. Single-unit physiology, scalp EEG, and fMRI research suggests that the prefrontal cortex (PFC) is a critical locus for object-spatial integration. To test the causal participation of the PFC in an object-spatial integration network, we studied ten patients with unilateral PFC damage performing a lateralized object-spatial integration task. Consistent with single-unit and neuroimaging studies, we found that PFC lesions result in a significant behavioral impairment in object-spatial integration. Furthermore, by manipulating inter-hemispheric transfer of object-spatial information, we found that masking of visual transfer impairs performance in the contralesional visual field in the PFC patients. Our results provide the first evidence that the PFC plays a key, causal role in an object-spatial integration network. Patient performance is also discussed within the context of compensation by the non-lesioned PFC.     

Correction to: Testing traditional hypotheses about prey capture efficiency in orb-web spiders

To capture prey, orb-web spiders create complex traps whose efficiency is contingent on a variety of factors that are not yet completely understood, including web size, competition for food, sun exposure, presence of web decorations and web orientation. Here we evaluate such factors in the field and ask which of them are the most influential variables affecting the quantity of prey captured in Argiope argentata webs. Webs were observed during the morning and the number of prey attached to each web was counted. Using the approach of information criteria based on the Akaike information criterion (AIC) values of each candidate model, we averaged the parameters of a global model, finding that the only predictor which 95% confidence interval did not include zero, was exposure to sunlight (whether the web is continuously shaded or continuously exposed to sunlight). All other variables did not explain variation in prey capture. We conclude that only sun exposure has an important effect on orb-web spiders’ prey capture efficiency in A. argentata. We additionally argue that silk decorations have different functions depending on the habitat and the species.

     

Fuzzy Rough Set algorithm with Binary Shuffled Frog-Leaping (BSFL-FRSA): An innovative approach for identifying main drivers of carbon exchange in temperate deciduous forests

The spatial variability, driving forces, and uncertainties of the net ecosystem exchange (NEE) of carbon between the temperate forests and the atmosphere remain elusive. Here, we proposed a fuzzy rough set algorithm with binary shuffled frog leaping (BSFL-FRSA) to identify main driving variables and define the contribution rate of main drivers to NEE. As a case study, we applied the approach to nine deciduous forest eddy covariance flux sites in the northeastern United States. The results show that the BSFL-FRSA effectively retained most information using just a few variables, and it performed better than the GA-FRSA (fuzzy rough set with genetic algorithm). Temperature, radiation, and soil water content were identified as the most influential variables (impact in descending order) to NEE across all sites. Soil temperature was the most important variable explained 59.6% of the NEE variance. Soil temperature and net radiation together, explained 72.7% of the NEE variance, was the most important two variables among all possible two-variable combinations. The most influential three variables on NEE among all possible three-variable combinations were soil temperature, net radiation, and soil water content or relative humidity (explained 81.1% of the NEE variance). The variance attribution approach presented here is generic and can be applied to other studies; the dominant influence of soil temperature begs for accurate characterization of soil temperature dynamics in time and space particularly in the global warming context.     

No need for a cognitive map: decentralized memory for insect navigation

In many animals the ability to navigate over long distances is an important prerequisite for foraging. For example, it is widely accepted that desert ants and honey bees, but also mammals, use path integration for finding the way back to their home site. It is however a matter of a long standing debate whether animals in addition are able to acquire and use so called cognitive maps. Such a 'map', a global spatial representation of the foraging area, is generally assumed to allow the animal to find shortcuts between two sites although the direct connection has never been travelled before. Using the artificial neural network approach, here we develop an artificial memory system which is based on path integration and various landmark guidance mechanisms (a bank of individual and independent landmark-defined memory elements). Activation of the individual memory elements depends on a separate motivation network and an, in part, asymmetrical lateral inhibition network. The information concerning the absolute position of the agent is present, but resides in a separate memory that can only be used by the path integration subsystem to control the behaviour, but cannot be used for computational purposes with other memory elements of the system. Thus, in this simulation there is no neural basis of a cognitive map. Nevertheless, an agent controlled by this network is able to accomplish various navigational tasks known from ants and bees and often discussed as being dependent on a cognitive map. For example, map-like behaviour as observed in honey bees arises as an emergent property from a decentralized system. This behaviour thus can be explained without referring to the assumption that a cognitive map, a coherent representation of foraging space, must exist. We hypothesize that the proposed network essentially resides in the mushroom bodies of the insect brain.     

General Relationship of Global Topology,Local Dynamics,and Directionality in Large-Scale Brain Networks

The balance of global integration and functional specialization is a critical feature of efficient brain networks, but the relationship of global topology, local node dynamics and information flow across networks has yet to be identified. One critical step in elucidating this relationship is the identification of governing principles underlying the directionality of interactions between nodes. Here, we demonstrate such principles through analytical solutions based on the phase lead/lag relationships of general oscillator models in networks. We confirm analytical results with computational simulations using general model networks and anatomical brain networks, as well as high-density electroencephalography collected from humans in the conscious and anesthetized states. Analytical, computational, and empirical results demonstrate that network nodes with more connections (i.e., higher degrees) have larger amplitudes and are directional targets (phase lag) rather than sources (phase lead). The relationship of node degree and directionality therefore appears to be a fundamental property of networks, with direct applicability to brain function. These results provide a foundation for a principled understanding of information transfer across networks and also demonstrate that changes in directionality patterns across states of human consciousness are driven by alterations of brain network topology.     

Population Dynamics Modelling in an Hierarchical Arborescent River Network: An Attempt with Salmo trutta

The balance between births and deaths in an age-structured population is strongly influenced by the spatial distribution of sub-populations. Our aim was to describe the demographic process of a fish population in an hierarchical dendritic river network, by taking into account the possible movements of individuals. We tried also to quantify the effect of river network changes (damming or channelling) on the global fish population dynamics. The Salmo trutta life pattern was taken as an example for.We proposed a model which includes the demographic and the migration processes, considering migration fast compared to demography. The population was divided into three age-classes and subdivided into fifteen spatial patches, thus having 45 state variables. Both processes were described by means of constant transfer coefficients, so we were dealing with a linear system of difference equations. The discrete case of the variable aggregation method allowed the study of the system through the dominant elements of a much simpler linear system with only three global variables: the total number of individuals in each age-class.From biological hypothesis on demographic and migratory parameters, we showed that the global population dynamics of fishes is well characterized in the reference river network, and that dams could have stronger effects on the global dynamics than channelling.     

Integrating multi-attribute similarity networks for robust representation of the protein space

MOTIVATION: A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust representation of the protein space can be realized if multiple sources of information are used. RESULTS: We propose a novel approach for analyzing multi-attribute similarity networks by combining random walks on graphs with Bayesian theory. A multi-attribute network is created by combining sequence and structure based similarity measures. For each attribute of the similarity network, one can compute a measure of affinity from a given protein to every other protein in the network using random walks. This process makes use of the implicit clustering information of the similarity network, and we show that it is superior to naive, local ranking methods. We then combine the computed affinities using a Bayesian framework. In particular, when we train a Bayesian model for automated classification of a novel protein, we achieve high classification accuracy and outperform single attribute networks. In addition, we demonstrate the effectiveness of our technique by comparison with a competing kernel-based information integration approach.     

Keystone species and food webs

Different species are of different importance in maintaining ecosystem functions in natural communities. Quantitative approaches are needed to identify unusually important or influential, ‘keystone’ species particularly for conservation purposes. Since the importance of some species may largely be the consequence of their rich interaction structure, one possible quantitative approach to identify the most influential species is to study their position in the network of interspecific interactions. In this paper, I discuss the role of network analysis (and centrality indices in particular) in this process and present a new and simple approach to characterizing the interaction structures of each species in a complex network. Understanding the linkage between structure and dynamics is a condition to test the results of topological studies, I briefly overview our current knowledge on this issue. The study of key nodes in networks has become an increasingly general interest in several disciplines: I will discuss some parallels. Finally, I will argue that conservation biology needs to devote more attention to identify and conserve keystone species and relatively less attention to rarity.     

Design and Analysis of Bioenergy Networks

This article presents a new methodology for designing industrial networks and analyzing them dynamically from the standpoint of sustainable development. The approach uses a combination of optimization and simulation tools. Assuming "top-down" overarching control of the network, we use global dynamic optimization to determine which evolutionary pathways are preferred in terms of economic, social, and environmental performance. Considering the autonomy of network entities and their actions, we apply agent-based simulation to analyze how the network actually evolves. These two perspectives are integrated into a powerful multiscale modeling framework for evaluating the consequences of new policy instruments or different business strategies aimed at stimulating sustainable development as well as identifying optimal leverage points for improved performance of the network in question. The approach is demonstrated for a regional network of interdependent organizations deploying a set of bioenergy technologies within a developing-economy context.     

生态学中的尺度问题——尺度上推

尺度推绎是生态学理论和应用的核心。如何在一个异质景观中进行尺度推绎仍然是一个悬而未决的科学难题,是对当今生态学家在全球变化背景下研究环境问题的重大挑战。就目前的研究,一般可分为四大类尺度推绎途径:空间分析法(如分维分析法和小波分析法)、基于相似性的尺度上推方法、基于局域动态模型的尺度上推方法、随机(模型)法。基于相似性的尺度上推方法来源于生物学上的异量关联,可将其思想延伸至空间上,研究物种丰富度、自然河网、地形特征、生态学格局或过程变量和景观指数等。基于局域动态模型的尺度上推方法需要首先确定是否进行跨尺度推绎,以及是否考虑空间单元之间的水平相互作用和反馈,然后再应用具体的方法或途径,如简单聚合法、有效值外推法、直接外推法、期望值外推、显式积分法和空间相互作用模拟法等。随机(模型)法以其它尺度上推方法为基础,根据研究的是单个景观,还是多个景观,采用不同的途径。理解、定量和降低尺度推绎结果的不确定性已经变得越来越重要,但相关研究仍然极少。以上所有有关尺度推绎的方法、途径和结果分析共同构成了尺度推绎的概念框架。     

Impact of the topology of global macroeconomic network on the spreading of economic crises

Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of a network of interacting macroeconomic agents. In this regard, from the perspective of collective network dynamics, here we explore how the topology of the global macroeconomic network affects the patterns of spreading of economic crises. Using a simple toy model of crisis spreading, we demonstrate that an individual country's role in crisis spreading is not only dependent on its gross macroeconomic capacities, but also on its local and global connectivity profile in the context of the world economic network. We find that on one hand clustering of weak links at the regional scale can significantly aggravate the spread of crises, but on the other hand the current network structure at the global scale harbors higher tolerance of extreme crises compared to more "globalized" random networks. These results suggest that there can be a potential hidden cost in the ongoing globalization movement towards establishing less-constrained, trans-regional economic links between countries, by increasing vulnerability of the global economic system to extreme crises.     

植物多样性监测研究进展

监测是植物保护工作的重要基础,也是管理者决策的重要依据,对生物资源的可持续利用和保护至关重要。本研究综述了植物多样性监测的进展,就未来的热点和方向进行了探讨。植物多样性监测正以全新的局面飞速发展,已经进入了智能化、宏观与微观结合、联网监测、大数据、大尺度、多学科、全方位、立体化、由物种水平扩展到科属水平、群落或生态系统层面的监测时代;生物多样性监测网络的建设促进了联网监测与核心监测指标体系的统一,网络资源成为植物多样性监测的大数据源。数据的标准化与有效利用、大数据共享、遗传多样性和个体物种水平监测是监测植物多样性的机遇和挑战。未来的生态监测是大尺度、智能化、统一化的监测,完善监测网络体系,寻找新方法、构建新模型,开展热点区域和优先物种监测,并兼顾群落或更大尺度,重视植物基础本底数据的收集,是今后监测的重点。     

Annotating gene function by combining expression data with a modular gene network

MOTIVATION: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks. RESULTS: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.     

A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana

One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.     

Optimization of bioreactor using metabolic control analysis approach

A new methodology based on a metabolic control analysis (MCA) approach is developed for the optimization of continuous cascade bioreactor system. A general framework for representation of a cascade bioreactor system consisting of a large number of reactors as a single network is proposed. The kinetic and transport processes occurring in the system are represented as a reaction network with appropriate stoichiometry. Such representation of the bioreactor systems makes it amenable to the direct application of the MCA approach. The process sensitivity information is extracted using MCA methodology in the form of flux and concentration control coefficients. The process sensitivity information is shown to be a useful guide for determining the choice of decision variables for the purpose of optimization. A generalized problem of optimization of the bioreactor is formulated in which the decision variables are the operating conditions and kinetic parameters. The gradient of the objective function to be maximized with respect to all decision variables is obtained in the form of response coefficients. This gradient information can be used in any gradient-based optimization algorithm. The efficiency of the proposed technique is demonstrated with two examples taken from literature: biotransformation of crotonobetaine and alcohol fermentation in cascade bioreactor system.     

Brain Anatomical Network and Intelligence

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.     

Graph theoretical analysis of complex networks in the brain

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.     

What do we mean by biological complexity?

The purpose of the present paper is to offer a precise definition of the concepts of integration, emergence and complexity in biological networks through the use of the information theory. If two distinct properties of a network are expressed by two discrete variables, the classical subadditivity principle of Shannon's information theory applies when all the nodes of the network are associated with these properties. If not, the subadditivity principle may not apply. This situation is often to be encountered with enzyme and metabolic networks, for some nodes may well not be associated with these two properties. This is precisely what is occurring with an enzyme that binds randomly its two substrates. This situation implies that an enzyme, or a metabolic network, may display a joint entropy equal, smaller, or larger than the corresponding sum of individual entropies of component sub-systems. In the first case, the collective properties of the network can be reduced to the individual properties of its components. Moreover, the network is devoid of any information. In the second case, the system displays integration effects, behaves as a coherent whole, and has positive information. But if the joint entropy of the network is smaller than the sum of the individual entropies of its components, then the system has emergent collective properties and can be considered complex. Moreover, under these conditions, its information is negative. The extent of negative information is enhanced if the enzyme, or the metabolic network, is far away from equilibrium.