A game theoretic algorithm to detect overlapping community structure in networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Zhao, Xiaohui; Liu, Yanheng; Sun, Geng

2018-04-01

Community detection can be used as an important technique for product and personalized service recommendation. A game theory based approach to detect overlapping community structure is introduced in this paper. The process of the community formation is converted into a game, when all agents (nodes) cannot improve their own utility, the game process will be terminated. The utility function is composed of a gain and a loss function and we present a new gain function in this paper. In addition, different from choosing action randomly among join, quit and switch for each agent to get new label, two new strategies for each agent to update its label are designed during the game, and the strategies are also evaluated and compared for each agent in order to find its best result. The overlapping community structure is naturally presented when the stop criterion is satisfied. The experimental results demonstrate that the proposed algorithm outperforms other similar algorithms for detecting overlapping communities in networks.

A new hierarchical method to find community structure in networks

NASA Astrophysics Data System (ADS)

Saoud, Bilal; Moussaoui, Abdelouahab

2018-04-01

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Detection of communities with Naming Game-based methods

PubMed Central

Ribeiro, Carlos Henrique Costa

2017-01-01

Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection. PMID:28797097

Detection of stable community structures within gut microbiota co-occurrence networks from different human populations.

PubMed

Jackson, Matthew A; Bonder, Marc Jan; Kuncheva, Zhana; Zierer, Jonas; Fu, Jingyuan; Kurilshikov, Alexander; Wijmenga, Cisca; Zhernakova, Alexandra; Bell, Jordana T; Spector, Tim D; Steves, Claire J

2018-01-01

Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.

Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics

PubMed Central

Sgier, Linn; Freimann, Remo; Zupanic, Anze; Kroll, Alexandra

2016-01-01

Biofilms serve essential ecosystem functions and are used in different technical applications. Studies from stream ecology and waste-water treatment have shown that biofilm functionality depends to a great extent on community structure. Here we present a fast and easy-to-use method for individual cell-based analysis of stream biofilms, based on stain-free flow cytometry and visualization of the high-dimensional data by viSNE. The method allows the combined assessment of community structure, decay of phototrophic organisms and presence of abiotic particles. In laboratory experiments, it allows quantification of cellular decay and detection of survival of larger cells after temperature stress, while in the field it enables detection of community structure changes that correlate with known environmental drivers (flow conditions, dissolved organic carbon, calcium) and detection of microplastic contamination. The method can potentially be applied to other biofilm types, for example, for inferring community structure for environmental and industrial research and monitoring. PMID:27188265

Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

NASA Astrophysics Data System (ADS)

Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

2016-11-01

Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

Overlapping community detection based on link graph using distance dynamics

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Jing; Cai, Li-Jun

2018-01-01

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

A Shadowing Problem in the Detection of Overlapping Communities: Lifting the Resolution Limit through a Cascading Procedure

PubMed Central

Young, Jean-Gabriel; Allard, Antoine; Hébert-Dufresne, Laurent; Dubé, Louis J.

2015-01-01

Community detection is the process of assigning nodes and links in significant communities (e.g. clusters, function modules) and its development has led to a better understanding of complex networks. When applied to sizable networks, we argue that most detection algorithms correctly identify prominent communities, but fail to do so across multiple scales. As a result, a significant fraction of the network is left uncharted. We show that this problem stems from larger or denser communities overshadowing smaller or sparser ones, and that this effect accounts for most of the undetected communities and unassigned links. We propose a generic cascading approach to community detection that circumvents the problem. Using real and artificial network datasets with three widely used community detection algorithms, we show how a simple cascading procedure allows for the detection of the missing communities. This work highlights a new detection limit of community structure, and we hope that our approach can inspire better community detection algorithms. PMID:26461919

Community detection in sequence similarity networks based on attribute clustering

DOE PAGES

Chowdhary, Janamejaya; Loeffler, Frank E.; Smith, Jeremy C.

2017-07-24

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs,more » for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments« less

Community detection in sequence similarity networks based on attribute clustering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chowdhary, Janamejaya; Loeffler, Frank E.; Smith, Jeremy C.

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs,more » for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments« less

A Stochastic Model for Detecting Overlapping and Hierarchical Community Structure

PubMed Central

Cao, Xiaochun; Wang, Xiao; Jin, Di; Guo, Xiaojie; Tang, Xianchao

2015-01-01

Community detection is a fundamental problem in the analysis of complex networks. Recently, many researchers have concentrated on the detection of overlapping communities, where a vertex may belong to more than one community. However, most current methods require the number (or the size) of the communities as a priori information, which is usually unavailable in real-world networks. Thus, a practical algorithm should not only find the overlapping community structure, but also automatically determine the number of communities. Furthermore, it is preferable if this method is able to reveal the hierarchical structure of networks as well. In this work, we firstly propose a generative model that employs a nonnegative matrix factorization (NMF) formulization with a l2,1 norm regularization term, balanced by a resolution parameter. The NMF has the nature that provides overlapping community structure by assigning soft membership variables to each vertex; the l2,1 regularization term is a technique of group sparsity which can automatically determine the number of communities by penalizing too many nonempty communities; and hence the resolution parameter enables us to explore the hierarchical structure of networks. Thereafter, we derive the multiplicative update rule to learn the model parameters, and offer the proof of its correctness. Finally, we test our approach on a variety of synthetic and real-world networks, and compare it with some state-of-the-art algorithms. The results validate the superior performance of our new method. PMID:25822148

Followers are not enough: a multifaceted approach to community detection in online social networks.

PubMed

Darmon, David; Omodei, Elisa; Garland, Joshua

2015-01-01

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a 'community' as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of 'community.' In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.

Locating Structural Centers: A Density-Based Clustering Method for Community Detection

PubMed Central

Liu, Gongshen; Li, Jianhua; Nees, Jan P.

2017-01-01

Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods. PMID:28046030

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

NASA Astrophysics Data System (ADS)

Cui, Yaozu; Wang, Xingyuan; Eustace, Justine

2014-12-01

Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.

A generalised significance test for individual communities in networks.

PubMed

Kojaku, Sadamori; Masuda, Naoki

2018-05-09

Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.

A clustering algorithm for determining community structure in complex networks

NASA Astrophysics Data System (ADS)

Jin, Hong; Yu, Wei; Li, ShiJun

2018-02-01

Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

NASA Astrophysics Data System (ADS)

Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

2016-07-01

The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

An ant colony based algorithm for overlapping community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

2015-06-01

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

Structure of Mesozooplankton Communities in the Coastal Waters of Morocco

NASA Astrophysics Data System (ADS)

Lidvanov, V. V.; Grabko, O. G.; Kukuev, E. I.; Korolkova, T. G.

2018-03-01

Mero- and holoplanktonic organisms from 23 large taxa have been detected in the coastal waters of Morocco. Seven Cladocera species and 164 Copepoda species were identified. Copepod fauna mostly consisted of oceanic epipelagic widely tropical species, but the constant species group (frequency of occurrence over 50%) included neritic and neritic-oceanic widely tropical species. The neritic community that formed a biotopic association with coastal upwelling waters and the distant-neritic community associated with Canary Current waters were the two major communities detected. The former community was characterized by a high abundance and biomass (5700 ind./m3 and 260 mg/m3) and predominance of neritic species. The trophic structure was dominated by thin filter feeders, mixed-food consumers, and small grabbers; the species structure was dominated by Paracalanus indicus, Acartia clausi, and Oncaea curta; the indices of species diversity (3.07 bit/ind.) and evenness (0.63) were relatively low. The latter community was characterized by low abundance and biomass (1150 ind./m3 and 90 mg/m3); variable biotopic, trophic, and species structure; and higher Shannon indices (3.99 bit/ind.) and Pielou (0.75). Seasonal variation of the abundance of organisms was not detected in the communities. Anomalous mesozooplankton states were observed in summer 1998 and winter 1998-1999.

Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

NASA Astrophysics Data System (ADS)

Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen

2013-08-01

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

Mixture models with entropy regularization for community detection in networks

NASA Astrophysics Data System (ADS)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Followers Are Not Enough: A Multifaceted Approach to Community Detection in Online Social Networks

PubMed Central

2015-01-01

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a ‘community’ as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of ‘community.’ In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure. PMID:26267868

Evolutionary method for finding communities in bipartite networks.

PubMed

Zhan, Weihua; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng

2011-06-01

An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework-detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

Fire modifies the phylogenetic structure of soil bacterial co-occurrence networks.

PubMed

Pérez-Valera, Eduardo; Goberna, Marta; Faust, Karoline; Raes, Jeroen; García, Carlos; Verdú, Miguel

2017-01-01

Fire alters ecosystems by changing the composition and community structure of soil microbes. The phylogenetic structure of a community provides clues about its main assembling mechanisms. While environmental filtering tends to reduce the community phylogenetic diversity by selecting for functionally (and hence phylogenetically) similar species, processes like competitive exclusion by limiting similarity tend to increase it by preventing the coexistence of functionally (and phylogenetically) similar species. We used co-occurrence networks to detect co-presence (bacteria that co-occur) or exclusion (bacteria that do not co-occur) links indicative of the ecological interactions structuring the community. We propose that inspecting the phylogenetic structure of co-presence or exclusion links allows to detect the main processes simultaneously assembling the community. We monitored a soil bacterial community after an experimental fire and found that fire altered its composition, richness and phylogenetic diversity. Both co-presence and exclusion links were more phylogenetically related than expected by chance. We interpret such a phylogenetic clustering in co-presence links as a result of environmental filtering, while that in exclusion links reflects competitive exclusion by limiting similarity. This suggests that environmental filtering and limiting similarity operate simultaneously to assemble soil bacterial communities, widening the traditional view that only environmental filtering structures bacterial communities. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Andy; Sanders, Geoffrey; Henson, Van

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is idealmore » for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.« less

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

Detection of forest stand-level spatial structure in ectomycorrhizal fungal communities

Treesearch

Erik A. Lilleskov; Thomas D. Bruns; Thomas R. Horton; D. Lee Taylor; Paul Grogan

2004-01-01

Ectomycorrhizal fungal (EMF) communities are highly diverse at the stand level. To begin to understand what might lead to such diversity, and to improve sampling designs, we investigated the spatial structure of these communities. We used EMF community data from a number of studies carried out in seven mature and one recently fire-initiated forest stand. We applied...

A density-based clustering model for community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhao, Xiang; Li, Yantao; Qu, Zehui

2018-04-01

Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

A Comparison of Microbial Community Structures by Depth and Season Under Switchgrass

NASA Astrophysics Data System (ADS)

Fansler, S. J.; Smith, J. L.; Bolton, H.; Bailey, V. L.

2008-12-01

As part of a multidisciplinary study of C sequestration in switchgrass production systems, the soil microbial community structure was monitored at 6 different depths (reaching 90 cm) in both spring and autumn. Microbial community structure was assessed using ribosomal intergenic spacer analysis (RISA), and primers were used specific to either bacteria or fungi, generating microbial community fingerprints for each taxonomic group. Diverse microbial communities for both groups were detected throughout the soil profile. It is notable that while community structure clearly changed with depth, there was the deepest soil samples still retained relatively diverse communities. Seasonally, differences are clearly evident within plots at the surface. As the plots were replicated, significant differences in the community fingerprints with depth and season are reported.

Approximation of Nash equilibria and the network community structure detection problem

PubMed Central

2017-01-01

Game theory based methods designed to solve the problem of community structure detection in complex networks have emerged in recent years as an alternative to classical and optimization based approaches. The Mixed Nash Extremal Optimization uses a generative relation for the characterization of Nash equilibria to identify the community structure of a network by converting the problem into a non-cooperative game. This paper proposes a method to enhance this algorithm by reducing the number of payoff function evaluations. Numerical experiments performed on synthetic and real-world networks show that this approach is efficient, with results better or just as good as other state-of-the-art methods. PMID:28467496

Ensemble method: Community detection based on game theory

NASA Astrophysics Data System (ADS)

Zhang, Xia; Xia, Zhengyou; Xu, Shengwu; Wang, J. D.

2014-08-01

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

The optimal community detection of software based on complex networks

NASA Astrophysics Data System (ADS)

Huang, Guoyan; Zhang, Peng; Zhang, Bing; Yin, Tengteng; Ren, Jiadong

2016-02-01

The community structure is important for software in terms of understanding the design patterns, controlling the development and the maintenance process. In order to detect the optimal community structure in the software network, a method Optimal Partition Software Network (OPSN) is proposed based on the dependency relationship among the software functions. First, by analyzing the information of multiple execution traces of one software, we construct Software Execution Dependency Network (SEDN). Second, based on the relationship among the function nodes in the network, we define Fault Accumulation (FA) to measure the importance of the function node and sort the nodes with measure results. Third, we select the top K(K=1,2,…) nodes as the core of the primal communities (only exist one core node). By comparing the dependency relationships between each node and the K communities, we put the node into the existing community which has the most close relationship. Finally, we calculate the modularity with different initial K to obtain the optimal division. With experiments, the method OPSN is verified to be efficient to detect the optimal community in various softwares.

Relationship between the Decomposition Process of Coarse Woody Debris and Fungal Community Structure as Detected by High-Throughput Sequencing in a Deciduous Broad-Leaved Forest in Japan

PubMed Central

Yamashita, Satoshi; Masuya, Hayato; Abe, Shin; Masaki, Takashi; Okabe, Kimiko

2015-01-01

We examined the relationship between the community structure of wood-decaying fungi, detected by high-throughput sequencing, and the decomposition rate using 13 years of data from a forest dynamics plot. For molecular analysis and wood density measurements, drill dust samples were collected from logs and stumps of Fagus and Quercus in the plot. Regression using a negative exponential model between wood density and time since death revealed that the decomposition rate of Fagus was greater than that of Quercus. The residual between the expected value obtained from the regression curve and the observed wood density was used as a decomposition rate index. Principal component analysis showed that the fungal community compositions of both Fagus and Quercus changed with time since death. Principal component analysis axis scores were used as an index of fungal community composition. A structural equation model for each wood genus was used to assess the effect of fungal community structure traits on the decomposition rate and how the fungal community structure was determined by the traits of coarse woody debris. Results of the structural equation model suggested that the decomposition rate of Fagus was affected by two fungal community composition components: one that was affected by time since death and another that was not affected by the traits of coarse woody debris. In contrast, the decomposition rate of Quercus was not affected by coarse woody debris traits or fungal community structure. These findings suggest that, in the case of Fagus coarse woody debris, the fungal community structure is related to the decomposition process of its host substrate. Because fungal community structure is affected partly by the decay stage and wood density of its substrate, these factors influence each other. Further research on interactive effects is needed to improve our understanding of the relationship between fungal community structure and the woody debris decomposition process. PMID:26110605

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

ERIC Educational Resources Information Center

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Sequential detection of temporal communities by estrangement confinement.

PubMed

Kawadia, Vikas; Sreenivasan, Sameet

2012-01-01

Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. To reliably detect temporal communities we need to not only find a good community partition in a given snapshot but also ensure that it bears some similarity to the partition(s) found in the previous snapshot(s), a particularly difficult task given the extreme sensitivity of community structure yielded by current methods to changes in the network structure. Here, motivated by the inertia of inter-node relationships, we present a new measure of partition distance called estrangement, and show that constraining estrangement enables one to find meaningful temporal communities at various degrees of temporal smoothness in diverse real-world datasets. Estrangement confinement thus provides a principled approach to uncovering temporal communities in evolving networks.

Subcommunities and Their Mutual Relationships in a Transaction Network

NASA Astrophysics Data System (ADS)

Iino, T.; Iyetomi, H.

We investigate a Japanese transaction network consisting ofabout 800 thousand firms (nodes) and four million business relations (links) with focus on its modular structure. Communities detected by maximizing modularity often are dominated by firms with common features or behaviors in the network, such as characterized by regions or industry sectors. However, it is well known that the modularity optimization approach has a resolution limit problem, that is, it fails in identifying fine communities buried in large communities. To unfold such hidden structures, we apply the community detection to each of subnetworks formed by isolating those communities from the whole body. Subcommunities thus identified are composed of firms with finer regions, more specified sectors or business affiliations. Also we introduce a new idea of reduced modularity matrix to measure the strength of relations between (sub)communities.

Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection.

PubMed

Surian, Didi; Nguyen, Dat Quoc; Kennedy, Georgina; Johnson, Mark; Coiera, Enrico; Dunn, Adam G

2016-08-29

In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy. The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines.

Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection

PubMed Central

Nguyen, Dat Quoc; Kennedy, Georgina; Johnson, Mark; Coiera, Enrico; Dunn, Adam G

2016-01-01

Background In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Objective Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. Methods The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. Results We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy. Conclusions The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines. PMID:27573910

A mathematical programming approach for sequential clustering of dynamic networks

NASA Astrophysics Data System (ADS)

Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

2016-02-01

A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

[Effects of Perfluoroalkyl Substances on the Microbial Community Structure in Surface Sediments of Typical River, China].

PubMed

Sun, Ya-jun; Wang, Tie-yu; Peng, Xia-wei; Wang, Pei

2015-07-01

In order to reveal the relationship between Perfluoroalkyl substances (PFASs) contamination and the bacterial community composition, surface sediment samples were collected along the Xiaoqing River in Shandong Province in April and July 2014 (XQ1-XQ10), where many PFASs manufacturers were located. PFASs were quantified by HPLC/MS-MS, related environmental factors affecting the microbial community structure were measured, and the microbial community structure in surface sediments was measured by the second-generation sequencing technology Illumina MiSeq. The results not only revealed the degree of PFASs pollution in the sediments of Xiaoqing River, but also illustrated the relationship between PFASs pollution and the microbial community structure. Among the twelve kinds of PFASs detected in this study, PFOA was the predominant compound, and the highest PFOA concentrations were detected in the sample of XQ5 (April: 456. 2 ng. g-1; July: 748.7 ng . g-1) located at the downstream of Xiaoqing River with many fluoropolymer producing facilities. PFOA contamination was the main factor affecting the microbial community structure in April, accordingly community richness and evenness were significantly negatively correlated with PFOA levels. The abundance of Thiobacillus increased with the increasing PFOA concentration in the sediment PFOA. This suggested that Thiobacillus was sensitive to PFOA pollution and might be the potential indicator to reveal the degree of PFOA pollution in sediment. When the concentrations of PFOA were below 100 ng . g-1, no significant effects on the microbial community structure were observed.

Social significance of community structure: Statistical view

NASA Astrophysics Data System (ADS)

Li, Hui-Jia; Daniels, Jasmine J.

2015-01-01

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p -value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

Information dynamics algorithm for detecting communities in networks

NASA Astrophysics Data System (ADS)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

An efficient semi-supervised community detection framework in social networks.

PubMed

Li, Zhen; Gong, Yong; Pan, Zhisong; Hu, Guyu

2017-01-01

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.

Scalable detection of statistically significant communities and hierarchies, using message passing for modularity

PubMed Central

Zhang, Pan; Moore, Cristopher

2014-01-01

Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods. PMID:25489096

Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation.

PubMed

Gadelkarim, Johnson J; Ajilore, Olusola; Schonfeld, Dan; Zhan, Liang; Thompson, Paul M; Feusner, Jamie D; Kumar, Anand; Altshuler, Lori L; Leow, Alex D

2014-05-01

In this article, we present path length associated community estimation (PLACE), a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, Ψ(PL), which measures the difference between intercommunity versus intracommunity path lengths. We compared community structures in human healthy brain networks generated using these two metrics and argued that Ψ(PL) may have theoretical advantages. PLACE consists of the following: (1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, (2) constructing and assessing mean group community structure, and (3) detecting node-level changes in community between groups. We applied PLACE and investigated the structural brain networks obtained from a sample of 25 euthymic bipolar I subjects versus 25 gender- and age-matched healthy controls. Results showed community structural differences in posterior default mode network regions, with the bipolar group exhibiting left-right decoupling. Copyright © 2013 Wiley Periodicals, Inc.

Community detection in complex networks using link prediction

NASA Astrophysics Data System (ADS)

Cheng, Hui-Min; Ning, Yi-Zi; Yin, Zhao; Yan, Chao; Liu, Xin; Zhang, Zhong-Yuan

2018-01-01

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

[Microbial Community Structure on the Root Surface of Patients with Periodontitis.

PubMed

Zhang, Ju-Mei; Zhou, Jian-Ye; Bo, Lei; Hu, Xiao-Pan; Jiao, Kang-Li; Li, Zhi-Jie; Li, Yue-Hong; Li, Zhi-Qiang

2016-11-01

To study the microbial community structure on the root surface of patients with periodontitis. Bacterial plaque and tissues from the root neck (RN group),root middle (RM group) and root tine (RT group) of six teeth with mobility 3 in one patient with periodontitis were sampled.The V3V4 region of 16S rRNA was sequenced on the Illumina MiSeq platform.The microbial community structure was analyzed by Mothur,Qiime and SPSS software. The principal component analysis (PCoA) results indicated that the RM samples had a similar microbial community structure as that of the RT samples,which was significant different from that of the RN samples.Thirteen phyla were detected in the three groups of samples,which included 7 dominant phyla.29 dominant genera were detected in 184 genera.The abundance of Bacteroidetes _[G-6] and Peptostre ptococcaceae _[XI][G-4] had a positive correlation with the depth of the collection site of samples ( P <0.05),while the abundance of Prevotella,Selenomonas,Corynebacterium and Olsenella had a negative correlation with the depth of the collection site of samples ( P <0.05). There is region-specificity of microbial community structure on the root surface of patients with periodontitis.

A novel community detection method in bipartite networks

NASA Astrophysics Data System (ADS)

Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

2018-02-01

Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

PubMed

Jeub, Lucas G S; Balachandran, Prakash; Porter, Mason A; Mucha, Peter J; Mahoney, Michael W

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

Think locally, act locally: Detection of small, medium-sized, and large communities in large networks

NASA Astrophysics Data System (ADS)

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

Effect of structured teaching programme on VIA test for early detection and diagnosis of cervical cancer.

PubMed

Chacko, Shiny

2014-01-01

The conceptual framework of the study, undertaken in select health centres of New Delhi, was based on General System Model. The research approach was evaluative with one group pre-test and post-test design. The study population comprised of Community Health Workers working in selected centres in Najafgarh, Delhi. Purposive sampling technique was used to select a sample of 30 Community Health Workers. A structured knowledge questionnaire was developed to assess the knowledge of subjects. A Structured Teaching Programme was developed to enhance the knowledge of Community Health Workers. Pre-test was given on day 1 and Structured Teaching Programme administered on same day. Post-test was conducted on day 7. Most of the Community Health Workers were in the age group of 21-30 years with academic qualification up to Higher Secondary level. Maximum Community Health Workers had professional qualification as ANM/MPHW (female). Majority of the Community Health Workers had experience up to 5 years. Initially there was deficit in scores of knowledge of Community Health Workers regarding Visual Inspection with Acetic Acid (VIA) test. Mean post-test knowledge scores of Community Health Workers were found to be signifi- cantly higher than their mean pre-test knowledge score. The Community Health Workers after expo- sure to Structured Teaching Programme gained a significant positive relationship between post-test knowledge scores. The study reveals the efficacy of Structured Teaching Programme in enhancing the knowledge of Community Health Workers regarding VIA test and a need for conducting a regular and well planned health teaching programme on VIA test for improving their knowledge on VIA test for the early detection and diagnosis of cervical cancer.

A novel dynamical community detection algorithm based on weighting scheme

NASA Astrophysics Data System (ADS)

Li, Ju; Yu, Kai; Hu, Ke

2015-12-01

Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

Efficient discovery of overlapping communities in massive networks

PubMed Central

Gopalan, Prem K.; Blei, David M.

2013-01-01

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. PMID:23950224

Active bacterial community structure along vertical redox gradients in Baltic Sea sediment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jansson, Janet; Edlund, Anna; Hardeman, Fredrik

Community structures of active bacterial populations were investigated along a vertical redox profile in coastal Baltic Sea sediments by terminal-restriction fragment length polymorphism (T-RFLP) and clone library analysis. According to correspondence analysis of T-RFLP results and sequencing of cloned 16S rRNA genes, the microbial community structures at three redox depths (179 mV, -64 mV and -337 mV) differed significantly. The bacterial communities in the community DNA differed from those in bromodeoxyuridine (BrdU)-labeled DNA, indicating that the growing members of the community that incorporated BrdU were not necessarily the most dominant members. The structures of the actively growing bacterial communities weremore » most strongly correlated to organic carbon followed by total nitrogen and redox potentials. Bacterial identification by sequencing of 16S rRNA genes from clones of BrdU-labeled DNA and DNA from reverse transcription PCR (rt-PCR) showed that bacterial taxa involved in nitrogen and sulfur cycling were metabolically active along the redox profiles. Several sequences had low similarities to previously detected sequences indicating that novel lineages of bacteria are present in Baltic Sea sediments. Also, a high number of different 16S rRNA gene sequences representing different phyla were detected at all sampling depths.« less

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Mineralization Of PAHs In Coal-Tar Impacted Aquifer Sediments And Associated Microbial Community Structure Investigated With FISH

EPA Science Inventory

The microbial community structure and mineralization of polycyclic aromatic hydrocarbons (PAHs) in a coal-tar contaminated aquifer were investigated spatially using fluorescence in situ hybridization (FISH) and in laboratory-scale incubations of the aquifer sediments. DAPI-detect...

Estimating the resolution limit of the map equation in community detection

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro; Rosvall, Martin

2015-01-01

A community detection algorithm is considered to have a resolution limit if the scale of the smallest modules that can be resolved depends on the size of the analyzed subnetwork. The resolution limit is known to prevent some community detection algorithms from accurately identifying the modular structure of a network. In fact, any global objective function for measuring the quality of a two-level assignment of nodes into modules must have some sort of resolution limit or an external resolution parameter. However, it is yet unknown how the resolution limit affects the so-called map equation, which is known to be an efficient objective function for community detection. We derive an analytical estimate and conclude that the resolution limit of the map equation is set by the total number of links between modules instead of the total number of links in the full network as for modularity. This mechanism makes the resolution limit much less restrictive for the map equation than for modularity; in practice, it is orders of magnitudes smaller. Furthermore, we argue that the effect of the resolution limit often results from shoehorning multilevel modular structures into two-level descriptions. As we show, the hierarchical map equation effectively eliminates the resolution limit for networks with nested multilevel modular structures.

Traveling salesman problems with PageRank Distance on complex networks reveal community structure

NASA Astrophysics Data System (ADS)

Jiang, Zhongzhou; Liu, Jing; Wang, Shuai

2016-12-01

In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.

Phase transition of Surprise optimization in community detection

NASA Astrophysics Data System (ADS)

Xiang, Ju; Tang, Yan-Ni; Gao, Yuan-Yuan; Liu, Lang; Hao, Yi; Li, Jian-Ming; Zhang, Yan; Chen, Shi

2018-02-01

Community detection is one of important issues in the research of complex networks. In literatures, many methods have been proposed to detect community structures in the networks, while they also have the scope of application themselves. In this paper, we investigate an important measure for community detection, Surprise (Aldecoa and Marín, Sci. Rep. 3 (2013) 1060), by focusing on the critical points in the merging and splitting of communities. We firstly analyze the critical behavior of Surprise and give the phase diagrams in community-partition transition. The results show that the critical number of communities for Surprise has a super-exponential increase with the increase of the link-density difference, while it is close to that of Modularity for small difference between inter- and intra-community link densities. By directly optimizing Surprise, we experimentally test the results on various networks, following a series of comparisons with other classical methods, and further find that the heterogeneity of networks could quicken the splitting of communities. On the whole, the results show that Surprise tends to split communities due to various reasons such as the heterogeneity in link density, degree and community size, and it thus exhibits higher resolution than other methods, e.g., Modularity, in community detection. Finally, we provide several approaches for enhancing Surprise.

The soil carbon/nitrogen ratio and moisture affect microbial community structures in alkaline permafrost-affected soils with different vegetation types on the Tibetan plateau.

PubMed

Zhang, Xinfang; Xu, Shijian; Li, Changming; Zhao, Lin; Feng, Huyuan; Yue, Guangyang; Ren, Zhengwei; Cheng, Guogdong

2014-01-01

In the Tibetan permafrost region, vegetation types and soil properties have been affected by permafrost degradation, but little is known about the corresponding patterns of their soil microbial communities. Thus, we analyzed the effects of vegetation types and their covariant soil properties on bacterial and fungal community structure and membership and bacterial community-level physiological patterns. Pyrosequencing and Biolog EcoPlates were used to analyze 19 permafrost-affected soil samples from four principal vegetation types: swamp meadow (SM), meadow (M), steppe (S) and desert steppe (DS). Proteobacteria, Acidobacteria, Bacteroidetes and Actinobacteria dominated bacterial communities and the main fungal phyla were Ascomycota, Basidiomycota and Mucoromycotina. The ratios of Proteobacteria/Acidobacteria decreased in the order: SM>M>S>DS, whereas the Ascomycota/Basidiomycota ratios increased. The distributions of carbon and nitrogen cycling bacterial genera detected were related to soil properties. The bacterial communities in SM/M soils degraded amines/amino acids very rapidly, while polymers were degraded rapidly by S/DS communities. UniFrac analysis of bacterial communities detected differences among vegetation types. The fungal UniFrac community patterns of SM differed from the others. Redundancy analysis showed that the carbon/nitrogen ratio had the main effect on bacteria community structures and their diversity in alkaline soil, whereas soil moisture was mainly responsible for structuring fungal communities. Thus, microbial communities and their functioning are probably affected by soil environmental change in response to permafrost degradation. Copyright © 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Improved graph clustering

DTIC Science & Technology

2013-01-01

5, pp. 75–174, 2010. [2] J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney , “Statistical properties of community structure in large social and...2011. [14] R. R. Nadakuditi and M. Newman , “Graph spectra and the detectability of community structure in networks,” Phys. Rev. Lett., vol. 108, no

Dynamic structure of stock communities: a comparative study between stock returns and turnover rates

NASA Astrophysics Data System (ADS)

Su, Li-Ling; Jiang, Xiong-Fei; Li, Sai-Ping; Zhong, Li-Xin; Ren, Fei

2017-07-01

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. An empirical study using the overall data set shows that for both returns and turnover rates the largest communities are composed of specific industrial or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. However, the community structure for turnover rates is more complex than that for returns, which indicates that the interactions between stocks revealed by turnover rates may contain more information. This conclusion is further confirmed by the analysis of the changes in the dynamics of community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to comprise a few of the largest communities in different sub-periods, and more interestingly several specific sectors appear in the communities with different rank orders for returns and turnover rates even in the same sub-period. To better understand their differences, a comparison between the evolution of the returns and turnover rates of the stocks from these sectors is conducted. We find that stock prices only had large changes around important events while turnover rates surged after each of these events relevant to specific sectors, which shows strong evidence that the turnover rates are more susceptible to exogenous shocks than returns and its measurement for community detection may contain more useful information about market structure.

Reproducibility and quantitation of amplicon sequencing-based detection

PubMed Central

Zhou, Jizhong; Wu, Liyou; Deng, Ye; Zhi, Xiaoyang; Jiang, Yi-Huei; Tu, Qichao; Xie, Jianping; Van Nostrand, Joy D; He, Zhili; Yang, Yunfeng

2011-01-01

To determine the reproducibility and quantitation of the amplicon sequencing-based detection approach for analyzing microbial community structure, a total of 24 microbial communities from a long-term global change experimental site were examined. Genomic DNA obtained from each community was used to amplify 16S rRNA genes with two or three barcode tags as technical replicates in the presence of a small quantity (0.1% wt/wt) of genomic DNA from Shewanella oneidensis MR-1 as the control. The technical reproducibility of the amplicon sequencing-based detection approach is quite low, with an average operational taxonomic unit (OTU) overlap of 17.2%±2.3% between two technical replicates, and 8.2%±2.3% among three technical replicates, which is most likely due to problems associated with random sampling processes. Such variations in technical replicates could have substantial effects on estimating β-diversity but less on α-diversity. A high variation was also observed in the control across different samples (for example, 66.7-fold for the forward primer), suggesting that the amplicon sequencing-based detection approach could not be quantitative. In addition, various strategies were examined to improve the comparability of amplicon sequencing data, such as increasing biological replicates, and removing singleton sequences and less-representative OTUs across biological replicates. Finally, as expected, various statistical analyses with preprocessed experimental data revealed clear differences in the composition and structure of microbial communities between warming and non-warming, or between clipping and non-clipping. Taken together, these results suggest that amplicon sequencing-based detection is useful in analyzing microbial community structure even though it is not reproducible and quantitative. However, great caution should be taken in experimental design and data interpretation when the amplicon sequencing-based detection approach is used for quantitative analysis of the β-diversity of microbial communities. PMID:21346791

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Zhao, Zhili; Wei, Jiaxuan; Hu, Rongjing

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

Detection of Anomalous Insiders in Collaborative Environments via Relational Analysis of Access Logs

PubMed Central

Chen, You; Malin, Bradley

2014-01-01

Collaborative information systems (CIS) are deployed within a diverse array of environments, ranging from the Internet to intelligence agencies to healthcare. It is increasingly the case that such systems are applied to manage sensitive information, making them targets for malicious insiders. While sophisticated security mechanisms have been developed to detect insider threats in various file systems, they are neither designed to model nor to monitor collaborative environments in which users function in dynamic teams with complex behavior. In this paper, we introduce a community-based anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on information recorded in the access logs of collaborative environments. CADS is based on the observation that typical users tend to form community structures, such that users with low a nity to such communities are indicative of anomalous and potentially illicit behavior. The model consists of two primary components: relational pattern extraction and anomaly detection. For relational pattern extraction, CADS infers community structures from CIS access logs, and subsequently derives communities, which serve as the CADS pattern core. CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies. To empirically evaluate the threat detection model, we perform an analysis with six months of access logs from a real electronic health record system in a large medical center, as well as a publicly-available dataset for replication purposes. The results illustrate that CADS can distinguish simulated anomalous users in the context of real user behavior with a high degree of certainty and with significant performance gains in comparison to several competing anomaly detection models. PMID:25485309

Linking microbial community structure and microbial processes: An empirical and conceptual overview

USGS Publications Warehouse

Bier, R.L.; Bernhardt, Emily S.; Boot, Claudia M.; Graham, Emily B.; Hall, Edward K.; Lennon, Jay T.; Nemergut, Diana R.; Osborne, Brooke B.; Ruiz-Gonzalez, Clara; Schimel, Joshua P.; Waldrop, Mark P.; Wallenstein, Matthew D.

2015-01-01

A major goal of microbial ecology is to identify links between microbial community structure and microbial processes. Although this objective seems straightforward, there are conceptual and methodological challenges to designing studies that explicitly evaluate this link. Here, we analyzed literature documenting structure and process responses to manipulations to determine the frequency of structure-process links and whether experimental approaches and techniques influence link detection. We examined nine journals (published 2009–13) and retained 148 experimental studies measuring microbial community structure and processes. Many qualifying papers (112 of 148) documented structure and process responses, but few (38 of 112 papers) reported statistically testing for a link. Of these tested links, 75% were significant and typically used Spearman or Pearson's correlation analysis (68%). No particular approach for characterizing structure or processes was more likely to produce significant links. Process responses were detected earlier on average than responses in structure or both structure and process. Together, our findings suggest that few publications report statistically testing structure-process links. However, when links are tested for they often occur but share few commonalities in the processes or structures that were linked and the techniques used for measuring them.

The way to uncover community structure with core and diversity

NASA Astrophysics Data System (ADS)

Chang, Y. F.; Han, S. K.; Wang, X. D.

2018-07-01

Communities are ubiquitous in nature and society. Individuals that share common properties often self-organize to form communities. Avoiding the shortages of computation complexity, pre-given information and unstable results in different run, in this paper, we propose a simple and efficient method to deepen our understanding of the emergence and diversity of communities in complex systems. By introducing the rational random selection, our method reveals the hidden deterministic and normal diverse community states of community structure. To demonstrate this method, we test it with real-world systems. The results show that our method could not only detect community structure with high sensitivity and reliability, but also provide instructional information about the hidden deterministic community world and the real normal diverse community world by giving out the core-community, the real-community, the tide and the diversity. Thizs is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in complex systems.

A FRAMEWORK FOR ATTRIBUTE-BASED COMMUNITY DETECTION WITH APPLICATIONS TO INTEGRATED FUNCTIONAL GENOMICS.

PubMed

Yu, Han; Hageman Blair, Rachael

2016-01-01

Understanding community structure in networks has received considerable attention in recent years. Detecting and leveraging community structure holds promise for understanding and potentially intervening with the spread of influence. Network features of this type have important implications in a number of research areas, including, marketing, social networks, and biology. However, an overwhelming majority of traditional approaches to community detection cannot readily incorporate information of node attributes. Integrating structural and attribute information is a major challenge. We propose a exible iterative method; inverse regularized Markov Clustering (irMCL), to network clustering via the manipulation of the transition probability matrix (aka stochastic flow) corresponding to a graph. Similar to traditional Markov Clustering, irMCL iterates between "expand" and "inflate" operations, which aim to strengthen the intra-cluster flow, while weakening the inter-cluster flow. Attribute information is directly incorporated into the iterative method through a sigmoid (logistic function) that naturally dampens attribute influence that is contradictory to the stochastic flow through the network. We demonstrate advantages and the exibility of our approach using simulations and real data. We highlight an application that integrates breast cancer gene expression data set and a functional network defined via KEGG pathways reveal significant modules for survival.

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

PubMed

Okamoto, Hiroshi

2016-08-01

Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

SCOUT: simultaneous time segmentation and community detection in dynamic networks

PubMed Central

Hulovatyy, Yuriy; Milenković, Tijana

2016-01-01

Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging. PMID:27881879

The phylogenetic composition and structure of soil microbial communities shifts in response to elevated carbon dioxide.

PubMed

He, Zhili; Piceno, Yvette; Deng, Ye; Xu, Meiying; Lu, Zhenmei; Desantis, Todd; Andersen, Gary; Hobbie, Sarah E; Reich, Peter B; Zhou, Jizhong

2012-02-01

One of the major factors associated with global change is the ever-increasing concentration of atmospheric CO(2). Although the stimulating effects of elevated CO(2) (eCO(2)) on plant growth and primary productivity have been established, its impacts on the diversity and function of soil microbial communities are poorly understood. In this study, phylogenetic microarrays (PhyloChip) were used to comprehensively survey the richness, composition and structure of soil microbial communities in a grassland experiment subjected to two CO(2) conditions (ambient, 368 p.p.m., versus elevated, 560 p.p.m.) for 10 years. The richness based on the detected number of operational taxonomic units (OTUs) significantly decreased under eCO(2). PhyloChip detected 2269 OTUs derived from 45 phyla (including two from Archaea), 55 classes, 99 orders, 164 families and 190 subfamilies. Also, the signal intensity of five phyla (Crenarchaeota, Chloroflexi, OP10, OP9/JS1, Verrucomicrobia) significantly decreased at eCO(2), and such significant effects of eCO(2) on microbial composition were also observed at the class or lower taxonomic levels for most abundant phyla, such as Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes and Acidobacteria, suggesting a shift in microbial community composition at eCO(2). Additionally, statistical analyses showed that the overall taxonomic structure of soil microbial communities was altered at eCO(2). Mantel tests indicated that such changes in species richness, composition and structure of soil microbial communities were closely correlated with soil and plant properties. This study provides insights into our understanding of shifts in the richness, composition and structure of soil microbial communities under eCO(2) and environmental factors shaping the microbial community structure.

Exploration of Microbial Diversity and Community Structure of Lonar Lake: The Only Hypersaline Meteorite Crater Lake within Basalt Rock

PubMed Central

Paul, Dhiraj; Kumbhare, Shreyas V.; Mhatre, Snehit S.; Chowdhury, Somak P.; Shetty, Sudarshan A.; Marathe, Nachiket P.; Bhute, Shrikant; Shouche, Yogesh S.

2016-01-01

Lonar Lake is a hypersaline and hyperalkaline soda lake and the only meteorite impact crater in the world situated in basalt rocks. Although culture-dependent studies have been reported, a comprehensive understanding of microbial community composition and structure in Lonar Lake remains elusive. In the present study, microbial community structure associated with Lonar Lake sediment and water samples was investigated using high-throughput sequencing. Microbial diversity analysis revealed the existence of diverse, yet largely consistent communities. Proteobacteria (30%), Actinobacteria (24%), Firmicutes (11%), and Cyanobacteria (5%) predominated in the sequencing survey, whereas Bacteroidetes (1.12%), BD1-5 (0.5%), Nitrospirae (0.41%), and Verrucomicrobia (0.28%) were detected in relatively minor abundances in the Lonar Lake ecosystem. Within the Proteobacteria phylum, the Gammaproteobacteria represented the most abundantly detected class (21–47%) within sediment samples, but only a minor population in the water samples. Proteobacteria and Firmicutes were found at significantly higher abundance (p ≥ 0.05) in sediment samples, whereas members of Actinobacteria, Candidate division TM7 and Cyanobacteria (p ≥ 0.05) were significantly abundant in water samples. Compared to the microbial communities of other hypersaline soda lakes, those of Lonar Lake formed a distinct cluster, suggesting a different microbial community composition and structure. Here we report for the first time, the difference in composition of indigenous microbial communities between the sediment and water samples of Lonar Lake. An improved census of microbial community structure in this Lake ecosystem provides a foundation for exploring microbial biogeochemical cycling and microbial function in hypersaline lake environments. PMID:26834712

Improving resolution of dynamic communities in human brain networks through targeted node removal

PubMed Central

Turner, Benjamin O.; Miller, Michael B.; Carlson, Jean M.

2017-01-01

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined “ground truth” communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters. PMID:29261662

Adaptations in bacterial catabolic enzyme activity and community structure in membrane-coupled bioreactors fed simple synthetic wastewater.

PubMed

LaPara, Timothy M; Klatt, Christian G; Chen, Ruoyu

2006-02-10

Membrane-coupled bioreactors (MBRs) offer substantial benefits compared to conventional reactor designs for biological wastewater treatment. MBR treatment efficiency, however, has not been optimized because the effects of the MBR on process microbiology are poorly understood. In this study, the structure and function of the microbial communities growing in MBRs fed simple synthetic wastewater were investigated. In four starch-fed MBRs, the bacterial community substantially increased its alpha-glucosidase affinity (>1000-fold), while the leucine aminopeptidase and heptanoate esterase affinities increased slightly (<40-fold) or remained relatively constant. Concomitant to these physiological adaptations, shifts in the bacterial community structure in two of the starch-fed MBRs were detected by PCR-DGGE. Four of the bacterial populations detected by PCR-DGGE were isolated and exhibited specific growth rates in batch culture ranging from 0.009 to 0.22 h(-1). Our results suggest that bacterial communities growing under increasingly stringent nutrient limitation adapt their enzyme activities primarily for the nutrients provided, but that there is also a more subtle response not linked to the substrates included in the feed medium. Our research also demonstrates that MBRs can support relatively complex bacterial communities even on simple feed media.

A Deep Stochastic Model for Detecting Community in Complex Networks

NASA Astrophysics Data System (ADS)

Fu, Jingcheng; Wu, Jianliang

2017-01-01

Discovering community structures is an important step to understanding the structure and dynamics of real-world networks in social science, biology and technology. In this paper, we develop a deep stochastic model based on non-negative matrix factorization to identify communities, in which there are two sets of parameters. One is the community membership matrix, of which the elements in a row correspond to the probabilities of the given node belongs to each of the given number of communities in our model, another is the community-community connection matrix, of which the element in the i-th row and j-th column represents the probability of there being an edge between a randomly chosen node from the i-th community and a randomly chosen node from the j-th community. The parameters can be evaluated by an efficient updating rule, and its convergence can be guaranteed. The community-community connection matrix in our model is more precise than the community-community connection matrix in traditional non-negative matrix factorization methods. Furthermore, the method called symmetric nonnegative matrix factorization, is a special case of our model. Finally, based on the experiments on both synthetic and real-world networks data, it can be demonstrated that our algorithm is highly effective in detecting communities.

Overlapping Community Detection based on Network Decomposition

NASA Astrophysics Data System (ADS)

Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin

2016-04-01

Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

A cooperative game framework for detecting overlapping communities in social networks

NASA Astrophysics Data System (ADS)

Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

2018-02-01

Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms.

Z-Score-Based Modularity for Community Detection in Networks

PubMed Central

Miyauchi, Atsushi; Kawase, Yasushi

2016-01-01

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function. PMID:26808270

Weighted compactness function based label propagation algorithm for community detection

NASA Astrophysics Data System (ADS)

Zhang, Weitong; Zhang, Rui; Shang, Ronghua; Jiao, Licheng

2018-02-01

Community detection in complex networks, is to detect the community structure with the internal structure relatively compact and the external structure relatively sparse, according to the topological relationship among nodes in the network. In this paper, we propose a compactness function which combines the weight of nodes, and use it as the objective function to carry out the node label propagation. Firstly, according to the node degree, we find the sets of core nodes which have great influence on the network. The more the connections between the core nodes and the other nodes are, the larger the amount of the information these kernel nodes receive and transform. Then, according to the similarity of the nodes between the core nodes sets and the nodes degree, we assign weights to the nodes in the network. So the label of the nodes with great influence will be the priority in the label propagation process, which effectively improves the accuracy of the label propagation. The compactness function between nodes and communities in this paper is based on the nodes influence. It combines the connections between nodes and communities with the degree of the node belongs to its neighbor communities based on calculating the node weight. The function effectively uses the information of nodes and connections in the network. The experimental results show that the proposed algorithm can achieve good results in the artificial network and large-scale real networks compared with the 8 contrast algorithms.

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

PubMed Central

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2016-01-01

It is common in the study of networks to investigate intermediate-sized (or “meso-scale”) features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify “communities,” which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that “communities” are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for “size-resolved community structure” that can arise in real (and realistic) networks: (i) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify “good” communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally-biased methods that focus on communities that are centered around a given seed node might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate subtler structural properties that are important to consider in the development of better benchmark networks to test methods for community detection. PMID:25679670

Evidence for the functional significance of diazotroph community structure in soil.

PubMed

Hsu, Shi-Fang; Buckley, Daniel H

2009-01-01

Microbial ecologists continue to seek a greater understanding of the factors that govern the ecological significance of microbial community structure. Changes in community structure have been shown to have functional significance for processes that are mediated by a narrow spectrum of organisms, such as nitrification and denitrification, but in some cases, functional redundancy in the community seems to buffer microbial ecosystem processes. The functional significance of microbial community structure is frequently obscured by environmental variation and is hard to detect in short-term experiments. We examine the functional significance of free-living diazotrophs in a replicated long-term tillage experiment in which extraneous variation is minimized and N-fixation rates can be related to soil characteristics and diazotroph community structure. Soil characteristics were found to be primarily impacted by tillage management, whereas N-fixation rates and diazotroph community structure were impacted by both biomass management practices and interactions between tillage and biomass management. The data suggest that the variation in diazotroph community structure has a greater impact on N-fixation rates than do soil characteristics at the site. N-fixation rates displayed a saturating response to increases in diazotroph community diversity. These results show that the changes in the community structure of free-living diazotrophs in soils can have ecological significance and suggest that this response is related to a change in community diversity.

Competition can lead to unexpected patterns in tropical ant communities

NASA Astrophysics Data System (ADS)

Ellwood, M. D. Farnon; Blüthgen, Nico; Fayle, Tom M.; Foster, William A.; Menzel, Florian

2016-08-01

Ecological communities are structured by competitive, predatory, mutualistic and parasitic interactions combined with chance events. Separating deterministic from stochastic processes is possible, but finding statistical evidence for specific biological interactions is challenging. We attempt to solve this problem for ant communities nesting in epiphytic bird's nest ferns (Asplenium nidus) in Borneo's lowland rainforest. By recording the frequencies with which each and every single ant species occurred together, we were able to test statistically for patterns associated with interspecific competition. We found evidence for competition, but the resulting co-occurrence pattern was the opposite of what we expected. Rather than detecting species segregation-the classical hallmark of competition-we found species aggregation. Moreover, our approach of testing individual pairwise interactions mostly revealed spatially positive rather than negative associations. Significant negative interactions were only detected among large ants, and among species of the subfamily Ponerinae. Remarkably, the results from this study, and from a corroborating analysis of ant communities known to be structured by competition, suggest that competition within the ants leads to species aggregation rather than segregation. We believe this unexpected result is linked with the displacement of species following asymmetric competition. We conclude that analysing co-occurrence frequencies across complete species assemblages, separately for each species, and for each unique pairwise combination of species, represents a subtle yet powerful way of detecting structure and compartmentalisation in ecological communities.

Overlapping communities from dense disjoint and high total degree clusters

NASA Astrophysics Data System (ADS)

Zhang, Hongli; Gao, Yang; Zhang, Yue

2018-04-01

Community plays an important role in the field of sociology, biology and especially in domains of computer science, where systems are often represented as networks. And community detection is of great importance in the domains. A community is a dense subgraph of the whole graph with more links between its members than between its members to the outside nodes, and nodes in the same community probably share common properties or play similar roles in the graph. Communities overlap when nodes in a graph belong to multiple communities. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is one of the most successful techniques dealing with large networks. The paper presents a density-based seeding method, in which dense disjoint local clusters are searched and selected as seeds. The proposed method selects a seed by the total degree and density of local clusters utilizing merely local structures of the network. Furthermore, this paper proposes a novel community refining phase via minimizing the conductance of each community, through which the quality of identified communities is largely improved in linear time. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art and the proposed refining method largely enhances the quality of the identified communities. Experimental results in real graphs with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with higher quality, and it obtains much more accurate community structure than the current local algorithms without any priori information.

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

Towards Online Multiresolution Community Detection in Large-Scale Networks

PubMed Central

Huang, Jianbin; Sun, Heli; Liu, Yaguang; Song, Qinbao; Weninger, Tim

2011-01-01

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks. PMID:21887325

Community structure in traffic zones based on travel demand

NASA Astrophysics Data System (ADS)

Sun, Li; Ling, Ximan; He, Kun; Tan, Qian

2016-09-01

Large structure in complex networks can be studied by dividing it into communities or modules. Urban traffic system is one of the most critical infrastructures. It can be abstracted into a complex network composed of tightly connected groups. Here, we analyze community structure in urban traffic zones based on the community detection method in network science. Spectral algorithm using the eigenvectors of matrices is employed. Our empirical results indicate that the traffic communities are variant with the travel demand distribution, since in the morning the majority of the passengers are traveling from home to work and in the evening they are traveling a contrary direction. Meanwhile, the origin-destination pairs with large number of trips play a significant role in urban traffic network's community division. The layout of traffic community in a city also depends on the residents' trajectories.

Natural attenuation of contaminated marine sediments from an old floating dock Part II: changes of sediment microbial community structure and its relationship with environmental variables.

PubMed

Wang, Ya-Fen; Tam, Nora Fung-Yee

2012-04-15

Changes of microbial community structure and its relationship with various environmental variables in surface marine sediments were examined for a one-year period after the removal of an old floating dock in Hong Kong SAR, South China. Temporal variations in the microbial community structure were clearly revealed by principal component analysis (PCA) of the microbial ester-linked fatty acid methyl ester (EL-FAME) profiles. The most obvious shift in microbial community structure was detected 6 months after the removal of the dock, although no significant decline in the levels of pollutants could be detected. As determined by EL-FAME profiles, the microbial diversity recovered and the predominance of gram-negative bacteria was gradually replaced by gram-positive bacteria and fungi in the impacted stations. With redundancy analysis (RDA), the concentration of total polycyclic aromatic hydrocarbons (PAHs) was found to be the second important determinant of microbial community structure, next to Time. The relative abundance of 18:1ω9c and hydroxyl fatty acids enriched in the PAH hot spots, whereas 16:1ω9 and 18:1ω9t were negatively correlated to total PAH concentration. The significant relationships observed between microbial EL-FAME profiles and pollutants, exampled by PAHs in the present study, suggested the potential of microbial community analysis in the assessment of the natural attenuation process in contaminated environments. Copyright © 2012 Elsevier B.V. All rights reserved.

Structure and inference in annotated networks

PubMed Central

Newman, M. E. J.; Clauset, Aaron

2016-01-01

For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this ‘metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains. PMID:27306566

Structure and inference in annotated networks

NASA Astrophysics Data System (ADS)

Newman, M. E. J.; Clauset, Aaron

2016-06-01

For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this `metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.

Investigation of methanogenic community structures in rural biogas digesters from different climatic regions in Yunnan, southwest China.

PubMed

Dong, Minghua; Wu, Yan; Li, Qiumin; Tian, Guangliang; Yang, Bin; Li, Yingjuan; Zhang, Lijuan; Wang, Yongxia; Xiao, Wei; Yin, Fang; Zhao, Xingling; Zhang, Wudi; Cui, Xiaolong

2015-05-01

Understanding of the microbial community structures of the biogas digesters in different climatic regions can help improve the methane production in the fermentation process. The methanogenic archaeal diversity in four rural biogas digesters (BNA, JSA, LJA, and XGA) was investigated by a culture-independent rRNA approach in different climatic regions in Yunnan. Community structure composed of 711 clones in the all libraries. A total of 33 operational taxonomic units (OTUs) were detected, and major groups of methanogens were the orders Methanosarcinales and Methanomicrobiales. 63.2 % of all archaeal OTUs belong to the order Methanosarcinales which mostly contain acetotrophic methanogens. Methanomicrobiales (19.5 % in all OTUs) were detected in considerable number. Additionally, there were minor rates of uncultured archaea. The principal component analysis indicated that the genus Methanosaeta was mainly affected by the fermentation temperatures.

Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure detection.

PubMed

Chai, Bian-fang; Yu, Jian; Jia, Cai-Yan; Yang, Tian-bao; Jiang, Ya-wen

2013-07-01

Latent community discovery that combines links and contents of a text-associated network has drawn more attention with the advance of social media. Most of the previous studies aim at detecting densely connected communities and are not able to identify general structures, e.g., bipartite structure. Several variants based on the stochastic block model are more flexible for exploring general structures by introducing link probabilities between communities. However, these variants cannot identify the degree distributions of real networks due to a lack of modeling of the differences among nodes, and they are not suitable for discovering communities in text-associated networks because they ignore the contents of nodes. In this paper, we propose a popularity-productivity stochastic block (PPSB) model by introducing two random variables, popularity and productivity, to model the differences among nodes in receiving links and producing links, respectively. This model has the flexibility of existing stochastic block models in discovering general community structures and inherits the richness of previous models that also exploit popularity and productivity in modeling the real scale-free networks with power law degree distributions. To incorporate the contents in text-associated networks, we propose a combined model which combines the PPSB model with a discriminative model that models the community memberships of nodes by their contents. We then develop expectation-maximization (EM) algorithms to infer the parameters in the two models. Experiments on synthetic and real networks have demonstrated that the proposed models can yield better performances than previous models, especially on networks with general structures.

Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure detection

NASA Astrophysics Data System (ADS)

Chai, Bian-fang; Yu, Jian; Jia, Cai-yan; Yang, Tian-bao; Jiang, Ya-wen

2013-07-01

Latent community discovery that combines links and contents of a text-associated network has drawn more attention with the advance of social media. Most of the previous studies aim at detecting densely connected communities and are not able to identify general structures, e.g., bipartite structure. Several variants based on the stochastic block model are more flexible for exploring general structures by introducing link probabilities between communities. However, these variants cannot identify the degree distributions of real networks due to a lack of modeling of the differences among nodes, and they are not suitable for discovering communities in text-associated networks because they ignore the contents of nodes. In this paper, we propose a popularity-productivity stochastic block (PPSB) model by introducing two random variables, popularity and productivity, to model the differences among nodes in receiving links and producing links, respectively. This model has the flexibility of existing stochastic block models in discovering general community structures and inherits the richness of previous models that also exploit popularity and productivity in modeling the real scale-free networks with power law degree distributions. To incorporate the contents in text-associated networks, we propose a combined model which combines the PPSB model with a discriminative model that models the community memberships of nodes by their contents. We then develop expectation-maximization (EM) algorithms to infer the parameters in the two models. Experiments on synthetic and real networks have demonstrated that the proposed models can yield better performances than previous models, especially on networks with general structures.

Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation.

PubMed

Taylor, Dane; Shai, Saray; Stanley, Natalie; Mucha, Peter J

2016-06-03

Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers' adjacency matrices for which we show the detectability limit vanishes as O(L^{-1/2}) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common-but not well understood-practice of thresholding pairwise-interaction data to obtain sparse network representations.

Persistence of bacterial and archaeal communities in sea ice through an Arctic winter

PubMed Central

Collins, R Eric; Rocap, Gabrielle; Deming, Jody W

2010-01-01

The structure of bacterial communities in first-year spring and summer sea ice differs from that in source seawaters, suggesting selection during ice formation in autumn or taxon-specific mortality in the ice during winter. We tested these hypotheses by weekly sampling (January–March 2004) of first-year winter sea ice (Franklin Bay, Western Arctic) that experienced temperatures from −9°C to −26°C, generating community fingerprints and clone libraries for Bacteria and Archaea. Despite severe conditions and significant decreases in microbial abundance, no significant changes in richness or community structure were detected in the ice. Communities of Bacteria and Archaea in the ice, as in under-ice seawater, were dominated by SAR11 clade Alphaproteobacteria and Marine Group I Crenarchaeota, neither of which is known from later season sea ice. The bacterial ice library contained clones of Gammaproteobacteria from oligotrophic seawater clades (e.g. OM60, OM182) but no clones from gammaproteobacterial genera commonly detected in later season sea ice by similar methods (e.g. Colwellia, Psychrobacter). The only common sea ice bacterial genus detected in winter ice was Polaribacter. Overall, selection during ice formation and mortality during winter appear to play minor roles in the process of microbial succession that leads to distinctive spring and summer sea ice communities. PMID:20192970

Functional gene array-based analysis of microbial community structure in groundwaters with a gradient of contaminant levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Waldron, P.J.; Wu, L.; Van Nostrand, J.D.

2009-06-15

To understand how contaminants affect microbial community diversity, heterogeneity, and functional structure, six groundwater monitoring wells from the Field Research Center of the U.S. Department of Energy Environmental Remediation Science Program (ERSP; Oak Ridge, TN), with a wide range of pH, nitrate, and heavy metal contamination were investigated. DNA from the groundwater community was analyzed with a functional gene array containing 2006 probes to detect genes involved in metal resistance, sulfate reduction, organic contaminant degradation, and carbon and nitrogen cycling. Microbial diversity decreased in relation to the contamination levels of the wells. Highly contaminated wells had lower gene diversity butmore » greater signal intensity than the pristine well. The microbial composition was heterogeneous, with 17-70% overlap between different wells. Metal-resistant and metal-reducing microorganisms were detected in both contaminated and pristine wells, suggesting the potential for successful bioremediation of metal-contaminated groundwaters. In addition, results of Mantel tests and canonical correspondence analysis indicate that nitrate, sulfate, pH, uranium, and technetium have a significant (p < 0.05) effect on microbial community structure. This study provides an overall picture of microbial community structure in contaminated environments with functional gene arrays by showing that diversity and heterogeneity can vary greatly in relation to contamination.« less

Constant Communities in Complex Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Tanmoy; Srinivasan, Sriram; Ganguly, Niloy; Bhowmick, Sanjukta; Mukherjee, Animesh

2013-05-01

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

Molecular Analysis of Microbial Community Structures in Pristine and Contaminated Aquifers: Field and Laboratory Microcosm Experiments

PubMed Central

Shi, Y.; Zwolinski, M. D.; Schreiber, M. E.; Bahr, J. M.; Sewell, G. W.; Hickey, W. J.

1999-01-01

This study used phylogenetic probes in hybridization analysis to (i) determine in situ microbial community structures in regions of a shallow sand aquifer that were oxygen depleted and fuel contaminated (FC) or aerobic and noncontaminated (NC) and (ii) examine alterations in microbial community structures resulting from exposure to toluene and/or electron acceptor supplementation (nitrate). The latter objective was addressed by using the NC and FC aquifer materials for anaerobic microcosm studies in which phylogenetic probe analysis was complemented by microbial activity assays. Domain probe analysis of the aquifer samples showed that the communities were predominantly Bacteria; Eucarya and Archaea were not detectable. At the phylum and subclass levels, the FC and NC aquifer material had similar relative abundance distributions of 43 to 65% β- and γ-Proteobacteria (B+G), 31 to 35% α-Proteobacteria (ALF), 15 to 18% sulfate-reducing bacteria, and 5 to 10% high G+C gram positive bacteria. Compared to that of the NC region, the community structure of the FC material differed mainly in an increased abundance of B+G relative to that of ALF. The microcosm communities were like those of the field samples in that they were predominantly Bacteria (83 to 101%) and lacked detectable Archaea but differed in that a small fraction (2 to 8%) of Eucarya was detected regardless of the treatment applied. The latter result was hypothesized to reflect enrichment of anaerobic protozoa. Addition of nitrate and/or toluene stimulated microbial activity in the microcosms, but only supplementation of toluene alone significantly altered community structures. For the NC material, the dominant subclass shifted from B+G to ALF, while in the FC microcosms 55 to 65% of the Bacteria community was no longer identifiable by the phylum or subclass probes used. The latter result suggested that toluene exposure fostered the proliferation of phylotype(s) that were otherwise minor constituents of the FC aquifer community. These studies demonstrated that alterations in aquifer microbial communities resulting from specific anthropogenic perturbances can be inferred from microcosm studies integrating chemical and phylogenetic probe analysis and in the case of hydrocarbon contamination may facilitate the identification of organisms important for in situ biodegradation processes. Further work integrating and coordinating microcosm and field experiments is needed to explore how differences in scale, substrate complexity, and other hydrogeological conditions may affect patterns observed in these systems. PMID:10224013

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Effect of disopyramide on bacterial diversity in drinking water

NASA Astrophysics Data System (ADS)

Wu, Qing; Zhao, Xiaofei; Tian, Qi; Wang, Lei; Zhao, Xinhua

2018-02-01

Disopyramide was detected in drinking water by LC-MS/MS and the microbial diversity was investigated by PCR and high-throughput sequencing. The results showed that bacteria community structure in drinking water changed a lot when added different concentrations of disopyramide. The results of Shannon index showed that the total number and abundance of bacterial community species in drinking water samples decreased significantly after the addition of disopyramide. However, the number and abundance of community structure did not change with the concentration of disopyramide. Disopyramide inhibits the activity of bacterial community in drinking water and also can reduce the bacterial community diversity in drinking water.

Impacts of exploratory drilling for oil and gas on the benthic environment of Georges Bank

USGS Publications Warehouse

Neff, J. M.; Bothner, Michael H.; Maciolek, N. J.; Grassle, J. F.

1989-01-01

Cluster analysis revealed a strong relationship between community structure and both sediment type and water depth. Little seasonal variation was detected, but some interannual differences were revealed by cluster analysis and correspondence analysis. The replicates from a station always resembled each other more than they resembled any replicates from other stations. In addition, the combined replicates from a station always clustered with samples from that station taken on other cruises. This excellent replication and uniformity of the benthic infaunal community at a station over time made it possible to detect very subtle changes in community parameters that might be related to discharges of drilling fluid and drill cuttings. Nevertheless, no changes were detected in benthic communities of Georges Bank that could be attributed to drilling activities.

Community detection in complex networks using proximate support vector clustering

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-03-01

Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

Detecting Anomalous Insiders in Collaborative Information Systems

PubMed Central

Chen, You; Nyemba, Steve; Malin, Bradley

2012-01-01

Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients’ records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients’ diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent. PMID:24489520

Discovering SIFIs in Interbank Communities

PubMed Central

Pecora, Nicolò; Rovira Kaltwasser, Pablo; Spelta, Alessandro

2016-01-01

This paper proposes a new methodology based on non-negative matrix factorization to detect communities and to identify central nodes in a network as well as within communities. The method is specifically designed for directed weighted networks and, consequently, it has been applied to the interbank network derived from the e-MID interbank market. In an interbank network indeed links are directed, representing flows of funds between lenders and borrowers. Besides distinguishing between Systemically Important Borrowers and Lenders, the technique complements the detection of systemically important banks, revealing the community structure of the network, that proxies the most plausible areas of contagion of institutions’ distress. PMID:28002445

Flat and complex temperate reefs provide similar support for fish: Evidence for a unimodal species-habitat relationship.

PubMed

Paxton, Avery B; Pickering, Emily A; Adler, Alyssa M; Taylor, J Christopher; Peterson, Charles H

2017-01-01

Structural complexity, a form of habitat heterogeneity, influences the structure and function of ecological communities, generally supporting increased species density, richness, and diversity. Recent research, however, suggests the most complex habitats may not harbor the highest density of individuals and number of species, especially in areas with elevated human influence. Understanding nuances in relationships between habitat heterogeneity and ecological communities is warranted to guide habitat-focused conservation and management efforts. We conducted fish and structural habitat surveys of thirty warm-temperate reefs on the southeastern US continental shelf to quantify how structural complexity influences fish communities. We found that intermediate complexity maximizes fish abundance on natural and artificial reefs, as well as species richness on natural reefs, challenging the current paradigm that abundance and other fish community metrics increase with increasing complexity. Naturally occurring rocky reefs of flat and complex morphologies supported equivalent abundance, biomass, species richness, and community composition of fishes. For flat and complex morphologies of rocky reefs to receive equal consideration as essential fish habitat (EFH), special attention should be given to detecting pavement type rocky reefs because their ephemeral nature makes them difficult to detect with typical seafloor mapping methods. Artificial reefs of intermediate complexity also maximized fish abundance, but human-made structures composed of low-lying concrete and metal ships differed in community types, with less complex, concrete structures supporting lower numbers of fishes classified largely as demersal species and metal ships protruding into the water column harboring higher numbers of fishes, including more pelagic species. Results of this study are essential to the process of evaluating habitat function provided by different types and shapes of reefs on the seafloor so that all EFH across a wide range of habitat complexity may be accurately identified and properly managed.

Chitinolytic and pectinolytic community in the vertical structure of chernozem's zone ecosystems

NASA Astrophysics Data System (ADS)

Lukacheva, E.; Manucharova, N.

2012-04-01

Chitin is a long-chain polymer of a N-acetylglucosamine and is found in many places throughout the natural world. Pectin is a structural heteropolysaccharide contained in the primary cell walls of terrestrial plants. Roots of the plants and root crops contain pectin. Chitin and pectin are widely distributed throughout the natural world. For this reason it is important to investigate the structural and functional properties of complex organisms, offering degradation of these biopolymers in the terrestrial and soil ecosystems. It is known that ecosystems have their own structure. It is possible to allocate some vertical tiers: phylloplane, litter (soil covering), soil. We investigated chitinolytic and pektinolytic microbial communities dedicated to different layers of the ecosystem of the chernozem zone. Quantity of eukaryote and procaryote organisms increased in the test samples with chitin and pectin. Increasing of eukaryote in samples with pectin was more then in samples with chitin. Also should be noted the significant increasing of actinomycet`s quantity in the samples with chitin in comparison with samples with pectin. The variety and abundance of bacteria in the litter samples increased an order of magnitude as compared to other options investigated. Further prokaryote community was investigated by method FISH (fluorescence in situ hybridization). FISH is a cytogenetic technique developed that is used to detect and localize the presence or absence of specific DNA sequences on chromosomes. Quantity of Actinomycets and Firmicutes was the largest among identified cells with metabolic activity in soil samples. Should be noted significant increasing of the quantity of Acidobateria and Bacteroidetes in pectinolytic community and Alphaproteobacteria in chitinolytic community. In considering of the phylogenetic structure investigated communities in samples of the litter should be noted increase in the segment of Proteobacteria. Increasing of this group of microorganisms was also detected in samples of the phylloplane. Also should be noted increasing of Baceroidetes in these samples. Further inoculation from investigated samples was provided. The dominant species of microorganisms were isolated on dense nutrient media. These microorganisms were detected by sequence analysis. Thus the differences of decomposing biopolymers were educed in the microbial communities in the terrestrial and soil ecosystems.

Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Wang, Bingbo; Yu, Liang

2018-01-01

Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

Detecting communities in large networks

NASA Astrophysics Data System (ADS)

Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

2005-07-01

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

Impact of dilution on microbial community structure and functional potential: comparison of numerical simulations and batch culture experiments

NASA Technical Reports Server (NTRS)

Franklin, R. B.; Garland, J. L.; Bolster, C. H.; Mills, A. L.

2001-01-01

A series of microcosm experiments was performed using serial dilutions of a sewage microbial community to inoculate a set of batch cultures in sterile sewage. After inoculation, the dilution-defined communities were allowed to regrow for several days and a number of community attributes were measured in the regrown assemblages. Based upon a set of numerical simulations, community structure was expected to differ along the dilution gradient; the greatest differences in structure were anticipated between the undiluted-low-dilution communities and the communities regrown from the very dilute (more than 10(-4)) inocula. Furthermore, some differences were expected among the lower-dilution treatments (e.g., between undiluted and 10(-1)) depending upon the evenness of the original community. In general, each of the procedures used to examine the experimental community structures separated the communities into at least two, often three, distinct groups. The groupings were consistent with the simulated dilution of a mixture of organisms with a very uneven distribution. Significant differences in community structure were detected with genetic (amplified fragment length polymorphism and terminal restriction fragment length polymorphism), physiological (community level physiological profiling), and culture-based (colony morphology on R2A agar) measurements. Along with differences in community structure, differences in community size (acridine orange direct counting), composition (ratio of sewage medium counts to R2A counts, monitoring of each colony morphology across the treatments), and metabolic redundancy (i.e., generalist versus specialist) were also observed, suggesting that the differences in structure and diversity of communities maintained in the same environment can be manifested as differences in community organization and function.

Sensitivity of system stability to model structure

USGS Publications Warehouse

Hosack, G.R.; Li, H.W.; Rossignol, P.A.

2009-01-01

A community is stable, and resilient, if the levels of all community variables can return to the original steady state following a perturbation. The stability properties of a community depend on its structure, which is the network of direct effects (interactions) among the variables within the community. These direct effects form feedback cycles (loops) that determine community stability. Although feedback cycles have an intuitive interpretation, identifying how they form the feedback properties of a particular community can be intractable. Furthermore, determining the role that any specific direct effect plays in the stability of a system is even more daunting. Such information, however, would identify important direct effects for targeted experimental and management manipulation even in complex communities for which quantitative information is lacking. We therefore provide a method that determines the sensitivity of community stability to model structure, and identifies the relative role of particular direct effects, indirect effects, and feedback cycles in determining stability. Structural sensitivities summarize the degree to which each direct effect contributes to stabilizing feedback or destabilizing feedback or both. Structural sensitivities prove useful in identifying ecologically important feedback cycles within the community structure and for detecting direct effects that have strong, or weak, influences on community stability. The approach may guide the development of management intervention and research design. We demonstrate its value with two theoretical models and two empirical examples of different levels of complexity. ?? 2009 Elsevier B.V. All rights reserved.

Dynamic social community detection and its applications.

PubMed

Nguyen, Nam P; Dinh, Thang N; Shen, Yilin; Thai, My T

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

Dynamic Social Community Detection and Its Applications

PubMed Central

Nguyen, Nam P.; Dinh, Thang N.; Shen, Yilin; Thai, My T.

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods. PMID:24722164

Covariance, correlation matrix, and the multiscale community structure of networks.

PubMed

Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

2010-07-01

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

Molecular analysis of microbial community structures in pristine and contaminated aquifers--Field and laboratory microcosm experiments

USGS Publications Warehouse

Shi, Y.; Zwolinski, M.D.; Schreiber, M.E.; Bahr, J.M.; Sewell, G.W.; Hickey, W.J.

1999-01-01

Molecular Analysis of Microbial Community Structures in Pristine and Contaminated Aquifers: Field and Laboratory Microcosm Experimentsvar callbackToken='531E8ACDB6C8511'; var subCode='asmjournal_sub'; var OAS_sitepage = 'aem.asm.org'; This study used phylogenetic probes in hybridization analysis to (i) determine in situ microbial community structures in regions of a shallow sand aquifer that were oxygen depleted and fuel contaminated (FC) or aerobic and noncontaminated (NC) and (ii) examine alterations in microbial community structures resulting from exposure to toluene and/or electron acceptor supplementation (nitrate). The latter objective was addressed by using the NC and FC aquifer materials for anaerobic microcosm studies in which phylogenetic probe analysis was complemented by microbial activity assays. Domain probe analysis of the aquifer samples showed that the communities were predominantlyBacteria; Eucarya and Archaea were not detectable. At the phylum and subclass levels, the FC and NC aquifer material had similar relative abundance distributions of 43 to 65% β- and γ-Proteobacteria (B+G), 31 to 35% α-Proteobacteria (ALF), 15 to 18% sulfate-reducing bacteria, and 5 to 10% high G+C gram positive bacteria. Compared to that of the NC region, the community structure of the FC material differed mainly in an increased abundance of B+G relative to that of ALF. The microcosm communities were like those of the field samples in that they were predominantly Bacteria (83 to 101%) and lacked detectable Archaea but differed in that a small fraction (2 to 8%) of Eucarya was detected regardless of the treatment applied. The latter result was hypothesized to reflect enrichment of anaerobic protozoa. Addition of nitrate and/or toluene stimulated microbial activity in the microcosms, but only supplementation of toluene alone significantly altered community structures. For the NC material, the dominant subclass shifted from B+G to ALF, while in the FC microcosms 55 to 65% of theBacteria community was no longer identifiable by the phylum or subclass probes used. The latter result suggested that toluene exposure fostered the proliferation of phylotype(s) that were otherwise minor constituents of the FC aquifer community. These studies demonstrated that alterations in aquifer microbial communities resulting from specific anthropogenic perturbances can be inferred from microcosm studies integrating chemical and phylogenetic probe analysis and in the case of hydrocarbon contamination may facilitate the identification of organisms important for in situ biodegradation processes. Further work integrating and coordinating microcosm and field experiments is needed to explore how differences in scale, substrate complexity, and other hydrogeological conditions may affect patterns observed in these systems.

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

PubMed Central

Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

2013-01-01

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks. PMID:24386268

Functional response of an adapted subtidal macrobenthic community to an oil spill: macrobenthic structure and bioturbation activity over time throughout an 18-month field experiment.

PubMed

Gilbert, Franck; Stora, Georges; Cuny, Philippe

2015-10-01

An experimental oil spill was carried out in order to assess in situ responses of a macrobenthic community of shallow subtidal sediments historically exposed to petroleum contamination. Both structural and functional (bioturbation activity) parameters of the community, subjected or not to a pulse acute contamination (25,000 ppm), were studied for 18 months. No difference in the community structure was detected between contaminated and control sediments, from 6 to 18 months of experimentation. Vertical distributions of organisms, however, were affected by the presence of oil contamination leading to a deeper burial of some polychaete species. In the same time, changes in sediment-reworking activity and more especially a deeper particle burying in sediments subjected to acute oil contamination were shown. These results highlight the need to complete the analysis of community structure by assessing functional aspects, such as bioturbation activity, a process integrating various aspects of benthic behaviour (e.g. feeding, locomotion, burrow building) in order to estimate real (structural and functional) and long-term effects of oil contamination on benthic communities.

GENETIC STRUCTURE OF TRIATOMA INFESTANS POPULATIONS IN RURAL COMMUNITIES OF SANTIAGO DEL ESTERO, NORTHERN ARGENTINA

PubMed Central

Marcet, PL; Mora, MS; Cutrera, AP; Jones, L; Gürtler, RE; Kitron, U; Dotson, EM

2008-01-01

To gain an understanding of the genetic structure and dispersal dynamics of T. infestans populations, we analyzed the multilocus genotype of 10 microsatellite loci for 352 T. infestans collected in 21 houses of 11 rural communities in October 2002. Genetic structure was analyzed at the community and house compound levels. Analysis revealed that vector control actions affected the genetic structure of T. infestans populations. Bug populations from communities under sustained vector control (core area) were highly structured and genetic differentiation between neighboring house compounds was significant. In contrast, bug populations from communities with sporadic vector control actions were more homogeneous and lacked defined genetic clusters. Genetic differentiation between population pairs did not fit a model of isolation by distance at the microgeographical level. Evidence consistent with flight or walking bug dispersal was detected within and among communities, dispersal was more female-biased in the core area and results suggested that houses received immigrants from more than one source. Putative sources and mechanisms of re-infestation are described. These data may be use to design improved vector control strategies PMID:18773972

Alternations of Structure and Functional Activity of Below Ground Microbial Communities at Elevated Atmospheric Carbon Dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Zhili; Xu, Meiying; Deng, Ye

2010-05-17

The global atmospheric concentration of CO2 has increased by more than 30percent since the industrial revolution. Although the stimulating effects of elevated CO2 (eCO2) on plant growth and primary productivity have been well studied, its influences on belowground microbial communities are poorly understood and controversial. In this study, we showed a significant change in the structure and functional potential of soil microbial communities at eCO2 in a grassland ecosystem, the BioCON (Biodiversity, CO2 and Nitrogen) experimental site (http://www.biocon.umn.edu/) using a comprehensive functional gene array, GeoChip 3.0, which contains about 28,0000 probes and covers approximately 57,000 gene variants from 292 functionalmore » gene families involved in carbon, nitrogen, phosphorus and sulfur cycles as well as other functional processes. GeoChip data indicated that the functional structure of microbial communities was markedly different between ambient CO2 (aCO2) and eCO2 by detrended correspondence analysis (DCA) of all 5001 detected functional gene probes although no significant differences were detected in the overall microbial diversity. A further analysis of 1503 detected functional genes involved in C, N, P, and S cycles showed that a considerable portion (39percent) of them were only detected under either aCO2 (14percent) or eCO2 (25percent), indicating that the functional characteristics of the microbial community were significantly altered by eCO2. Also, for those shared genes (61percent) detected, some significantly (p<0.05) changed their abundance at eCO2. Especially, genes involved in labile C degradation, such as amyA, egl, and ara for starch, cellulose, and hemicelluloses, respectively, C fixation (e.g., rbcL, pcc/acc), N fixation (nifH), and phosphorus utilization (ppx) were significantly increased under eCO2, while those involved in decomposing recalcitrant C, such as glx, lip, and mnp for lignin degradation remained unchanged. This study provides insights into our understanding of belowground microbial communities and their feedbacks to terrestrial ecosystems at eCO2.« less

Detecting network communities beyond assortativity-related attributes

NASA Astrophysics Data System (ADS)

Liu, Xin; Murata, Tsuyoshi; Wakita, Ken

2014-07-01

In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.

Land-use change and soil type are drivers of fungal and archaeal communities in the Pampa biome.

PubMed

Lupatini, Manoeli; Jacques, Rodrigo Josemar Seminoti; Antoniolli, Zaida Inês; Suleiman, Afnan Khalil Ahmad; Fulthorpe, Roberta R; Roesch, Luiz Fernando Würdig

2013-02-01

The current study aimed to test the hypothesis that both land-use change and soil type are responsible for the major changes in the fungal and archaeal community structure and functioning of the soil microbial community in Brazilian Pampa biome. Soil samples were collected at sites with different land-uses (native grassland, native forest, Eucalyptus and Acacia plantation, soybean and watermelon field) and in a typical toposequence in Pampa biome formed by Paleudult, Albaqualf and alluvial soils. The structure of soil microbial community (archaeal and fungal) was evaluated by ribosomal intergenic spacer analysis and soil functional capabilities were measured by microbial biomass carbon and metabolic quotient. We detected different patterns in microbial community driven by land-use change and soil type, showing that both factors are significant drivers of fungal and archaeal community structure and biomass and microbial activity. Fungal community structure was more affected by land-use and archaeal community was more affected by soil type. Irrespective of the land-use or soil type, a large percentage of operational taxonomic unit were shared among the soils. We accepted the hypothesis that both land-use change and soil type are drivers of archaeal and fungal community structure and soil functional capabilities. Moreover, we also suggest the existence of a soil microbial core.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

NASA Astrophysics Data System (ADS)

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

[Fungal community structure in phase II composting of Volvariella volvacea].

PubMed

Chen, Changqing; Li, Tong; Jiang, Yun; Li, Yu

2014-12-04

To understand the fungal community succession during the phase II of Volvariella volvacea compost and clarify the predominant fungi in different fermentation stages, to monitor the dynamic compost at the molecular level accurately and quickly, and reveal the mechanism. The 18S rDNA-denaturing gradient gel electrophoresis (DGGE) and sequencing methods were used to analyze the fungal community structure during the course of compost. The DGGE profile shows that there were differences in the diversity of fungal community with the fermentation progress. The diversity was higher in the stages of high temperature. And the dynamic changes of predominant community and relative intensity was observed. Among the 20 predominant clone strains, 9 were unknown eukaryote and fungi, the others were Eurotiales, Aspergillus sp., Melanocarpus albomyces, Colletotrichum sp., Rhizomucor sp., Verticillium sp., Penicillium commune, Microascus trigonosporus and Trichosporon lactis. The 14 clone strains were detected in the stages of high and durative temperature. The fungal community structure and predominant community have taken dynamic succession during the phase II of Volvariella volvacea compost.

The Interplay between Environmental Filtering and Spatial Processes in Structuring Communities: The Case of Neotropical Snake Communities

PubMed Central

Cavalheri, Hamanda; Both, Camila; Martins, Marcio

2015-01-01

Both habitat filters and spatial processes can influence community structure. Space alone affects species immigration from the regional species pool, whereas habitat filters affect species distribution and inter-specific interactions. This study aimed to understand how the interplay between environmental and geographical processes influenced the structure of Neotropical snake communities in different habitat types. We selected six studies that sampled snakes in forests, four conducted in savannas and two in grasslands (the latter two are grouped in a non-forest category). We used the net relatedness and nearest taxon indices to assess phylogenetic structure within forest and non-forest areas. We also used the phylogenetic fuzzy-weighting algorithm to characterize phylogenetic structure across communities and the relation of phylogenetic composition patterns to habitat type, structure, and latitude. Finally, we tested for morphological trait convergence and phylogenetic niche conservatism using four forest and four non-forest areas for which morphological data were available. Community phylogenetic composition changed across forest and non-forest areas suggesting that environmental filtering influences community structure. Species traits were affected by habitat type, indicating convergence at the metacommunity level. Tail length, robustness, and number of ventral scales maximized community convergence among forest and non-forest areas. The observed patterns suggested environmental filtering, indicating that less vertically structured habitats represent a strong filter. Despite the fact that phylogenetic structure was not detected individually for each community, we observed a trend towards communities composed by more closely related species in higher latitudes and more overdispersed compositions in lower latitudes. Such pattern suggests that the limited distribution of major snake lineages constrained species distributions. Structure indices for each community were also related to habitat type, showing that communities from non-forest areas tend to be more clustered. Our study showed that both environmental filtering and spatial gradients play important roles in shaping the composition of Neotropical snake communities. PMID:26061038

The Interplay between Environmental Filtering and Spatial Processes in Structuring Communities: The Case of Neotropical Snake Communities.

PubMed

Cavalheri, Hamanda; Both, Camila; Martins, Marcio

2015-01-01

Both habitat filters and spatial processes can influence community structure. Space alone affects species immigration from the regional species pool, whereas habitat filters affect species distribution and inter-specific interactions. This study aimed to understand how the interplay between environmental and geographical processes influenced the structure of Neotropical snake communities in different habitat types. We selected six studies that sampled snakes in forests, four conducted in savannas and two in grasslands (the latter two are grouped in a non-forest category). We used the net relatedness and nearest taxon indices to assess phylogenetic structure within forest and non-forest areas. We also used the phylogenetic fuzzy-weighting algorithm to characterize phylogenetic structure across communities and the relation of phylogenetic composition patterns to habitat type, structure, and latitude. Finally, we tested for morphological trait convergence and phylogenetic niche conservatism using four forest and four non-forest areas for which morphological data were available. Community phylogenetic composition changed across forest and non-forest areas suggesting that environmental filtering influences community structure. Species traits were affected by habitat type, indicating convergence at the metacommunity level. Tail length, robustness, and number of ventral scales maximized community convergence among forest and non-forest areas. The observed patterns suggested environmental filtering, indicating that less vertically structured habitats represent a strong filter. Despite the fact that phylogenetic structure was not detected individually for each community, we observed a trend towards communities composed by more closely related species in higher latitudes and more overdispersed compositions in lower latitudes. Such pattern suggests that the limited distribution of major snake lineages constrained species distributions. Structure indices for each community were also related to habitat type, showing that communities from non-forest areas tend to be more clustered. Our study showed that both environmental filtering and spatial gradients play important roles in shaping the composition of Neotropical snake communities.

Detection of Inter- and Intra-Specific Competition in the Insect Communities of British Trees: A Practical Exercise in Community Ecology.

ERIC Educational Resources Information Center

Putman, R. J.

1984-01-01

Describes an activity (suitable for high school or college) in which the effects of competition in the structuring of ecological communities are examined. The exercise also offers an introduction into species diversity; more advanced classes may be encouraged to seek reasons for differences in insect diversity on different trees. (Author/JN)

Liking and hyperlinking: Community detection in online child sexual exploitation networks.

PubMed

Westlake, Bryce G; Bouchard, Martin

2016-09-01

The online sexual exploitation of children is facilitated by websites that form virtual communities, via hyperlinks, to distribute images, videos, and other material. However, how these communities form, are structured, and evolve over time is unknown. Collected using a custom-designed webcrawler, we begin from known child sexual exploitation (CE) seed websites and follow hyperlinks to connected, related, websites. Using a repeated measure design we analyze 10 networks of 300 + websites each - over 4.8 million unique webpages in total, over a period of 60 weeks. Community detection techniques reveal that CE-related networks were dominated by two large communities hosting varied material -not necessarily matching the seed website. Community stability, over 60 weeks, varied across networks. Reciprocity in hyperlinking between community members was substantially higher than within the full network, however, websites were not more likely to connect to homogeneous-content websites. Copyright © 2016 Elsevier Inc. All rights reserved.

Detection of core-periphery structure in networks based on 3-tuple motifs

NASA Astrophysics Data System (ADS)

Ma, Chuang; Xiang, Bing-Bing; Chen, Han-Shuang; Small, Michael; Zhang, Hai-Feng

2018-05-01

Detecting mesoscale structure, such as community structure, is of vital importance for analyzing complex networks. Recently, a new mesoscale structure, core-periphery (CP) structure, has been identified in many real-world systems. In this paper, we propose an effective algorithm for detecting CP structure based on a 3-tuple motif. In this algorithm, we first define a 3-tuple motif in terms of the patterns of edges as well as the property of nodes, and then a motif adjacency matrix is constructed based on the 3-tuple motif. Finally, the problem is converted to find a cluster that minimizes the smallest motif conductance. Our algorithm works well in different CP structures: including single or multiple CP structure, and local or global CP structures. Results on the synthetic and the empirical networks validate the high performance of our method.

Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia.

PubMed

Robinson, Lucy F; Atlas, Lauren Y; Wager, Tor D

2015-03-01

We present a new method, State-based Dynamic Community Structure, that detects time-dependent community structure in networks of brain regions. Most analyses of functional connectivity assume that network behavior is static in time, or differs between task conditions with known timing. Our goal is to determine whether brain network topology remains stationary over time, or if changes in network organization occur at unknown time points. Changes in network organization may be related to shifts in neurological state, such as those associated with learning, drug uptake or experimental conditions. Using a hidden Markov stochastic blockmodel, we define a time-dependent community structure. We apply this approach to data from a functional magnetic resonance imaging experiment examining how contextual factors influence drug-induced analgesia. Results reveal that networks involved in pain, working memory, and emotion show distinct profiles of time-varying connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

Quality Audit in the Fastener Industry

NASA Technical Reports Server (NTRS)

Reagan, John R.

1995-01-01

Both the financial and quality communities rely on audits to verify customers records. The financial community is highly structured around three categories of risk, INHERENT RISK, CONTROL RISK, and DETECTION RISK. Combined, the product of these three categories constitute the AUDIT RISK. The financial community establishes CONTROL RISK based in large part on a systems level understanding of the process flow. This system level understanding is best expressed in a flowchart. The quality community may be able to adopt this structure and thereby reduce cost while maintaining and enhancing quality. The quality community should attempt to flowchart the systems level quality process before beginning substantive testing. This theory needs to be applied in several trial cases to prove or disprove this hypothesis

Quality audit in the fastener industry

NASA Astrophysics Data System (ADS)

Reagan, John R.

1995-09-01

Both the financial and quality communities rely on audits to verify customers records. The financial community is highly structured around three categories of risk, INHERENT RISK, CONTROL RISK, and DETECTION RISK. Combined, the product of these three categories constitute the AUDIT RISK. The financial community establishes CONTROL RISK based in large part on a systems level understanding of the process flow. This system level understanding is best expressed in a flowchart. The quality community may be able to adopt this structure and thereby reduce cost while maintaining and enhancing quality. The quality community should attempt to flowchart the systems level quality process before beginning substantive testing. This theory needs to be applied in several trial cases to prove or disprove this hypothesis

Community structure from spectral properties in complex networks

NASA Astrophysics Data System (ADS)

Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

2005-06-01

We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

Links between ammonia oxidizer species composition, functional diversity and nitrification kinetics in grassland soils.

PubMed

Webster, Gordon; Embley, T Martin; Freitag, Thomas E; Smith, Zena; Prosser, James I

2005-05-01

Molecular approaches have revealed considerable diversity and uncultured novelty in natural prokaryotic populations, but not direct links between the new genotypes detected and ecosystem processes. Here we describe the influence of the structure of communities of ammonia-oxidizing bacteria on nitrogen cycling in microcosms containing natural and managed grasslands and amended with artificial sheep urine, a major factor determining local ammonia concentrations in these environments. Nitrification kinetics were assessed by analysis of changes in urea, ammonia, nitrite and nitrate concentrations and ammonia oxidizer communities were characterized by analysis of 16S rRNA genes amplified from extracted DNA using ammonia oxidizer-specific primers. In natural soils, ammonia oxidizer community structure determined the delay preceding nitrification, which depended on the relative abundance of two Nitrosospira clusters, termed 3a and 3b. In batch cultures, pure culture and enrichment culture representatives of Nitrosospira 3a were sensitive to high ammonia concentration, while Nitrosospira cluster 3b representatives and Nitrosomonas europaea were tolerant. Delays in nitrification occurred in natural soils dominated by Nitrosospira cluster 3a and resulted from the time required for growth of low concentrations of Nitrosospira cluster 3b. In microcosms dominated by Nitrosospira cluster 3b and Nitrosomonas, no substantial delays were observed. In managed soils, no delays in nitrification were detected, regardless of initial ammonia oxidizer community structure, most probably resulting from higher ammonia oxidizer cell concentrations. The data therefore demonstrate a direct link between bacterial community structure, physiological diversity and ecosystem function.

Seasonal Dynamics of the Airborne Bacterial Community and Selected Viruses in a Children's Daycare Center.

PubMed

Prussin, Aaron J; Vikram, Amit; Bibby, Kyle J; Marr, Linsey C

2016-01-01

Children's daycare centers appear to be hubs of respiratory infectious disease transmission, yet there is only limited information about the airborne microbial communities that are present in daycare centers. We have investigated the microbial community of the air in a daycare center, including seasonal dynamics in the bacterial community and the presence of specific viral pathogens. We collected filters from the heating, ventilation, and air conditioning (HVAC) system of a daycare center every two weeks over the course of a year. Amplifying and sequencing the 16S rRNA gene revealed that the air was dominated by Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes that are commonly associated with the human skin flora. Clear seasonal differences in the microbial community were not evident; however, the community structure differed when the daycare center was closed and unoccupied for a 13-day period. These results suggest that human occupancy, rather than the environment, is the major driver in shaping the microbial community structure in the air of the daycare center. Using PCR for targeted viruses, we detected a seasonal pattern in the presence of respiratory syncytial virus that included the period of typical occurrence of the disease related to the virus; however, we did not detect the presence of adenovirus or rotavirus at any time.

Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI.

PubMed

He, Ye; Lim, Sol; Fortunato, Santo; Sporns, Olaf; Zhang, Lei; Qiu, Jiang; Xie, Peng; Zuo, Xi-Nian

2018-04-01

Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system.

Seasonal Dynamics of the Airborne Bacterial Community and Selected Viruses in a Children’s Daycare Center

PubMed Central

Prussin, Aaron J.; Vikram, Amit; Bibby, Kyle J.; Marr, Linsey C.

2016-01-01

Children’s daycare centers appear to be hubs of respiratory infectious disease transmission, yet there is only limited information about the airborne microbial communities that are present in daycare centers. We have investigated the microbial community of the air in a daycare center, including seasonal dynamics in the bacterial community and the presence of specific viral pathogens. We collected filters from the heating, ventilation, and air conditioning (HVAC) system of a daycare center every two weeks over the course of a year. Amplifying and sequencing the 16S rRNA gene revealed that the air was dominated by Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes that are commonly associated with the human skin flora. Clear seasonal differences in the microbial community were not evident; however, the community structure differed when the daycare center was closed and unoccupied for a 13-day period. These results suggest that human occupancy, rather than the environment, is the major driver in shaping the microbial community structure in the air of the daycare center. Using PCR for targeted viruses, we detected a seasonal pattern in the presence of respiratory syncytial virus that included the period of typical occurrence of the disease related to the virus; however, we did not detect the presence of adenovirus or rotavirus at any time. PMID:26942410

Effect of Increasing Nitrogen Deposition on Soil Microbial Communities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiao, Shengmu; Xue, Kai; He, Zhili

2010-05-17

Increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity are three main environmental changes occurring on a global scale. The BioCON (Biodiversity, CO2, and Nitrogen) ecological experiment site at the University of Minnesota's Cedar Creek Ecosystem Science Reserve started in 1997, to better understand how these changes would affect soil systems. To understand how increasing nitrogen deposition affects the microbial community diversity, heterogeneity, and functional structure impact soil microbial communities, 12 samples were collected from the BioCON plots in which nitrogenous fertilizer was added to simulate the effect of increasing nitrogen deposition and 12 samples from without added fertilizer. DNAmore » from the 24 samples was extracted using a freeze-grind protocol, amplified, labeled with a fluorescent dye, and then hybridized to GeoChip, a functional gene array containing probes for genes involved in N, S and C cycling, metal resistance and organic contaminant degradation. Detrended correspondence analysis (DCA) of all genes detected was performed to analyze microbial community patterns. The first two axes accounted for 23.5percent of the total variation. The samples fell into two major groups: fertilized and non-fertilized, suggesting that nitrogenous fertilizer had a significant impact on soil microbial community structure and diversity. The functional gene numbers detected in fertilized samples was less that detected in non-fertilizer samples. Functional genes involving in the N cycling were mainly discussed.« less

Evidence of community structure in biomedical research grant collaborations.

PubMed

Nagarajan, Radhakrishnan; Kalinka, Alex T; Hogan, William R

2013-02-01

Recent studies have clearly demonstrated a shift towards collaborative research and team science approaches across a spectrum of disciplines. Such collaborative efforts have also been acknowledged and nurtured by popular extramurally funded programs including the Clinical Translational Science Award (CTSA) conferred by the National Institutes of Health. Since its inception, the number of CTSA awardees has steadily increased to 60 institutes across 30 states. One of the objectives of CTSA is to accelerate translation of research from bench to bedside to community and train a new genre of researchers under the translational research umbrella. Feasibility of such a translation implicitly demands multi-disciplinary collaboration and mentoring. Networks have proven to be convenient abstractions for studying research collaborations. The present study is a part of the CTSA baseline study and investigates existence of possible community-structure in Biomedical Research Grant Collaboration (BRGC) networks across data sets retrieved from the internally developed grants management system, the Automated Research Information Administrator (ARIA) at the University of Arkansas for Medical Sciences (UAMS). Fastgreedy and link-community community-structure detection algorithms were used to investigate the presence of non-overlapping and overlapping community-structure and their variation across years 2006 and 2009. A surrogate testing approach in conjunction with appropriate discriminant statistics, namely: the modularity index and the maximum partition density is proposed to investigate whether the community-structure of the BRGC networks were different from those generated by certain types of random graphs. Non-overlapping as well as overlapping community-structure detection algorithms indicated the presence of community-structure in the BRGC network. Subsequent, surrogate testing revealed that random graph models considered in the present study may not necessarily be appropriate generative mechanisms of the community-structure in the BRGC networks. The discrepancy in the community-structure between the BRGC networks and the random graph surrogates was especially pronounced at 2009 as opposed to 2006 indicating a possible shift towards team-science and formation of non-trivial modular patterns with time. The results also clearly demonstrate presence of inter-departmental and multi-disciplinary collaborations in BRGC networks. While the results are presented on BRGC networks as a part of the CTSA baseline study at UAMS, the proposed methodologies are as such generic with potential to be extended across other CTSA organizations. Understanding the presence of community-structure can supplement more traditional network analysis as they're useful in identifying research teams and their inter-connections as opposed to the role of individual nodes in the network. Such an understanding can be a critical step prior to devising meaningful interventions for promoting team-science, multi-disciplinary collaborations, cross-fertilization of ideas across research teams and identifying suitable mentors. Understanding the temporal evolution of these communities may also be useful in CTSA evaluation. Copyright © 2012. Published by Elsevier Inc.

Identifying and characterizing key nodes among communities based on electrical-circuit networks.

PubMed

Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

2014-01-01

Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

Microbial community structure associated with the high loading anaerobic codigestion of olive mill and abattoir wastewaters.

PubMed

Gannoun, Hana; Omri, Ilhem; Chouari, Rakia; Khelifi, Eltaief; Keskes, Sajiaa; Godon, Jean-Jacques; Hamdi, Moktar; Sghir, Abdelghani; Bouallagui, Hassib

2016-02-01

The effect of increasing the organic loading rates (OLRs) on the performance of the anaerobic codigestion of olive mill (OMW) and abattoir wastewaters (AW) was investigated under mesophilic and thermophilic conditions. The structure of the microbial community was also monitored. Increasing OLR to 9g of chemical oxygen demand (COD) L(-1)d(-1) affected significantly the biogas yield and microbial diversity at 35°C. However, at 55°C digester remained stable until OLR of 12g of CODL(-1)d(-1) with higher COD removal (80%) and biogas yield (0.52Lg(-1) COD removed). Significant differences in the bacterial communities were detected between mesophilic and thermophilic conditions. The dominant phyla detected in the digester at both phases were the Firmicutes, Actinobacteria, Bacteroidetes, Synergistetes and Spirochaete. However, Verrucomicrobia, Proteobacteria and the candidate division BRC1 were only detected at thermophilic conditions. The Methanobacteriales and the Thermoplasmales were found as a high predominant archaeal member in the anaerobic sludge. Copyright © 2015 Elsevier Ltd. All rights reserved.

Finding community structure in very large networks

NASA Astrophysics Data System (ADS)

Clauset, Aaron; Newman, M. E. J.; Moore, Cristopher

2004-12-01

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(mdlogn) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with mtilde n and dtilde logn , in which case our algorithm runs in essentially linear time, O(nlog2n) . As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2×106 edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Improving the recommender algorithms with the detected communities in bipartite networks

NASA Astrophysics Data System (ADS)

Zhang, Peng; Wang, Duo; Xiao, Jinghua

2017-04-01

Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.

Microbial community pattern detection in human body habitats via ensemble clustering framework.

PubMed

Yang, Peng; Su, Xiaoquan; Ou-Yang, Le; Chua, Hon-Nian; Li, Xiao-Li; Ning, Kang

2014-01-01

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.

Microbial community pattern detection in human body habitats via ensemble clustering framework

PubMed Central

2014-01-01

Background The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. Results To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. Conclusions In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome. PMID:25521415

Risk Assessment and effect of Penicillin-G on bacterial diversity in drinking water

NASA Astrophysics Data System (ADS)

Wu, Qing; Zhao, Xiaofei; Peng, Sen; Wang, Lei; Zhao, Xinhua

2018-02-01

Penicillin-G was detected in drinking water by LC-MS/MS and the bacterial diversity was investigated by PCR and high-throughput sequencing. The results showed that bacteria community structure in drinking water has undergone major changes when added different concentrations of penicillin-G. The diversity index of each sample was calculated. The results showed that the total number and abundance of bacterial community species in drinking water samples decreased significantly after the addition of penicillin-G. However, the number and abundance of community structure did not change with the concentration. Penicillin-G inhibits the activity of bacterial community in drinking water and can reduce the bacterial diversity in drinking water.

Assessment of pollution impact on biological activity and structure of seabed bacterial communities in the Port of Livorno (Italy).

PubMed

Iannelli, Renato; Bianchi, Veronica; Macci, Cristina; Peruzzi, Eleonora; Chiellini, Carolina; Petroni, Giulio; Masciandaro, Grazia

2012-06-01

The main objective of this study was to assess the impact of pollution on seabed bacterial diversity, structure and activity in the Port of Livorno. Samples of seabed sediments taken from five selected sites within the port were subjected to chemical analyses, enzymatic activity detection, bacterial count and biomolecular analysis. Five different statistics were used to correlate the level of contamination with the detected biological indicators. The results showed that the port is mainly contaminated by variable levels of petroleum hydrocarbons and heavy metals, which affect the structure and activity of the bacterial population. Irrespective of pollution levels, the bacterial diversity did not diverge significantly among the assessed sites and samples, and no dominance was observed. The type of impact of hydrocarbons and heavy metals was controversial, thus enforcing the supposition that the structure of the bacterial community is mainly driven by the levels of nutrients. The combined use of chemical and biological essays resulted in an in-depth observation and analysis of the existing links between pollution macro-indicators and biological response of seabed bacterial communities. Copyright © 2012 Elsevier B.V. All rights reserved.

Flat and complex temperate reefs provide similar support for fish: Evidence for a unimodal species-habitat relationship

PubMed Central

Pickering, Emily A.; Adler, Alyssa M.; Taylor, J. Christopher; Peterson, Charles H.

2017-01-01

Structural complexity, a form of habitat heterogeneity, influences the structure and function of ecological communities, generally supporting increased species density, richness, and diversity. Recent research, however, suggests the most complex habitats may not harbor the highest density of individuals and number of species, especially in areas with elevated human influence. Understanding nuances in relationships between habitat heterogeneity and ecological communities is warranted to guide habitat-focused conservation and management efforts. We conducted fish and structural habitat surveys of thirty warm-temperate reefs on the southeastern US continental shelf to quantify how structural complexity influences fish communities. We found that intermediate complexity maximizes fish abundance on natural and artificial reefs, as well as species richness on natural reefs, challenging the current paradigm that abundance and other fish community metrics increase with increasing complexity. Naturally occurring rocky reefs of flat and complex morphologies supported equivalent abundance, biomass, species richness, and community composition of fishes. For flat and complex morphologies of rocky reefs to receive equal consideration as essential fish habitat (EFH), special attention should be given to detecting pavement type rocky reefs because their ephemeral nature makes them difficult to detect with typical seafloor mapping methods. Artificial reefs of intermediate complexity also maximized fish abundance, but human-made structures composed of low-lying concrete and metal ships differed in community types, with less complex, concrete structures supporting lower numbers of fishes classified largely as demersal species and metal ships protruding into the water column harboring higher numbers of fishes, including more pelagic species. Results of this study are essential to the process of evaluating habitat function provided by different types and shapes of reefs on the seafloor so that all EFH across a wide range of habitat complexity may be accurately identified and properly managed. PMID:28873447

Bacterial community structure in experimental methanogenic bioreactors and search for pathogenic clostridia as community members.

PubMed

Dohrmann, Anja B; Baumert, Susann; Klingebiel, Lars; Weiland, Peter; Tebbe, Christoph C

2011-03-01

Microbial conversion of organic waste or harvested plant material into biogas has become an attractive technology for energy production. Biogas is produced in reactors under anaerobic conditions by a consortium of microorganisms which commonly include bacteria of the genus Clostridium. Since the genus Clostridium also harbors some highly pathogenic members in its phylogenetic cluster I, there has been some concern that an unintended growth of such pathogens might occur during the fermentation process. Therefore this study aimed to follow how process parameters affect the diversity of Bacteria in general, and the diversity of Clostridium cluster I members in particular. The development of both communities was followed in model biogas reactors from start-up during stable methanogenic conditions. The biogas reactors were run with either cattle or pig manures as substrates, and both were operated at mesophilic and thermophilic conditions. The structural diversity was analyzed independent of cultivation using a PCR-based detection of 16S rRNA genes and genetic profiling by single-strand conformation polymorphism (SSCP). Genetic profiles indicated that both bacterial and clostridial communities evolved in parallel, and the community structures were highly influenced by both substrate and temperature. Sequence analysis of 16S rRNA genes recovered from prominent bands from SSCP profiles representing Clostridia detected no pathogenic species. Thus, this study gave no indication that pathogenic clostridia would be enriched as dominant community members in biogas reactors fed with manure.

Determination of nasal and oropharyngeal microbiomes in a multicenter population-based study - findings from Pretest 1 of the German National Cohort.

PubMed

Akmatov, Manas K; Koch, Nadine; Vital, Marius; Ahrens, Wolfgang; Flesch-Janys, Dieter; Fricke, Julia; Gatzemeier, Anja; Greiser, Halina; Günther, Kathrin; Illig, Thomas; Kaaks, Rudolf; Krone, Bastian; Kühn, Andrea; Linseisen, Jakob; Meisinger, Christine; Michels, Karin; Moebus, Susanne; Nieters, Alexandra; Obi, Nadia; Schultze, Anja; Six-Merker, Julia; Pieper, Dietmar H; Pessler, Frank

2017-05-12

We examined acceptability, preference and feasibility of collecting nasal and oropharyngeal swabs, followed by microbiome analysis, in a population-based study with 524 participants. Anterior nasal and oropharyngeal swabs were collected by certified personnel. In addition, participants self-collected nasal swabs at home four weeks later. Four swab types were compared regarding (1) participants' satisfaction and acceptance and (2) detection of microbial community structures based on deep sequencing of the 16 S rRNA gene V1-V2 variable regions. All swabbing methods were highly accepted. Microbial community structure analysis revealed 846 phylotypes, 46 of which were unique to oropharynx and 164 unique to nares. The calcium alginate tipped swab was found unsuitable for microbiome determinations. Among the remaining three swab types, there were no differences in oropharyngeal microbiomes detected and only marginal differences in nasal microbiomes. Microbial community structures did not differ between staff-collected and self-collected nasal swabs. These results suggest (1) that nasal and oropharyngeal swabbing are highly feasible methods for human population-based studies that include the characterization of microbial community structures in these important ecological niches, and (2) that self-collection of nasal swabs at home can be used to reduce cost and resources needed, particularly when serial measurements are to be taken.

Soil shapes community structure through fire.

PubMed

Ojeda, Fernando; Pausas, Juli G; Verdú, Miguel

2010-07-01

Recurrent wildfires constitute a major selecting force in shaping the structure of plant communities. At the regional scale, fire favours phenotypic and phylogenetic clustering in Mediterranean woody plant communities. Nevertheless, the incidence of fire within a fire-prone region may present strong variations at the local, landscape scale. This study tests the prediction that woody communities on acid, nutrient-poor soils should exhibit more pronounced phenotypic and phylogenetic clustering patterns than woody communities on fertile soils, as a consequence of their higher flammability and, hence, presumably higher propensity to recurrent fire. Results confirm the predictions and show that habitat filtering driven by fire may be detected even in local communities from an already fire-filtered regional flora. They also provide a new perspective from which to consider a preponderant role of fire as a key evolutionary force in acid, infertile Mediterranean heathlands.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

Multi-Level Anomaly Detection on Time-Varying Graph Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Robert A; Collins, John P; Ferragut, Erik M

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, thismore » multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

Controls on bacterial and archaeal community structure and greenhouse gas production in natural, mined, and restored Canadian peatlands

PubMed Central

Basiliko, Nathan; Henry, Kevin; Gupta, Varun; Moore, Tim R.; Driscoll, Brian T.; Dunfield, Peter F.

2013-01-01

Northern peatlands are important global C reservoirs, largely because of their slow rates of microbial C mineralization. Particularly in sites that are heavily influenced by anthropogenic disturbances, there is scant information about microbial ecology and whether or not microbial community structure influences greenhouse gas production. This work characterized communities of bacteria and archaea using terminal restriction fragment length polymorphism (T-RFLP) and sequence analysis of 16S rRNA and functional genes across eight natural, mined, or restored peatlands in two locations in eastern Canada. Correlations were explored among chemical properties of peat, bacterial and archaeal community structure, and carbon dioxide (CO2) and methane (CH4) production rates under oxic and anoxic conditions. Bacteria and archaea similar to those found in other peat soil environments were detected. In contrast to other reports, methanogen diversity was low in our study, with only 2 groups of known or suspected methanogens. Although mining and restoration affected substrate availability and microbial activity, these land-uses did not consistently affect bacterial or archaeal community composition. In fact, larger differences were observed between the two locations and between oxic and anoxic peat samples than between natural, mined, and restored sites, with anoxic samples characterized by less detectable bacterial diversity and stronger dominance by members of the phylum Acidobacteria. There were also no apparent strong linkages between prokaryote community structure and CH4 or CO2 production, suggesting that different organisms exhibit functional redundancy and/or that the same taxa function at very different rates when exposed to different peat substrates. In contrast to other earlier work focusing on fungal communities across similar mined and restored peatlands, bacterial and archaeal communities appeared to be more resistant or resilient to peat substrate changes brought about by these land uses. PMID:23914185

Bank-firm credit network in Japan: an analysis of a bipartite network.

PubMed

Marotta, Luca; Miccichè, Salvatore; Fujiwara, Yoshi; Iyetomi, Hiroshi; Aoyama, Hideaki; Gallegati, Mauro; Mantegna, Rosario N

2015-01-01

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-year-long analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms.

Bank-Firm Credit Network in Japan: An Analysis of a Bipartite Network

PubMed Central

Marotta, Luca; Miccichè, Salvatore; Fujiwara, Yoshi; Iyetomi, Hiroshi; Aoyama, Hideaki; Gallegati, Mauro; Mantegna, Rosario N.

2015-01-01

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-year-long analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms. PMID:25933413

Microbial community functional structure in response to antibiotics in pharmaceutical wastewater treatment systems.

PubMed

Zhang, Yu; Xie, Jianping; Liu, Miaomiao; Tian, Zhe; He, Zhili; van Nostrand, Joy D; Ren, Liren; Zhou, Jizhong; Yang, Min

2013-10-15

It is widely demonstrated that antibiotics in the environment affect microbial community structure. However, direct evidence regarding the impacts of antibiotics on microbial functional structures in wastewater treatment systems is limited. Herein, a high-throughput functional gene array (GeoChip 3.0) in combination with quantitative PCR and clone libraries were used to evaluate the microbial functional structures in two biological wastewater treatment systems, which treat antibiotic production wastewater mainly containing oxytetracycline. Despite the bacteriostatic effects of antibiotics, the GeoChip detected almost all key functional gene categories, including carbon cycling, nitrogen cycling, etc., suggesting that these microbial communities were functionally diverse. Totally 749 carbon-degrading genes belonging to 40 groups (24 from bacteria and 16 from fungi) were detected. The abundance of several fungal carbon-degrading genes (e.g., glyoxal oxidase (glx), lignin peroxidase or ligninase (lip), manganese peroxidase (mnp), endochitinase, exoglucanase_genes) was significantly correlated with antibiotic concentrations (Mantel test; P < 0.05), showing that the fungal functional genes have been enhanced by the presence of antibiotics. However, from the fact that the majority of carbon-degrading genes were derived from bacteria and diverse antibiotic resistance genes were detected in bacteria, it was assumed that many bacteria could survive in the environment by acquiring antibiotic resistance and may have maintained the position as a main player in nutrient removal. Variance partitioning analysis showed that antibiotics could explain 24.4% of variations in microbial functional structure of the treatment systems. This study provides insights into the impacts of antibiotics on microbial functional structure of a unique system receiving antibiotic production wastewater, and reveals the potential importance of the cooperation between fungi and bacteria with antibiotic resistance in maintaining the stability and performance of the systems. Copyright © 2013 Elsevier Ltd. All rights reserved.

A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies

PubMed Central

2017-01-01

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems. PMID:29121100

Finding overlapping communities in multilayer networks

PubMed Central

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks. PMID:29694387

Finding overlapping communities in multilayer networks.

PubMed

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

Malaria case detection using rapid diagnostic test at the community level in Ghana: consumer perception and practitioners' experiences.

PubMed

Danquah, Daniel A; Buabeng, Kwame O; Asante, Kwaku P; Mahama, Emmanuel; Bart-Plange, Constance; Owusu-Dabo, Ellis

2016-01-22

Ghana has scaled-up malaria control strategies over the past decade. Much as malaria morbidity and mortality seem to have declined with these efforts, there appears to be increased consumption of artemisinin-based combination therapy (ACT). This study explored the perception and experiences of community members and medicines outlet practitioners on malaria case detection using rapid diagnostic test (RDTs) to guide malaria therapy. This was a cross-sectional study using both quantitative and qualitative approaches for data. In-depth interviews with structured questionnaires were conducted among 197 practitioners randomly selected from community pharmacies and over-the-counter medicine sellers shops within two metropolis (Kumasi and Obuasi) in the Ashanti Region of Ghana. Two focus group discussions were also held in the two communities among female adult caregivers. Medicine outlet practitioners and community members often used raised body temperature of individuals as an index for malaria case detection. The raised body temperature was presumptively determined by touching the forehead with hands. Seventy percent of the practitioners' perceived malaria RDTs are used in hospitals and clinics but not in retail medicines outlets. Many of the practitioners and community members agreed to the need for using RDT for malaria case detection at medicine outlets. However, about 30% of the practitioners (n = 59) and some community members (n = 6) held the view that RDT negative results does not mean no malaria illness and would use ACT. Though malaria RDT use in medicines outlets was largely uncommon, both community members and medicine outlet practitioners welcomed its use. Public education is however needed to improve malaria case detection using RDTs at the community level, to inform appropriate use of ACT.

Fuel-reduction treatment effects on avian community structure and diversity

Treesearch

Sarah R. Hurteau; Thomas D. Sisk; William M. Block; Brett D. Dickson

2008-01-01

We assessed responses of the breeding bird community to mechanical thinning and prescribed surface fire, alone and in combination, between 2000 and 2006 in ponderosa pine (Pinus ponderosa) forests in northern Arizona, USA. Fuel-reduction treatments did not affect species richness or evenness, and effects on density of 5 commonly detected species...

The Cultural and Social Context of Oral and Pharyngeal Cancer Risk and Control among Hispanics in New York

PubMed Central

Cruz, Gustavo D.; Shulman, Lawrence C.; Kumar, Jayanth V.; Salazar, Christian R.

2007-01-01

New York City (NYC) has one of the highest incidence and mortality rates of oral and pharyngeal cancer (OPC) for Hispanics of any major U.S. city. This qualitative assessment explores OPC awareness, attitudes, and screening practices among at-risk Hispanics, health care providers, and community leaders in a Hispanic neighborhood of NYC. Four focus groups (N=39) were conducted with at-risk Hispanics. Structured interviews were conducted with ten health care providers (four physicians, four dentists, two dental hygienists) and three key community leaders. Results showed major gaps in OPC awareness across all key stakeholders. Focus group participants expressed difficulty in accessing appropriate health care. Health care providers were not familiar with OPC prevention and early detection practices. Community leaders lacked the knowledge and resources necessary for advocating prevention and early detection for their constituencies. All participants reported cultural, social, and structural barriers to prevention. There is a need for developing a comprehensive, culturally competent health communication program that targets all key stakeholders in the at-risk Hispanic community of NYC. PMID:17982210

Community structure of planktonic methane-oxidizing bacteria in a subtropical reservoir characterized by dominance of phylotype closely related to nitrite reducer

NASA Astrophysics Data System (ADS)

Kojima, Hisaya; Tokizawa, Riho; Kogure, Kouhei; Kobayashi, Yuki; Itoh, Masayuki; Shiah, Fuh-Kwo; Okuda, Noboru; Fukui, Manabu

2014-07-01

Methane-oxidizing bacteria (MOB) gain energy from the oxidation of methane and may play important roles in freshwater ecosystems. In this study, the community structure of planktonic MOB was investigated in a subtropical reservoir. Bacterial community structure was investigated through the analysis of the 16S rRNA gene. Three groups of phylogenetically distinct MOB were detected in the clone libraries of polymerase chain reaction products obtained with universal primers. The groups belonged to the class Gammaproteobacteria, the class Alphaproteobacteria, and the candidate phylum NC10. The last group, which consists of close relatives of the nitrite reducer `Candidatus Methylomirabilis oxyfera', was frequently detected in the clone libraries of deep-water environments. The presence of 3 groups of MOB in deep water was also shown by a cloning analysis of the pmoA gene encoding particulate methane monooxygenase. The dominance of `M. oxyfera'-like organisms in deep water was confirmed by catalyzed reporter deposition-fluorescence in situ hybridization, in which cells stained with a specific probe accounted for 16% of total microbial cells. This is the first study to demonstrate that close relatives of the nitrite reducer can be major component of planktonic MOB community which may affect carbon flow in aquatic ecosystems.

The use of plant community attributes to detect habitat quality in coastal environments

PubMed Central

Del Vecchio, Silvia; Slaviero, Antonio; Fantinato, Edy; Buffa, Gabriella

2016-01-01

The monitoring of biodiversity has mainly focused on the species level. However, researchers and land managers are making increasing use of complementary assessment tools that address higher levels of biological organization, i.e. communities, habitats and ecosystems. Recently, a variety of frameworks have been proposed for assessing the conservation status of communities or ecosystems. Among the various criteria proposed, all the protocols suggest considering (i) spatial aspects (range and area), and (ii) qualitative aspects of specific structures and functions. However, changes to ecological function are difficult to quantify and many protocols end up by using qualitative criteria. The aim of this work was to test the efficacy of some plant community attributes for the detection of vegetation quality in sand dune plant communities. We chose plant community attributes that either help to distinguish a habitat from others (diagnostic components) or play a significant role in habitat function and persistence over time. We used a diachronic approach by contrasting up-to-date vegetation data with data from previous studies carried out within the same areas. Changes in species composition were detected through detrended correspondence analyses (detrended correspondence analyses), Multi-Response Permutation Procedures and Indicator Species Analysis, while structural changes were analyzed by comparing species richness, total species cover, ecological groups of species and growth forms through null models. Ecological groups such as native focal species and aliens, and growth forms proved their efficacy in discriminating between habitat types and in describing their changes over time. The approach used in this study may provide an instrument for the assessment of plant community quality that can be applied to other coastal ecosystems. PMID:27255516

Context-aided analysis of community evolution in networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure basedmore » on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.« less

Context-aided analysis of community evolution in networks

DOE PAGES

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose; ...

2017-09-15

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure basedmore » on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.« less

Detecting livestock production zones.

PubMed

Grisi-Filho, J H H; Amaku, M; Ferreira, F; Dias, R A; Neto, J S Ferreira; Negreiros, R L; Ossada, R

2013-07-01

Communities are sets of nodes that are related in an important way, most likely sharing common properties and/or playing similar roles within a network. Unraveling a network structure, and hence the trade preferences and pathways, could be useful to a researcher or a decision maker. We implemented a community detection algorithm to find livestock communities, which is consistent with the definition of a livestock production zone, assuming that a community is a group of farm premises in which an animal is more likely to stay during its lifetime than expected by chance. We applied this algorithm to the network of animal movements within the state of Mato Grosso for 2007. This database holds information concerning 87,899 premises and 521,431 movements throughout the year, totaling 15,844,779 animals moved. The community detection algorithm achieved a network partition that shows a clear geographical and commercial pattern, two crucial features for preventive veterinary medicine applications; this algorithm provides also a meaningful interpretation to trade networks where links emerge based on trader node choices. Copyright © 2013 Elsevier B.V. All rights reserved.

Detecting macroecological patterns in bacterial communities across independent studies of global soils.

PubMed

Ramirez, Kelly S; Knight, Christopher G; de Hollander, Mattias; Brearley, Francis Q; Constantinides, Bede; Cotton, Anne; Creer, Si; Crowther, Thomas W; Davison, John; Delgado-Baquerizo, Manuel; Dorrepaal, Ellen; Elliott, David R; Fox, Graeme; Griffiths, Robert I; Hale, Chris; Hartman, Kyle; Houlden, Ashley; Jones, David L; Krab, Eveline J; Maestre, Fernando T; McGuire, Krista L; Monteux, Sylvain; Orr, Caroline H; van der Putten, Wim H; Roberts, Ian S; Robinson, David A; Rocca, Jennifer D; Rowntree, Jennifer; Schlaeppi, Klaus; Shepherd, Matthew; Singh, Brajesh K; Straathof, Angela L; Bhatnagar, Jennifer M; Thion, Cécile; van der Heijden, Marcel G A; de Vries, Franciska T

2018-02-01

The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential 'indicator' taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past.

Mutualistic mimicry and filtering by altitude shape the structure of Andean butterfly communities.

PubMed

Chazot, Nicolas; Willmott, Keith R; Santacruz Endara, Paola G; Toporov, Alexandre; Hill, Ryan I; Jiggins, Chris D; Elias, Marianne

2014-01-01

Both the abiotic environment and abiotic interactions among species contribute to shaping species assemblages. While the roles of habitat filtering and competitive interactions are clearly established, less is known about how positive interactions, whereby species benefit from the presence of one another, affect community structure. Here we assess the importance of positive interactions by studying Andean communities of butterflies that interact mutualistically via Müllerian mimicry. We show that communities at similar altitudes have a similar phylogenetic composition, confirming that filtering by altitude is an important process. We also provide evidence that species that interact mutualistically (i.e., species that share the same mimicry wing pattern) coexist at large scales more often than expected by chance. Furthermore, we detect an association between mimicry structure and altitude that is stronger than expected even when phylogeny is corrected for, indicating adaptive convergence for wing pattern and/or altitudinal range driven by mutualistic interactions. Positive interactions extend far beyond Müllerian mimicry, with many examples in plants and animals, and their role in the evolution and assembly of communities may be more pervasive than is currently appreciated. Our findings have strong implications for the evolution and resilience of community structure in a changing world.

Molecular tools for investigating microbial community structure and function in oxygen-deficient marine waters.

PubMed

Hawley, Alyse K; Kheirandish, Sam; Mueller, Andreas; Leung, Hilary T C; Norbeck, Angela D; Brewer, Heather M; Pasa-Tolic, Ljiljana; Hallam, Steven J

2013-01-01

Water column oxygen (O2)-deficiency shapes food-web structure by progressively directing nutrients and energy away from higher trophic levels into microbial community metabolism resulting in fixed nitrogen loss and greenhouse gas production. Although respiratory O2 consumption during organic matter degradation is a natural outcome of a productive surface ocean, global-warming-induced stratification intensifies this process leading to oxygen minimum zone (OMZ) expansion. Here, we describe useful tools for detection and quantification of potential key microbial players and processes in OMZ community metabolism including quantitative polymerase chain reaction primers targeting Marine Group I Thaumarchaeota, SUP05, Arctic96BD-19, and SAR324 small-subunit ribosomal RNA genes and protein extraction methods from OMZ waters compatible with high-resolution mass spectrometry for profiling microbial community structure and functional dynamics. © 2013 Elsevier Inc. All rights reserved.

Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes.

PubMed

Logares, Ramiro; Tesson, Sylvie V M; Canbäck, Björn; Pontarp, Mikael; Hedlund, Katarina; Rengefors, Karin

2018-05-04

Whether or not communities of microbial eukaryotes are structured in the same way as bacteria is a general and poorly explored question in ecology. Here, we investigated this question in a set of planktonic lake microbiotas in Eastern Antarctica that represent a natural community ecology experiment. Most of the analysed lakes emerged from the sea during the last 6,000 years, giving rise to waterbodies that originally contained marine microbiotas and that subsequently evolved into habitats ranging from freshwater to hypersaline. We show that habitat diversification has promoted selection driven by the salinity gradient in bacterial communities (explaining ∼72% of taxa turnover), while microeukaryotic counterparts were predominantly structured by ecological drift (∼72% of the turnover). Nevertheless, we also detected a number of microeukaryotes with specific responses to salinity, indicating that albeit minor, selection has had a role in the structuring of specific members of their communities. In sum, we conclude that microeukaryotes and bacteria inhabiting the same communities can be structured predominantly by different processes. This should be considered in future studies aiming to understand the mechanisms that shape microbial assemblages. This article is protected by copyright. All rights reserved. © 2018 Society for Applied Microbiology and John Wiley & Sons Ltd.

Network clustering and community detection using modulus of families of loops.

PubMed

Shakeri, Heman; Poggi-Corradini, Pietro; Albin, Nathan; Scoglio, Caterina

2017-01-01

We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link usage optimally. We propose weighting networks using these expected link usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

Exploring anti-community structure in networks with application to incompatibility of traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Zhu, Jiajing; Liu, Yongguo; Zhang, Yun; Liu, Xiaofeng; Xiao, Yonghua; Wang, Shidong; Wu, Xindong

2017-11-01

Community structure is one of the most important properties in networks, in which a node shares its most connections with the others in the same community. On the contrary, the anti-community structure means the nodes in the same group have few or no connections with each other. In Traditional Chinese Medicine (TCM), the incompatibility problem of herbs is a challenge to the clinical medication safety. In this paper, we propose a new anti-community detection algorithm, Random non-nEighboring nOde expansioN (REON), to find anti-communities in networks, in which a new evaluation criterion, anti-modularity, is designed to measure the quality of the obtained anti-community structure. In order to establish anti-communities in REON, we expand the node set by non-neighboring node expansion and regard the node set with the highest anti-modularity as an anti-community. Inspired by the phenomenon that the node with higher degree has greater contribution to the anti-modularity, an improved algorithm called REONI is developed by expanding node set by the non-neighboring node with the maximum degree, which greatly enhances the efficiency of REON. Experiments on synthetic and real-world networks demonstrate the superiority of the proposed algorithms over the existing methods. In addition, by applying REONI to the herb network, we find that it can discover incompatible herb combinations.

Community detection in complex networks using deep auto-encoded extreme learning machine

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-06-01

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

A fast community detection method in bipartite networks by distance dynamics

NASA Astrophysics Data System (ADS)

Sun, Hong-liang; Ch'ng, Eugene; Yong, Xi; Garibaldi, Jonathan M.; See, Simon; Chen, Duan-bing

2018-04-01

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(| E |) in sparse networks, where | E | is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle.

PubMed

Ferrari, Renata; Marzinelli, Ezequiel M; Ayroza, Camila Rezende; Jordan, Alan; Figueira, Will F; Byrne, Maria; Malcolm, Hamish A; Williams, Stefan B; Steinberg, Peter D

2018-01-01

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate 'no-take' and 'general-use' (fishing allowed) zones within three MPAs along 7o of latitude. We recorded 92 taxa and 38 morpho-groups across three large MPAs. We found that important habitat-forming biota (e.g. massive sponges) were more prevalent and abundant in no-take zones, while short ephemeral algae were more abundant in general-use zones, suggesting potential short-term effects of zoning (5-10 years). Yet, short-term effects of zoning were not detected at the community level (community structure or composition), while community structure varied significantly among MPAs. We conclude that by allowing rapid, simultaneous assessments at multiple spatial scales, autonomous underwater vehicles are useful to document changes in marine communities and identify adequate scales to manage them. This study advanced knowledge of marine benthic communities and their conservation in three ways. First, we quantified benthic biodiversity and abundance, generating the first baseline of these benthic communities against which the effectiveness of three large MPAs can be assessed. Second, we identified the taxonomic resolution necessary to assess both short and long-term effects of MPAs, concluding that coarse taxonomic resolution is sufficient given that analyses of community structure at different taxonomic levels were generally consistent. Yet, observed differences were taxa-specific and may have not been evident using our broader taxonomic classifications, a classification of mid to high taxonomic resolution may be necessary to determine zoning effects on key taxa. Third, we provide an example of statistical analyses and sampling design that once temporal sampling is incorporated will be useful to detect changes of marine benthic communities across multiple spatial and temporal scales.

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle

PubMed Central

Ayroza, Camila Rezende; Jordan, Alan; Figueira, Will F.; Byrne, Maria; Malcolm, Hamish A.; Williams, Stefan B.; Steinberg, Peter D.

2018-01-01

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate ‘no-take’ and ‘general-use’ (fishing allowed) zones within three MPAs along 7o of latitude. We recorded 92 taxa and 38 morpho-groups across three large MPAs. We found that important habitat-forming biota (e.g. massive sponges) were more prevalent and abundant in no-take zones, while short ephemeral algae were more abundant in general-use zones, suggesting potential short-term effects of zoning (5–10 years). Yet, short-term effects of zoning were not detected at the community level (community structure or composition), while community structure varied significantly among MPAs. We conclude that by allowing rapid, simultaneous assessments at multiple spatial scales, autonomous underwater vehicles are useful to document changes in marine communities and identify adequate scales to manage them. This study advanced knowledge of marine benthic communities and their conservation in three ways. First, we quantified benthic biodiversity and abundance, generating the first baseline of these benthic communities against which the effectiveness of three large MPAs can be assessed. Second, we identified the taxonomic resolution necessary to assess both short and long-term effects of MPAs, concluding that coarse taxonomic resolution is sufficient given that analyses of community structure at different taxonomic levels were generally consistent. Yet, observed differences were taxa-specific and may have not been evident using our broader taxonomic classifications, a classification of mid to high taxonomic resolution may be necessary to determine zoning effects on key taxa. Third, we provide an example of statistical analyses and sampling design that once temporal sampling is incorporated will be useful to detect changes of marine benthic communities across multiple spatial and temporal scales. PMID:29547656

Niche partitioning in arbuscular mycorrhizal communities in temperate grasslands: a lesson from adjacent serpentine and nonserpentine habitats.

PubMed

Kohout, Petr; Doubková, Pavla; Bahram, Mohammad; Suda, Jan; Tedersoo, Leho; Voříšková, Jana; Sudová, Radka

2015-04-01

Arbuscular mycorrhizal fungi (AMF) represent an important soil microbial group playing a fundamental role in many terrestrial ecosystems. We explored the effects of deterministic (soil characteristics, host plant life stage, neighbouring plant communities) and stochastic processes on AMF colonization, richness and community composition in roots of Knautia arvensis (Dipsacaceae) plants from three serpentine grasslands and adjacent nonserpentine sites. Methodically, the study was based on 454-sequencing of the ITS region of rDNA. In total, we detected 81 molecular taxonomical operational units (MOTUs) belonging to the Glomeromycota. Serpentine character of the site negatively influenced AMF root colonization, similarly as higher Fe concentration. AMF MOTUs richness linearly increased along a pH gradient from 3.5 to 5.8. Contrary, K and Cr soil concentration had a negative influence on AMF MOTUs richness. We also detected a strong relation between neighbouring plant community composition and AMF MOTUs richness. Although spatial distance between the sampled sites (c. 0.3-3 km) contributed to structuring AMF communities in K. arvensis roots, environmental parameters were key factors in this respect. In particular, the composition of AMF communities was shaped by the complex of serpentine conditions, pH and available soil Ni concentration. The composition of AMF communities was also dependent on host plant life stage (vegetative vs. generative). Our study supports the dominance of deterministic factors in structuring AMF communities in heterogeneous environment composed of an edaphic mosaic of serpentine and nonserpentine soils. © 2015 John Wiley & Sons Ltd.

Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

PubMed

Hassett, Michael J; Uno, Hajime; Cronin, Angel M; Carroll, Nikki M; Hornbrook, Mark C; Ritzwoller, Debra

2017-12-01

Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations. We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each cancer in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by cancer type/data source, and contrasted with an existing binary detection rule. Detection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by cancer type and dataset challenged efforts to create 1 common algorithm for all scenarios. Valid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and colorectal cancer patients and those who develop recurrence.

Microbial community dynamics in the rhizosphere of a cadmium hyper-accumulator

NASA Astrophysics Data System (ADS)

Wood, J. L.; Zhang, C.; Mathews, E. R.; Tang, C.; Franks, A. E.

2016-11-01

Phytoextraction is influenced by the indigenous soil microbial communities during the remediation of heavy metal contaminated soils. Soil microbial communities can affect plant growth, metal availability and the performance of phytoextraction-assisting inocula. Understanding the basic ecology of indigenous soil communities associated with the phytoextraction process, including the interplay between selective pressures upon the communities, is an important step towards phytoextraction optimization. This study investigated the impact of cadmium (Cd), and the presence of a Cd-accumulating plant, Carpobrotus rossii (Haw.) Schwantes, on the structure of soil-bacterial and fungal communities using automated ribosomal intergenic spacer analysis (ARISA) and quantitative PCR (qPCR). Whilst Cd had no detectable influence upon fungal communities, bacterial communities underwent significant structural changes with no reduction in 16S rRNA copy number. The presence of C. rossii influenced the structure of all communities and increased ITS copy number. Suites of operational taxonomic units (OTUs) changed in abundance in response to either Cd or C. rossii, however we found little evidence to suggest that the two selective pressures were acting synergistically. The Cd-induced turnover in bacterial OTUs suggests that Cd alters competition dynamics within the community. Further work to understand how competition is altered could provide a deeper understanding of the microbiome-plant-environment and aid phytoextraction optimization.

Microbial community dynamics in the rhizosphere of a cadmium hyper-accumulator

PubMed Central

Wood, J. L.; Zhang, C.; Mathews, E. R.; Tang, C.; Franks, A. E.

2016-01-01

Phytoextraction is influenced by the indigenous soil microbial communities during the remediation of heavy metal contaminated soils. Soil microbial communities can affect plant growth, metal availability and the performance of phytoextraction-assisting inocula. Understanding the basic ecology of indigenous soil communities associated with the phytoextraction process, including the interplay between selective pressures upon the communities, is an important step towards phytoextraction optimization. This study investigated the impact of cadmium (Cd), and the presence of a Cd-accumulating plant, Carpobrotus rossii (Haw.) Schwantes, on the structure of soil-bacterial and fungal communities using automated ribosomal intergenic spacer analysis (ARISA) and quantitative PCR (qPCR). Whilst Cd had no detectable influence upon fungal communities, bacterial communities underwent significant structural changes with no reduction in 16S rRNA copy number. The presence of C. rossii influenced the structure of all communities and increased ITS copy number. Suites of operational taxonomic units (OTUs) changed in abundance in response to either Cd or C. rossii, however we found little evidence to suggest that the two selective pressures were acting synergistically. The Cd-induced turnover in bacterial OTUs suggests that Cd alters competition dynamics within the community. Further work to understand how competition is altered could provide a deeper understanding of the microbiome-plant-environment and aid phytoextraction optimization. PMID:27805014

A life detection problem in a High Arctic microbial community

NASA Astrophysics Data System (ADS)

Rogers, J. D.; Perreault, N. N.; Niederberger, T. D.; Lichten, C.; Whyte, L. G.; Nadeau, J. L.

2010-03-01

Fluorescent labeling of bacterial cell walls, DNA, and metabolic processes demonstrates high (potentially single molecule) sensitivity, is non-invasive, and in some cases can differentiate strains and species. Robust microscopes such as the custom instruments presented here can provide good image quality in the field and are potentially suitable for flight. However, ambiguous or false-positive results with bacterial stains can occur and can create difficulties in interpretation even on Earth. We present a "real" life detection problem in a sample of biofilms taken from the Canadian High Arctic. The samples consisted of numerous small sulfur-oxidizing bacteria and larger structures resembling fungi or diatoms. The identity of these latter structures remained ambiguous until electron microscopy and X-ray spectroscopy were performed, indicating that they were unusual sulfur minerals probably precipitated by the bacterial communities. While such mineral structures may possibly serve as biosignatures after the cells have disappeared, it is important that they not be mistaken for cells themselves. It is also possible that unusual mineral structures will be performed under extraterrestrial conditions, so great care is needed to differentiate cell structures from minerals.

Climate extremes drive changes in functional community structure.

PubMed

Boucek, Ross E; Rehage, Jennifer S

2014-06-01

The response of communities to climate extremes can be quite variable. Much of this variation has been attributed to differences in community-specific functional trait diversity, as well as community composition. Yet, few if any studies have explicitly tested the response of the functional trait structure of communities following climate extremes (CEs). Recently in South Florida, two independent, but sequential potential CEs took place, a 2010 cold front, followed by a 2011 drought, both of which had profound impacts on a subtropical estuarine fish community. These CEs provided an opportunity to test whether the structure of South Florida fish communities following each extreme was a result of species-specific differences in functional traits. From historical temperature (1927-2012) and freshwater inflows records into the estuary (1955-2012), we determined that the cold front was a statistically extreme disturbance, while the drought was not, but rather a decadal rare disturbance. The two disturbances predictably affected different parts of functional community structure and thus different component species. The cold front virtually eliminated tropical species, including large-bodied snook, mojarra species, nonnative cichlids, and striped mullet, while having little affect on temperate fishes. Likewise, the drought severely impacted freshwater fishes including Florida gar, bowfin, and two centrarchids, with little effect on euryhaline species. Our findings illustrate the ability of this approach to predict and detect both the filtering effects of different types of disturbances and the implications of the resulting changes in community structure. Further, we highlight the value of this approach to developing predictive frameworks for better understanding community responses to global change. © 2014 John Wiley & Sons Ltd.

Investigation of Water Quality and Aquatic-Community Structure in Village and Valley Creeks, City of Birmingham, Jefferson County, Alabama 2000-01

NASA Astrophysics Data System (ADS)

McPherson, A. K.

2002-12-01

The U.S. Geological Survey conducted a 16-month investigation of water quality, aquatic-community structure, bed sediment, and fish tissue in Village and Valley Creeks, two urban streams that drain areas of residential, commercial, and industrial land use in Birmingham, Alabama. Water-quality data were collected between February 2000 and March 2001 at four sites on Village Creek, three sites on Valley Creek, and at two reference sites near Birmingham, Fivemile Creek and Little Cahaba River, that drain less urbanized areas. The occurrence and distribution of chemical constituents in the water column and bed sediment provided an initial assessment of water quality in the streams. Aquatic-community structure, physical condition of fish, and analysis of fish tissue provided an indication of the cumulative effects of the water quality on the aquatic biota. Degraded water quality was seen at the more urbanized sites on Village and Valley Creeks. Elevated concentrations of nutrients, bacteria, trace elements, and organic contaminants were detected in the water column. Trace-element priority pollutants, pesticides, and other organic compounds were detected in higher concentrations in bed sediment and fish tissue at the Village and Valley Creek sites than at the reference site. The richness and density of the fish and benthic-invertebrate communities indicate that the integrity of the aquatic communities in Village and Valley Creeks is poor in comparison to that observed at the two reference sites. Correlations between land use and aquatic-community structure, water quality, bed sediment, and fish tissue were observed. The abundance of mayflies and the number of EPT (ephemeroptera, plecoptera, tricoptera) taxa were negatively correlated with industrial land use. The abundance of midges (an indicator of poor water quality) was positively correlated with industrial land use; the percentage of mosquitofishes (a tolerant species) was positively correlated with commercial land use. In contrast, the numbers of fish species, fish families, and the percentage of sunfishes (intolerant species) were positively correlated with forested land use, indicating that the more diverse fish communities were found in basins with a higher percentage of forested land. The concentrations of 12 water-quality constituents and 18 organic compounds detected in bed sediment were positively correlated with industrial land use. Mercury and molybdenum concentrations detected in fish-liver tissue also were positively correlated with industrial land use. The water quality and aquatic-community structure in Village and Valley Creeks are degraded in comparison to streams flowing through less urbanized areas. Decreased diversity and elevated concentrations of trace elements and organic contaminants in the water column, bed sediment, and fish tissues at Village and Valley Creeks are indicative of the effects of urbanization. Industrial land use, in particular, was significantly correlated to elevated contaminant levels in the water column, bed sediment, fish tissues, and to the declining health of the benthic-invertebrate communities. The results of this 16-month study have long-range watershed management implications, demonstrating the association between urban development and stream degradation. These data can serve as a baseline from which to determine the effectiveness of stream-restoration programs.

Ectomycorrhizal fungal diversity and community structure associated with cork oak in different landscapes.

PubMed

Reis, Francisca; Valdiviesso, Teresa; Varela, Carolina; Tavares, Rui M; Baptista, Paula; Lino-Neto, Teresa

2018-05-01

Cork oak (Quercus suber L.) forests play an important ecological and economic role. Ectomycorrhizal fungi (ECMF) are key components for the sustainability and functioning of these ecosystems. The community structure and composition of ECMF associated with Q. suber in different landscapes of distinct Mediterranean bioclimate regions have not previously been compared. In this work, soil samples from cork oak forests residing in different bioclimates (arid, semi-arid, sub-humid, and humid) were collected and surveyed for ectomycorrhizal (ECM) root tips. A global analysis performed on 3565 ECM root tips revealed that the ECMF community is highly enriched in Russula, Tomentella, and Cenoccocum, which correspond to the ECMF genera that mainly contribute to community differences. The ECMF communities from the rainiest and the driest cork oak forests were distinct, with soils from the rainiest climates being more heterogeneous than those from the driest climates. The analyses of several abiotic factors on the ECMF communities revealed that bioclimate, precipitation, soil texture, and forest management strongly influenced ECMF structure. Shifts in ECMF with different hyphal exploration types were also detected among forests, with precipitation, forest system, and soil texture being the main drivers controlling their composition. Understanding the effects of environmental factors on the structuring of ECM communities could be the first step for promoting the sustainability of this threatened ecosystem.

Altered fish community and feeding behaviour in close proximity to boat moorings in an urban estuary.

PubMed

Lanham, Brendan S; Vergés, Adriana; Hedge, Luke H; Johnston, Emma L; Poore, Alistair G B

2018-04-01

Coastal urbanization has led to large-scale transformation of estuaries, with artificial structures now commonplace. Boat moorings are known to reduce seagrass cover, but little is known about their effect on fish communities. We used underwater video to quantify abundance, diversity, composition and feeding behaviour of fish assemblages on two scales: with increasing distance from moorings on fine scales, and among locations where moorings were present or absent. Fish were less abundant in close proximity to boat moorings, and the species composition varied on fine scales, leading to lower predation pressure near moorings. There was no relationship at the location with seagrass. On larger scales, we detected no differences in abundance or community composition among locations where moorings were present or absent. These findings show a clear impact of moorings on fish and highlight the importance of fine-scale assessments over location-scale comparisons in the detection of the effects of artificial structures. Copyright © 2018 Elsevier Ltd. All rights reserved.

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

Genetic characterization of uncultured fungal endophytes from Bouteloua eriopoda and Atriplex canescens

Treesearch

Mary E. Lucero; Jerry R. Barrow; Ruth Sedillo; Pedro Osuna-Avila; Isaac Reyes-Vera

2008-01-01

Obligate fungal endophytes form cryptic communities in vascular plants that can defy detection and isolation by microscopic examination of reproductive structures. Molecular detection by PCR amplification of fungal DNA sequences alone is insufficient, since target endophyte sequences are unknown and difficult to distinguish from sequences already characterized as plant...

RNA-based analyses reveal fungal communities structured by a senescence gradient in the moss Dicranum scoparium and the presence of putative multi-trophic fungi.

PubMed

Chen, Ko-Hsuan; Liao, Hui-Ling; Arnold, A Elizabeth; Bonito, Gregory; Lutzoni, François

2018-06-01

Diverse plant-associated fungi are thought to have symbiotrophic and saprotrophic states because they can be isolated from both dead and living plant tissues. However, such tissues often are separated in time and space, and fungal activity at various stages of plant senescence is rarely assessed directly in fungal community studies. We used fungal ribosomal RNA metatranscriptomics to detect active fungal communities across a natural senescence gradient within wild-collected gametophytes of Dicranum scoparium (Bryophyta) to understand the distribution of active fungal communities in adjacent living, senescing and dead tissues. Ascomycota were active in all tissues across the senescence gradient. By contrast, Basidiomycota were prevalent and active in senescing and dead tissues. Several fungi were detected as active in living and dead tissues, suggesting their capacity for multi-trophy. Differences in community assembly detected by metatranscriptomics were echoed by amplicon sequencing of cDNA and compared to culture-based inferences and observation of fungal fruit bodies in the field. The combination of amplicon sequencing of cDNA and metatranscriptomics is promising for studying symbiotic systems with complex microbial diversity, allowing for the simultaneous detection of their presence and activity. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

Using phylogeny and functional traits for assessing community assembly along environmental gradients: A deterministic process driven by elevation.

PubMed

Xu, Jinshi; Chen, Yu; Zhang, Lixia; Chai, Yongfu; Wang, Mao; Guo, Yaoxin; Li, Ting; Yue, Ming

2017-07-01

Community assembly processes is the primary focus of community ecology. Using phylogenetic-based and functional trait-based methods jointly to explore these processes along environmental gradients are useful ways to explain the change of assembly mechanisms under changing world. Our study combined these methods to test assembly processes in wide range gradients of elevation and other habitat environmental factors. We collected our data at 40 plots in Taibai Mountain, China, with more than 2,300 m altitude difference in study area and then measured traits and environmental factors. Variance partitioning was used to distinguish the main environment factors leading to phylogeny and traits change among 40 plots. Principal component analysis (PCA) was applied to colligate other environment factors. Community assembly patterns along environmental gradients based on phylogenetic and functional methods were studied for exploring assembly mechanisms. Phylogenetic signal was calculated for each community along environmental gradients in order to detect the variation of trait performance on phylogeny. Elevation showed a better explanatory power than other environment factors for phylogenetic and most traits' variance. Phylogenetic and several functional structure clustered at high elevation while some conserved traits overdispersed. Convergent tendency which might be caused by filtering or competition along elevation was detected based on functional traits. Leaf dry matter content (LDMC) and leaf nitrogen content along PCA 1 axis showed conflicting patterns comparing to patterns showed on elevation. LDMC exhibited the strongest phylogenetic signal. Only the phylogenetic signal of maximum plant height showed explicable change along environmental gradients. Synthesis . Elevation is the best environment factors for predicting phylogeny and traits change. Plant's phylogenetic and some functional structures show environmental filtering in alpine region while it shows different assembly processes in middle- and low-altitude region by different trait/phylogeny. The results highlight deterministic processes dominate community assembly in large-scale environmental gradients. Performance of phylogeny and traits along gradients may be independent with each other. The novel method for calculating functional structure which we used in this study and the focus of phylogenetic signal change along gradients may provide more useful ways to detect community assembly mechanisms.

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Robert A.; Collins, John P.; Ferragut, Erik M.

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitatesmore » intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

DOE PAGES

Bridges, Robert A.; Collins, John P.; Ferragut, Erik M.; ...

2016-01-01

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitatesmore » intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

Parameterized centrality metric for network analysis

NASA Astrophysics Data System (ADS)

Ghosh, Rumi; Lerman, Kristina

2011-06-01

A variety of metrics have been proposed to measure the relative importance of nodes in a network. One of these, alpha-centrality [P. Bonacich, Am. J. Sociol.0002-960210.1086/228631 92, 1170 (1987)], measures the number of attenuated paths that exist between nodes. We introduce a normalized version of this metric and use it to study network structure, for example, to rank nodes and find community structure of the network. Specifically, we extend the modularity-maximization method for community detection to use this metric as the measure of node connectivity. Normalized alpha-centrality is a powerful tool for network analysis, since it contains a tunable parameter that sets the length scale of interactions. Studying how rankings and discovered communities change when this parameter is varied allows us to identify locally and globally important nodes and structures. We apply the proposed metric to several benchmark networks and show that it leads to better insights into network structure than alternative metrics.

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

The ecology of parasites of freshwater fishes: the search for patterns.

PubMed

Kennedy, C R

2009-10-01

Developments in the study of the ecology of helminth parasites of freshwater fishes over the last half century are reviewed. Most research has of necessity been field based and has involved the search for patterns in population and community dynamics that are repeatable in space and time. Mathematical models predict that under certain conditions host and parasite populations can attain equilibrial levels through operation of regulatory factors. Such factors have been identified in several host-parasite systems and some parasite populations have been shown to persist over long time-periods. However, there is no convincing evidence that fish parasite populations are stable and regulated since in all cases alternative explanations are equally acceptable and it appears that they are non-equilibrial systems. It has proved particularly difficult to detect replicable patterns in parasite communities. Inter-specific competition, evidenced by functional and numerical responses, has been detected in several communities but its occurrence is erratic and its significance unclear. Some studies have failed to find any nested patterns in parasite community structure and richness, whereas others have identified such patterns although they are seldom constant over space and time. Departures from randomness appear to be the exception and then only temporary. It appears that parasite communities are non-equilibrial, stochastic assemblages rather than structured and organized.

Metagenomic Analysis of Fungal Diversity on Strawberry Plants and the Effect of Management Practices on the Fungal Community Structure of Aerial Organs

PubMed Central

Abdelfattah, Ahmed; Wisniewski, Michael; Li Destri Nicosia, Maria Giulia; Cacciola, Santa Olga

2016-01-01

An amplicon metagenomic approach based on the ITS2 region of fungal rDNA was used to identify the composition of fungal communities associated with different strawberry organs (leaves, flowers, immature and mature fruits), grown on a farm using management practices that entailed the routine use of various chemical pesticides. ITS2 sequences clustered into 316 OTUs and Ascomycota was the dominant phyla (95.6%) followed by Basidiomycota (3.9%). Strawberry plants supported a high diversity of microbial organisms, but two genera, Botrytis and Cladosporium, were the most abundant, representing 70–99% of the relative abundance (RA) of all detected sequences. According to alpha and beta diversity analyses, strawberry organs displayed significantly different fungal communities with leaves having the most diverse fungal community, followed by flowers, and fruit. The interruption of chemical treatments for one month resulted in a significant modification in the structure of the fungal community of leaves and flowers while immature and mature fruit were not significantly affected. Several plant pathogens of other plant species, that would not be intuitively expected to be present on strawberry plants such as Erysiphe, were detected, while some common strawberry pathogens, such as Rhizoctonia, were less evident or absent. PMID:27490110

Vertical Structure of Phyllosphere Fungal Communities in a Tropical Forest in Thailand Uncovered by High-Throughput Sequencing.

PubMed

Izuno, Ayako; Kanzaki, Mamoru; Artchawakom, Taksin; Wachrinrat, Chongrak; Isagi, Yuji

2016-01-01

Phyllosphere fungi harbor a tremendous species diversity and play important ecological roles. However, little is known about their distribution patterns within forest ecosystems. We examined how species diversity and community composition of phyllosphere fungi change along a vertical structure in a tropical forest in Thailand. Fungal communities in 144 leaf samples from 19 vertical layers (1.28-34.4 m above ground) of 73 plant individuals (27 species) were investigated by metabarcoding analysis using Ion Torrent sequencing. In total, 1,524 fungal operational taxonomic units (OTUs) were detected among 890,710 reads obtained from the 144 leaf samples. Taxonomically diverse fungi belonging to as many as 24 orders of Ascomycota and 21 orders of Basidiomycota were detected, most of which inhabited limited parts of the lowest layers closest to the forest floor. Species diversity of phyllosphere fungi was the highest in the lowest layers closest to the forest floor, decreased with increasing height, and lowest in the canopy; 742 and 55 fungal OTUs were detected at the lowest and highest layer, respectively. On the layers close to the forest floor, phyllosphere fungal communities were mainly composed of low frequency OTUs and largely differentiated among plant individuals. Conversely, in the canopy, fungal communities consisted of similar OTUs across plant individuals, and as many as 86.1%-92.7% of the OTUs found in the canopy (≥22 m above ground) were also distributed in the lower layers. Overall, our study showed the variability of phyllosphere fungal communities along the vertical gradient of plant vegetation and environmental conditions, suggesting the significance of biotic and abiotic variation for the species diversity of phyllosphere fungi.

Microbial diversity in hummock and hollow soils of three wetlands on the Qinghai-Tibetan Plateau revealed by 16S rRNA pyrosequencing.

PubMed

Deng, Yongcui; Cui, Xiaoyong; Hernández, Marcela; Dumont, Marc G

2014-01-01

The wetlands of the Qinghai-Tibetan Plateau are believed to play an important role in global nutrient cycling, but the composition and diversity of microorganisms in this ecosystem are poorly characterized. An understanding of the effects of geography and microtopography on microbial populations will provide clues to the underlying mechanisms that structure microbial communities. In this study, we used pyrosequencing-based analysis of 16S rRNA gene sequences to assess and compare the composition of soil microbial communities present in hummock and hollow soils from three wetlands (Dangxiong, Hongyuan and Maduo) on the Qinghai-Tibetan Plateau, the world's highest plateau. A total of 36 bacterial phyla were detected. Proteobacteria (34.5% average relative abundance), Actinobacteria (17.3%) and Bacteroidetes (11%) had the highest relative abundances across all sites. Chloroflexi, Acidobacteria, Verrucomicrobia, Firmicutes, and Planctomycetes were also relatively abundant (1-10%). In addition, archaeal sequences belonging to Euryarchaea, Crenarchaea and Thaumarchaea were detected. Alphaproteobacteria sequences, especially of the order Rhodospirillales, were significantly more abundant in Maduo than Hongyuan and Dangxiong wetlands. Compared with Hongyuan soils, Dangxiong and Maduo had significantly higher relative abundances of Gammaproteobacteria sequences (mainly order Xanthomonadales). Hongyuan wetland had a relatively high abundance of methanogens (mainly genera Methanobacterium, Methanosarcina and Methanosaeta) and methanotrophs (mainly Methylocystis) compared with the other two wetlands. Principal coordinate analysis (PCoA) indicated that the microbial community structure differed between locations and microtopographies and canonical correspondence analysis indicated an association between microbial community structure and soil properties or geography. These insights into the microbial community structure and the main controlling factors in wetlands of the Qinghai-Tibetan Plateau provide a valuable background for further studies on biogeochemical processes in this distinct ecosystem.

Microbial Diversity in Hummock and Hollow Soils of Three Wetlands on the Qinghai-Tibetan Plateau Revealed by 16S rRNA Pyrosequencing

PubMed Central

Deng, Yongcui; Cui, Xiaoyong; Hernández, Marcela; Dumont, Marc G.

2014-01-01

The wetlands of the Qinghai-Tibetan Plateau are believed to play an important role in global nutrient cycling, but the composition and diversity of microorganisms in this ecosystem are poorly characterized. An understanding of the effects of geography and microtopography on microbial populations will provide clues to the underlying mechanisms that structure microbial communities. In this study, we used pyrosequencing-based analysis of 16S rRNA gene sequences to assess and compare the composition of soil microbial communities present in hummock and hollow soils from three wetlands (Dangxiong, Hongyuan and Maduo) on the Qinghai-Tibetan Plateau, the world’s highest plateau. A total of 36 bacterial phyla were detected. Proteobacteria (34.5% average relative abundance), Actinobacteria (17.3%) and Bacteroidetes (11%) had the highest relative abundances across all sites. Chloroflexi, Acidobacteria, Verrucomicrobia, Firmicutes, and Planctomycetes were also relatively abundant (1–10%). In addition, archaeal sequences belonging to Euryarchaea, Crenarchaea and Thaumarchaea were detected. Alphaproteobacteria sequences, especially of the order Rhodospirillales, were significantly more abundant in Maduo than Hongyuan and Dangxiong wetlands. Compared with Hongyuan soils, Dangxiong and Maduo had significantly higher relative abundances of Gammaproteobacteria sequences (mainly order Xanthomonadales). Hongyuan wetland had a relatively high abundance of methanogens (mainly genera Methanobacterium, Methanosarcina and Methanosaeta) and methanotrophs (mainly Methylocystis) compared with the other two wetlands. Principal coordinate analysis (PCoA) indicated that the microbial community structure differed between locations and microtopographies and canonical correspondence analysis indicated an association between microbial community structure and soil properties or geography. These insights into the microbial community structure and the main controlling factors in wetlands of the Qinghai-Tibetan Plateau provide a valuable background for further studies on biogeochemical processes in this distinct ecosystem. PMID:25078273

Similar soil microbial community structure across different environments after long-term succession: evidence from volcanoes of different ages.

PubMed

Xu, Shangqi; Zhang, Jianfeng; Luo, Shasha; Zhou, Xue; Shi, Shaohua; Tian, Chunjie

2018-06-08

Soil microbes play critical roles in global biogeochemical cycles, but their succession patterns across long temporal scales have rarely been studied. In this study, soil samples were collected from three volcanoes in Wudalianchi, northeastern China: Laoheishan (LH, approximately 240 years old), Dongjiaodebushan (DJ, 0.45-0.6 million years old), and Nangelaqiushan (NG, 0.8-1.3 million years old). For each volcano, both southern (S) and northern (N) slope aspects were sampled. Soil microbial communities were analyzed using phospholipid fatty acid analysis (PLFA). The results showed that soil properties and microbial biomass changed perceptibly among different volcanoes and different slope aspects. Almost all of the detected soil nutrient contents of LH were lowest, and total microbial biomass of LH was 40 and 36% lower than those of NG and DJ, respectively. LH was significantly different from NG and DJ in soil microbial community structure with a higher relative abundance of fungi and a lower relative abundance of actinomycetes and bacteria. However, for the two ancient volcanoes (NG and DJ), soil microbial community structures were highly similar among different ages and different slope aspects. No difference was detected in any of the measured microbial indices, including richness, evenness, Shannon's diversity, Simpson's diversity and the relative abundance of different microbial groups. The results indicated that while soil microbial biomass may change across different soil environments after long-term succession, soil microbial community structure can remain relatively stable. The results further indicated that soil microbes may show different successional patterns in different stages of succession. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Community detection using preference networks

NASA Astrophysics Data System (ADS)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

Characterization of the bacterial communities of life stages of free living lone star ticks (Amblyomma americanum).

PubMed

Williams-Newkirk, Amanda Jo; Rowe, Lori A; Mixson-Hayden, Tonya R; Dasch, Gregory A

2014-01-01

The lone star tick (Amblyomma americanum) is an abundant and aggressive biter of humans, domestic animals, and wildlife in the southeastern-central USA and an important vector of several known and suspected zoonotic bacterial pathogens. However, the biological drivers of bacterial community variation in this tick are still poorly defined. Knowing the community context in which tick-borne bacterial pathogens exist and evolve is required to fully understand the ecology and immunobiology of the ticks and to design effective public health and veterinary interventions. We performed a metagenomic survey of the bacterial communities of questing A. americanum and tested 131 individuals (66 nymphs, 24 males, and 41 females) from five sites in three states. Pyrosequencing was performed with barcoded eubacterial primers targeting variable 16S rRNA gene regions 5-3. The bacterial communities were dominated by Rickettsia (likely R. amblyommii) and an obligate Coxiella symbiont, together accounting for 6.7-100% of sequences per tick. DNAs from Midichloria, Borrelia, Wolbachia, Ehrlichia, Pseudomonas, or unidentified Bacillales, Enterobacteriaceae, or Rhizobiales groups were also detected frequently. Wolbachia and Midichloria significantly co-occurred in Georgia (p<0.00001), but not in other states. The significance of the Midichloria-Wolbachia co-occurrence is unknown. Among ticks collected in Georgia, nymphs differed from adults in both the composition (p = 0.002) and structure (p = 0.002) of their bacterial communities. Adults differed only in their community structure (p = 0.002) with males containing more Rickettsia and females containing more Coxiella. Comparisons among adult ticks collected in New York and North Carolina supported the findings from the Georgia collection despite differences in geography, collection date, and sample handling, implying that the differences detected are consistent attributes. The data also suggest that some members of the bacterial community change during the tick life cycle and that some sex-specific attributes may be detectable in nymphs.

Combined node and link partitions method for finding overlapping communities in complex networks

PubMed Central

Jin, Di; Gabrys, Bogdan; Dang, Jianwu

2015-01-01

Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures. PMID:25715829

Complementary Microorganisms in Highly Corrosive Biofilms from an Offshore Oil Production Facility.

PubMed

Vigneron, Adrien; Alsop, Eric B; Chambers, Brian; Lomans, Bartholomeus P; Head, Ian M; Tsesmetzis, Nicolas

2016-04-01

Offshore oil production facilities are frequently victims of internal piping corrosion, potentially leading to human and environmental risks and significant economic losses. Microbially influenced corrosion (MIC) is believed to be an important factor in this major problem for the petroleum industry. However, knowledge of the microbial communities and metabolic processes leading to corrosion is still limited. Therefore, the microbial communities from three anaerobic biofilms recovered from the inside of a steel pipe exhibiting high corrosion rates, iron oxide deposits, and substantial amounts of sulfur, which are characteristic of MIC, were analyzed in detail. Bacterial and archaeal community structures were investigated by automated ribosomal intergenic spacer analysis, multigenic (16S rRNA and functional genes) high-throughput Illumina MiSeq sequencing, and quantitative PCR analysis. The microbial community analysis indicated that bacteria, particularly Desulfovibrio species, dominated the biofilm microbial communities. However, other bacteria, such as Pelobacter, Pseudomonas, and Geotoga, as well as various methanogenic archaea, previously detected in oil facilities were also detected. The microbial taxa and functional genes identified suggested that the biofilm communities harbored the potential for a number of different but complementary metabolic processes and that MIC in oil facilities likely involves a range of microbial metabolisms such as sulfate, iron, and elemental sulfur reduction. Furthermore, extreme corrosion leading to leakage and exposure of the biofilms to the external environment modify the microbial community structure by promoting the growth of aerobic hydrocarbon-degrading organisms. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

GeoChip-Based Analysis of the Functional Gene Diversity and Metabolic Potential of Microbial Communities in Acid Mine Drainage▿ †

PubMed Central

Xie, Jianping; He, Zhili; Liu, Xinxing; Liu, Xueduan; Van Nostrand, Joy D.; Deng, Ye; Wu, Liyou; Zhou, Jizhong; Qiu, Guanzhou

2011-01-01

Acid mine drainage (AMD) is an extreme environment, usually with low pH and high concentrations of metals. Although the phylogenetic diversity of AMD microbial communities has been examined extensively, little is known about their functional gene diversity and metabolic potential. In this study, a comprehensive functional gene array (GeoChip 2.0) was used to analyze the functional diversity, composition, structure, and metabolic potential of AMD microbial communities from three copper mines in China. GeoChip data indicated that these microbial communities were functionally diverse as measured by the number of genes detected, gene overlapping, unique genes, and various diversity indices. Almost all key functional gene categories targeted by GeoChip 2.0 were detected in the AMD microbial communities, including carbon fixation, carbon degradation, methane generation, nitrogen fixation, nitrification, denitrification, ammonification, nitrogen reduction, sulfur metabolism, metal resistance, and organic contaminant degradation, which suggested that the functional gene diversity was higher than was previously thought. Mantel test results indicated that AMD microbial communities are shaped largely by surrounding environmental factors (e.g., S, Mg, and Cu). Functional genes (e.g., narG and norB) and several key functional processes (e.g., methane generation, ammonification, denitrification, sulfite reduction, and organic contaminant degradation) were significantly (P < 0.10) correlated with environmental variables. This study presents an overview of functional gene diversity and the structure of AMD microbial communities and also provides insights into our understanding of metabolic potential in AMD ecosystems. PMID:21097602

Complementary Microorganisms in Highly Corrosive Biofilms from an Offshore Oil Production Facility

PubMed Central

Alsop, Eric B.; Chambers, Brian; Lomans, Bartholomeus P.; Head, Ian M.; Tsesmetzis, Nicolas

2016-01-01

Offshore oil production facilities are frequently victims of internal piping corrosion, potentially leading to human and environmental risks and significant economic losses. Microbially influenced corrosion (MIC) is believed to be an important factor in this major problem for the petroleum industry. However, knowledge of the microbial communities and metabolic processes leading to corrosion is still limited. Therefore, the microbial communities from three anaerobic biofilms recovered from the inside of a steel pipe exhibiting high corrosion rates, iron oxide deposits, and substantial amounts of sulfur, which are characteristic of MIC, were analyzed in detail. Bacterial and archaeal community structures were investigated by automated ribosomal intergenic spacer analysis, multigenic (16S rRNA and functional genes) high-throughput Illumina MiSeq sequencing, and quantitative PCR analysis. The microbial community analysis indicated that bacteria, particularly Desulfovibrio species, dominated the biofilm microbial communities. However, other bacteria, such as Pelobacter, Pseudomonas, and Geotoga, as well as various methanogenic archaea, previously detected in oil facilities were also detected. The microbial taxa and functional genes identified suggested that the biofilm communities harbored the potential for a number of different but complementary metabolic processes and that MIC in oil facilities likely involves a range of microbial metabolisms such as sulfate, iron, and elemental sulfur reduction. Furthermore, extreme corrosion leading to leakage and exposure of the biofilms to the external environment modify the microbial community structure by promoting the growth of aerobic hydrocarbon-degrading organisms. PMID:26896143

Community detection, link prediction, and layer interdependence in multilayer networks.

PubMed

De Bacco, Caterina; Power, Eleanor A; Larremore, Daniel B; Moore, Cristopher

2017-04-01

Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

Community detection, link prediction, and layer interdependence in multilayer networks

NASA Astrophysics Data System (ADS)

De Bacco, Caterina; Power, Eleanor A.; Larremore, Daniel B.; Moore, Cristopher

2017-04-01

Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

First Comparative Analysis of the Community Structures and Carbon Metabolic Pathways of the Bacteria Associated with Alvinocaris longirostris in a Hydrothermal Vent of Okinawa Trough.

PubMed

Sun, Qing-Lei; Zeng, Zhi-Gang; Chen, Shuai; Sun, Li

2016-01-01

Alvinocaris longirostris is a species of shrimp existing in the hydrothermal fields of Okinawa Trough. To date the structure and function of the microbial community associated with A. longirostris are essentially unknown. In this study, by employment of the techniques of high through-put sequencing and clone library construction and analysis, we compared for the first time the community structures and metabolic profiles of microbes associated with the gill and gut of A. longirostris in a hydrothermal field of Okinawa Trough. Fourteen phyla were detected in the gill and gut communities, of which 11 phyla were shared by both tissues. Proteobacteria made up a substantial proportion in both tissues, while Firmicutes was abundant only in gut. Although gill and gut communities were similar in bacterial diversities, the bacterial community structures in these two tissues were significantly different. Further, we discovered for the first time the existence in the gill and gut communities of A. longirostris the genes (cbbM and aclB) encoding the key enzymes of Calvin-Benson-Bassham (CBB) cycle and the reductive tricarboxylic acid (rTCA) cycle, and that both cbbM and aclB were significantly more abundant in gill than in gut. Taken together, these results provide the first evidence that at least two carbon fixation pathways are present in both the gill and the gut communities of A. longirostris, and that the communities in different tissues likely differ in autotrophic productivity.

[Epiphytic communities of arboreal formations in Southern Vietnam: an analysis of species composition and synusias structure in dependence on the extent of anthropogenic impact].

PubMed

Es'kov, A K

2013-01-01

Species composition of epiphytic communities within different formations of Phú Quôc Island (Southern Vietnam) is studied. The dependence of species composition and structural complexity of epiphytic communities on formation quality is demonstrated. Representatives of different families differ notably in their sensitivity to disturbances. Most vulnerable are Orchidaceae which represent the dominant group in epiphytic community of rain forest and which drop out almost completely under anthropogenic impacts. In less disturbed forests, epiphyte species diversity increases mainly at the expense of "lower" synusias and directly depends on the formation layering. Diminishing of layering numbers leads to dropping out of species belonging to "lower" synusias. Among epiphytes, the indicators of disturbed communities can be detected, namely species of ruderal strategy (explerents). In primal rain forest, they are absent or barely noticeable. An index is proposed for estimation of epiphytic communitiy complexity.

Community analysis of biofilms on flame-oxidized stainless steel anodes in microbial fuel cells fed with different substrates.

PubMed

Eyiuche, Nweze Julius; Asakawa, Shiho; Yamashita, Takahiro; Ikeguchi, Atsuo; Kitamura, Yutaka; Yokoyama, Hiroshi

2017-06-29

The flame-oxidized stainless steel anode (FO-SSA) is a newly developed electrode that enhances microbial fuel cell (MFC) power generation; however, substrate preference and community structure of the biofilm developed on FO-SSA have not been well characterized. Herein, we investigated the community on FO-SSA using high-throughput sequencing of the 16S rRNA gene fragment in acetate-, starch-, glucose-, and livestock wastewater-fed MFCs. Furthermore, to analyze the effect of the anode material, the acetate-fed community formed on a common carbon-based electrode-carbon-cloth anode (CCA)-was examined for comparison. Substrate type influenced the power output of MFCs using FO-SSA; the highest electricity was generated using acetate as a substrate, followed by peptone, starch and glucose, and wastewater. Intensity of power generation using FO-SSA was related to the abundance of exoelectrogenic genera, namely Geobacter and Desulfuromonas, of the phylum Proteobacteria, which were detected at a higher frequency in acetate-fed communities than in communities fed with other substrates. Lactic acid bacteria (LAB)-Enterococcus and Carnobacterium-were predominant in starch- and glucose-fed communities, respectively. In the wastewater-fed community, members of phylum Planctomycetes were frequently detected (36.2%). Exoelectrogenic genera Geobacter and Desulfuromonas were also detected in glucose-, starch-, and wastewater-fed communities on FO-SSA, but with low frequency (0-3.2%); the lactate produced by Carnobacterium and Enterococcus in glucose- and starch-fed communities might affect exoelectrogenic bacterial growth, resulting in low power output by MFCs fed with these substrates. Furthermore, in the acetate-fed community on FO-SSA, Desulfuromonas was abundant (15.4%) and Geobacter had a minor proportion (0.7%), while in that on CCA, both Geobacter and Desulfuromonas were observed at similar frequencies (6.0-9.8%), indicating that anode material affects exoelectrogenic genus enrichment in anodic biofilm. Anodic community structure was dependent on both substrate and anode material. Although Desulfuromonas spp. are marine microorganisms, they were abundant in the acetate-fed community on FO-SSA, implying the presence of novel non-halophilic and exoelectrogenic species in this genus. Power generation using FO-SSA was positively related to the frequency of exoelectrogenic genera in the anodic community. Predominant LAB in saccharide-fed anodic biofilm caused low abundance of exoelectrogenic genera and consequent low power generation.

Social and place-focused communities in location-based online social networks

NASA Astrophysics Data System (ADS)

Brown, Chloë; Nicosia, Vincenzo; Scellato, Salvatore; Noulas, Anastasios; Mascolo, Cecilia

2013-06-01

Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.

Comparison of bacterial community structure and dynamics during the thermophilic composting of different types of solid wastes: anaerobic digestion residue, pig manure and chicken manure

PubMed Central

Song, Caihong; Li, Mingxiao; Jia, Xuan; Wei, Zimin; Zhao, Yue; Xi, Beidou; Zhu, Chaowei; Liu, Dongming

2014-01-01

This study investigated the impact of composting substrate types on the bacterial community structure and dynamics during composting processes. To this end, pig manure (PM), chicken manure (CM), a mixture of PM and CM (PM + CM), and a mixture of PM, CM and anaerobic digestion residue (ADR) (PM + CM + ADR) were selected for thermophilic composting. The bacterial community structure and dynamics during the composting process were detected and analysed by polymerase chain reaction–denaturing gradient gel electrophoresis (DGGE) coupled with a statistic analysis. The physical-chemical analyses indicated that compared to single-material composting (PM, CM), co-composting (PM + CM, PM + CM + ADR) could promote the degradation of organic matter and strengthen the ability of conserving nitrogen. A DGGE profile and statistical analysis demonstrated that co-composting, especially PM + CM + ADR, could improve the bacterial community structure and functional diversity, even in the thermophilic stage. Therefore, co-composting could weaken the screening effect of high temperature on bacterial communities. Dominant sequencing analyses indicated a dramatic shift in the dominant bacterial communities from single-material composting to co-composting. Notably, compared with PM, PM + CM increased the quantity of xylan-degrading bacteria and reduced the quantity of human pathogens. PMID:24963997

GeoChip-based Analysis of Groundwater Microbial Diversity in Norman Landfill

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Zhenmei; He, Zhili; Parisi, Victoria

The Norman Landfill is a closed municipal solid waste landfill located on an alluvium associated with the Canadian River in Norman, Oklahoma. It has operated as a research site since 1994 because it is typical of many closed landfill sites across the U.S. Leachate from the unlined landfill forms a groundwater plume that extends downgradient approximately 250 m from the landfill toward the Canadian River. To investigate the impact of the landfill leachate on the diversity and functional structure of microbial communities, groundwater samples were taken from eight monitoring wells at a depth of 5m, and analyzed using a comprehensivemore » functional gene array covering about 50,000 genes involved in key microbial processes, such as biogeochemical cycling of C, N, P, and S, and bioremediation of organic contaminants and metals. Wells are located within a transect along a presumed flow path with different distances to the center of the leachate plume. Our analyses showed that microbial communities were obviously impacted by the leachate-component from the landfill. The number of genes detected and microbial diversity indices in the center (LF2B) and its closest (MLS35) wells were significantly less than those detected in other more downgradient wells, while no significant changes were observed in the relative abundance (i.e., percentage of each gene category) for most gene categories. However, the microbial community composition or structure of the landfill groundwater did not clearly show a significant correlation with the distance from well LF2B. Burkholderia sp. and Pseudomonas sp. were found to be the dominant microbial populations detected in all wells, while Bradyrhizobium sp. and Ralstonia sp. were dominant populations for seven wells except LF2B. In addition, Mantel test and canonical correspondence analysis (CCA) indicate that pH, sulfate, ammonia nitrogen and dissolved organic carbon (DOC) have significant effects on the microbial community structure. The results suggest that the leachate from unlined landfills significantly impact the structures of groundwater microbial communities, and that more distal wells recover by natural attenuation.« less

Spatial and Temporal Variation in DeSoto Canyon Macrofaunal Community Structure

NASA Astrophysics Data System (ADS)

Baco-Taylor, A.; Shantharam, A. K.

2016-02-01

Sediment-dwelling macrofauna (polychaetes, bivalves, and assorted crustaceans ≥ 300 µm) have long served as biological indicators of ecosystem stress. As part of evaluating the 2010 impact from the Deepwater Horizon blowout, we sampled 12 sites along and transverse to the DeSoto Canyon axis, Gulf of Mexico, as well as 2 control sites outside the Canyon. Sites ranged in depth from 479-2310 m. Three of the sites (PCB06, S36, and XC4) were sampled annually from 2012-2014. We provide an overview of the macrofauna community structure of canyon and non-canyon sites, as well as trends in community structure and diversity at the time-series sites. Compositionally, polychaetes dominated the communities, followed by tanaid crustaceans and bivalves. The total number of individuals was not significantly correlated with depth while the total number of taxa and species richness were. Rarefaction shows the deepest station, XC4 (2310 m) had the lowest diversity while NT800 (a non-canyon control at 800m) had the highest. Multivariate analysis shows the canyon assemblages fall into eight clusters with the non-canyon stations forming a separate ninth cluster, indicating a detectable difference in canyon and non-canyon communities. Time series stations show an increase in diversity from 2012-2014 with a strong overlap in community structure in 2013 and 2014 samples. Environmental analysis, via BEST, using data from 10 canyon sites and the controls, indicated depth in combination with latitude explain the most variation in macrofaunal community structure.

Bacterial Communities in Boreal Forest Mushrooms Are Shaped Both by Soil Parameters and Host Identity

PubMed Central

Pent, Mari; Põldmaa, Kadri; Bahram, Mohammad

2017-01-01

Despite recent advances in understanding the microbiome of eukaryotes, little is known about microbial communities in fungi. Here we investigate the structure of bacterial communities in mushrooms, including common edible ones, with respect to biotic and abiotic factors in the boreal forest. Using a combination of culture-based and Illumina high-throughput sequencing, we characterized the bacterial communities in fruitbodies of fungi from eight genera spanning four orders of the class Agaricomycetes (Basidiomycota). Our results revealed that soil pH followed by fungal identity are the main determinants of the structure of bacterial communities in mushrooms. While almost half of fruitbody bacteria were also detected from soil, the abundance of several bacterial taxa differed considerably between the two environments. The effect of host identity was significant at the fungal genus and order level and could to some extent be ascribed to the distinct bacterial community of the chanterelle, representing Cantharellales—the earliest diverged group of mushroom-forming basidiomycetes. These data suggest that besides the substantial contribution of soil as a major taxa source of bacterial communities in mushrooms, the structure of these communities is also affected by the identity of the host. Thus, bacteria inhabiting fungal fruitbodies may be non-randomly selected from environment based on their symbiotic functions and/or habitat requirements. PMID:28539921

Temporal changes in soil bacterial and archaeal communities with different fertilizers in tea orchards.

PubMed

Wang, Hua; Yang, Shao-hui; Yang, Jing-ping; Lv, Ya-min; Zhao, Xing; Pang, Ji-liang

2014-11-01

It is important to understand the effects of temporal changes in microbial communities in the acidic soils of tea orchards with different fertilizers. A field experiment involving organic fertilizer (OF), chemical fertilizer (CF), and unfertilized control (CK) treatments was arranged to analyze the temporal changes in the bacterial and archaeal communities at bimonthly intervals based on the 16S ribosomal RNA (rRNA) gene using terminal restriction fragment length polymorphism (T-RFLP) profiling. The abundances of total bacteria, total archaea, and selected functional genes (bacterial and archaeal amoA, bacterial narG, nirK, nirS, and nosZ) were determined by quantitative polymerase chain reaction (qPCR). The results indicate that the structures of bacterial and archaeal communities varied significantly with time and fertilization based on changes in the relative abundance of dominant T-RFs. The abundancy of the detected genes changed with time. The total bacteria, total archaea, and archaeal amoA were less abundant in July. The bacterial amoA and denitrifying genes were less abundant in September, except the nirK gene. The OF treatment increased the abundance of the observed genes, while the CF treatment had little influence on them. The soil temperature significantly affected the bacterial and archaeal community structures. The soil moisture was significantly correlated with the abundance of denitrifying genes. Of the soil chemical properties, soil organic carbon was the most important factor and was significantly correlated with the abundance of the detected genes, except the nirK gene. Overall, this study demonstrated the effects of both temporal alteration and organic fertilizer on the structures of microbial communities and the abundance of genes involved in the nitrogen cycle.

Temporal changes in soil bacterial and archaeal communities with different fertilizers in tea orchards* #

PubMed Central

Wang, Hua; Yang, Shao-hui; Yang, Jing-ping; Lv, Ya-min; Zhao, Xing; Pang, Ji-liang

2014-01-01

It is important to understand the effects of temporal changes in microbial communities in the acidic soils of tea orchards with different fertilizers. A field experiment involving organic fertilizer (OF), chemical fertilizer (CF), and unfertilized control (CK) treatments was arranged to analyze the temporal changes in the bacterial and archaeal communities at bimonthly intervals based on the 16S ribosomal RNA (rRNA) gene using terminal restriction fragment length polymorphism (T-RFLP) profiling. The abundances of total bacteria, total archaea, and selected functional genes (bacterial and archaeal amoA, bacterial narG, nirK, nirS, and nosZ) were determined by quantitative polymerase chain reaction (qPCR). The results indicate that the structures of bacterial and archaeal communities varied significantly with time and fertilization based on changes in the relative abundance of dominant T-RFs. The abundancy of the detected genes changed with time. The total bacteria, total archaea, and archaeal amoA were less abundant in July. The bacterial amoA and denitrifying genes were less abundant in September, except the nirK gene. The OF treatment increased the abundance of the observed genes, while the CF treatment had little influence on them. The soil temperature significantly affected the bacterial and archaeal community structures. The soil moisture was significantly correlated with the abundance of denitrifying genes. Of the soil chemical properties, soil organic carbon was the most important factor and was significantly correlated with the abundance of the detected genes, except the nirK gene. Overall, this study demonstrated the effects of both temporal alteration and organic fertilizer on the structures of microbial communities and the abundance of genes involved in the nitrogen cycle. PMID:25367788

Temperature and Nutrient Effects on Periphyton Associated Bacterial Communities in Continuous Flow-Through Estuarine Mesocosms

NASA Astrophysics Data System (ADS)

Houghton, K.; James, J. B.; Devereux, R.; Friedman, S. D.

2016-02-01

Nutrient pollution is a leading cause of water quality impairments and degraded aquatic ecosystem condition. Reliable and reproducible indicators of ecosystem condition are needed to help manage nutrient pollution. The diatom component of periphyton has been used as a water quality indicator due to identifiable cell morphology and existence of relationships between nutrient concentration and diatom community composition. However, morphological identification of diatoms requires highly specialized personnel, is very time consuming, and can produce variable results, suggesting the need for alternative methods that are less expensive and more reproducible. DNA sequencing of the bacterial 16S rRNA gene is well documented and provides genus-level resolution of the community structure. The goal of this study was to evaluate the effects of nutrient loading and temperature on periphyton-associated bacterial communities using standard periphytometer techniques and next generation sequencing technologies. Continuous flow mesocosms were established in an eight tank system consisting of two temperature conditions (10°C and 20°C) and four nutrient conditions (1x to 6x ambient concentrations). Experimental conditions were replicated in July/August 2013 and September 2013. Replicate DNA samples were extracted and the 16S rRNA gene was sequenced using universal Bacterial primers. Initial analyses revealed strong differences in community structure based on temperature (p < 0.01, R = 0.997) and sampling month (p < 0.01, R = 0.993) while no significant differences were detected between nutrient treatments. These results suggest that the method can detect changes in periphyton associated bacterial communities based on temperature but a more refined approach, as might be based on functional genes instead of structural genes, may be needed to differentiate nutrient effects.

Changes in the Structure and Function of Microbial Communities in Drinking Water Treatment Bioreactors upon Addition of Phosphorus▿ †

PubMed Central

Li, Xu; Upadhyaya, Giridhar; Yuen, Wangki; Brown, Jess; Morgenroth, Eberhard; Raskin, Lutgarde

2010-01-01

Phosphorus was added as a nutrient to bench-scale and pilot-scale biologically active carbon (BAC) reactors operated for perchlorate and nitrate removal from contaminated groundwater. The two bioreactors responded similarly to phosphorus addition in terms of microbial community function (i.e., reactor performance), while drastically different responses in microbial community structure were detected. Improvement in reactor performance with respect to perchlorate and nitrate removal started within a few days after phosphorus addition for both reactors. Microbial community structures were evaluated using molecular techniques targeting 16S rRNA genes. Clone library results showed that the relative abundance of perchlorate-reducing bacteria (PRB) Dechloromonas and Azospira in the bench-scale reactor increased from 15.2% and 0.6% to 54.2% and 11.7% after phosphorus addition, respectively. Real-time quantitative PCR (qPCR) experiments revealed that these increases started within a few days after phosphorus addition. In contrast, after phosphorus addition, the relative abundance of Dechloromonas in the pilot-scale reactor decreased from 7.1 to 0.6%, while Zoogloea increased from 17.9 to 52.0%. The results of this study demonstrated that similar operating conditions for bench-scale and pilot-scale reactors resulted in similar contaminant removal performances, despite dramatically different responses from microbial communities. These findings suggest that it is important to evaluate the microbial community compositions inside bioreactors used for drinking water treatment, as they determine the microbial composition in the effluent and impact downstream treatment requirements for drinking water production. This information could be particularly relevant to drinking water safety, if pathogens or disinfectant-resistant bacteria are detected in the bioreactors. PMID:20889793

Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach

NASA Astrophysics Data System (ADS)

Lu, Feng; Liu, Kang; Duan, Yingying; Cheng, Shifen; Du, Fei

2018-07-01

A better characterization of the traffic influence among urban roads is crucial for traffic control and traffic forecasting. The existence of spatial heterogeneity imposes great influence on modeling the extent and degree of road traffic correlation, which is usually neglected by the traditional distance based method. In this paper, we propose a traffic-enhanced community detection approach to spatially reveal the traffic correlation in city road networks. First, the road network is modeled as a traffic-enhanced dual graph with the closeness between two road segments determined not only by their topological connection, but also by the traffic correlation between them. Then a flow-based community detection algorithm called Infomap is utilized to identify the road segment clusters. Evaluated by Moran's I, Calinski-Harabaz Index and the traffic interpolation application, we find that compared to the distance based method and the community based method, our proposed traffic-enhanced community based method behaves better in capturing the extent of traffic relevance as both the topological structure of the road network and the traffic correlations among urban roads are considered. It can be used in more traffic-related applications, such as traffic forecasting, traffic control and guidance.

To cut or not to cut? Assessing the modular structure of brain networks.

PubMed

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M

2014-05-01

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Forestry impacts on the hidden fungal biodiversity associated with bryophytes.

PubMed

Davey, Marie L; Kauserud, Håvard; Ohlson, Mikael

2014-10-01

Recent studies have revealed an unexpectedly high, cryptic diversity of fungi associated with boreal forest bryophytes. Forestry practices heavily influence the boreal forest and fundamentally transform the landscape. However, little is known about how bryophyte-associated fungal communities are affected by these large-scale habitat transformations. This study assesses to what degree bryophyte-associated fungal communities are structured across the forest successional stages created by current forestry practices. Shoots of Hylocomium splendens were collected in Picea abies dominated forests of different ages, and their associated fungal communities were surveyed by pyrosequencing of ITS2 amplicons. Although community richness, diversity and evenness were relatively stable across the forest types and all were consistently dominated by ascomycete taxa, there was a marked shift in fungal community composition between young and old forests. Numerous fungal operational taxonomic units (OTUs) showed distinct affinities for different forest ages. Spatial structure was also detected among the sites, suggesting that environmental gradients resulting from the topography of the study area and dispersal limitations may also significantly affect bryophyte-associated fungal community structure. This study confirms that Hylocomium splendens hosts an immense diversity of fungi and demonstrates that this community is structured in part by forest age, and as such is highly influenced by modern forestry practices. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Land-use changes influence soil bacterial communities in a meadow grassland in Northeast China

NASA Astrophysics Data System (ADS)

Cao, Chengyou; Zhang, Ying; Qian, Wei; Liang, Caiping; Wang, Congmin; Tao, Shuang

2017-10-01

The conversion of natural grassland into agricultural fields is an intensive anthropogenic perturbation commonly occurring in semiarid regions, and this perturbation strongly affects soil microbiota. In this study, the influences of land-use conversion on the soil properties and bacterial communities in the Horqin Grasslands in Northeast China were assessed. This study aimed to investigate (1) how the abundances of soil bacteria changed across land-use types, (2) how the structure of the soil bacterial community was altered in each land-use type, and (3) how these variations were correlated with soil physical and chemical properties. Variations in the diversities and compositions of bacterial communities and the relative abundances of dominant taxa were detected in four distinct land-use systems, namely, natural meadow grassland, paddy field, upland field, and poplar plantation, through the high-throughput Illumina MiSeq sequencing technique. The results indicated that land-use changes primarily affected the soil physical and chemical properties and bacterial community structure. Soil properties, namely, organic matter, pH, total N, total P, available N and P, and microbial biomass C, N, and P, influenced the bacterial community structure. The dominant phyla and genera were almost the same among the land-use types, but their relative abundances were significantly different. The effects of land-use changes on the structure of soil bacterial communities were more quantitative than qualitative.

Chlorophyll a might structure a community of potentially pathogenic culturable Vibrionaceae. Insights from a one-year study of water and mussels surveyed on the French Atlantic coast.

PubMed

Deter, J; Lozach, S; Derrien, A; Véron, A; Chollet, J; Hervio-Heath, D

2010-02-01

The present study focused on the isolation of culturable bacteria from mussels and sea water to identify Vibrionaceae potentially pathogenic for humans. Three sites located on the French Atlantic coast were monitored monthly (twice each month during summer) for 1 year. Environmental parameters were surveyed (water temperature, salinity, turbidity, chlorophyll a) and bacteria were detected by culture and identified by API 20E(®) systems (BioMérieux) and PCR. A total of seven species were detected (Grimontia hollisae, Photobacterium damselae, Vibrio alginolyticus, V. cholerae, V. fluvialis, V. vulnificus and V. parahaemolyticus) and species diversity was higher at the end of summer. Surprisingly, V. cholerae non-O1/non-O139 was detected in spring. No site effect was detected. Using Sørensen similarity indices and statistical analyses, we showed that chlorophyll a had a significant influence on the bacterial community detected in mussels and assemblages were more similar to one another when chlorophyll a values were above 20 µg l(-1) . No significant effect of any parameter was found on the community detected in water samples. Such surveys are essential for the understanding of sanitary crises and detection of emerging pathogens. © 2010 Society for Applied Microbiology and Blackwell Publishing Ltd.

Phylogenetic and gene expression analysis of cyanobacteria and diatoms in the twilight waters of the temperate northeast Pacific Ocean.

PubMed

Gao, Weimin; Shi, Xu; Wu, Jieying; Jin, Yuguang; Zhang, Weiwen; Meldrum, Deirdre R

2011-11-01

In this study, to explore the microbial community structure and its functionality in the deep-sea environments, we initially performed a 16S ribosomal RNA (rRNA)-based community structure analyses for microbial communities in the sea water collected from sites of 765-790 m in depth in the Pacific Ocean. Interestingly, in the clone library we detected the presence of both photoautotrophic bacteria such as cyanobacteria and photoheterotrophic bacteria, such as Chloroflexus sp. To further explore the existence and diversity of possible light-utilizing microorganisms, we then constructed and analyzed a 23S rRNA plastid gene cloning library. The results showed that the majority of this cloning library was occupied by oxygenic photoautotrophic organisms, such as diatoms Thalassiosira spp. and cyanobacterium Synechococcus sp. In addition, the diversity of these oxygenic photoautotrophic organisms was very limited. Moreover, both reverse-transcription PCR and quantitative reverse-transcription PCR approaches had been employed to detect expression of the genes involved in protein synthesis and photosynthesis of photoautotrophic organisms, and the positive results were obtained. The possible mechanisms underlying the existence of very limited diversity of photosynthetic organisms at this depth of ocean, as well as the positive detection of rRNA and mRNA of diatom and cyanobacteria, were discussed.

Structure of the human gastric bacterial community in relation to Helicobacter pylori status.

PubMed

Maldonado-Contreras, Ana; Goldfarb, Kate C; Godoy-Vitorino, Filipa; Karaoz, Ulas; Contreras, Mónica; Blaser, Martin J; Brodie, Eoin L; Dominguez-Bello, Maria G

2011-04-01

The human stomach is naturally colonized by Helicobacter pylori, which, when present, dominates the gastric bacterial community. In this study, we aimed to characterize the structure of the bacterial community in the stomach of patients of differing H. pylori status. We used a high-density 16S rRNA gene microarray (PhyloChip, Affymetrix, Inc.) to hybridize 16S rRNA gene amplicons from gastric biopsy DNA of 10 rural Amerindian patients from Amazonas, Venezuela, and of two immigrants to the United States (from South Asia and Africa, respectively). H. pylori status was determined by PCR amplification of H. pylori glmM from gastric biopsy samples. Of the 12 patients, 8 (6 of the 10 Amerindians and the 2 non-Amerindians) were H. pylori glmM positive. Regardless of H. pylori status, the PhyloChip detected Helicobacteriaceae DNA in all patients, although with lower relative abundance in patients who were glmM negative. The G2-chip taxonomy analysis of PhyloChip data indicated the presence of 44 bacterial phyla (of which 16 are unclassified by the Taxonomic Outline of the Bacteria and Archaea taxonomy) in a highly uneven community dominated by only four phyla: Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Positive H. pylori status was associated with increased relative abundance of non-Helicobacter bacteria from the Proteobacteria, Spirochetes and Acidobacteria, and with decreased abundance of Actinobacteria, Bacteroidetes and Firmicutes. The PhyloChip detected richness of low abundance phyla, and showed marked differences in the structure of the gastric bacterial community according to H. pylori status.

Structure of the human gastric bacterial community in relation to Helicobacter pylori status

PubMed Central

Maldonado-Contreras, Ana; Goldfarb, Kate C; Godoy-Vitorino, Filipa; Karaoz, Ulas; Contreras, Mónica; Blaser, Martin J; Brodie, Eoin L; Dominguez-Bello, Maria G

2011-01-01

The human stomach is naturally colonized by Helicobacter pylori, which, when present, dominates the gastric bacterial community. In this study, we aimed to characterize the structure of the bacterial community in the stomach of patients of differing H. pylori status. We used a high-density 16S rRNA gene microarray (PhyloChip, Affymetrix, Inc.) to hybridize 16S rRNA gene amplicons from gastric biopsy DNA of 10 rural Amerindian patients from Amazonas, Venezuela, and of two immigrants to the United States (from South Asia and Africa, respectively). H. pylori status was determined by PCR amplification of H. pylori glmM from gastric biopsy samples. Of the 12 patients, 8 (6 of the 10 Amerindians and the 2 non-Amerindians) were H. pylori glmM positive. Regardless of H. pylori status, the PhyloChip detected Helicobacteriaceae DNA in all patients, although with lower relative abundance in patients who were glmM negative. The G2-chip taxonomy analysis of PhyloChip data indicated the presence of 44 bacterial phyla (of which 16 are unclassified by the Taxonomic Outline of the Bacteria and Archaea taxonomy) in a highly uneven community dominated by only four phyla: Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Positive H. pylori status was associated with increased relative abundance of non-Helicobacter bacteria from the Proteobacteria, Spirochetes and Acidobacteria, and with decreased abundance of Actinobacteria, Bacteroidetes and Firmicutes. The PhyloChip detected richness of low abundance phyla, and showed marked differences in the structure of the gastric bacterial community according to H. pylori status. PMID:20927139

Influence of substrate type on microbial community structure in vertical-flow constructed wetland treating polluted river water.

PubMed

Guan, Wei; Yin, Min; He, Tao; Xie, Shuguang

2015-10-01

Microorganisms attached on the surfaces of substrate materials in constructed wetland play crucial roles in the removal of organic and inorganic pollutants. However, the impact of substrate material on wetland microbial community structure remains unclear. Moreover, little is known about microbial community in constructed wetland purifying polluted surface water. In this study, Illumina high-throughput sequencing was applied to profile the spatial variation of microbial communities in three pilot-scale surface water constructed wetlands with different substrate materials (sand, zeolite, and gravel). Bacterial community diversity and structure showed remarkable spatial variation in both sand and zeolite wetland systems, but changed slightly in gravel wetland system. Bacterial community was found to be significantly influenced by wetland substrate type. A number of bacterial groups were detected in wetland systems, including Proteobacteria, Chloroflexi, Bacteroidetes, Acidobacteria, Cyanobacteria, Nitrospirae, Planctomycetes, Actinobacteria, Firmicutes, Chlorobi, Spirochaetae, Gemmatimonadetes, Deferribacteres, OP8, WS3, TA06, and OP3, while Proteobacteria (accounting for 29.1-62.3 %), mainly composed of Alpha-, Beta-, Gamma-, and Deltaproteobacteria, showed the dominance and might contribute to the effective reduction of organic pollutants. In addition, Nitrospira-like microorganisms were abundant in surface water constructed wetlands.

Community Detection in Signed Networks: the Role of Negative ties in Different Scales

PubMed Central

Esmailian, Pouya; Jalili, Mahdi

2015-01-01

Extracting community structure of complex network systems has many applications from engineering to biology and social sciences. There exist many algorithms to discover community structure of networks. However, it has been significantly under-explored for networks with positive and negative links as compared to unsigned ones. Trying to fill this gap, we measured the quality of partitions by introducing a Map Equation for signed networks. It is based on the assumption that negative relations weaken positive flow from a node towards a community, and thus, external (internal) negative ties increase the probability of staying inside (escaping from) a community. We further extended the Constant Potts Model, providing a map spectrum for signed networks. Accordingly, a partition is selected through balancing between abridgment and expatiation of a signed network. Most importantly, multi-scale spectrum of signed networks revealed how informative are negative ties in different scales, and quantified the topological placement of negative ties between dense positive ones. Moreover, an inconsistency was found in the signed Modularity: as the number of negative ties increases, the density of positive ties is neglected more. These results shed lights on the community structure of signed networks. PMID:26395815

Identifying influential user communities on the social network

NASA Astrophysics Data System (ADS)

Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

2015-10-01

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome

PubMed Central

Schloter-Hai, Brigitte; Kublik, Susanne; Granitsiotis, Michael S.; Boschetto, Piera; Stendardo, Mariarita; Barta, Imre; Dome, Balazs; Deleuze, Jean-François; Boland, Anne; Müller-Quernheim, Joachim; Prasse, Antje; Welte, Tobias; Hohlfeld, Jens; Subramanian, Deepak; Parr, David; Gut, Ivo Glynne; Greulich, Timm; Koczulla, Andreas Rembert; Nowinski, Adam; Gorecka, Dorota; Singh, Dave; Gupta, Sumit; Brightling, Christopher E.; Hoffmann, Harald; Frankenberger, Marion; Hofer, Thomas P.; Burggraf, Dorothe; Heiss-Neumann, Marion; Ziegler-Heitbrock, Loems; Schloter, Michael; zu Castell, Wolfgang

2017-01-01

Background Changes in microbial community composition in the lung of patients suffering from moderate to severe COPD have been well documented. However, knowledge about specific microbiome structures in the human lung associated with CT defined abnormalities is limited. Methods Bacterial community composition derived from brush samples from lungs of 16 patients suffering from different CT defined subtypes of COPD and 9 healthy subjects was analyzed using a cultivation independent barcoding approach applying 454-pyrosequencing of 16S rRNA gene fragment amplicons. Results We could show that bacterial community composition in patients with changes in CT (either airway or emphysema type changes, designated as severe subtypes) was different from community composition in lungs of patients without visible changes in CT as well as from healthy subjects (designated as mild COPD subtype and control group) (PC1, Padj = 0.002). Higher abundance of Prevotella in samples from patients with mild COPD subtype and from controls and of Streptococcus in the severe subtype cases mainly contributed to the separation of bacterial communities of subjects. No significant effects of treatment with inhaled glucocorticoids on bacterial community composition were detected within COPD cases with and without abnormalities in CT in PCoA. Co-occurrence analysis suggests the presence of networks of co-occurring bacteria. Four communities of positively correlated bacteria were revealed. The microbial communities can clearly be distinguished by their associations with the CT defined disease phenotype. Conclusion Our findings indicate that CT detectable structural changes in the lung of COPD patients, which we termed severe subtypes, are associated with alterations in bacterial communities, which may induce further changes in the interaction between microbes and host cells. This might result in a changed interplay with the host immune system. PMID:28704452

Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome.

PubMed

Engel, Marion; Endesfelder, David; Schloter-Hai, Brigitte; Kublik, Susanne; Granitsiotis, Michael S; Boschetto, Piera; Stendardo, Mariarita; Barta, Imre; Dome, Balazs; Deleuze, Jean-François; Boland, Anne; Müller-Quernheim, Joachim; Prasse, Antje; Welte, Tobias; Hohlfeld, Jens; Subramanian, Deepak; Parr, David; Gut, Ivo Glynne; Greulich, Timm; Koczulla, Andreas Rembert; Nowinski, Adam; Gorecka, Dorota; Singh, Dave; Gupta, Sumit; Brightling, Christopher E; Hoffmann, Harald; Frankenberger, Marion; Hofer, Thomas P; Burggraf, Dorothe; Heiss-Neumann, Marion; Ziegler-Heitbrock, Loems; Schloter, Michael; Zu Castell, Wolfgang

2017-01-01

Changes in microbial community composition in the lung of patients suffering from moderate to severe COPD have been well documented. However, knowledge about specific microbiome structures in the human lung associated with CT defined abnormalities is limited. Bacterial community composition derived from brush samples from lungs of 16 patients suffering from different CT defined subtypes of COPD and 9 healthy subjects was analyzed using a cultivation independent barcoding approach applying 454-pyrosequencing of 16S rRNA gene fragment amplicons. We could show that bacterial community composition in patients with changes in CT (either airway or emphysema type changes, designated as severe subtypes) was different from community composition in lungs of patients without visible changes in CT as well as from healthy subjects (designated as mild COPD subtype and control group) (PC1, Padj = 0.002). Higher abundance of Prevotella in samples from patients with mild COPD subtype and from controls and of Streptococcus in the severe subtype cases mainly contributed to the separation of bacterial communities of subjects. No significant effects of treatment with inhaled glucocorticoids on bacterial community composition were detected within COPD cases with and without abnormalities in CT in PCoA. Co-occurrence analysis suggests the presence of networks of co-occurring bacteria. Four communities of positively correlated bacteria were revealed. The microbial communities can clearly be distinguished by their associations with the CT defined disease phenotype. Our findings indicate that CT detectable structural changes in the lung of COPD patients, which we termed severe subtypes, are associated with alterations in bacterial communities, which may induce further changes in the interaction between microbes and host cells. This might result in a changed interplay with the host immune system.

Developing a multidisciplinary approach within the ED towards domestic violence presentations.

PubMed

Basu, Subhashis; Ratcliffe, Giles

2014-03-01

To improve the detection and quality of care of patients who attend the emergency department (ED) with confirmed or suspected domestic abuse (DA). A quality improvement report on the design, implementation and evaluation of a specialised service and structured training programme to detect and manage DA presentations within an emergency medicine department. The study was set in the ED at the Northern General Hospital, Sheffield, UK. Key measures for improvement included introducing a service within the ED to help staff manage DA and coordinate responses; improve staff confidence in detecting DA; develop a structured and consistent process by which to manage DA presentations. An Independent Domestic Violence Advocate service was introduced into the department in July 2011 through a multiagency agreement. A structured training and education programme was delivered to ED staff. A 'communications form' was developed for DA risk assessment and case management. The process was reviewed quarterly. One hundred and seventy-two referrals were made to the service (121 distinct clients) over a 12-month period. Staff reported greater confidence in detecting DA, and community partners highlighted the role the service had in improving DA detection and care quality within the city. Strong leadership and prioritising the issue within the department has facilitated the development of the process and contributed substantially to its success. Support from community partners has been invaluable in tailoring the service and education programme to the needs of staff and patients within the department.

Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field

NASA Technical Reports Server (NTRS)

Franklin, Rima B.; Mills, Aaron L.

2003-01-01

To better understand the distribution of soil microbial communities at multiple spatial scales, a survey was conducted to examine the spatial organization of community structure in a wheat field in eastern Virginia (USA). Nearly 200 soil samples were collected at a variety of separation distances ranging from 2.5 cm to 11 m. Whole-community DNA was extracted from each sample, and community structure was compared using amplified fragment length polymorphism (AFLP) DNA fingerprinting. Relative similarity was calculated between each pair of samples and compared using geostatistical variogram analysis to study autocorrelation as a function of separation distance. Spatial autocorrelation was found at scales ranging from 30 cm to more than 6 m, depending on the sampling extent considered. In some locations, up to four different correlation length scales were detected. The presence of nested scales of variability suggests that the environmental factors regulating the development of the communities in this soil may operate at different scales. Kriging was used to generate maps of the spatial organization of communities across the plot, and the results demonstrated that bacterial distributions can be highly structured, even within a habitat that appears relatively homogeneous at the plot and field scale. Different subsets of the microbial community were distributed differently across the plot, and this is thought to be due to the variable response of individual populations to spatial heterogeneity associated with soil properties. c2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

[Effect of ground mulch managements on soil bacterial community structure and diversity in the non-irrigated apple orchard in Weibei Loess Plateau].

PubMed

Chen, Yuexing; Wen, Xiaoxia; Sun, Yulin; Zhang, Junli; Lin, Xiaoli; Liao, Yuncheng

2015-07-04

We studied the changes in soil bacterial communities induced by ground mulch managements at different apple growth periods. We adopted the denaturing gradient gel electrophoresis (DGGE) with PCR-amplified 16S rRNA fragments to determine soil bacterial community structure and diversity. Soil bacterial community structure with different ground mulch managements were significantly different. Both the mulch management strategies and apple growth periods affected the predominant groups and their abundance in soil bacterial communities. Grass mulch and cornstalk mulch treatments had higher bacterial diversity and richness than the control at young fruit period and fruit expanding period, whereas film mulch treatment had no significant difference compared with the control. During mature period, bacterial diversity in the control reached its maximum, which may be ascribed to the rapid growth and reproduction of the r-selection bacteria. The clustering and detrended correspondence analysis revealed that differences in soil bacterial communities were closely correlated to apple growth periods and ground mulch managements. Soil samples from the grass mulch and cornstalk mulch treatments clustered together while those mulched with plastic film treatment were similar to the control. The most abundant phylum in soil bacterial community was Proteobacteria followed by Bacteroidetes. Some other phyla were also detected, such as Acidobacteria, Firmicutes, Actinobacteria and Chloroflexi. Mulching with plant (Grass/Cornstalk) had great effects on soil bacterial community structure and enhanced the diversity while film mulch management had no significant effects.

Cophylogenetic signal is detectable in pollination interactions across ecological scales.

PubMed

Hutchinson, Matthew C; Cagua, Edgar Fernando; Stouffer, Daniel B

2017-10-01

That evolutionary history can influence the way that species interact is a basic tenet of evolutionary ecology. However, when the role of evolution in determining ecological interactions is investigated, focus typically centers on just one side of the interaction. A cophylogenetic signal, the congruence of evolutionary history across both sides of an ecological interaction, extends these previous explorations and provides a more complete picture of how evolutionary patterns influence the way species interact. To date, cophylogenetic signal has most typically been studied in interactions that occur between fine taxonomic clades that show high intimacy. In this study, we took an alternative approach and made an exhaustive assessment of cophylogeny in pollination interactions. To do so, we assessed the strength of cophylogenetic signal at four distinct scales of pollination interaction: (1) across plant-pollinator associations globally, (2) in local pollination communities, (3) within the modular structure of those communities, and (4) in individual modules. We did so using a globally distributed dataset comprised of 54 pollination networks, over 4000 species, and over 12,000 interactions. Within these data, we detected cophylogenetic signal at all four scales. Cophylogenetic signal was found at the level of plant-pollinator interactions on a global scale and in the majority of pollination communities. At the scale defined by the modular structure within those communities, however, we observed a much weaker cophylogenetic signal. Cophylogenetic signal was detectable in a significant proportion of individual modules and most typically when within-module phylogenetic diversity was low. In sum, the detection of cophylogenetic signal in pollination interactions across scales provides a new dimension to the story of how past evolution shapes extant pollinator-angiosperm interactions. © 2017 by the Ecological Society of America.

Algorithmic detectability threshold of the stochastic block model

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Detecting communities using asymptotical surprise

NASA Astrophysics Data System (ADS)

Traag, V. A.; Aldecoa, R.; Delvenne, J.-C.

2015-08-01

Nodes in real-world networks are repeatedly observed to form dense clusters, often referred to as communities. Methods to detect these groups of nodes usually maximize an objective function, which implicitly contains the definition of a community. We here analyze a recently proposed measure called surprise, which assesses the quality of the partition of a network into communities. In its current form, the formulation of surprise is rather difficult to analyze. We here therefore develop an accurate asymptotic approximation. This allows for the development of an efficient algorithm for optimizing surprise. Incidentally, this leads to a straightforward extension of surprise to weighted graphs. Additionally, the approximation makes it possible to analyze surprise more closely and compare it to other methods, especially modularity. We show that surprise is (nearly) unaffected by the well-known resolution limit, a particular problem for modularity. However, surprise may tend to overestimate the number of communities, whereas they may be underestimated by modularity. In short, surprise works well in the limit of many small communities, whereas modularity works better in the limit of few large communities. In this sense, surprise is more discriminative than modularity and may find communities where modularity fails to discern any structure.

[Study on Microbial Diversity of Peri-implantitis Subgingival by High-throughput Sequencing].

PubMed

Li, Zhi-jie; Wang, Shao-guo; Li, Yue-hong; Tu, Dong-xiang; Liu, Shi-yun; Nie, Hong-bing; Li, Zhi-qiang; Zhang, Ju-mei

2015-07-01

To study microbial diversity of peri-implantitis subgingival with high-throughput sequencing, and investigate microbiological etiology of peri-implantitis. Subgingival plaques were sampled from the patients with peri-implantitis (D group) and non-peri-implantitis subjects (N group). The microbiological diversity of the subgingival plaques was detected by sequencing V4 region of 16S rRNA with Illumina Miseq platform. The diversity of the community structure was analyzed using Mothur software. A total of 156 507 gene sequences were detected in nine samples and 4 402 operational taxonomic units (OTUs) were found. Selenomonas, Pseudomonas, and Fusobacterium were dominant bacteria in D group, while Fusobacterium, Veillonella and Streptococcus were dominant bacteria in N group. Differences between peri-implantitis and non-peri-implantitis bacterial communities were observed at all phylogenetic levels by LEfSe, which was also found in PcoA test. The occurrence of peri-implantitis is not only related to periodontitis pathogenic microbe, but also related with the changes of oral microbial community structure. Treponema, Herbaspirillum, Butyricimonas and Phaeobacte may be closely related to the occurrence and development of peri-implantitis.

Effects of multiple spreaders in community networks

NASA Astrophysics Data System (ADS)

Hu, Zhao-Long; Ren, Zhuo-Ming; Yang, Guang-Yong; Liu, Jian-Guo

2014-12-01

Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan-Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

Beneficial changes in rumen bacterial community profile in sheep and dairy calves as a result of feeding the probiotic Bacillus amyloliquefaciens H57.

PubMed

Schofield, B J; Lachner, N; Le, O T; McNeill, D M; Dart, P; Ouwerkerk, D; Hugenholtz, P; Klieve, A V

2018-03-01

The probiotic Bacillus amyloliquefaciens H57 increased weight gain, increased nitrogen retention and increased feed intake in ruminants when administered to the diet. This study aims to develop a better understanding of this probiotic effect by analysing changes in the rumen prokaryotic community. Sequencing the 16S rRNA gene PCR amplicons of the rumen microbiome, revealed that ewes fed H57 had a significantly different rumen microbial community structure to Control sheep. In contrast, dairy calves showed no significant differences in rumen community structure between treatment groups. In both instances, H57 was below detection in the rumen community profile and was only present at low relative abundance as determined by qPCR. The altered rumen microbial community in sheep likely contributes to increased weight gain through more efficient digestion of plant material. As no change occurred in the rumen community of dairy calves it is suggested that increased weight gain may be due to changes in community function rather than structure. The low relative abundance of H57 as determined by qPCR, suggests that weight gain was not directly mediated by the probiotic, but rather by influencing animal behaviour (feed consumption) and/or altering the native rumen community structure or function. This study provides a novel look at the rumen prokaryotic community in both sheep and dairy calves when fed H57. These findings improve our understanding for the potential rumen community involvement in H57-enabled weight gain. The study reveals that the probiotic B. amyloliquefaciens H57 is capable of benefiting ruminants without colonizing the rumen, suggesting an indirect mechanism of action. © 2018 The Society for Applied Microbiology.

Microbial Community Analysis of a Coastal Salt Marsh Affected by the Deepwater Horizon Oil Spill

PubMed Central

Beazley, Melanie J.; Martinez, Robert J.; Rajan, Suja; Powell, Jessica; Piceno, Yvette M.; Tom, Lauren M.; Andersen, Gary L.; Hazen, Terry C.; Van Nostrand, Joy D.; Zhou, Jizhong; Mortazavi, Behzad; Sobecky, Patricia A.

2012-01-01

Coastal salt marshes are highly sensitive wetland ecosystems that can sustain long-term impacts from anthropogenic events such as oil spills. In this study, we examined the microbial communities of a Gulf of Mexico coastal salt marsh during and after the influx of petroleum hydrocarbons following the Deepwater Horizon oil spill. Total hydrocarbon concentrations in salt marsh sediments were highest in June and July 2010 and decreased in September 2010. Coupled PhyloChip and GeoChip microarray analyses demonstrated that the microbial community structure and function of the extant salt marsh hydrocarbon-degrading microbial populations changed significantly during the study. The relative richness and abundance of phyla containing previously described hydrocarbon-degrading bacteria (Proteobacteria, Bacteroidetes, and Actinobacteria) increased in hydrocarbon-contaminated sediments and then decreased once hydrocarbons were below detection. Firmicutes, however, continued to increase in relative richness and abundance after hydrocarbon concentrations were below detection. Functional genes involved in hydrocarbon degradation were enriched in hydrocarbon-contaminated sediments then declined significantly (p<0.05) once hydrocarbon concentrations decreased. A greater decrease in hydrocarbon concentrations among marsh grass sediments compared to inlet sediments (lacking marsh grass) suggests that the marsh rhizosphere microbial communities could also be contributing to hydrocarbon degradation. The results of this study provide a comprehensive view of microbial community structural and functional dynamics within perturbed salt marsh ecosystems. PMID:22815990

A similarity based agglomerative clustering algorithm in networks

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

Bacterial community structure in the rhizosphere of a Cry1Ac Bt-brinjal crop and comparison to its non-transgenic counterpart in the tropical soil.

PubMed

Singh, Amit Kishore; Rai, Govind Kumar; Singh, Major; Dubey, Suresh Kumar

2013-11-01

To elucidate whether the transgenic crop alters the rhizospheric bacterial community structure, a 2-year study was performed with Cry1Ac gene-inserted brinjal crop (Bt) and their near isogenic non-transformed trait (non-Bt). The event of Bt crop (VRBT-8) was screened using an insect bioassay and enzyme-linked immunosorbent assay. Soil moisture, NH4 (+)-N, NO3 (-)-N, and PO4 (-)-P level had non-significant variation. Quantitative polymerase chain reaction revealed that abundance of bacterial 16S rRNA gene copies were lower in soils associated with Bt brinjal. Microbial biomass carbon (MBC) showed slight reduction in Bt brinjal soils. Higher MBC values in the non-Bt crop soil may be attributed to increased root activity and availability of readily metabolizable carbon compounds. The restriction fragment length polymorphism of PCR-amplified rRNA gene fragments detected 13 different bacterial groups with the exclusive presence of β-Proteobacteria, Chloroflexus, Planctomycetes, and Fusobacteria in non-Bt, and Cyanobacteria and Bacteroidetes in Bt soils, respectively, reflecting minor changes in the community structure. Despite the detection of Cry1Ac protein in the rhizospheric soil, the overall impact of Cry1Ac expressing Bt brinjal was less compared to that due to seasonal changes.

Significant Scales in Community Structure

NASA Astrophysics Data System (ADS)

Traag, V. A.; Krings, G.; van Dooren, P.

2013-10-01

Many complex networks show signs of modular structure, uncovered by community detection. Although many methods succeed in revealing various partitions, it remains difficult to detect at what scale some partition is significant. This problem shows foremost in multi-resolution methods. We here introduce an efficient method for scanning for resolutions in one such method. Additionally, we introduce the notion of ``significance'' of a partition, based on subgraph probabilities. Significance is independent of the exact method used, so could also be applied in other methods, and can be interpreted as the gain in encoding a graph by making use of a partition. Using significance, we can determine ``good'' resolution parameters, which we demonstrate on benchmark networks. Moreover, optimizing significance itself also shows excellent performance. We demonstrate our method on voting data from the European Parliament. Our analysis suggests the European Parliament has become increasingly ideologically divided and that nationality plays no role.

Inference methods for spatial variation in species richness and community composition when not all species are detected

USGS Publications Warehouse

Nichols, J.D.; Boulinier, T.; Hines, J.E.; Pollock, K.H.; Sauer, J.R.

1998-01-01

Inferences about spatial variation in species richness and community composition are important both to ecological hypotheses about the structure and function of communities and to community-level conservation and management. Few sampling programs for animal communities provide censuses, and usually some species present. We present estimators useful for drawing inferences about comparative species richness and composition between different sampling locations when not all species are detected in sampling efforts. Based on capture-recapture models using the robust design, our methods estimate relative species richness, proportion of species in one location that are also found in another, and number of species found in one location but not in another. The methods use data on the presence or absence of each species at different sampling occasions (or locations) to estimate the number of species not detected at any occasions (or locations). This approach permits estimation of the number of species in the sampled community and in subsets of the community useful for estimating the fraction of species shared by two communities. We provide an illustration of our estimation methods by comparing bird species richness and composition in two locations sampled by routes of the North American Breeding Bird Survey. In this example analysis, the two locations (an associated bird communities) represented different levels of urbanization. Estimates of relative richness, proportion of shared species, and number of species present on one route but not the other indicated that the route with the smaller fraction of urban area had greater richness and a larer number of species that were not found on the more urban route than vice versa. We developed a software package, COMDYN, for computing estimates based on the methods. Because these estimation methods explicitly deal with sampling in which not all species are detected, we recommend their use for addressing questions about species richness and community composition.

Comparison of bacterial community structure and dynamics during the thermophilic composting of different types of solid wastes: anaerobic digestion residue, pig manure and chicken manure.

PubMed

Song, Caihong; Li, Mingxiao; Jia, Xuan; Wei, Zimin; Zhao, Yue; Xi, Beidou; Zhu, Chaowei; Liu, Dongming

2014-09-01

This study investigated the impact of composting substrate types on the bacterial community structure and dynamics during composting processes. To this end, pig manure (PM), chicken manure (CM), a mixture of PM and CM (PM + CM), and a mixture of PM, CM and anaerobic digestion residue (ADR) (PM + CM + ADR) were selected for thermophilic composting. The bacterial community structure and dynamics during the composting process were detected and analysed by polymerase chain reaction-denaturing gradient gel electrophoresis (DGGE) coupled with a statistic analysis. The physical-chemical analyses indicated that compared to single-material composting (PM, CM), co-composting (PM + CM, PM + CM + ADR) could promote the degradation of organic matter and strengthen the ability of conserving nitrogen. A DGGE profile and statistical analysis demonstrated that co-composting, especially PM + CM + ADR, could improve the bacterial community structure and functional diversity, even in the thermophilic stage. Therefore, co-composting could weaken the screening effect of high temperature on bacterial communities. Dominant sequencing analyses indicated a dramatic shift in the dominant bacterial communities from single-material composting to co-composting. Notably, compared with PM, PM + CM increased the quantity of xylan-degrading bacteria and reduced the quantity of human pathogens. © 2014 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Plant DNA barcodes and assessment of phylogenetic community structure of a tropical mixed dipterocarp forest in Brunei Darussalam (Borneo)

PubMed Central

Abu Salim, Kamariah; Chase, Mark W.; Dexter, Kyle G.; Pennington, R. Toby; Tan, Sylvester; Kaye, Maria Ellen; Samuel, Rosabelle

2017-01-01

DNA barcoding is a fast and reliable tool to assess and monitor biodiversity and, via community phylogenetics, to investigate ecological and evolutionary processes that may be responsible for the community structure of forests. In this study, DNA barcodes for the two widely used plastid coding regions rbcL and matK are used to contribute to identification of morphologically undetermined individuals, as well as to investigate phylogenetic structure of tree communities in 70 subplots (10 × 10m) of a 25-ha forest-dynamics plot in Brunei (Borneo, Southeast Asia). The combined matrix (rbcL + matK) comprised 555 haplotypes (from ≥154 genera, 68 families and 25 orders sensu APG, Angiosperm Phylogeny Group, 2016), making a substantial contribution to tree barcode sequences from Southeast Asia. Barcode sequences were used to reconstruct phylogenetic relationships using maximum likelihood, both with and without constraining the topology of taxonomic orders to match that proposed by the Angiosperm Phylogeny Group. A third phylogenetic tree was reconstructed using the program Phylomatic to investigate the influence of phylogenetic resolution on results. Detection of non-random patterns of community assembly was determined by net relatedness index (NRI) and nearest taxon index (NTI). In most cases, community assembly was either random or phylogenetically clustered, which likely indicates the importance to community structure of habitat filtering based on phylogenetically correlated traits in determining community structure. Different phylogenetic trees gave similar overall results, but the Phylomatic tree produced greater variation across plots for NRI and NTI values, presumably due to noise introduced by using an unresolved phylogenetic tree. Our results suggest that using a DNA barcode tree has benefits over the traditionally used Phylomatic approach by increasing precision and accuracy and allowing the incorporation of taxonomically unidentified individuals into analyses. PMID:29049301

Effects of grazing on spatiotemporal variations in community structure and ecosystem function on the grasslands of Inner Mongolia, China.

PubMed

Su, Rina; Cheng, Junhui; Chen, Dima; Bai, Yongfei; Jin, Hua; Chao, Lumengqiqige; Wang, Zhijun; Li, Junqing

2017-02-28

Grasslands worldwide are suffering from overgrazing, which greatly alters plant community structure and ecosystem functioning. However, the general effects of grazing on community structure and ecosystem function at spatial and temporal scales has rarely been examined synchronously in the same grassland. Here, during 2011-2013, we investigated community structure (cover, height, and species richness) and aboveground biomass (AGB) using 250 paired field sites (grazed vs. fenced) across three vegetation types (meadow, typical, and desert steppes) on the Inner Mongolian Plateau. Grazing, vegetation type, and year all had significant effects on cover, height, species richness, and AGB, although the primary factor influencing variations in these variables was vegetation type. Spatially, grazing significantly reduced the measured variables in meadow and typical steppes, whereas no changes were observed in desert steppe. Temporally, both linear and quadratic relationships were detected between growing season precipitation and cover, height, richness, or AGB, although specific relationships varied among observation years and grazing treatments. In each vegetation type, the observed community properties were significantly correlated with each other, and the shape of the relationship was unaffected by grazing treatment. These findings indicate that vegetation type is the most important factor to be considered in grazing management for this semi-arid grassland.

Diversity of the Sediment Microbial Community in the Aha Watershed (Southwest China) in Response to Acid Mine Drainage Pollution Gradients

PubMed Central

Sun, Weimin; Sun, Min; Dong, Yiran; Ning, Zengping; Xiao, Enzong; Tang, Song; Li, Jiwei

2015-01-01

Located in southwest China, the Aha watershed is continually contaminated by acid mine drainage (AMD) produced from upstream abandoned coal mines. The watershed is fed by creeks with elevated concentrations of aqueous Fe (total Fe > 1 g/liter) and SO42− (>6 g/liter). AMD contamination gradually decreases throughout downstream rivers and reservoirs, creating an AMD pollution gradient which has led to a suite of biogeochemical processes along the watershed. In this study, sediment samples were collected along the AMD pollution sites for geochemical and microbial community analyses. High-throughput sequencing found various bacteria associated with microbial Fe and S cycling within the watershed and AMD-impacted creek. A large proportion of Fe- and S-metabolizing bacteria were detected in this watershed. The dominant Fe- and S-metabolizing bacteria were identified as microorganisms belonging to the genera Metallibacterium, Aciditerrimonas, Halomonas, Shewanella, Ferrovum, Alicyclobacillus, and Syntrophobacter. Among them, Halomonas, Aciditerrimonas, Metallibacterium, and Shewanella have previously only rarely been detected in AMD-contaminated environments. In addition, the microbial community structures changed along the watershed with different magnitudes of AMD pollution. Moreover, the canonical correspondence analysis suggested that temperature, pH, total Fe, sulfate, and redox potentials (Eh) were significant factors that structured the microbial community compositions along the Aha watershed. PMID:25979900

Global and local-scale variation in bacterial community structure of snow from the Swiss and Australian Alps.

PubMed

Wunderlin, Tina; Ferrari, Belinda; Power, Michelle

2016-09-01

Seasonally, snow environments cover up to 50% of the land's surface, yet the microbial diversity and ecosystem functioning within snow, particularly from alpine regions are not well described. This study explores the bacterial diversity in snow using next-generation sequencing technology. Our data expand the global inventory of snow microbiomes by focusing on two understudied regions, the Swiss Alps and the Australian Alps. A total biomass similar to cell numbers in polar snow was detected, with 5.2 to 10.5 × 10(3) cells mL(-1) of snow. We found that microbial community structure of surface snow varied by country and site and along the altitudinal range (alpine and sub-alpine). The bacterial communities present were diverse, spanning 25 distinct phyla, but the six phyla Proteobacteria (Alpha- and Betaproteobacteria), Acidobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria and Firmicutes, accounted for 72%-98% of the total relative abundance. Taxa such as Acidobacteriaceae and Methylocystaceae, associated with cold soils, may be part of the atmospherically sourced snow community, while families like Sphingomonadaceae were detected in every snow sample and are likely part of the common snow biome. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Critical slowing down as early warning for the onset of collapse in mutualistic communities.

PubMed

Dakos, Vasilis; Bascompte, Jordi

2014-12-09

Tipping points are crossed when small changes in external conditions cause abrupt unexpected responses in the current state of a system. In the case of ecological communities under stress, the risk of approaching a tipping point is unknown, but its stakes are high. Here, we test recently developed critical slowing-down indicators as early-warning signals for detecting the proximity to a potential tipping point in structurally complex ecological communities. We use the structure of 79 empirical mutualistic networks to simulate a scenario of gradual environmental change that leads to an abrupt first extinction event followed by a sequence of species losses until the point of complete community collapse. We find that critical slowing-down indicators derived from time series of biomasses measured at the species and community level signal the proximity to the onset of community collapse. In particular, we identify specialist species as likely the best-indicator species for monitoring the proximity of a community to collapse. In addition, trends in slowing-down indicators are strongly correlated to the timing of species extinctions. This correlation offers a promising way for mapping species resilience and ranking species risk to extinction in a given community. Our findings pave the road for combining theory on tipping points with patterns of network structure that might prove useful for the management of a broad class of ecological networks under global environmental change.

The relationship between species diversity and genetic structure in the rare Picea chihuahuana tree species community, Mexico.

PubMed

Simental-Rodríguez, Sergio Leonel; Quiñones-Pérez, Carmen Zulema; Moya, Daniel; Hernández-Tecles, Enrique; López-Sánchez, Carlos Antonio; Wehenkel, Christian

2014-01-01

Species diversity and genetic diversity, the most basic elements of biodiversity, have long been treated as separate topics, although populations evolve within a community context. Recent studies on community genetics and ecology have suggested that genetic diversity is not completely independent of species diversity. The Mexican Picea chihuahuana Martínez is an endemic species listed as "Endangered" on the Red List. Forty populations of Chihuahua spruce have been identified. This species is often associated with tree species of eight genera in gallery forests. This rare Picea chihuahuana tree community covers an area no more than 300 ha and has been subject of several studies involving different topics such as ecology, genetic structure and climate change. The overall aim of these studies was to obtain a dataset for developing management tools to help decision makers implement preservation and conservation strategies. However, this unique forest tree community may also represent an excellent subject for helping us to understand the interplay between ecological and evolutionary processes in determining community structure and dynamics. The AFLP technique and species composition data were used together to test the hypothesis that species diversity is related to the adaptive genetic structure of some dominant tree species (Picea chihuahuana, Pinus strobiformis, Pseudotsuga menziesii and Populus tremuloides) of the Picea chihuahuana tree community at fourteen locations. The Hill numbers were used as a diversity measure. The results revealed a significant correlation between tree species diversity and genetic structure in Populus tremuloides. Because the relationship between the two levels of diversity was found to be positive for the putative adaptive AFLP detected, genetic and species structures of the tree community were possibly simultaneously adapted to a combination of ecological or environmental factors. The present findings indicate that interactions between genetic variants and species diversity may be crucial in shaping tree communities.

Human and Environmental Impacts on River Sediment Microbial Communities

DOE PAGES

Gibbons, Sean M.; Jones, Edwin; Bearquiver, Angelita; ...

2014-05-19

Sediment microbial communities are responsible for a majority of the metabolic activity in river and stream ecosystems. Understanding the dynamics in community structure and function across freshwater environments will help us to predict how these ecosystems will change in response to human land-use practices. Here we present a spatiotemporal study of sediments in the Tongue River (Montana, USA), comprising six sites along 134 km of river sampled in both spring and fall for two years. Sequencing of 16S rRNA amplicons and shotgun metagenomes revealed that these sediments are the richest (~65,000 microbial ‘species’ identified) and most novel (93% of OTUsmore » do not match known microbial diversity) ecosystems analyzed by the Earth Microbiome Project to date, and display more functional diversity than was detected in a recent review of global soil metagenomes. Community structure and functional potential have been significantly altered by anthropogenic drivers, including increased pathogenicity and antibiotic metabolism markers near towns and metabolic signatures of coal and coalbed methane extraction byproducts. The core (OTUs shared across all samples) and the overall microbial community exhibited highly similar structure, and phylogeny was weakly coupled with functional potential. Together, these results suggest that microbial community structure is shaped by environmental drivers and niche filtering, though stochastic assembly processes likely play a role as well. These results indicate that sediment microbial communities are highly complex and sensitive to changes in land use practices.« less

Ecotoxicological assessment of pesticides and their combination on rhizospheric microbial community structure and function of Vigna radiata.

PubMed

Walvekar, Varsha Ashok; Bajaj, Swati; Singh, Dileep K; Sharma, Shilpi

2017-07-01

India is one of the leading countries in production and indiscriminate consumption of pesticides. Owing to their xenobiotic nature, pesticides affect soil microorganisms that serve as mediators in plant growth promotion. Our study aimed to deliver a comprehensive picture, by comparing the effects of synthetic pesticides (chlorpyriphos, cypermethrin, and a combination of both) with a biopesticide (azadirachtin) at their recommended field application level (L), and three times the recommended dosage (H) on structure and function of microbial community in rhizosphere of Vigna radiata. Effect on culturable fraction was assessed by enumeration on selective media, while PCR-denaturing gradient gel electrophoresis (DGGE) was employed to capture total bacterial community diversity. This was followed by a metabolic sketch using community-level physiological profiling (CLPP), to obtain a broader picture of the non-target effects on rhizospheric microbial community. Although plant parameters were not significantly affected by pesticide application, the microbial community structure experienced an undesirable impact as compared to control devoid of pesticide treatment. Examination of DGGE banding patterns through cluster analysis revealed that microbial community structure of pesticide-treated soils had only 70% resemblance to control rhizospheric soil even at 45 days post application. Drastic changes in the metabolic profiles of pesticide-treated soils were also detected in terms of substrate utilization, rhizospheric diversity, and evenness. It is noteworthy that the effects exacerbated by biopesticide were comparable to that of synthetic pesticides, thus emphasizing the significance of ecotoxicological assessments before tagging biopesticides as "safe alternatives."

A mixing evolution model for bidirectional microblog user networks

NASA Astrophysics Data System (ADS)

Yuan, Wei-Guo; Liu, Yun

2015-08-01

Microblogs have been widely used as a new form of online social networking. Based on the user profile data collected from Sina Weibo, we find that the number of microblog user bidirectional friends approximately corresponds with the lognormal distribution. We then build two microblog user networks with real bidirectional relationships, both of which have not only small-world and scale-free but also some special properties, such as double power-law degree distribution, disassortative network, hierarchical and rich-club structure. Moreover, by detecting the community structures of the two real networks, we find both of their community scales follow an exponential distribution. Based on the empirical analysis, we present a novel evolution network model with mixed connection rules, including lognormal fitness preferential and random attachment, nearest neighbor interconnected in the same community, and global random associations in different communities. The simulation results show that our model is consistent with real network in many topology features.

Bacterial Microbiota Associated with the Glacier Ice Worm Is Dominated by Both Worm-Specific and Glacier-Derived Facultative Lineages

PubMed Central

Murakami, Takumi; Segawa, Takahiro; Dial, Roman; Takeuchi, Nozomu; Kohshima, Shiro; Hongoh, Yuichi

2017-01-01

The community structure of bacteria associated with the glacier ice worm Mesenchytraeus solifugus was analyzed by amplicon sequencing of 16S rRNA genes and their transcripts. Ice worms were collected from two distinct glaciers in Alaska, Harding Icefield and Byron Glacier, and glacier surfaces were also sampled for comparison. Marked differences were observed in bacterial community structures between the ice worm and glacier surface samples. Several bacterial phylotypes were detected almost exclusively in the ice worms, and these bacteria were phylogenetically affiliated with either animal-associated lineages or, interestingly, clades mostly consisting of glacier-indigenous species. The former included bacteria that belong to Mollicutes, Chlamydiae, Rickettsiales, and Lachnospiraceae, while the latter included Arcicella and Herminiimonas phylotypes. Among these bacteria enriched in ice worm samples, Mollicutes, Arcicella, and Herminiimonas phylotypes were abundantly and consistently detected in the ice worm samples; these phylotypes constituted the core microbiota associated with the ice worm. A fluorescence in situ hybridization analysis showed that Arcicella cells specifically colonized the epidermis of the ice worms. Other bacterial phylotypes detected in the ice worm samples were also abundantly recovered from the respective habitat glaciers; these bacteria may be food for ice worms to digest or temporary residents. Nevertheless, some were overrepresented in the ice worm RNA samples; they may also function as facultative gut bacteria. Our results indicate that the community structure of bacteria associated with ice worms is distinct from that in the associated glacier and includes worm-specific and facultative, glacier-indigenous lineages. PMID:28302989

Bacterial Microbiota Associated with the Glacier Ice Worm Is Dominated by Both Worm-Specific and Glacier-Derived Facultative Lineages.

PubMed

Murakami, Takumi; Segawa, Takahiro; Dial, Roman; Takeuchi, Nozomu; Kohshima, Shiro; Hongoh, Yuichi

2017-03-31

The community structure of bacteria associated with the glacier ice worm Mesenchytraeus solifugus was analyzed by amplicon sequencing of 16S rRNA genes and their transcripts. Ice worms were collected from two distinct glaciers in Alaska, Harding Icefield and Byron Glacier, and glacier surfaces were also sampled for comparison. Marked differences were observed in bacterial community structures between the ice worm and glacier surface samples. Several bacterial phylotypes were detected almost exclusively in the ice worms, and these bacteria were phylogenetically affiliated with either animal-associated lineages or, interestingly, clades mostly consisting of glacier-indigenous species. The former included bacteria that belong to Mollicutes, Chlamydiae, Rickettsiales, and Lachnospiraceae, while the latter included Arcicella and Herminiimonas phylotypes. Among these bacteria enriched in ice worm samples, Mollicutes, Arcicella, and Herminiimonas phylotypes were abundantly and consistently detected in the ice worm samples; these phylotypes constituted the core microbiota associated with the ice worm. A fluorescence in situ hybridization analysis showed that Arcicella cells specifically colonized the epidermis of the ice worms. Other bacterial phylotypes detected in the ice worm samples were also abundantly recovered from the respective habitat glaciers; these bacteria may be food for ice worms to digest or temporary residents. Nevertheless, some were overrepresented in the ice worm RNA samples; they may also function as facultative gut bacteria. Our results indicate that the community structure of bacteria associated with ice worms is distinct from that in the associated glacier and includes worm-specific and facultative, glacier-indigenous lineages.

Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

PubMed

Lohse, Christian; Bassett, Danielle S; Lim, Kelvin O; Carlson, Jean M

2014-10-01

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

Research on energy stock market associated network structure based on financial indicators

NASA Astrophysics Data System (ADS)

Xi, Xian; An, Haizhong

2018-01-01

A financial market is a complex system consisting of many interacting units. In general, due to the various types of information exchange within the industry, there is a relationship between the stocks that can reveal their clear structural characteristics. Complex network methods are powerful tools for studying the internal structure and function of the stock market, which allows us to better understand the stock market. Applying complex network methodology, a stock associated network model based on financial indicators is created. Accordingly, we set threshold value and use modularity to detect the community network, and we analyze the network structure and community cluster characteristics of different threshold situations. The study finds that the threshold value of 0.7 is the abrupt change point of the network. At the same time, as the threshold value increases, the independence of the community strengthens. This study provides a method of researching stock market based on the financial indicators, exploring the structural similarity of financial indicators of stocks. Also, it provides guidance for investment and corporate financial management.

Are there keystone mycorrhizal fungi associated to tropical epiphytic orchids?

PubMed

Cevallos, Stefania; Sánchez-Rodríguez, Aminael; Decock, Cony; Declerck, Stéphane; Suárez, Juan Pablo

2017-04-01

In epiphytic orchids, distinctive groups of fungi are involved in the symbiotic association. However, little is known about the factors that determine the mycorrhizal community structure. Here, we analyzed the orchid mycorrhizal fungi communities associated with three sympatric Cymbidieae epiphytic tropical orchids (Cyrtochilum flexuosum, Cyrtochilum myanthum, and Maxillaria calantha) at two sites located within the mountain rainforest of southern Ecuador. To characterize these communities at each orchid population, the ITS2 region was analyzed by Illumina MiSeq technology. Fifty-five mycorrhizal fungi operational taxonomic units (OTUs) putatively attributed to members of Serendipitaceae, Ceratobasidiaceae and Tulasnellaceae were identified. Significant differences in mycorrhizal communities were detected between the three sympatric orchid species as well as among sites/populations. Interestingly, some mycorrhizal OTUs overlapped among orchid populations. Our results suggested that populations of studied epiphytic orchids have site-adjusted mycorrhizal communities structured around keystone fungal species. Interaction with multiple mycorrhizal fungi could favor orchid site occurrence and co-existence among several orchid species.

Bacterial community compositions of coking wastewater treatment plants in steel industry revealed by Illumina high-throughput sequencing.

PubMed

Ma, Qiao; Qu, Yuanyuan; Shen, Wenli; Zhang, Zhaojing; Wang, Jingwei; Liu, Ziyan; Li, Duanxing; Li, Huijie; Zhou, Jiti

2015-03-01

In this study, Illumina high-throughput sequencing was used to reveal the community structures of nine coking wastewater treatment plants (CWWTPs) in China for the first time. The sludge systems exhibited a similar community composition at each taxonomic level. Compared to previous studies, some of the core genera in municipal wastewater treatment plants such as Zoogloea, Prosthecobacter and Gp6 were detected as minor species. Thiobacillus (20.83%), Comamonas (6.58%), Thauera (4.02%), Azoarcus (7.78%) and Rhodoplanes (1.42%) were the dominant genera shared by at least six CWWTPs. The percentages of autotrophic ammonia-oxidizing bacteria and nitrite-oxidizing bacteria were unexpectedly low, which were verified by both real-time PCR and fluorescence in situ hybridization analyses. Hierarchical clustering and canonical correspondence analysis indicated that operation mode, flow rate and temperature might be the key factors in community formation. This study provides new insights into our understanding of microbial community compositions and structures of CWWTPs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparisons of the fungal and protistan communities among different marine sponge holobionts by pyrosequencing.

PubMed

He, Liming; Liu, Fang; Karuppiah, Valliappan; Ren, Yi; Li, Zhiyong

2014-05-01

To date, the knowledge of eukaryotic communities associated with sponges remains limited compared with prokaryotic communities. In a manner similar to prokaryotes, it could be hypothesized that sponge holobionts have phylogenetically diverse eukaryotic symbionts, and the eukaryotic community structures in different sponge holobionts were probably different. In order to test this hypothesis, the communities of eukaryota associated with 11 species of South China Sea sponges were compared with the V4 region of 18S ribosomal ribonucleic acid gene using 454 pyrosequencing. Consequently, 135 and 721 unique operational taxonomic units (OTUs) of fungi and protists were obtained at 97 % sequence similarity, respectively. These sequences were assigned to 2 phyla of fungi (Ascomycota and Basidiomycota) and 9 phyla of protists including 5 algal phyla (Chlorophyta, Haptophyta, Streptophyta, Rhodophyta, and Stramenopiles) and 4 protozoal phyla (Alveolata, Cercozoa, Haplosporidia, and Radiolaria) including 47 orders (12 fungi, 35 protists). Entorrhizales of fungi and 18 orders of protists were detected in marine sponges for the first time. Particularly, Tilletiales of fungi and Chlorocystidales of protists were detected for the first time in marine habitats. Though Ascomycota, Alveolata, and Radiolaria were detected in all the 11 sponge species, sponge holobionts have different fungi and protistan communities according to OTU comparison and principal component analysis at the order level. This study provided the first insights into the fungal and protistan communities associated with different marine sponge holobionts using pyrosequencing, thus further extending the knowledge on sponge-associated eukaryotic diversity.

Microbial Community Structure in a Serpentine-Hosted Abiotic Gas Seepage at the Chimaera Ophiolite, Turkey

PubMed Central

Sun, Li; Müller, Bettina; Ivarsson, Magnus; Hosgörmez, Hakan; Özcan, Dogacan; Broman, Curt; Schnürer, Anna

2017-01-01

ABSTRACT The surface waters at the ultramafic ophiolitic outcrop in Chimaera, Turkey, are characterized by high pH values and high metal levels due to the percolation of fluids through areas of active serpentinization. We describe the influence of the liquid chemistry, mineralogy, and H2 and CH4 levels on the bacterial community structure in a semidry, exposed, ultramafic environment. The bacterial and archaeal community structures were monitored using Illumina sequencing targeting the 16S rRNA gene. At all sampling points, four phyla, Proteobacteria, Actinobacteria, Chloroflexi, and Acidobacteria, accounted for the majority of taxa. Members of the Chloroflexi phylum dominated low-diversity sites, whereas Proteobacteria dominated high-diversity sites. Methane, nitrogen, iron, and hydrogen oxidizers were detected as well as archaea and metal-resistant bacteria. IMPORTANCE Our study is a comprehensive microbial investigation of the Chimaera ophiolite. DNA has been extracted from 16 sites in the area and has been studied from microbial and geochemical points of view. We describe a microbial community structure that is dependent on terrestrial, serpentinization-driven abiotic H2, which is poorly studied due to the rarity of these environments on Earth. PMID:28389534

Microbial Community Structure in a Serpentine-Hosted Abiotic Gas Seepage at the Chimaera Ophiolite, Turkey.

PubMed

Neubeck, Anna; Sun, Li; Müller, Bettina; Ivarsson, Magnus; Hosgörmez, Hakan; Özcan, Dogacan; Broman, Curt; Schnürer, Anna

2017-06-15

The surface waters at the ultramafic ophiolitic outcrop in Chimaera, Turkey, are characterized by high pH values and high metal levels due to the percolation of fluids through areas of active serpentinization. We describe the influence of the liquid chemistry, mineralogy, and H 2 and CH 4 levels on the bacterial community structure in a semidry, exposed, ultramafic environment. The bacterial and archaeal community structures were monitored using Illumina sequencing targeting the 16S rRNA gene. At all sampling points, four phyla, Proteobacteria , Actinobacteria , Chloroflexi , and Acidobacteria , accounted for the majority of taxa. Members of the Chloroflexi phylum dominated low-diversity sites, whereas Proteobacteria dominated high-diversity sites. Methane, nitrogen, iron, and hydrogen oxidizers were detected as well as archaea and metal-resistant bacteria. IMPORTANCE Our study is a comprehensive microbial investigation of the Chimaera ophiolite. DNA has been extracted from 16 sites in the area and has been studied from microbial and geochemical points of view. We describe a microbial community structure that is dependent on terrestrial, serpentinization-driven abiotic H 2 , which is poorly studied due to the rarity of these environments on Earth. Copyright © 2017 Neubeck et al.

Various forms of organic and inorganic P fertilizers did not negatively affect soil- and root-inhabiting AM fungi in a maize-soybean rotation system.

PubMed

Beauregard, M S; Gauthier, M-P; Hamel, C; Zhang, T; Welacky, T; Tan, C S; St-Arnaud, M

2013-02-01

Arbuscular mycorrhizal (AM) fungi are key components of most agricultural ecosystems. Therefore, understanding the impact of agricultural practices on their community structure is essential to improve nutrient mobilization and reduce plant stress in the field. The effects of five different organic or mineral sources of phosphorus (P) for a maize-soybean rotation system on AM fungal diversity in roots and soil were assessed over a 3-year period. Total DNA was extracted from root and soil samples collected at three different plant growth stages. An 18S rRNA gene fragment was amplified and taxa were detected and identified using denaturing gradient gel electrophoresis followed by sequencing. AM fungal biomass was estimated by fatty acid methyl ester analysis. Soil P fertility parameters were also monitored and analyzed for possible changes related with fertilization or growth stages. Seven AM fungal ribotypes were detected. Fertilization significantly modified soil P flux, but had barely any effect on AM fungi community structure or biomass. There was no difference in the AM fungal community between plant growth stages. Specific ribotypes could not be significantly associated to P treatment. Ribotypes were associated with root or soil samples with variable detection frequencies between seasons. AM fungal biomass remained stable throughout the growing seasons. This study demonstrated that roots and soil host distinct AM fungal communities and that these are very temporally stable. The influence of contrasting forms of P fertilizers was not significant over 3 years of crop rotation.

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations.

PubMed

Almog, Assaf; Besamusca, Ferry; MacMahon, Mel; Garlaschelli, Diego

2015-01-01

The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by "communities" of units whose dynamics, represented by time series of activity, is more strongly correlated internally than with the rest of the system. Recent studies have shown that the binary projections of various financial and neural time series exhibit nontrivial dynamical features that resemble those of the original data. This implies that a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. Here, we explore whether the binary signatures of multiple time series can replicate the same complex community organization of the financial market, as the original weighted time series. We adopt a method that has been specifically designed to detect communities from cross-correlation matrices of time series data. Our analysis shows that the simpler binary representation leads to a community structure that is almost identical with that obtained using the full weighted representation. These results confirm that binary projections of financial time series contain significant structural information.

Matrix composition and community structure analysis of a novel bacterial pyrite leaching community.

PubMed

Ziegler, Sibylle; Ackermann, Sonia; Majzlan, Juraj; Gescher, Johannes

2009-09-01

Here we describe a novel bacterial community that is embedded in a matrix of carbohydrates and bio/geochemical products of pyrite (FeS(2)) oxidation. This community grows in stalactite-like structures--snottites--on the ceiling of an abandoned pyrite mine at pH values of 2.2-2.6. The aqueous phase in the matrix contains 200 mM of sulfate and total iron concentrations of 60 mM. Micro-X-ray diffraction analysis showed that jarosite [(K,Na,H(3)O)Fe(3)(SO(4))(2)(OH)(6)] is the major mineral embedded in the snottites. X-ray absorption near-edge structure experiments revealed three different sulfur species. The major signal can be ascribed to sulfate, and the other two features may correspond to thiols and sulfoxides. Arabinose was detected as the major sugar component in the extracellular polymeric substance. Via restriction fragment length polymorphism analysis, a community was found that mainly consists of iron oxidizing Leptospirillum and Ferrovum species but also of bacteria that could be involved in dissimilatory sulfate and dissimilatory iron reduction. Each snottite can be regarded as a complex, self-contained consortium of bacterial species fuelled by the decomposition of pyrite.

Good initialization model with constrained body structure for scene text recognition

NASA Astrophysics Data System (ADS)

Zhu, Anna; Wang, Guoyou; Dong, Yangbo

2016-09-01

Scene text recognition has gained significant attention in the computer vision community. Character detection and recognition are the promise of text recognition and affect the overall performance to a large extent. We proposed a good initialization model for scene character recognition from cropped text regions. We use constrained character's body structures with deformable part-based models to detect and recognize characters in various backgrounds. The character's body structures are achieved by an unsupervised discriminative clustering approach followed by a statistical model and a self-build minimum spanning tree model. Our method utilizes part appearance and location information, and combines character detection and recognition in cropped text region together. The evaluation results on the benchmark datasets demonstrate that our proposed scheme outperforms the state-of-the-art methods both on scene character recognition and word recognition aspects.

Differences in microbial community structure and nitrogen cycling in natural and drained tropical peatland soils.

PubMed

Espenberg, Mikk; Truu, Marika; Mander, Ülo; Kasak, Kuno; Nõlvak, Hiie; Ligi, Teele; Oopkaup, Kristjan; Maddison, Martin; Truu, Jaak

2018-03-16

Tropical peatlands, which play a crucial role in the maintenance of different ecosystem services, are increasingly drained for agriculture, forestry, peat extraction and human settlement purposes. The present study investigated the differences between natural and drained sites of a tropical peatland in the community structure of soil bacteria and archaea and their potential to perform nitrogen transformation processes. The results indicate significant dissimilarities in the structure of soil bacterial and archaeal communities as well as nirK, nirS, nosZ, nifH and archaeal amoA gene-possessing microbial communities. The reduced denitrification and N 2 -fixing potential was detected in the drained tropical peatland soil. In undisturbed peatland soil, the N 2 O emission was primarily related to nirS-type denitrifiers and dissimilatory nitrate reduction to ammonium, while the conversion of N 2 O to N 2 was controlled by microbes possessing nosZ clade I genes. The denitrifying microbial community of the drained site differed significantly from the natural site community. The main reducers of N 2 O were microbes harbouring nosZ clade II genes in the drained site. Additionally, the importance of DNRA process as one of the controlling mechanisms of N 2 O fluxes in the natural peatlands of the tropics revealed from the results of the study.

Sedimentological imprint on subseafloor microbial communities in Western Mediterranean Sea Quaternary sediments

NASA Astrophysics Data System (ADS)

Ciobanu, M.-C.; Rabineau, M.; Droz, L.; Révillon, S.; Ghiglione, J.-F.; Dennielou, B.; Jorry, S.-J.; Kallmeyer, J.; Etoubleau, J.; Pignet, P.; Crassous, P.; Vandenabeele-Trambouze, O.; Laugier, J.; Guégan, M.; Godfroy, A.; Alain, K.

2012-09-01

An interdisciplinary study was conducted to evaluate the relationship between geological and paleoenvironmental parameters and the bacterial and archaeal community structure of two contrasting subseafloor sites in the Western Mediterranean Sea (Ligurian Sea and Gulf of Lion). Both depositional environments in this area are well-documented from paleoclimatic and paleooceanographic point of views. Available data sets allowed us to calibrate the investigated cores with reference and dated cores previously collected in the same area, and notably correlated to Quaternary climate variations. DNA-based fingerprints showed that the archaeal diversity was composed by one group, Miscellaneous Crenarchaeotic Group (MCG), within the Gulf of Lion sediments and of nine different lineages (dominated by MCG, South African Gold Mine Euryarchaeotal Group (SAGMEG) and Halobacteria) within the Ligurian Sea sediments. Bacterial molecular diversity at both sites revealed mostly the presence of the classes Alphaproteobacteria, Betaproteobacteria and Gammaproteobacteria within Proteobacteria phylum, and also members of Bacteroidetes phylum. The second most abundant lineages were Actinobacteria and Firmicutes at the Gulf of Lion site and Chloroflexi at the Ligurian Sea site. Various substrates and cultivation conditions allowed us to isolate 75 strains belonging to four lineages: Alpha-, Gammaproteobacteria, Firmicutes and Actinobacteria. In molecular surveys, the Betaproteobacteria group was consistently detected in the Ligurian Sea sediments, characterized by a heterolithic facies with numerous turbidites from a deep-sea levee. Analysis of relative betaproteobacterial abundances and turbidite frequency suggested that the microbial diversity was a result of main climatic changes occurring during the last 20 ka. Statistical direct multivariate canonical correspondence analyses (CCA) showed that the availability of electron acceptors and the quality of electron donors (indicated by age) strongly influenced the community structure. In contrast, within the Gulf of Lion core, characterized by a homogeneous lithological structure of upper-slope environment, most detected groups were Bacteroidetes and, to a lesser extent, Betaproteobacteria. At both site, the detection of Betaproteobacteria coincided with increased terrestrial inputs, as confirmed by the geochemical measurements (Si, Sr, Ti and Ca). In the Gulf of Lion, geochemical parameters were also found to drive microbial community composition. Taken together, our data suggest that the palaeoenvironmental history of erosion and deposition recorded in the Western Mediterranean Sea sediments has left its imprint on the sedimentological context for microbial habitability, and then indirectly on structure and composition of the microbial communities during the late Quaternary.

Horizontal gene transfer in an acid mine drainage microbial community.

PubMed

Guo, Jiangtao; Wang, Qi; Wang, Xiaoqi; Wang, Fumeng; Yao, Jinxian; Zhu, Huaiqiu

2015-07-04

Horizontal gene transfer (HGT) has been widely identified in complete prokaryotic genomes. However, the roles of HGT among members of a microbial community and in evolution remain largely unknown. With the emergence of metagenomics, it is nontrivial to investigate such horizontal flow of genetic materials among members in a microbial community from the natural environment. Because of the lack of suitable methods for metagenomics gene transfer detection, microorganisms from a low-complexity community acid mine drainage (AMD) with near-complete genomes were used to detect possible gene transfer events and suggest the biological significance. Using the annotation of coding regions by the current tools, a phylogenetic approach, and an approximately unbiased test, we found that HGTs in AMD organisms are not rare, and we predicted 119 putative transferred genes. Among them, 14 HGT events were determined to be transfer events among the AMD members. Further analysis of the 14 transferred genes revealed that the HGT events affected the functional evolution of archaea or bacteria in AMD, and it probably shaped the community structure, such as the dominance of G-plasma in archaea in AMD through HGT. Our study provides a novel insight into HGT events among microorganisms in natural communities. The interconnectedness between HGT and community evolution is essential to understand microbial community formation and development.

Serpentine Soils Do Not Limit Mycorrhizal Fungal Diversity

PubMed Central

Branco, Sara; Ree, Richard H.

2010-01-01

Background Physiologically stressful environments tend to host depauperate and specialized biological communities. Serpentine soils exemplify this phenomenon by imposing well-known constraints on plants; however, their effect on other organisms is still poorly understood. Methodology/Principal Findings We used a combination of field and molecular approaches to test the hypothesis that serpentine fungal communities are species-poor and specialized. We conducted surveys of ectomycorrhizal fungal diversity from adjacent serpentine and non-serpentine sites, described fungal communities using nrDNA Internal Transcribed Spacer (ITS) fragment and sequence analyses, and compared their phylogenetic community structure. Although we detected low fungal overlap across the two habitats, we found serpentine soils to support rich fungal communities that include representatives from all major fungal lineages. We failed to detect the phylogenetic signature of endemic clades that would result from specialization and adaptive radiation within this habitat. Conclusions/Significance Our results indicate that serpentine soils do not constitute an extreme environment for ectomycorrhizal fungi, and raise important questions about the role of symbioses in edaphic tolerance and the maintenance of biodiversity. PMID:20668696

Microbial community analysis in rice paddy soils irrigated by acid mine drainage contaminated water.

PubMed

Sun, Min; Xiao, Tangfu; Ning, Zengping; Xiao, Enzong; Sun, Weimin

2015-03-01

Five rice paddy soils located in southwest China were selected for geochemical and microbial community analysis. These rice fields were irrigated with river water which was contaminated by Fe-S-rich acid mine drainage. Microbial communities were characterized by high-throughput sequencing, which showed 39 different phyla/groups in these samples. Among these phyla/groups, Proteobacteria was the most abundant phylum in all samples. Chloroflexi, Acidobacteria, Nitrospirae, and Bacteroidetes exhibited higher relative abundances than other phyla. A number of rare and candidate phyla were also detected. Moreover, canonical correspondence analysis suggested that pH, sulfate, and nitrate were significant factors that shaped the microbial community structure. In addition, a wide diversity of Fe- and S-related bacteria, such as GOUTA19, Shewanella, Geobacter, Desulfobacca, Thiobacillus, Desulfobacterium, and Anaeromyxobacter, might be responsible for biogeochemical Fe and S cycles in the tested rice paddy soils. Among the dominant genera, GOUTA19 and Shewanella were seldom detected in rice paddy soils.

Estimating the effectiveness of further sampling in species inventories

USGS Publications Warehouse

Keating, K.A.; Quinn, J.F.; Ivie, M.A.; Ivie, L.L.

1998-01-01

Estimators of the number of additional species expected in the next ??n samples offer a potentially important tool for improving cost-effectiveness of species inventories but are largely untested. We used Monte Carlo methods to compare 11 such estimators, across a range of community structures and sampling regimes, and validated our results, where possible, using empirical data from vascular plant and beetle inventories from Glacier National Park, Montana, USA. We found that B. Efron and R. Thisted's 1976 negative binomial estimator was most robust to differences in community structure and that it was among the most accurate estimators when sampling was from model communities with structures resembling the large, heterogeneous communities that are the likely targets of major inventory efforts. Other estimators may be preferred under specific conditions, however. For example, when sampling was from model communities with highly even species-abundance distributions, estimates based on the Michaelis-Menten model were most accurate; when sampling was from moderately even model communities with S=10 species or communities with highly uneven species-abundance distributions, estimates based on Gleason's (1922) species-area model were most accurate. We suggest that use of such methods in species inventories can help improve cost-effectiveness by providing an objective basis for redirecting sampling to more-productive sites, methods, or time periods as the expectation of detecting additional species becomes unacceptably low.

Text and Structural Data Mining of Influenza Mentions in Web and Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corley, Courtney D.; Cook, Diane; Mikler, Armin R.

Text and structural data mining of Web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5-October-2008 to 21-March-2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like-illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

Structure of Infaunal Communities on the Beaufort Sea Shelf and Slope: Insights from Morphological and Environmental DNA Sequencing Approaches

NASA Astrophysics Data System (ADS)

Hardy, S. M.; Bik, H.; Walker, A.; Sharma, J.; Blanchard, A.

2016-02-01

Rapid change is occurring in the Arctic concurrently with increased human activity, yet our knowledge of the structure and function of high-Arctic sediment communities is still rudimentary. The Beaufort Sea is particularly poorly sampled, and largely unexplored at slope depths, providing little information with which to assess the impacts of petroleum exploration activities now beginning in this area. We are investigating diversity and community structure of meio- and macrobenthic infauna on the continental shelf and slope of the Beaufort Sea across a range of depths (50 to 1000 m) using traditional taxonomic and environmental DNA sequencing approaches, and comparing results to additional sites in the adjacent NE Chukchi Sea petroleum lease-sale area. The Beaufort slope is topographically complex and characterized by an east-west gradient in benthic habitat characteristics, with heavy input of terrestrial organic matter particularly in the region of the Mackenzie River delta. Warmer, saltier subsurface Atlantic water masses impact benthic communities at mid-slope depths, likely influencing turnover in community structure observed with depth. Food resources are variable across the region, with very high sediment chlorophyll concentrations at 350 m depth in some areas. Differences in nematode assemblages were detected across the Beaufort Sea shelf/slope, across depths within the Beaufort Sea, and between the Beaufort and adjacent NE Chukchi Sea. These differences were apparent in both morphological and environmental sequencing data. Macrofaunal communities showed variable community structure among transects, with high abundance and high dominance in polychaete assemblages coincident with the chlorophyll maximum. Sequencing data also revealed an abundance of protists in sediments which have been mostly ignored in studies of ecosystem dynamics in this region, and may represent an important component of the food web.

Diversity and Dynamics of Microbial Community Structure in Different Mangrove, Marine and Freshwater Sediments During Anaerobic Debromination of PBDEs

PubMed Central

Wang, Ya Fen; Zhu, Hao Wen; Wang, Ying; Zhang, Xiang Ling; Tam, Nora Fung Yee

2018-01-01

Little is known about the diversity and succession of indigenous microbial community during debromination of polybrominated diphenyl ethers (PBDEs). This study examined the diversity and dynamics of microbial community structure in eight saline (mangrove and marine) and freshwater sediment microcosms exhibiting different debrominating capabilities for hexa-BDE 153, a common congener in sediments, using terminal restriction fragment length polymorphism (T-RFLP) and clone library analyses. The results showed that microbial community structure greatly differed between the saline and freshwater microcosms, likely leading to distinct variations in their debrominating capabilities and pathways. Higher relative abundances of Chloroflexi and Deltaproteobacteria succeed by Alphaproteobacteria and Betaproteobacteria were detected in the two mangrove microcosms with the fastest debrominating capabilities mainly via para pathway, respectively; the dominance of Alphaproteobacteria resulted in less accumulation of tetra-BDEs and more complete debromination of lower brominated congeners (from di- to tetra-BDEs). Meanwhile, the shifts in both microbial community structure and PBDE profiles were relatively small in the less efficient freshwater microcosms, with relatively more ortho and meta brominated products of BDE-153 resulted. Coincidently, one of the freshwater microcosms showed sudden increases of Chloroflexi and Deltaproteobacteria by the end of incubation, which synchronized with the increase in the removal rate of BDE-153. The significant relationship between microbial community structure and PBDEs was confirmed by redundancy analysis (18.7% of total variance explained, P = 0.002). However, the relative abundance of the well-known dechlorinator Dehalococcoides showed no clear correlation with the debrominating capability across different microcosms. These findings shed light in the significance of microbial community network in different saline environments on enhancement of PBDE intrinsic debromination. PMID:29867858

Diversity and Dynamics of Microbial Community Structure in Different Mangrove, Marine and Freshwater Sediments During Anaerobic Debromination of PBDEs.

PubMed

Wang, Ya Fen; Zhu, Hao Wen; Wang, Ying; Zhang, Xiang Ling; Tam, Nora Fung Yee

2018-01-01

Little is known about the diversity and succession of indigenous microbial community during debromination of polybrominated diphenyl ethers (PBDEs). This study examined the diversity and dynamics of microbial community structure in eight saline (mangrove and marine) and freshwater sediment microcosms exhibiting different debrominating capabilities for hexa-BDE 153, a common congener in sediments, using terminal restriction fragment length polymorphism (T-RFLP) and clone library analyses. The results showed that microbial community structure greatly differed between the saline and freshwater microcosms, likely leading to distinct variations in their debrominating capabilities and pathways. Higher relative abundances of Chloroflexi and Deltaproteobacteria succeed by Alphaproteobacteria and Betaproteobacteria were detected in the two mangrove microcosms with the fastest debrominating capabilities mainly via para pathway, respectively; the dominance of Alphaproteobacteria resulted in less accumulation of tetra-BDEs and more complete debromination of lower brominated congeners (from di- to tetra-BDEs). Meanwhile, the shifts in both microbial community structure and PBDE profiles were relatively small in the less efficient freshwater microcosms, with relatively more ortho and meta brominated products of BDE-153 resulted. Coincidently, one of the freshwater microcosms showed sudden increases of Chloroflexi and Deltaproteobacteria by the end of incubation, which synchronized with the increase in the removal rate of BDE-153. The significant relationship between microbial community structure and PBDEs was confirmed by redundancy analysis (18.7% of total variance explained, P = 0.002). However, the relative abundance of the well-known dechlorinator Dehalococcoides showed no clear correlation with the debrominating capability across different microcosms. These findings shed light in the significance of microbial community network in different saline environments on enhancement of PBDE intrinsic debromination.

[Taxonomic and functional diversity of the bycatch fishes community of trawl fishing from Northern Gulf of California, Mexico].

PubMed

Herrera-Valdivia, Eloísa; López-Martínez, Juana; Castillo Vargasmachuca, Sergio; García-Juárez, Rosa

2016-06-01

The Northern Gulf of California (NGC) is a mega diverse area of high endemism with major economic interest because of the multi-specific fisheries developed, mainly shrimp. There is a lack of recent studies on bycatch fish assemblages, so during the fishing seasons from 2009-2010 and 2010-2011, on board 13 shrimp boats, 14 commercial fishing trips were performed from 5 m - 90 m in depth with a total of 119 catches. The 119 catches were analyzed to assess fish community structure using taxonomic diversity indices to detect changes in the community following the taxonomic distinctness average Δ+ and the diversity index Δ* (TAXDEST of the PRIMER v6 program). To confirm the structure of functional groups, we considered similarities of ecologic and morphologic traits among species. The results showed that the indices Δ+ and Δ* were within the expected average and confidence intervals at 95%, finding significant differences between the indices. The analyses showed a well-structured community because of the great variety of forms and functions of the species within the community. In the community of the functional groups, reproduction was the ecological attribute that contributed the most to their structure. The community structure was represented by intermediate trophic levels (3-3.9), preferably primary and secondary carnivores within the trophic categories of predators of benthic ichthyo-fauna that belong to demersal species of soft bottoms and mostly fusiform body. To conclude, the NGC showed high functional redundancy according to the estimated functional groups, thus the ecosystem was considered stable and with great diversity. This type of studies should be followed using fishing and environmental effort due to the great biological and ecologic importance in the area.

Inference and Analysis of Population Structure Using Genetic Data and Network Theory

PubMed Central

Greenbaum, Gili; Templeton, Alan R.; Bar-David, Shirli

2016-01-01

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition’s modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/). PMID:26888080

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

PubMed

Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

2016-04-01

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/). Copyright © 2016 by the Genetics Society of America.

An improved label propagation algorithm based on node importance and random walk for community detection

NASA Astrophysics Data System (ADS)

Ma, Tianren; Xia, Zhengyou

2017-05-01

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.

Structure and function of methanotrophic communities in a landfill-cover soil.

PubMed

Henneberger, Ruth; Lüke, Claudia; Mosberger, Lona; Schroth, Martin H

2012-07-01

In landfill-cover soils, aerobic methane-oxidizing bacteria (MOB) convert CH(4) to CO(2), mitigating emissions of the greenhouse gas CH(4) to the atmosphere. We investigated overall MOB community structure and assessed spatial differences in MOB diversity, abundance and activity in a Swiss landfill-cover soil. Molecular cloning, terminal restriction-fragment length polymorphism (T-RFLP) and quantitative PCR of pmoA genes were applied to soil collected from 16 locations at three different depths to study MOB community structure, diversity and abundance; MOB activity was measured in the field using gas push-pull tests. The MOB community was highly diverse but dominated by Type Ia MOB, with novel pmoA sequences present. Type II MOB were detected mainly in deeper soil with lower nutrient and higher CH(4) concentrations. Substantial differences in MOB community structure were observed between one high- and one low-activity location. MOB abundance was highly variable across the site [4.0 × 10(4) to 1.1 × 10(7) (g soil dry weight)(-1)]. Potential CH(4) oxidation rates were high [1.8-58.2 mmol CH(4) (L soil air)(-1) day(-1) ] but showed significant lateral variation and were positively correlated with mean CH(4) concentrations (P < 0.01), MOB abundance (P < 0.05) and MOB diversity (weak correlation, P < 0.17). Our findings indicate that Methylosarcina and closely related MOB are key players and that MOB abundance and community structure are driving factors in CH(4) oxidation at this landfill. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Surprisingly little population genetic structure in a fungus-associated beetle despite its exploitation of multiple hosts

PubMed Central

Wood, Corlett W; Donald, Hannah M; Formica, Vincent A; Brodie, Edmund D

2013-01-01

In heterogeneous environments, landscape features directly affect the structure of genetic variation among populations by functioning as barriers to gene flow. Resource-associated population genetic structure, in which populations that use different resources (e.g., host plants) are genetically distinct, is a well-studied example of how environmental heterogeneity structures populations. However, the pattern that emerges in a given landscape should depend on its particular combination of resources. If resources constitute barriers to gene flow, population differentiation should be lowest in homogeneous landscapes, and highest where resources exist in equal proportions. In this study, we tested whether host community diversity affects population genetic structure in a beetle (Bolitotherus cornutus) that exploits three sympatric host fungi. We collected B. cornutus from plots containing the three host fungi in different proportions and quantified population genetic structure in each plot using a panel of microsatellite loci. We found no relationship between host community diversity and population differentiation in this species; however, we also found no evidence of resource-associated differentiation, suggesting that host fungi are not substantial barriers to gene flow. Moreover, we detected no genetic differentiation among B. cornutus populations separated by several kilometers, even though a previous study demonstrated moderate genetic structure on the scale of a few hundred meters. Although we found no effect of community diversity on population genetic structure in this study, the role of host communities in the structuring of genetic variation in heterogeneous landscapes should be further explored in a species that exhibits resource-associated population genetic structure. PMID:23789061

Alignment and integration of complex networks by hypergraph-based spectral clustering

NASA Astrophysics Data System (ADS)

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Alignment and integration of complex networks by hypergraph-based spectral clustering.

PubMed

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Differential resistance of drinking water bacterial populations to monochloramine disinfection.

PubMed

Chiao, Tzu-Hsin; Clancy, Tara M; Pinto, Ameet; Xi, Chuanwu; Raskin, Lutgarde

2014-04-01

The impact of monochloramine disinfection on the complex bacterial community structure in drinking water systems was investigated using culture-dependent and culture-independent methods. Changes in viable bacterial diversity were monitored using culture-independent methods that distinguish between live and dead cells based on membrane integrity, providing a highly conservative measure of viability. Samples were collected from lab-scale and full-scale drinking water filters exposed to monochloramine for a range of contact times. Culture-independent detection of live cells was based on propidium monoazide (PMA) treatment to selectively remove DNA from membrane-compromised cells. Quantitative PCR (qPCR) and pyrosequencing of 16S rRNA genes was used to quantify the DNA of live bacteria and characterize the bacterial communities, respectively. The inactivation rate determined by the culture-independent PMA-qPCR method (1.5-log removal at 664 mg·min/L) was lower than the inactivation rate measured by the culture-based methods (4-log removal at 66 mg·min/L). Moreover, drastic changes in the live bacterial community structure were detected during monochloramine disinfection using PMA-pyrosequencing, while the community structure appeared to remain stable when pyrosequencing was performed on samples that were not subject to PMA treatment. Genera that increased in relative abundance during monochloramine treatment include Legionella, Escherichia, and Geobacter in the lab-scale system and Mycobacterium, Sphingomonas, and Coxiella in the full-scale system. These results demonstrate that bacterial populations in drinking water exhibit differential resistance to monochloramine, and that the disinfection process selects for resistant bacterial populations.

Impacts of Edaphic Factors on Communities of Ammonia-Oxidizing Archaea, Ammonia-Oxidizing Bacteria and Nitrification in Tropical Soils

PubMed Central

de Gannes, Vidya; Eudoxie, Gaius; Hickey, William J.

2014-01-01

Nitrification is a key process in soil nitrogen (N) dynamics, but relatively little is known about it in tropical soils. In this study, we examined soils from Trinidad to determine the edaphic drivers affecting nitrification levels and community structure of ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) in non-managed soils. The soils were naturally vegetated, ranged in texture from sands to clays and spanned pH 4 to 8. The AOA were detected by qPCR in all soils (ca. 105 to 106 copies archaeal amoA g−1 soil), but AOB levels were low and bacterial amoA was infrequently detected. AOA abundance showed a significant negative correlation (p<0.001) with levels of soil organic carbon, clay and ammonium, but was not correlated to pH. Structures of AOA and AOB communities, as determined by amoA terminal restriction fragment (TRF) analysis, differed significantly between soils (p<0.001). Variation in AOA TRF profiles was best explained by ammonium-N and either Kjeldahl N or total N (p<0.001) while variation in AOB TRF profiles was best explained by phosphorus, bulk density and iron (p<0.01). In clone libraries, phylotypes of archaeal amoA (predominantly Nitrososphaera) and bacterial amoA (predominanatly Nitrosospira) differed between soils, but variation was not correlated with pH. Nitrification potential was positively correlated with clay content and pH (p<0.001), but not to AOA or AOB abundance or community structure. Collectively, the study showed that AOA and AOB communities were affected by differing sets of edaphic factors, notably that soil N characteristics were significant for AOA, but not AOB, and that pH was not a major driver for either community. Thus, the effect of pH on nitrification appeared to mainly reflect impacts on AOA or AOB activity, rather than selection for AOA or AOB phylotypes differing in nitrifying capacity. PMID:24586878

Designing long-term fish community assessments in connecting channels: Lessons from the Saint Marys River

USGS Publications Warehouse

Schaeffer, Jeff; Rogers, Mark W.; Fielder, David G.; Godby, Neal; Bowen, Anjanette K.; O'Connor, Lisa; Parrish, Josh; Greenwood, Susan; Chong, Stephen; Wright, Greg

2014-01-01

Long-term surveys are useful in understanding trends in connecting channel fish communities; a gill net assessment in the Saint Marys River performed periodically since 1975 is the most comprehensive connecting channels sampling program within the Laurentian Great Lakes. We assessed efficiency of that survey, with intent to inform development of assessments at other connecting channels. We evaluated trends in community composition, effort versus estimates of species richness, ability to detect abundance changes for four species, and effects of subsampling yellow perch catches on size and age-structure metrics. Efficiency analysis revealed low power to detect changes in species abundance, whereas reduced effort could be considered to index species richness. Subsampling simulations indicated that subsampling would have allowed reliable estimates of yellow perch (Perca flavescens) population structure, while greatly reducing the number of fish that were assigned ages. Analyses of statistical power and efficiency of current sampling protocols are useful for managers collecting and using these types of data as well as for the development of new monitoring programs. Our approach provides insight into whether survey goals and objectives were being attained and can help evaluate ability of surveys to answer novel questions that arise as management strategies are refined.

New sensors and techniques for the structural health monitoring of propulsion systems.

PubMed

Woike, Mark; Abdul-Aziz, Ali; Oza, Nikunj; Matthews, Bryan

2013-01-01

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA), through the Aviation Safety Program (AVSP), has taken a lead role in the development of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. This paper presents a summary of key results and findings obtained from three different structural health monitoring approaches that have been investigated. This includes evaluating the performance of a novel microwave blade tip clearance sensor; a vibration based crack detection technique using an externally mounted capacitive blade tip clearance sensor; and lastly the results of using data driven anomaly detection algorithms for detecting cracks in a rotating disk.

New Sensors and Techniques for the Structural Health Monitoring of Propulsion Systems

PubMed Central

2013-01-01

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA), through the Aviation Safety Program (AVSP), has taken a lead role in the development of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. This paper presents a summary of key results and findings obtained from three different structural health monitoring approaches that have been investigated. This includes evaluating the performance of a novel microwave blade tip clearance sensor; a vibration based crack detection technique using an externally mounted capacitive blade tip clearance sensor; and lastly the results of using data driven anomaly detection algorithms for detecting cracks in a rotating disk. PMID:23935425

Characterisation of the bacterial community structures in the intestine of Lampetra morii.

PubMed

Li, Yingying; Xie, Wenfang; Li, Qingwei

2016-07-01

The metagenomic analysis and 16S rDNA sequencing method were used to investigate the bacterial community in the intestines of Lampetra morii. The bacterial community structure in L. morii intestine was relatively simple. Eight different operational taxonomic units were observed. Chitinophagaceae_unclassified (26.5 %) and Aeromonas spp. (69.6 %) were detected as dominant members at the genus level. The non-dominant genera were as follows: Acinetobacter spp. (1.4 %), Candidatus Bacilloplasma (2.5 %), Enterobacteria spp. (1.5 %), Shewanella spp. (0.04 %), Vibrio spp. (0.09 %), and Yersinia spp. (1.8 %). The Shannon-Wiener (H) and Simpson (1-D) indexes were 0.782339 and 0.5546, respectively. The rarefaction curve representing the bacterial community richness and Shannon-Wiener curve representing the bacterial community diversity reached asymptote, which indicated that the sequence depth were sufficient to represent the majority of species richness and bacterial community diversity. The number of Aeromonas in lamprey intestine was two times higher after stimulation by lipopolysaccharide than PBS. This study provides data for understanding the bacterial community harboured in lamprey intestines and exploring potential key intestinal symbiotic bacteria essential for the L. morii immune response.

Correlations between Community Structure and Link Formation in Complex Networks

PubMed Central

Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

2013-01-01

Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

Simultaneous Amplicon Sequencing to Explore Co-Occurrence Patterns of Bacterial, Archaeal and Eukaryotic Microorganisms in Rumen Microbial Communities

PubMed Central

Kittelmann, Sandra; Seedorf, Henning; Walters, William A.; Clemente, Jose C.; Knight, Rob; Gordon, Jeffrey I.; Janssen, Peter H.

2013-01-01

Ruminants rely on a complex rumen microbial community to convert dietary plant material to energy-yielding products. Here we developed a method to simultaneously analyze the community's bacterial and archaeal 16S rRNA genes, ciliate 18S rRNA genes and anaerobic fungal internal transcribed spacer 1 genes using 12 DNA samples derived from 11 different rumen samples from three host species (Ovis aries, Bos taurus, Cervus elephas) and multiplex 454 Titanium pyrosequencing. We show that the mixing ratio of the group-specific DNA templates before emulsion PCR is crucial to compensate for differences in amplicon length. This method, in contrast to using a non-specific universal primer pair, avoids sequencing non-targeted DNA, such as plant- or endophyte-derived rRNA genes, and allows increased or decreased levels of community structure resolution for each microbial group as needed. Communities analyzed with different primers always grouped by sample origin rather than by the primers used. However, primer choice had a greater impact on apparent archaeal community structure than on bacterial community structure, and biases for certain methanogen groups were detected. Co-occurrence analysis of microbial taxa from all three domains of life suggested strong within- and between-domain correlations between different groups of microorganisms within the rumen. The approach used to simultaneously characterize bacterial, archaeal and eukaryotic components of a microbiota should be applicable to other communities occupying diverse habitats. PMID:23408926

Simultaneous amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic microorganisms in rumen microbial communities.

PubMed

Kittelmann, Sandra; Seedorf, Henning; Walters, William A; Clemente, Jose C; Knight, Rob; Gordon, Jeffrey I; Janssen, Peter H

2013-01-01

Ruminants rely on a complex rumen microbial community to convert dietary plant material to energy-yielding products. Here we developed a method to simultaneously analyze the community's bacterial and archaeal 16S rRNA genes, ciliate 18S rRNA genes and anaerobic fungal internal transcribed spacer 1 genes using 12 DNA samples derived from 11 different rumen samples from three host species (Ovis aries, Bos taurus, Cervus elephas) and multiplex 454 Titanium pyrosequencing. We show that the mixing ratio of the group-specific DNA templates before emulsion PCR is crucial to compensate for differences in amplicon length. This method, in contrast to using a non-specific universal primer pair, avoids sequencing non-targeted DNA, such as plant- or endophyte-derived rRNA genes, and allows increased or decreased levels of community structure resolution for each microbial group as needed. Communities analyzed with different primers always grouped by sample origin rather than by the primers used. However, primer choice had a greater impact on apparent archaeal community structure than on bacterial community structure, and biases for certain methanogen groups were detected. Co-occurrence analysis of microbial taxa from all three domains of life suggested strong within- and between-domain correlations between different groups of microorganisms within the rumen. The approach used to simultaneously characterize bacterial, archaeal and eukaryotic components of a microbiota should be applicable to other communities occupying diverse habitats.

Uncovering the overlapping community structure of complex networks by maximal cliques

NASA Astrophysics Data System (ADS)

Li, Junqiu; Wang, Xingyuan; Cui, Yaozu

2014-12-01

In this paper, a unique algorithm is proposed to detect overlapping communities in the un-weighted and weighted networks with considerable accuracy. The maximal cliques, overlapping vertex, bridge vertex and isolated vertex are introduced. First, all the maximal cliques are extracted by the algorithm based on the deep and bread searching. Then two maximal cliques can be merged into a larger sub-graph by some given rules. In addition, the proposed algorithm successfully finds overlapping vertices and bridge vertices between communities. Experimental results using some real-world networks data show that the performance of the proposed algorithm is satisfactory.

Diversity of the Sediment Microbial Community in the Aha Watershed (Southwest China) in Response to Acid Mine Drainage Pollution Gradients.

PubMed

Sun, Weimin; Xiao, Tangfu; Sun, Min; Dong, Yiran; Ning, Zengping; Xiao, Enzong; Tang, Song; Li, Jiwei

2015-08-01

Located in southwest China, the Aha watershed is continually contaminated by acid mine drainage (AMD) produced from upstream abandoned coal mines. The watershed is fed by creeks with elevated concentrations of aqueous Fe (total Fe > 1 g/liter) and SO4 (2-) (>6 g/liter). AMD contamination gradually decreases throughout downstream rivers and reservoirs, creating an AMD pollution gradient which has led to a suite of biogeochemical processes along the watershed. In this study, sediment samples were collected along the AMD pollution sites for geochemical and microbial community analyses. High-throughput sequencing found various bacteria associated with microbial Fe and S cycling within the watershed and AMD-impacted creek. A large proportion of Fe- and S-metabolizing bacteria were detected in this watershed. The dominant Fe- and S-metabolizing bacteria were identified as microorganisms belonging to the genera Metallibacterium, Aciditerrimonas, Halomonas, Shewanella, Ferrovum, Alicyclobacillus, and Syntrophobacter. Among them, Halomonas, Aciditerrimonas, Metallibacterium, and Shewanella have previously only rarely been detected in AMD-contaminated environments. In addition, the microbial community structures changed along the watershed with different magnitudes of AMD pollution. Moreover, the canonical correspondence analysis suggested that temperature, pH, total Fe, sulfate, and redox potentials (Eh) were significant factors that structured the microbial community compositions along the Aha watershed. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Nekton community response to a large-scale Mississippi River discharge: Examining spatial and temporal response to river management

USGS Publications Warehouse

Piazza, Bryan P.; La Peyre, M.K.

2011-01-01

Freshwater flow is generally held to be one of the most influential factors affecting community structure and production in estuaries. In coastal Louisiana, the Caernarvon Freshwater Diversion (CFD) is managed to control freshwater discharge from the Mississippi River into Breton Sound basin. Operational since 1991, CFD has undergone several changes in management strategy including pulsed spring flooding, which was introduced in 2001. We used a 20-yr time series of fisheries-independent data to investigate how variation in freshwater inflow (i.e., pre- and post-CFD, and pre and post spring pulsing management) influences the downstream nekton community (abundance, diversity, and assemblage). Analyses of long-term data demonstrated that while there were effects from the CFD, they largely involved subtle changes in community structure. Spatially, effects were largely limited to the sites immediately downstream of the diversion and extended only occasionally to more down-estuary sites. Temporally, effects were 1) immediate (detected during spring diversion events) or 2) delayed (detected several months post-diversion). Analysis of river management found that pulsed spring-time inflow resulted in more significant changes in nekton assemblages, likely due to higher discharge rates that 1) increased marsh flooding, thus increasing marsh habitat accessibility for small resident marsh species, and 2) reduced salinity, possibly causing displacement of marine pelagic species down estuary. ?? 2010.

The structuring process of the macroparasite community of an experimental population of cichlasoma urophthalmus through time

PubMed

Vidal-Martinez; Kennedy; Aguirre-Macedo

1998-09-01

The structuring process of the macroparasite community of caged Cichlasoma urophthalmus was studied over time using sentinel fish. Three thousand uninfected cichlids were stocked in floating cages introduced into a quarry in which a wild population of the same species was present. Caged and wild cichlids were sampled monthly over 6 and 7 months, respectively. Seventeen macroparasite species were found in the wild C. urophthalmus population, ten of which were detected in the caged population after 6 months. Early infections were by those species that were more frequent and abundant in the wild population, while helminths with a low prevalence and abundance in the wild appeared later in the caged fish population. The results suggested that the structuring process of the macroparasite community of caged C. urophthalmus followed a predictable pattern, in which those species that were most frequent and abundant in the wild were the first to establish in sentinel fish.

Gut microbial communities of American pikas (Ochotona princeps): Evidence for phylosymbiosis and adaptations to novel diets.

PubMed

Kohl, Kevin D; Varner, Johanna; Wilkening, Jennifer L; Dearing, M Denise

2018-03-01

Gut microbial communities provide many physiological functions to their hosts, especially in herbivorous animals. We still lack an understanding of how these microbial communities are structured across hosts in nature, especially within a given host species. Studies on laboratory mice have demonstrated that host genetics can influence microbial community structure, but that diet can overwhelm these genetic effects. We aimed to test these ideas in a natural system, the American pika (Ochotona princeps). First, pikas are high-elevation specialists with significant population structure across various mountain ranges in the USA, allowing us to investigate whether similarities in microbial communities match host genetic differences. Additionally, pikas are herbivorous, with some populations exhibiting remarkable dietary plasticity and consuming high levels of moss, which is exceptionally high in fibre and low in protein. This allows us to investigate adaptations to an herbivorous diet, as well as to the especially challenging diet of moss. Here, we inventoried the microbial communities of pika caecal pellets from various populations using 16S rRNA sequencing to investigate structuring of microbial communities across various populations with different natural diets. Microbial communities varied significantly across populations, and differences in microbial community structure were congruent with genetic differences in host population structure, a pattern known as "phylosymbiosis." Several microbial members (Ruminococcus, Prevotella, Oxalobacter and Coprococcus) were detected across all samples, and thus likely represent a "core microbiome." These genera are known to perform a number of services for herbivorous hosts such as fibre fermentation and the degradation of plant defensive compounds, and thus are likely important for herbivory in pikas. Moreover, pikas that feed on moss harboured microbial communities highly enriched in Melainabacteria. This uncultivable candidate phylum has been proposed to ferment fibre for herbivores, and thus may contribute to the ability of some pika populations to consume high amounts of moss. These findings demonstrate that both host genetics and diet can influence the microbial communities of the American pika. These animals may be novel sources of fibre-degrading microbes. Last, we discuss the implications of population-specific microbial communities for conservation efforts in this species. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Habitat heterogeneity and associated microbial community structure in a small-scale floodplain hyporheic flow path.

PubMed

Lowell, Jennifer L; Gordon, Nathan; Engstrom, Dale; Stanford, Jack A; Holben, William E; Gannon, James E

2009-10-01

The Nyack floodplain is located on the Middle Fork of the Flathead River, an unregulated, pristine, fifth-order stream in Montana, USA, bordering Glacier National Park. The hyporheic zone is a nutritionally heterogeneous floodplain component harboring a diverse array of microbial assemblages essential in fluvial biogeochemical cycling, riverine ecosystem productivity, and trophic interactions. Despite these functions, microbial community structure in pristine hyporheic systems is not well characterized. The current study was designed to assess whether physical habitat heterogeneity within the hyporheic zone of the Nyack floodplain was sufficient to drive bacterial beta diversity between three different hyporheic flow path locations. Habitat heterogeneity was assessed by measuring soluble reactive phosphorous, nitrate, dissolved organic carbon, dissolved oxygen, and soluble total nitrogen levels seasonally at surface water infiltration, advection, and exfiltration zones. Significant spatial differences were detected in dissolved oxygen and nitrate levels, and seasonal differences were detected in dissolved oxygen, nitrate, and dissolved organic carbon levels. Denaturing gradient gel electrophoresis (DGGE) and cell counts indicated that bacterial diversity increased with abundance, and DGGE fingerprints covaried with nitrate levels where water infiltrated the hyporheic zone. The ribosomal gene phylogeny revealed that hyporheic habitat heterogeneity was sufficient to drive beta diversity between bacterial assemblages. Phylogenetic (P) tests detected sequence disparity between the flow path locations. Small distinct lineages of Firmicutes, Actinomycetes, Planctomycetes, and Acidobacteria defined the infiltration zone and alpha- and beta-proteobacterial lineages delineated the exfiltration and advection zone communities. These data suggest that spatial habitat heterogeneity drives hyporheic microbial community development and that attempts to understand functional differences between bacteria inhabiting nutritionally heterogeneous hyporheic environments might begin by focusing on the biology of these taxa.

Community structures of fecal bacteria in cattle from different animal feeding operations

EPA Science Inventory

The fecal microbiome of cattle plays a critical role not only in animal health and productivity, but also in methane emissions, food safety, pathogen shedding, and the performance of fecal pollution detection methods. Unfortunately, most published molecular surveys fail to provid...

Diversity and Spatial Structure of Belowground Plant–Fungal Symbiosis in a Mixed Subtropical Forest of Ectomycorrhizal and Arbuscular Mycorrhizal Plants

PubMed Central

Toju, Hirokazu; Sato, Hirotoshi; Tanabe, Akifumi S.

2014-01-01

Plant–mycorrhizal fungal interactions are ubiquitous in forest ecosystems. While ectomycorrhizal plants and their fungi generally dominate temperate forests, arbuscular mycorrhizal symbiosis is common in the tropics. In subtropical regions, however, ectomycorrhizal and arbuscular mycorrhizal plants co-occur at comparable abundances in single forests, presumably generating complex community structures of root-associated fungi. To reveal root-associated fungal community structure in a mixed forest of ectomycorrhizal and arbuscular mycorrhizal plants, we conducted a massively-parallel pyrosequencing analysis, targeting fungi in the roots of 36 plant species that co-occur in a subtropical forest. In total, 580 fungal operational taxonomic units were detected, of which 132 and 58 were probably ectomycorrhizal and arbuscular mycorrhizal, respectively. As expected, the composition of fungal symbionts differed between fagaceous (ectomycorrhizal) and non-fagaceous (possibly arbuscular mycorrhizal) plants. However, non-fagaceous plants were associated with not only arbuscular mycorrhizal fungi but also several clades of ectomycorrhizal (e.g., Russula) and root-endophytic ascomycete fungi. Many of the ectomycorrhizal and root-endophytic fungi were detected from both fagaceous and non-fagaceous plants in the community. Interestingly, ectomycorrhizal and arbuscular mycorrhizal fungi were concurrently detected from tiny root fragments of non-fagaceous plants. The plant–fungal associations in the forest were spatially structured, and non-fagaceous plant roots hosted ectomycorrhizal fungi more often in the proximity of ectomycorrhizal plant roots. Overall, this study suggests that belowground plant–fungal symbiosis in subtropical forests is complex in that it includes “non-typical” plant–fungal combinations (e.g., ectomycorrhizal fungi on possibly arbuscular mycorrhizal plants) that do not fall within the conventional classification of mycorrhizal symbioses, and in that associations with multiple functional (or phylogenetic) groups of fungi are ubiquitous among plants. Moreover, ectomycorrhizal fungal symbionts of fagaceous plants may “invade” the roots of neighboring non-fagaceous plants, potentially influencing the interactions between non-fagaceous plants and their arbuscular-mycorrhizal fungal symbionts at a fine spatial scale. PMID:24489745

Diversity and spatial structure of belowground plant-fungal symbiosis in a mixed subtropical forest of ectomycorrhizal and arbuscular mycorrhizal plants.

PubMed

Toju, Hirokazu; Sato, Hirotoshi; Tanabe, Akifumi S

2014-01-01

Plant-mycorrhizal fungal interactions are ubiquitous in forest ecosystems. While ectomycorrhizal plants and their fungi generally dominate temperate forests, arbuscular mycorrhizal symbiosis is common in the tropics. In subtropical regions, however, ectomycorrhizal and arbuscular mycorrhizal plants co-occur at comparable abundances in single forests, presumably generating complex community structures of root-associated fungi. To reveal root-associated fungal community structure in a mixed forest of ectomycorrhizal and arbuscular mycorrhizal plants, we conducted a massively-parallel pyrosequencing analysis, targeting fungi in the roots of 36 plant species that co-occur in a subtropical forest. In total, 580 fungal operational taxonomic units were detected, of which 132 and 58 were probably ectomycorrhizal and arbuscular mycorrhizal, respectively. As expected, the composition of fungal symbionts differed between fagaceous (ectomycorrhizal) and non-fagaceous (possibly arbuscular mycorrhizal) plants. However, non-fagaceous plants were associated with not only arbuscular mycorrhizal fungi but also several clades of ectomycorrhizal (e.g., Russula) and root-endophytic ascomycete fungi. Many of the ectomycorrhizal and root-endophytic fungi were detected from both fagaceous and non-fagaceous plants in the community. Interestingly, ectomycorrhizal and arbuscular mycorrhizal fungi were concurrently detected from tiny root fragments of non-fagaceous plants. The plant-fungal associations in the forest were spatially structured, and non-fagaceous plant roots hosted ectomycorrhizal fungi more often in the proximity of ectomycorrhizal plant roots. Overall, this study suggests that belowground plant-fungal symbiosis in subtropical forests is complex in that it includes "non-typical" plant-fungal combinations (e.g., ectomycorrhizal fungi on possibly arbuscular mycorrhizal plants) that do not fall within the conventional classification of mycorrhizal symbioses, and in that associations with multiple functional (or phylogenetic) groups of fungi are ubiquitous among plants. Moreover, ectomycorrhizal fungal symbionts of fagaceous plants may "invade" the roots of neighboring non-fagaceous plants, potentially influencing the interactions between non-fagaceous plants and their arbuscular-mycorrhizal fungal symbionts at a fine spatial scale.

Structure Computation of Quiet Spike[Trademark] Flight-Test Data During Envelope Expansion

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

System identification or mathematical modeling is used in the aerospace community for development of simulation models for robust control law design. These models are often described as linear time-invariant processes. Nevertheless, it is well known that the underlying process is often nonlinear. The reason for using a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades, the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B Quiet Spike(TradeMark) aeroservoelastic flight-test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description that may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance for the development of robust parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion, which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B Quiet Spike aeroservoelastic flight-test data for several flight conditions that 1) linear models are inefficient for modeling aeroservoelastic data, 2) nonlinear identification provides a parsimonious model description while providing a high percent fit for cross-validated data, and 3) the model structure and parameters vary as the flight condition is altered.

Bacterial community structures in air conditioners installed in Japanese residential buildings.

PubMed

Hatayama, Kouta; Oikawa, Yurika; Ito, Hiroyuki

2018-01-01

The bacterial community structures in four Japanese split-type air conditioners were analyzed using a next-generation sequencer. A variety of bacteria were detected in the air filter of an air conditioner installed on the first floor. In the evaporator of this air conditioner, bacteria belonging to the genus Methylobacterium, or the family of Sphingomonadaceae, were predominantly detected. On the other hand, the majority of bacteria detected in the air filters and evaporators of air conditioners installed on the fifth and twelfth floors belonged to the family Enterobacteriaceae. The source of bacteria belonging to the family Enterobacteriaceae may have been aerosols generated by toilet flushing in the buildings. Our results suggested the possibility that the bacterial contamination in the air conditioners was affected by the floor level on which they were installed. The air conditioner installed on the lower floor, near the ground, may have been contaminated by a variety of outdoor bacteria, whereas the air conditioners installed on floors more distant from the ground may have been less contaminated by outdoor bacteria. However, these suppositions may apply only to the specific split-type air conditioners that we analyzed, because our sample size was small.

Assessment of Overlap of Phylogenetic Transmission Clusters and Communities in Simple Sexual Contact Networks: Applications to HIV-1

PubMed Central

Villandre, Luc; Günthard, Huldrych F.; Kouyos, Roger; Stadler, Tanja

2016-01-01

Background Transmission patterns of sexually-transmitted infections (STIs) could relate to the structure of the underlying sexual contact network, whose features are therefore of interest to clinicians. Conventionally, we represent sexual contacts in a population with a graph, that can reveal the existence of communities. Phylogenetic methods help infer the history of an epidemic and incidentally, may help detecting communities. In particular, phylogenetic analyses of HIV-1 epidemics among men who have sex with men (MSM) have revealed the existence of large transmission clusters, possibly resulting from within-community transmissions. Past studies have explored the association between contact networks and phylogenies, including transmission clusters, producing conflicting conclusions about whether network features significantly affect observed transmission history. As far as we know however, none of them thoroughly investigated the role of communities, defined with respect to the network graph, in the observation of clusters. Methods The present study investigates, through simulations, community detection from phylogenies. We simulate a large number of epidemics over both unweighted and weighted, undirected random interconnected-islands networks, with islands corresponding to communities. We use weighting to modulate distance between islands. We translate each epidemic into a phylogeny, that lets us partition our samples of infected subjects into transmission clusters, based on several common definitions from the literature. We measure similarity between subjects’ island membership indices and transmission cluster membership indices with the adjusted Rand index. Results and Conclusion Analyses reveal modest mean correspondence between communities in graphs and phylogenetic transmission clusters. We conclude that common methods often have limited success in detecting contact network communities from phylogenies. The rarely-fulfilled requirement that network communities correspond to clades in the phylogeny is their main drawback. Understanding the link between transmission clusters and communities in sexual contact networks could help inform policymaking to curb HIV incidence in MSMs. PMID:26863322

Environmental Control on Fish and Macrocrustacean Spring Community-Structure, on an Intertidal Sandy Beach

PubMed Central

Benazza, Achwak; Selleslagh, Jonathan; Breton, Elsa; Rabhi, Khalef; Cornille, Vincent; Bacha, Mahmoud; Lecuyer, Eric; Amara, Rachid

2015-01-01

The inter-annual variability of the fish and macrocrustacean spring community on an intertidal sandy beach near the Canche estuary (North of France) was studied from 2000 to 2013 based on weekly spring sampling over an 11-year period. Twenty-eight species representing 21 families were collected during the course of the study. The community was dominated by a few abundant species accounting for > 99% of the total species densities. Most individuals caught were young-of-the-year indicating the importance of this ecosystem for juvenile fishes and macrocrustaceans. Although standard qualitative community ecology metrics (species composition, richness, diversity, evenness and similarity) indicated notable stability over the study period, community structure showed a clear change since 2009. Densities of P. platessa, P. microps and A. tobianus decreased significantly since 2009, whereas over the period 2010-2013, the contribution of S. sprattus to total species density increased 4-fold. Co-inertia and generalised linear model analyses identified winter NAO index, water temperature, salinity and suspended particular matter as the major environmental factors explaining these changes. Although the recurrent and dense spring blooms of the Prymnesiophyte Phaeocystis globosa is one of the main potential threats in shallow waters of the eastern English Channel, no negative impact of its temporal change was detected on the fish and macrocrustacean spring community structure. PMID:25617852

Microbial community structure and function in sediments from e-waste contaminated rivers at Guiyu area of China.

PubMed

Liu, Jun; Chen, Xi; Shu, Hao-Yue; Lin, Xue-Rui; Zhou, Qi-Xing; Bramryd, Torleif; Shu, Wen-Sheng; Huang, Li-Nan

2018-04-01

The release of toxic organic pollutants and heavy metals by primitive electronic waste (e-waste) processing to waterways has raised significant concerns, but little is known about their potential ecological effects on aquatic biota especially microorganisms. We characterized the microbial community composition and diversity in sediments sampled along two rivers consistently polluted by e-waste, and explored how community functions may respond to the complex combined pollution. High-throughput 16S rRNA gene sequencing showed that Proteobacteria (particularly Deltaproteobacteria) dominated the sediment microbial assemblages followed by Bacteroidetes, Acidobacteria, Chloroflexi and Firmicutes. PICRUSt metagenome inference provided an initial insight into the metabolic potentials of these e-waste affected communities, speculating that organic pollutants degradation in the sediment might be mainly performed by some of the dominant genera (such as Sulfuricurvum, Thiobacillus and Burkholderia) detected in situ. Statistical analyses revealed that toxic organic compounds contributed more to the observed variations in sediment microbial community structure and predicted functions (24.68% and 8.89%, respectively) than heavy metals (12.18% and 4.68%), and Benzo(a)pyrene, bioavailable lead and electrical conductivity were the key contributors. These results have shed light on the microbial assemblages in e-waste contaminated river sediments, indicating a potential influence of e-waste pollution on the microbial community structure and function in aquatic ecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

nirS-Encoding denitrifier community composition, distribution, and abundance along the coastal wetlands of China.

PubMed

Gao, Juan; Hou, Lijun; Zheng, Yanling; Liu, Min; Yin, Guoyu; Li, Xiaofei; Lin, Xianbiao; Yu, Chendi; Wang, Rong; Jiang, Xiaofen; Sun, Xiuru

2016-10-01

For the past few decades, human activities have intensively increased the reactive nitrogen enrichment in China's coastal wetlands. Although denitrification is a critical pathway of nitrogen removal, the understanding of denitrifier community dynamics driving denitrification remains limited in the coastal wetlands. In this study, the diversity, abundance, and community composition of nirS-encoding denitrifiers were analyzed to reveal their variations in China's coastal wetlands. Diverse nirS sequences were obtained and more than 98 % of them shared considerable phylogenetic similarity with sequences obtained from aquatic systems (marine/estuarine/coastal sediments and hypoxia sea water). Clone library analysis revealed that the distribution and composition of nirS-harboring denitrifiers had a significant latitudinal differentiation, but without a seasonal shift. Canonical correspondence analysis showed that the community structure of nirS-encoding denitrifiers was significantly related to temperature and ammonium concentration. The nirS gene abundance ranged from 4.3 × 10(5) to 3.7 × 10(7) copies g(-1) dry sediment, with a significant spatial heterogeneity. Among all detected environmental factors, temperature was a key factor affecting not only the nirS gene abundance but also the community structure of nirS-type denitrifiers. Overall, this study significantly enhances our understanding of the structure and dynamics of denitrifying communities in the coastal wetlands of China.

Microbial community analysis of field-grown soybeans with different nodulation phenotypes.

PubMed

Ikeda, Seishi; Rallos, Lynn Esther E; Okubo, Takashi; Eda, Shima; Inaba, Shoko; Mitsui, Hisayuki; Minamisawa, Kiwamu

2008-09-01

Microorganisms associated with the stems and roots of nonnodulated (Nod(-)), wild-type nodulated (Nod(+)), and hypernodulated (Nod(++)) soybeans [Glycine max (L.) Merril] were analyzed by ribosomal intergenic transcribed spacer analysis (RISA) and automated RISA (ARISA). RISA of stem samples detected no bands specific to the nodulation phenotype, whereas RISA of root samples revealed differential bands for the nodulation phenotypes. Pseudomonas fluorescens was exclusively associated with Nod(+) soybean roots. Fusarium solani was stably associated with nodulated (Nod(+) and Nod(++)) roots and less abundant in Nod(-) soybeans, whereas the abundance of basidiomycetes was just the opposite. The phylogenetic analyses suggested that these basidiomycetous fungi might represent a root-associated group in the Auriculariales. Principal-component analysis of the ARISA results showed that there was no clear relationship between nodulation phenotype and bacterial community structure in the stem. In contrast, both the bacterial and fungal community structures in the roots were related to nodulation phenotype. The principal-component analysis further suggested that bacterial community structure in roots could be classified into three groups according to the nodulation phenotype (Nod(-), Nod(+), or Nod(++)). The analysis of root samples indicated that the microbial community in Nod(-) soybeans was more similar to that in Nod(++) soybeans than to that in Nod(+) soybeans.

Microbial Community Analysis of Field-Grown Soybeans with Different Nodulation Phenotypes▿

PubMed Central

Ikeda, Seishi; Rallos, Lynn Esther E.; Okubo, Takashi; Eda, Shima; Inaba, Shoko; Mitsui, Hisayuki; Minamisawa, Kiwamu

2008-01-01

Microorganisms associated with the stems and roots of nonnodulated (Nod−), wild-type nodulated (Nod+), and hypernodulated (Nod++) soybeans [Glycine max (L.) Merril] were analyzed by ribosomal intergenic transcribed spacer analysis (RISA) and automated RISA (ARISA). RISA of stem samples detected no bands specific to the nodulation phenotype, whereas RISA of root samples revealed differential bands for the nodulation phenotypes. Pseudomonas fluorescens was exclusively associated with Nod+ soybean roots. Fusarium solani was stably associated with nodulated (Nod+ and Nod++) roots and less abundant in Nod− soybeans, whereas the abundance of basidiomycetes was just the opposite. The phylogenetic analyses suggested that these basidiomycetous fungi might represent a root-associated group in the Auriculariales. Principal-component analysis of the ARISA results showed that there was no clear relationship between nodulation phenotype and bacterial community structure in the stem. In contrast, both the bacterial and fungal community structures in the roots were related to nodulation phenotype. The principal-component analysis further suggested that bacterial community structure in roots could be classified into three groups according to the nodulation phenotype (Nod−, Nod+, or Nod++). The analysis of root samples indicated that the microbial community in Nod− soybeans was more similar to that in Nod++ soybeans than to that in Nod+ soybeans. PMID:18658280

Tidal Energy: The benthic effects of an operational tidal stream turbine.

PubMed

O'Carroll, J P J; Kennedy, R M; Creech, A; Savidge, G

2017-08-01

The effect of modified flow on epifaunal boulder reef communities adjacent to the SeaGen, the world's first grid-compliant tidal stream turbine, were assessed. The wake of the SeaGen was modelled and the outputs were used in conjunction with positional and substrate descriptor variables, to relate variation in epifaunal community structure to the modified physical environment. An Artificial Neural Network (ANN) and Generalised Linear Model (GLM) were used to make predictions on the distribution of Ecological Status (ES) of epifaunal communities in relation to the turbulent wake of the SeaGen. ES was assigned using the High Energy Hard Substrate (HEHS) index. ES was largely High throughout the survey area and it was not possible to make predictions on the spatial distribution of ES using an ANN or GLM. Spatial pattern in epifaunal community structure was detected when the study area was partitioned into three treatment areas: area D1; within one rotor diameter (16 m) of the centre of SeaGen, area D2; between one and three rotor diameters, and area D3; outside of three rotor diameters. Area D1 was found to be significantly more variable than D2 and D3 in terms of epifaunal community structure, bare rock distributions and ES. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating carnivore community structures

PubMed Central

Jiménez, José; Nuñez-Arjona, Juan Carlos; Rueda, Carmen; González, Luis Mariano; García-Domínguez, Francisco; Muñoz-Igualada, Jaime; López-Bao, José Vicente

2017-01-01

Obtaining reliable estimates of the structure of carnivore communities is of paramount importance because of their ecological roles, ecosystem services and impact on biodiversity conservation, but they are still scarce. This information is key for carnivore management: to build support for and acceptance of management decisions and policies it is crucial that those decisions are based on robust and high quality information. Here, we combined camera and live-trapping surveys, as well as telemetry data, with spatially-explicit Bayesian models to show the usefulness of an integrated multi-method and multi-model approach to monitor carnivore community structures. Our methods account for imperfect detection and effectively deal with species with non-recognizable individuals. In our Mediterranean study system, the terrestrial carnivore community was dominated by red foxes (0.410 individuals/km2); Egyptian mongooses, feral cats and stone martens were similarly abundant (0.252, 0.249 and 0.240 individuals/km2, respectively), whereas badgers and common genets were the least common (0.130 and 0.087 individuals/km2, respectively). The precision of density estimates improved by incorporating multiple covariates, device operation, and accounting for the removal of individuals. The approach presented here has substantial implications for decision-making since it allows, for instance, the evaluation, in a standard and comparable way, of community responses to interventions. PMID:28120871

Pyrosequencing reveals bacterial community differences in composting and vermicomposting on the stabilization of mixed sewage sludge and cattle dung.

PubMed

Lv, Baoyi; Xing, Meiyan; Yang, Jian; Zhang, Liangbo

2015-12-01

This study aimed to compare the microbial community structures and compositions in composting and vermicomposting processes. We applied 454 high-throughput pyrosequencing to analyze the 16S rRNA gene of bacteria obtained from bio-stabilization of sewage sludge and cattle dung. Results demonstrated that vermicomposting process presented higher operational taxonomic units and bacterial diversity than the composting. Analysis using weighted UniFrac indicated that composting exhibited higher effects on shaping microbial community structure than the vermicomposting. The succession of dominant bacteria was also detected during composting. Firmicutes was the dominant bacteria in the thermophilic phase of composting and shifted to Actinomycetes in the maturing stage. By contrast, Proteobacteria accounted for the highest proportions in the whole process of the vermicomposting. Furthermore, vermicomposting contained more uncultured and unidentified bacteria at the taxonomy level of genus than the composting. In summary, the bacterial community during composting significantly differed from that during vermicomposting. These two techniques played different roles in changing the diversity and composition of microbial communities.

Streptomycin Application Has No Detectable Effect on Bacterial Community Structure in Apple Orchard Soil

PubMed Central

Shade, Ashley; Klimowicz, Amy K.; Spear, Russell N.; Linske, Matthew; Donato, Justin J.; Hogan, Clifford S.; McManus, Patricia S.

2013-01-01

Streptomycin is commonly used to control fire blight disease on apple trees. Although the practice has incited controversy, little is known about its nontarget effects in the environment. We investigated the impact of aerial application of streptomycin on nontarget bacterial communities in soil beneath streptomycin-treated and untreated trees in a commercial apple orchard. Soil samples were collected in two consecutive years at 4 or 10 days before spraying streptomycin and 8 or 9 days after the final spray. Three sources of microbial DNA were profiled using tag-pyrosequencing of 16S rRNA genes: uncultured bacteria from the soil (culture independent) and bacteria cultured on unamended or streptomycin-amended (15 μg/ml) media. Multivariate tests for differences in community structure, Shannon diversity, and Pielou's evenness test results showed no evidence of community response to streptomycin. The results indicate that use of streptomycin for disease management has minimal, if any, immediate effect on apple orchard soil bacterial communities. This study contributes to the profile of an agroecosystem in which antibiotic use for disease prevention appears to have minimal consequences for nontarget bacteria. PMID:23974143

Effect of phenol on the nitrogen removal performance and microbial community structure and composition of an anammox reactor.

PubMed

Pereira, Alyne Duarte; Leal, Cíntia Dutra; Dias, Marcela França; Etchebehere, Claudia; Chernicharo, Carlos Augusto L; de Araújo, Juliana Calabria

2014-08-01

The effects of phenol on the nitrogen removal performance of a sequencing batch reactor (SBR) with anammox activity and on the microbial community within the reactor were evaluated. A phenol concentration of 300 mg L(-1) reduced the ammonium-nitrogen removal efficiency of the SBR from 96.5% to 47%. The addition of phenol changed the microbial community structure and composition considerably, as shown by denaturing gradient gel electrophoresis and 454 pyrosequencing of 16S rRNA genes. Some phyla, such as Proteobacteria, Verrucomicrobia, and Firmicutes, increased in abundance, whereas others, such as Acidobacteria, Chloroflexi, Planctomycetes, GN04, WS3, and NKB19, decreased. The diversity of the anammox bacteria was also affected by phenol: sequences related to Candidatus Brocadia fulgida were no longer detected, whereas sequences related to Ca. Brocadia sp. 40 and Ca. Jettenia asiatica persisted. These results indicate that phenol adversely affects anammox metabolism and changes the bacterial community within the anammox reactor. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Community Structure of the European Network of Interlocking Directorates 2005–2010

PubMed Central

Heemskerk, Eelke M.; Daolio, Fabio; Tomassini, Marco

2013-01-01

The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer. PMID:23894318

The community structure of the European network of interlocking directorates 2005-2010.

PubMed

Heemskerk, Eelke M; Daolio, Fabio; Tomassini, Marco

2013-01-01

The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer.

Metabolically active microbial communities in marine sediment under high-CO2 and low-pH extremes

PubMed Central

Yanagawa, Katsunori; Morono, Yuki; de Beer, Dirk; Haeckel, Matthias; Sunamura, Michinari; Futagami, Taiki; Hoshino, Tatsuhiko; Terada, Takeshi; Nakamura, Ko-ichi; Urabe, Tetsuro; Rehder, Gregor; Boetius, Antje; Inagaki, Fumio

2013-01-01

Sediment-hosting hydrothermal systems in the Okinawa Trough maintain a large amount of liquid, supercritical and hydrate phases of CO2 in the seabed. The emission of CO2 may critically impact the geochemical, geophysical and ecological characteristics of the deep-sea sedimentary environment. So far it remains unclear whether microbial communities that have been detected in such high-CO2 and low-pH habitats are metabolically active, and if so, what the biogeochemical and ecological consequences for the environment are. In this study, RNA-based molecular approaches and radioactive tracer-based respiration rate assays were combined to study the density, diversity and metabolic activity of microbial communities in CO2-seep sediment at the Yonaguni Knoll IV hydrothermal field of the southern Okinawa Trough. In general, the number of microbes decreased sharply with increasing sediment depth and CO2 concentration. Phylogenetic analyses of community structure using reverse-transcribed 16S ribosomal RNA showed that the active microbial community became less diverse with increasing sediment depth and CO2 concentration, indicating that microbial activity and community structure are sensitive to CO2 venting. Analyses of RNA-based pyrosequences and catalyzed reporter deposition-fluorescence in situ hybridization data revealed that members of the SEEP-SRB2 group within the Deltaproteobacteria and anaerobic methanotrophic archaea (ANME-2a and -2c) were confined to the top seafloor, and active archaea were not detected in deeper sediments (13–30 cm in depth) characterized by high CO2. Measurement of the potential sulfate reduction rate at pH conditions of 3–9 with and without methane in the headspace indicated that acidophilic sulfate reduction possibly occurs in the presence of methane, even at very low pH of 3. These results suggest that some members of the anaerobic methanotrophs and sulfate reducers can adapt to the CO2-seep sedimentary environment; however, CO2 and pH in the deep-sea sediment were found to severely impact the activity and structure of the microbial community. PMID:23096400

Selected water-quality and biological characteristics of streams in some forested basins of North Carolina, 1985-88

USGS Publications Warehouse

Caldwell, W.S.

1992-01-01

Selected physical, chemical and biological components of streams draining undeveloped, forested basins in North Carolina were characterized on the basis of samples collected at nine sites on streams in basins that ranged in size from 0.67 to 11.2 sq mi. Water analysis included specific conductance, dissolved oxygen, water temperature, suspended sediment, pH, major dissolved constituents, nutrients, minor constituents, organochlorine insecticides, and biochemical oxygen demand. Biological characteristics included fish tissue analysis for minor constituents and synthetic organic compounds, fish community structure, and benthic macroinvertebrates. Precipitation is the source of 10 to 40% of the chloride concentration and 20 to 30% of the sulfate concentration in stormflow. Mean total nitrogen concentrations ranged from 0.16 mg/L during low-flow conditions to 1.2 mg/L during stormflow. Organic nitrogen was 60 to 85% of the total nitrogen concentration. Stream water was free of organochlorine insecticides. DDD, DDE, DDT, Lindane, and Mirex were detected in 18 of 60 samples of streambed material. About 35% of fish tissue analyses showed detectable concentrations of copper, lead, mercury and nickel. Synthetic organic chemicals were not detected in fish tissue. Fish community structure data were rated using Karr's Index of Biotic Integrity. Streams rated poor to good because of natural stresses on fish communities. Five streams in the Piedmont and mountains received excellent bioclassification ratings based on benthic macroinvertebrtate data. Two streams in the Coastal Plain rated good to fair because of natural stresses.

Phylogenetic analysis of TCE-dechlorinating consortia enriched on a variety of electron donors.

PubMed

Freeborn, Ryan A; West, Kimberlee A; Bhupathiraju, Vishvesh K; Chauhan, Sadhana; Rahm, Brian G; Richardson, Ruth E; Alvarez-Cohen, Lisa

2005-11-01

Two rapidly fermented electron donors, lactate and methanol, and two slowly fermented electron donors, propionate and butyrate, were selected for enrichment studies to evaluate the characteristics of anaerobic microbial consortia that reductively dechlorinate TCE to ethene. Each electron donor enrichment subculture demonstrated the ability to dechlorinate TCE to ethene through several serial transfers. Microbial community analyses based upon 16S rDNA, including terminal restriction fragment length polymorphism (T-RFLP) and clone library/sequencing, were performed to assess major changes in microbial community structure associated with electron donors capable of stimulating reductive dechlorination. Results demonstrated that five phylogenic subgroups or genera of bacteria were present in all consortia, including Dehalococcoides sp., low G+C Gram-positives (mostly Clostridium and Eubacterium sp.), Bacteroides sp., Citrobacter sp., and delta Proteobacteria (mostly Desulfovibrio sp.). Phylogenetic association indicates that only minor shifts in the microbial community structure occurred between the four alternate electron donor enrichments and the parent consortium. Inconsistent detection of Dehalococcoides spp. in clone libraries and T-RFLP of enrichment subcultures was resolved using quantitative polymerase chain reaction (Q-PCR). Q-PCR with primers specific to Dehalococcoides 16S rDNA resulted in positive detection of this species in all enrichments. Our results suggest that TCE-dechlorinating consortia can be stably maintained on a variety of electron donors and that quantities of Dehalococcoides cells detected with Dehalococcoides specific 16S rDNA primer/probe sets do not necessarily correlate well with solvent degradation rates.

Geographical variation in soil bacterial community structure in tropical forests in Southeast Asia and temperate forests in Japan based on pyrosequencing analysis of 16S rRNA.

PubMed

Ito, Natsumi; Iwanaga, Hiroko; Charles, Suliana; Diway, Bibian; Sabang, John; Chong, Lucy; Nanami, Satoshi; Kamiya, Koichi; Lum, Shawn; Siregar, Ulfah J; Harada, Ko; Miyashita, Naohiko T

2017-09-12

Geographical variation in soil bacterial community structure in 26 tropical forests in Southeast Asia (Malaysia, Indonesia and Singapore) and two temperate forests in Japan was investigated to elucidate the environmental factors and mechanisms that influence biogeography of soil bacterial diversity and composition. Despite substantial environmental differences, bacterial phyla were represented in similar proportions, with Acidobacteria and Proteobacteria the dominant phyla in all forests except one mangrove forest in Sarawak, although highly significant heterogeneity in frequency of individual phyla was detected among forests. In contrast, species diversity (α-diversity) differed to a much greater extent, being nearly six-fold higher in the mangrove forest (Chao1 index = 6,862) than in forests in Singapore and Sarawak (~1,250). In addition, natural mixed dipterocarp forests had lower species diversity than acacia and oil palm plantations, indicating that aboveground tree composition does not influence soil bacterial diversity. Shannon and Chao1 indices were correlated positively, implying that skewed operational taxonomic unit (OTU) distribution was associated with the abundance of overall and rare (singleton) OTUs. No OTUs were represented in all 28 forests, and forest-specific OTUs accounted for over 70% of all detected OTUs. Forests that were geographically adjacent and/or of the same forest type had similar bacterial species composition, and a positive correlation was detected between species divergence (β-diversity) and direct distance between forests. Both α- and β-diversities were correlated with soil pH. These results suggest that soil bacterial communities in different forests evolve largely independently of each other and that soil bacterial communities adapt to their local environment, modulated by bacterial dispersal (distance effect) and forest type. Therefore, we conclude that the biogeography of soil bacteria communities described here is non-random, reflecting the influences of contemporary environmental factors and evolutionary history.

Both sulfate-reducing bacteria and Enterobacteriaceae take part in marine biocorrosion of carbon steel.

PubMed

Bermont-Bouis, D; Janvier, M; Grimont, P A D; Dupont, I; Vallaeys, T

2007-01-01

In order to evaluate the part played in biocorrosion by microbial groups other than sulfate-reducing bacteria (SRB), we characterized the phylogenetic diversity of a corrosive marine biofilm attached to a harbour pile structure as well as to carbon steel surfaces (coupons) immersed in seawater for increasing time periods (1 and 8 months). We thus experimentally checked corroding abilities of defined species mixtures. Microbial community analysis was performed using both traditional cultivation techniques and polymerase chain reaction cloning-sequencing of 16S rRNA genes. Community structure of biofilms developing with time on immersed coupons tended to reach after 8 months, a steady state similar to the one observed on a harbour pile structure. Phylogenetic affiliations of isolates and cloned 16S rRNA genes (rrs) indicated that native biofilms (developing after 1-month immersion) were mainly colonized by gamma-proteobacteria. Among these, Vibrio species were detected in majority with molecular methods while cultivation techniques revealed dominance of Enterobacteriaceae such as Citrobacter, Klebsiella and Proteus species. Conversely, in mature biofilms (8-month immersion and pile structure), SRB, and to a lesser extent, spirochaetes were dominant. Corroding activity detection assays confirmed that Enterobacteriaceae (members of the gamma-proteobacteria) were involved in biocorrosion of metallic material in marine conditions. In marine biofilms, metal corrosion may be initiated by Enterobacteriaceae.

Influence of hydraulic regimes on bacterial community structure and composition in an experimental drinking water distribution system.

PubMed

Douterelo, I; Sharpe, R L; Boxall, J B

2013-02-01

Microbial biofilms formed on the inner-pipe surfaces of drinking water distribution systems (DWDS) can alter drinking water quality, particularly if they are mechanically detached from the pipe wall to the bulk water, such as due to changes in hydraulic conditions. Results are presented here from applying 454 pyrosequencing of the 16S ribosomal RNA (rRNA) gene to investigate the influence of different hydrological regimes on bacterial community structure and to study the potential mobilisation of material from the pipe walls to the network using a full scale, temperature-controlled experimental pipeline facility accurately representative of live DWDS. Analysis of pyrosequencing and water physico-chemical data showed that habitat type (water vs. biofilm) and hydraulic conditions influenced bacterial community structure and composition in our experimental DWDS. Bacterial community composition clearly differed between biofilms and bulk water samples. Gammaproteobacteria and Betaproteobacteria were the most abundant phyla in biofilms while Alphaproteobacteria was predominant in bulk water samples. This suggests that bacteria inhabiting biofilms, predominantly species belonging to genera Pseudomonas, Zooglea and Janthinobacterium, have an enhanced ability to express extracellular polymeric substances to adhere to surfaces and to favour co-aggregation between cells than those found in the bulk water. Highest species richness and diversity were detected in 28 days old biofilms with this being accentuated at highly varied flow conditions. Flushing altered the pipe-wall bacterial community structure but did not completely remove bacteria from the pipe walls, particularly under highly varied flow conditions, suggesting that under these conditions more compact biofilms were generated. This research brings new knowledge regarding the influence of different hydraulic regimes on the composition and structure of bacterial communities within DWDS and the implication that this might have on drinking water quality. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cecal bacterial communities in wild Japanese rock ptarmigans and captive Svalbard rock ptarmigans.

PubMed

Ushida, Kazunari; Segawa, Takahiro; Tsuchida, Sayaka; Murata, Koichi

2016-02-01

Preservation of indigenous gastrointestinal microbiota is deemed to be critical for successful captive breeding of endangered wild animals, yet its biology is poorly understood. Here, we investigated cecal bacterial communities in wild Japanese rock ptarmigans (Lagopus muta japonica) and compared them with those in Svalbard rock ptarmigans (L. m. hyperborea) in captivity. Ultra-deep sequencing of 16S rRNA gene indicated that the community structure of cecal microbiota in wild rock ptarmigans was remarkably different from that in captive Svalbard rock ptarmigans. Fundamental differences between bacterial communities in the two groups of birds were detected at the phylum level. Firmicutes, Actinobacteria, Bacteroidetes and Synergistetes were the major phyla detected in wild Japanese rock ptarmigans, whereas Firmicutes alone occupied more than 80% of abundance in captive Svalbard rock ptarmigans. Furthermore, unclassified genera of Coriobacteriaceae, Synergistaceae, Bacteroidaceae, Actinomycetaceae, Veillonellaceae and Clostridiales were the major taxa detected in wild individuals, whereas in zoo-reared birds, major genera were Ruminococcus, Blautia, Faecalibacterium and Akkermansia. Zoo-reared birds seemed to lack almost all rock ptarmigan-specific bacteria in their intestine, which may explain the relatively high rate of pathogenic infections affecting them. We show evidence that preservation and reconstitution of indigenous cecal microflora are critical for successful ex situ conservation and future re-introduction plan for the Japanese rock ptarmigan.

Relationships between Host Phylogeny, Host Type and Bacterial Community Diversity in Cold-Water Coral Reef Sponges

PubMed Central

Schöttner, Sandra; Hoffmann, Friederike; Cárdenas, Paco; Rapp, Hans Tore; Boetius, Antje; Ramette, Alban

2013-01-01

Cold-water coral reefs are known to locally enhance the diversity of deep-sea fauna as well as of microbes. Sponges are among the most diverse faunal groups in these ecosystems, and many of them host large abundances of microbes in their tissues. In this study, twelve sponge species from three cold-water coral reefs off Norway were investigated for the relationship between sponge phylogenetic classification (species and family level), as well as sponge type (high versus low microbial abundance), and the diversity of sponge-associated bacterial communities, taking also geographic location and water depth into account. Community analysis by Automated Ribosomal Intergenic Spacer Analysis (ARISA) showed that as many as 345 (79%) of the 437 different bacterial operational taxonomic units (OTUs) detected in the dataset were shared between sponges and sediments, while only 70 (16%) appeared purely sponge-associated. Furthermore, changes in bacterial community structure were significantly related to sponge species (63% of explained community variation), sponge family (52%) or sponge type (30%), whereas mesoscale geographic distances and water depth showed comparatively small effects (<5% each). In addition, a highly significant, positive relationship between bacterial community dissimilarity and sponge phylogenetic distance was observed within the ancient family of the Geodiidae. Overall, the high diversity of sponges in cold-water coral reefs, combined with the observed sponge-related variation in bacterial community structure, support the idea that sponges represent heterogeneous, yet structured microbial habitats that contribute significantly to enhancing bacterial diversity in deep-sea ecosystems. PMID:23393586

Seasonal changes in the sensitivity of river microalgae to atrazine and isoproturon along a contamination gradient.

PubMed

Dorigo, Ursula; Bourrain, Xavier; Bérard, Annette; Leboulanger, Christophe

2004-01-05

A study was undertaken to investigate the environmental impact of herbicides on natural communities of freshwater periphyton and phytoplankton in the river Ozanne and in related nearby water reservoirs, including both pristine and pesticide- (atrazine and isoproturon) contaminated stations. The microalgal toxicity of both herbicides was investigated by short-term studies, using the in vivo fluorescence pattern to perform dose-effect experiments. The taxonomic composition of the communities sampled was assessed by microscopy and by HPLC pigment analysis. The EC50 (periphyton) or EC125 (phytoplankton) values, calculated using in vivo fluorescence endpoints, increased with the herbicide concentration found in the water. In contrast, the structure of the algal communities (periphyton) inhabiting the contaminated stations seemed to be permanently affected when compared to the reference community. A 'memory effect' could be detected, both in herbicide sensitivity and in the structure of periphytic communities that persisted even when peak contaminations had disappeared. This study shows that the response of algal communities is likely to reflect past selection pressures, and suggests that the function and structure of a community could both be modified by the persistent or repeated presence of microcontaminants in natural environments. We could use short-term ecotoxicological tests on freshwater microalgae to assess the effects of past temporary contaminations by agricultural pesticides, and combining this with diversity indices could make it possible to assess the ecotoxicological risk of herbicide contamination even when a complete chemical analysis of the contamination is not feasible.

Spatiotemporal patterns in community structure of macroinvertebrates inhabiting calcareous periphyton mats

USGS Publications Warehouse

Liston, S.E.; Trexler, J.C.

2005-01-01

Calcareous floating periphyton mats in the southern Everglades provide habitat for a diverse macroinvertebrate community that has not been well characterized. Our study described this community in an oligotrophic marsh, compared it with the macroinvertebrate community associated with adjacent epiphytic algae attached to macrophytes in the water column, and detected spatial patterns in density and community structure. The floating periphyton mat (floating mat) and epiphytic algae in the water column (submerged epiphyton) were sampled at 4 sites (???1 km apart) in northern Shark River Slough, Everglades National Park (ENP), in the early (July) and late (November) wet season. Two perpendicular 90-m transects were established at each site and ???100 samples were taken in a nested design. Sites were located in wet-prairie spikerush-dominated sloughs with similar water depths and emergent macrophyte communities. Floating mats were sampled by taking cores (6-cm diameter) that were sorted under magnification to enumerate infauna retained on a 250-??m-mesh sieve and with a maximum dimension >1 mm. Our results showed that floating mats provide habitat for a macroinvertebrate community with higher densities (no. animals/g ash-free dry mass) of Hyalella azteca, Dasyhelea spp., and Cladocera, and lower densities of Chironomidae and Planorbella spp. than communities associated with submerged epiphyton. Densities of the most common taxa increased 3x to 15x from early to late wet season, and community differences between the 2 habitat types became more pronounced. Floating-mat coverage and estimated floating-mat biomass increased 20 to 30%, and 30 to 110%, respectively, at most sites in the late wet season. Some intersite variation was observed in individual taxa, but no consistent spatial pattern in any taxon was detected at any scale (from 0.2 m to 3 km). Floating mats and their resident macroinvertebrate communities are important components in the Everglades food web. This community should be included in environmental monitoring programs because degradation and eventual loss of the calcareous periphyton mat is associated with P enrichment in this ecosystem. ?? 2005 by The North American Benthological Society.

Microbial biogeography of arctic streams: exploring influences of lithology and habitat.

PubMed

Larouche, Julia R; Bowden, William B; Giordano, Rosanna; Flinn, Michael B; Crump, Byron C

2012-01-01

Terminal restriction fragment length polymorphism and 16S rRNA gene sequencing were used to explore the community composition of bacterial communities in biofilms on sediments (epipssamon) and rocks (epilithon) in stream reaches that drain watersheds with contrasting lithologies in the Noatak National Preserve, Alaska. Bacterial community composition varied primarily by stream habitat and secondarily by lithology. Positive correlations were detected between bacterial community structure and nutrients, base cations, and dissolved organic carbon. Our results showed significant differences at the stream habitat, between epipssamon and epilithon bacterial communities, which we expected. Our results also showed significant differences at the landscape scale that could be related to different lithologies and associated stream biogeochemistry. These results provide insight into the bacterial community composition of little known and pristine arctic stream ecosystems and illustrate how differences in the lithology, soils, and vegetation community of the terrestrial environment interact to influence stream bacterial taxonomic richness and composition.

Microbial Biogeography of Arctic Streams: Exploring Influences of Lithology and Habitat

PubMed Central

Larouche, Julia R.; Bowden, William B.; Giordano, Rosanna; Flinn, Michael B.; Crump, Byron C.

2012-01-01

Terminal restriction fragment length polymorphism and 16S rRNA gene sequencing were used to explore the community composition of bacterial communities in biofilms on sediments (epipssamon) and rocks (epilithon) in stream reaches that drain watersheds with contrasting lithologies in the Noatak National Preserve, Alaska. Bacterial community composition varied primarily by stream habitat and secondarily by lithology. Positive correlations were detected between bacterial community structure and nutrients, base cations, and dissolved organic carbon. Our results showed significant differences at the stream habitat, between epipssamon and epilithon bacterial communities, which we expected. Our results also showed significant differences at the landscape scale that could be related to different lithologies and associated stream biogeochemistry. These results provide insight into the bacterial community composition of little known and pristine arctic stream ecosystems and illustrate how differences in the lithology, soils, and vegetation community of the terrestrial environment interact to influence stream bacterial taxonomic richness and composition. PMID:22936932

Rapid Bioassessment Methods for Assessing Stream Macroinvertebrate Community on the Savannah River Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Specht, W.L.

Macroinvertebrate sampling was performed at 16 locations in the Savannah River Site (SRS) streams using Hester-Dendy multiplate samplers and EPA Rapid Bioassessment Protocols (RBP). Some of the sampling locations were unimpacted, while other locations had been subject to various forms of perturbation by SRS activities. In general, the data from the Hester-Dendy multiplate samplers were more sensitive at detecting impacts than were the RBP data. We developed a Biotic Index for the Hester-Dendy data which incorporated eight community structure, function, and balance parameters. when tested using a data set that was unrelated to the data set that was used inmore » developing the Biotic Index, the index was very successful at detecting impact.« less

Opportunistic Pathogens and Microbial Communities and Their Associations with Sediment Physical Parameters in Drinking Water Storage Tank Sediments.

PubMed

Qin, Ke; Struewing, Ian; Domingo, Jorge Santo; Lytle, Darren; Lu, Jingrang

2017-10-26

The occurrence and densities of opportunistic pathogens (OPs), the microbial community structure, and their associations with sediment elements from eight water storage tanks in Ohio, West Virginia, and Texas were investigated. The elemental composition of sediments was measured through X-ray fluorescence (XRF) spectra. The occurrence and densities of OPs and amoeba hosts (i.e., Legionella spp. and L . pneumophila , Mycobacterium spp., P. aeruginosa , V. vermiformis, Acanthamoeba spp.) were determined using genus- or species-specific qPCR assays. Microbial community analysis was performed using next generation sequencing on the Illumina Miseq platform. Mycobacterium spp. were most frequently detected in the sediments and water samples (88% and 88%), followed by Legionella spp. (50% and 50%), Acanthamoeba spp. (63% and 13%), V. vermiformis (50% and 25%), and P. aeruginosa (0 and 50%) by qPCR method. Comamonadaceae (22.8%), Sphingomonadaceae (10.3%), and Oxalobacteraceae (10.1%) were the most dominant families by sequencing method. Microbial communities in water samples were mostly separated with those in sediment samples, suggesting differences of communities between two matrices even in the same location. There were associations of OPs with microbial communities. Both OPs and microbial community structures were positively associated with some elements (Al and K) in sediments mainly from pipe material corrosions. Opportunistic pathogens presented in both water and sediments, and the latter could act as a reservoir of microbial contamination. There appears to be an association between potential opportunistic pathogens and microbial community structures. These microbial communities may be influenced by constituents within storage tank sediments. The results imply that compositions of microbial community and elements may influence and indicate microbial water quality and pipeline corrosion, and that these constituents may be important for optimal storage tank management within a distribution system.

Opportunistic Pathogens and Microbial Communities and Their Associations with Sediment Physical Parameters in Drinking Water Storage Tank Sediments

PubMed Central

Qin, Ke; Struewing, Ian; Domingo, Jorge Santo; Lytle, Darren

2017-01-01

The occurrence and densities of opportunistic pathogens (OPs), the microbial community structure, and their associations with sediment elements from eight water storage tanks in Ohio, West Virginia, and Texas were investigated. The elemental composition of sediments was measured through X-ray fluorescence (XRF) spectra. The occurrence and densities of OPs and amoeba hosts (i.e., Legionella spp. and L. pneumophila, Mycobacterium spp., P. aeruginosa, V. vermiformis, Acanthamoeba spp.) were determined using genus- or species-specific qPCR assays. Microbial community analysis was performed using next generation sequencing on the Illumina Miseq platform. Mycobacterium spp. were most frequently detected in the sediments and water samples (88% and 88%), followed by Legionella spp. (50% and 50%), Acanthamoeba spp. (63% and 13%), V. vermiformis (50% and 25%), and P. aeruginosa (0 and 50%) by qPCR method. Comamonadaceae (22.8%), Sphingomonadaceae (10.3%), and Oxalobacteraceae (10.1%) were the most dominant families by sequencing method. Microbial communities in water samples were mostly separated with those in sediment samples, suggesting differences of communities between two matrices even in the same location. There were associations of OPs with microbial communities. Both OPs and microbial community structures were positively associated with some elements (Al and K) in sediments mainly from pipe material corrosions. Opportunistic pathogens presented in both water and sediments, and the latter could act as a reservoir of microbial contamination. There appears to be an association between potential opportunistic pathogens and microbial community structures. These microbial communities may be influenced by constituents within storage tank sediments. The results imply that compositions of microbial community and elements may influence and indicate microbial water quality and pipeline corrosion, and that these constituents may be important for optimal storage tank management within a distribution system. PMID:29072631

Community core detection in transportation networks

NASA Astrophysics Data System (ADS)

De Leo, Vincenzo; Santoboni, Giovanni; Cerina, Federica; Mureddu, Mario; Secchi, Luca; Chessa, Alessandro

2013-10-01

This work analyzes methods for the identification and the stability under perturbation of a territorial community structure with specific reference to transportation networks. We considered networks of commuters for a city and an insular region. In both cases, we have studied the distribution of commuters’ trips (i.e., home-to-work trips and vice versa). The identification and stability of the communities’ cores are linked to the land-use distribution within the zone system, and therefore their proper definition may be useful to transport planners.

Microbial communities related to volatile organic compound emission in automobile air conditioning units.

PubMed

Diekmann, Nina; Burghartz, Melanie; Remus, Lars; Kaufholz, Anna-Lena; Nawrath, Thorben; Rohde, Manfred; Schulz, Stefan; Roselius, Louisa; Schaper, Jörg; Mamber, Oliver; Jahn, Dieter; Jahn, Martina

2013-10-01

During operation of mobile air conditioning (MAC) systems in automobiles, malodours can occur. We studied the microbial communities found on contaminated heat exchanger fins of 45 evaporators from car MAC systems which were operated in seven different regions of the world and identified corresponding volatile organic compounds. Collected biofilms were examined by scanning electron microscopy and fluorescent in situ hybridization. The detected bacteria were loosely attached to the metal surface. Further analyses of the bacteria using PCR-based single-strand conformation polymorphism and sequencing of isolated 16S rRNA gene fragments identified highly divergent microbial communities with multiple members of the Alphaproteobacteriales, Methylobacteria were the prevalent bacteria. In addition, Sphingomonadales, Burkholderiales, Bacillales, Alcanivorax spp. and Stenotrophomonas spp. were found among many others depending on the location the evaporators were operated. Interestingly, typical pathogenic bacteria related to air conditioning systems including Legionella spp. were not found. In order to determine the nature of the chemical compounds produced by the bacteria, the volatile organic compounds were examined by closed loop stripping analysis and identified by combined gas chromatography/mass spectrometry. Sulphur compounds, i.e. di-, tri- and multiple sulphides, acetylthiazole, aromatic compounds and diverse substituted pyrazines were detected. Mathematical clustering of the determined microbial community structures against their origin identified a European/American/Arabic cluster versus two mainly tropical Asian clusters. Interestingly, clustering of the determined volatiles against the origin of the corresponding MAC revealed a highly similar pattern. A close relationship of microbial community structure and resulting malodours to the climate and air quality at the location of MAC operation was concluded.

A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities

NASA Astrophysics Data System (ADS)

Muscoloni, Alessandro; Vittorio Cannistraci, Carlo

2018-05-01

The investigation of the hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The popularity-similarity-optimization (PSO) model simulates how random geometric graphs grow in the hyperbolic space, generating realistic networks with clustering, small-worldness, scale-freeness and rich-clubness. However, it misses to reproduce an important feature of real complex networks, which is the community organization. The geometrical-preferential-attachment (GPA) model was recently developed in order to confer to the PSO also a soft community structure, which is obtained by forcing different angular regions of the hyperbolic disk to have a variable level of attractiveness. However, the number and size of the communities cannot be explicitly controlled in the GPA, which is a clear limitation for real applications. Here, we introduce the nonuniform PSO (nPSO) model. Differently from GPA, the nPSO generates synthetic networks in the hyperbolic space where heterogeneous angular node attractiveness is forced by sampling the angular coordinates from a tailored nonuniform probability distribution (for instance a mixture of Gaussians). The nPSO differs from GPA in other three aspects: it allows one to explicitly fix the number and size of communities; it allows one to tune their mixing property by means of the network temperature; it is efficient to generate networks with high clustering. Several tests on the detectability of the community structure in nPSO synthetic networks and wide investigations on their structural properties confirm that the nPSO is a valid and efficient model to generate realistic complex networks with communities.

Active link selection for efficient semi-supervised community detection

NASA Astrophysics Data System (ADS)

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-03-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches.

Active link selection for efficient semi-supervised community detection

PubMed Central

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-01-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

Caribbean-wide, long-term study of seagrass beds reveals local variations, shifts in community structure and occasional collapse.

PubMed

van Tussenbroek, Brigitta I; Cortés, Jorge; Collin, Rachel; Fonseca, Ana C; Gayle, Peter M H; Guzmán, Hector M; Jácome, Gabriel E; Juman, Rahanna; Koltes, Karen H; Oxenford, Hazel A; Rodríguez-Ramirez, Alberto; Samper-Villarreal, Jimena; Smith, Struan R; Tschirky, John J; Weil, Ernesto

2014-01-01

The CARICOMP monitoring network gathered standardized data from 52 seagrass sampling stations at 22 sites (mostly Thalassia testudinum-dominated beds in reef systems) across the Wider Caribbean twice a year over the period 1993 to 2007 (and in some cases up to 2012). Wide variations in community total biomass (285 to >2000 g dry m(-2)) and annual foliar productivity of the dominant seagrass T. testudinum (<200 and >2000 g dry m(-2)) were found among sites. Solar-cycle related intra-annual variations in T. testudinum leaf productivity were detected at latitudes > 16°N. Hurricanes had little to no long-term effects on these well-developed seagrass communities, except for 1 station, where the vegetation was lost by burial below ∼1 m sand. At two sites (5 stations), the seagrass beds collapsed due to excessive grazing by turtles or sea-urchins (the latter in combination with human impact and storms). The low-cost methods of this regional-scale monitoring program were sufficient to detect long-term shifts in the communities, and fifteen (43%) out of 35 long-term monitoring stations (at 17 sites) showed trends in seagrass communities consistent with expected changes under environmental deterioration.

Different slopes of a mountain can determine the structure of ferns and lycophytes communities in a tropical forest of Brazil.

PubMed

Nettesheim, Felipe C; Damasceno, Elaine R; Sylvestre, Lana S

2014-03-01

A community of Ferns and Lycophytes was investigated by comparing the occurrence of species on different slopes of a paleoisland in Southeastern Brazil. Our goal was to evaluate the hypothesis that slopes with different geographic orientations determine a differentiation of Atlantic Forest ferns and lycophytes community. We recorded these plants at slopes turned towards the continent and at slopes turned towards the open sea. Analysis consisted of a preliminary assessment on fern beta diversity, a Non Metric Multidimensional Scaling (NMDS) and a Student t-test to confirm if sites sampling units ordination was different at each axis. We further used the Pearson coefficient to relate fern species to the differentiation pattern and again Student's t-test to determine if richness, plant cover and abundance varied between the two sites. There was a relatively low number of shared species between the two sites and ferns and lycophytes community variation was confirmed. Some species were detected as indicators of the community variation but we were unable to detect richness, plant cover or abundance differences. Despite the evidence of this variation between the slopes, further works are needed to evaluate which processes are contributing to determine this pattern.

Structural damage identification using damping: a compendium of uses and features

NASA Astrophysics Data System (ADS)

Cao, M. S.; Sha, G. G.; Gao, Y. F.; Ostachowicz, W.

2017-04-01

The vibration responses of structures under controlled or ambient excitation can be used to detect structural damage by correlating changes in structural dynamic properties extracted from responses with damage. Typical dynamic properties refer to modal parameters: natural frequencies, mode shapes, and damping. Among these parameters, natural frequencies and mode shapes have been investigated extensively for their use in damage characterization by associating damage with reduction in local stiffness of structures. In contrast, the use of damping as a dynamic property to represent structural damage has not been comprehensively elucidated, primarily due to the complexities of damping measurement and analysis. With advances in measurement technologies and analysis tools, the use of damping to identify damage is becoming a focus of increasing attention in the damage detection community. Recently, a number of studies have demonstrated that damping has greater sensitivity for characterizing damage than natural frequencies and mode shapes in various applications, but damping-based damage identification is still a research direction ‘in progress’ and is not yet well resolved. This situation calls for an overall survey of the state-of-the-art and the state-of-the-practice of using damping to detect structural damage. To this end, this study aims to provide a comprehensive survey of uses and features of applying damping in structural damage detection. First, we present various methods for damping estimation in different domains including the time domain, the frequency domain, and the time-frequency domain. Second, we investigate the features and applications of damping-based damage detection methods on the basis of two predominant infrastructure elements, reinforced concrete structures and fiber-reinforced composites. Third, we clarify the influential factors that can impair the capability of damping to characterize damage. Finally, we recommend future research directions for advancing damping-based damage detection. This work holds the promise of (a) helping researchers identify crucial components in damping-based damage detection theories, methods, and technologies, and (b) leading practitioners to better implement damping-based structural damage identification.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

PubMed Central

Geng, Haifeng; Tran-Gyamfi, Mary B.; Lane, Todd W.; Sale, Kenneth L.; Yu, Eizadora T.

2016-01-01

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations. PMID:27507966

Spatial and Temporal Variation in Enterococcal Abundance and Its Relationship to the Microbial Community in Hawaii Beach Sand and Water

PubMed Central

Cui, Henglin; Yang, Kun; Pagaling, Eulyn

2013-01-01

Recent studies have reported high levels of fecal indicator enterococci in marine beach sand. This study aimed to determine the spatial and temporal variation of enterococcal abundance and to evaluate its relationships with microbial community parameters in Hawaii beach sand and water. Sampling at 23 beaches on the Island of Oahu detected higher levels of enterococci in beach foreshore sand than in beach water on a mass unit basis. Subsequent 8-week consecutive samplings at two selected beaches (Waialae and Kualoa) consistently detected significantly higher levels of enterococci in backshore sand than in foreshore/nearshore sand and beach water. Comparison between the abundance of enterococci and the microbial communities showed that enterococci correlated significantly with total Vibrio in all beach zones but less significantly with total bacterial density and Escherichia coli. Samples from the different zones of Waialae beach were sequenced by 16S rRNA gene pyrosequencing to determine the microbial community structure and diversity. The backshore sand had a significantly more diverse community and contained different major bacterial populations than the other beach zones, which corresponded to the spatial distribution pattern of enterococcal abundance. Taken together, multiple lines of evidence support the possibility of enterococci as autochthonous members of the microbial community in Hawaii beach sand. PMID:23563940

Conditionally Rare Taxa Disproportionately Contribute to Temporal Changes in Microbial Diversity

DOE PAGES

Shade, Ashley; Jones, Stuart E.; Caporaso, J. Gregory; ...

2014-07-15

Microbial communities typically contain many rare taxa that make up the majority of the observed membership, yet the contribution of this microbial “rare biosphere” to community dynamics is unclear. Using 16S rRNA amplicon sequencing of 3,237 samples from 42 time series of microbial communities from nine different ecosystems (air; marine; lake; stream; adult human skin, tongue, and gut; infant gut; and brewery wastewater treatment), we introduce a new method to detect typically rare microbial taxa that occasionally become very abundant (conditionally rare taxa [CRT]) and then quantify their contributions to temporal shifts in community structure. We discovered that CRT mademore » up 1.5 to 28% of the community membership, represented a broad diversity of bacterial and archaeal lineages, and explained large amounts of temporal community dissimilarity (i.e., up to 97% of Bray-Curtis dissimilarity). Most of the CRT were detected at multiple time points, though we also identified “one-hit wonder” CRT that were observed at only one time point. Using a case study from a temperate lake, we gained additional insights into the ecology of CRT by comparing routine community time series to large disturbance events. Our results reveal that many rare taxa contribute a greater amount to microbial community dynamics than is apparent from their low proportional abundances. In conclusion, this observation was true across a wide range of ecosystems, indicating that these rare taxa are essential for understanding community changes over time.« less

Co-existence of Anaerobic Ammonium Oxidation Bacteria and Denitrifying Anaerobic Methane Oxidation Bacteria in Sewage Sludge: Community Diversity and Seasonal Dynamics.

PubMed

Xu, Sai; Lu, Wenjing; Mustafa, Muhammad Farooq; Caicedo, Luis Miguel; Guo, Hanwen; Fu, Xindi; Wang, Hongtao

2017-11-01

Anaerobic ammonium oxidation (ANAMMOX) and denitrifying anaerobic methane oxidation (DAMO) have been recently discovered as relevant processes in the carbon and nitrogen cycles of wastewater treatment plants. In this study, the seasonal dynamics of ANAMMOX and DAMO bacterial community structures and their abundance in sewage sludge collected from wastewater treatment plants were analysed. Results indicated that ANAMMOX and DAMO bacteria co-existed in sewage sludge in different seasons and their abundance was positively correlated (P < 0.05). The high abundance of ANAMMOX and DAMO bacteria in autumn and winter indicated that these seasons were the preferred time to favour the growth of ANAMMOX and DAMO bacteria. The community structure of ANNAMOX and DAMO bacteria could also shift with seasonal changes. The "Candidatus Brocadia" genus of ANAMMOX bacteria was mainly recovered in spring and summer, and an unknown cluster was primarily detected in autumn and winter. Similar patterns of seasonal variation in the community structure of DAMO bacteria were also observed. Group B was the dominant in spring and summer, whereas in autumn and winter, group A and group B presented almost the same proportion. The redundancy analysis revealed that pH and nitrate were the most significant factors affecting community structures of these two groups (P < 0.01). This study reported the diversity of ANAMMOX and DAMO in wastewater treatment plants that may be the basis for new nitrogen removal technologies.

Too risky to settle: avian community structure changes in response to perceived predation risk on adults and offspring

USGS Publications Warehouse

Hua, Fangyuan; Fletcher, Robert J.; Sieving, Kathryn E.; Dorazio, Robert M.

2013-01-01

Predation risk is widely hypothesized as an important force structuring communities, but this potential force is rarely tested experimentally, particularly in terrestrial vertebrate communities. How animals respond to predation risk is generally considered predictable from species life-history and natural-history traits, but rigorous tests of these predictions remain scarce. We report on a large-scale playback experiment with a forest bird community that addresses two questions: (i) does perceived predation risk shape the richness and composition of a breeding bird community? And (ii) can species life-history and natural-history traits predict prey community responses to different types of predation risk? On 9 ha plots, we manipulated cues of three avian predators that preferentially prey on either adult birds or offspring, or both, throughout the breeding season. We found that increased perception of predation risk led to generally negative responses in the abundance, occurrence and/or detection probability of most prey species, which in turn reduced the species richness and shifted the composition of the breeding bird community. Species-level responses were largely predicted from the key natural-history trait of body size, but we did not find support for the life-history theory prediction of the relationship between species' slow/fast life-history strategy and their response to predation risk.

Community structure and diversity of tropical forest mammals: data from a global camera trap network.

PubMed

Ahumada, Jorge A; Silva, Carlos E F; Gajapersad, Krisna; Hallam, Chris; Hurtado, Johanna; Martin, Emanuel; McWilliam, Alex; Mugerwa, Badru; O'Brien, Tim; Rovero, Francesco; Sheil, Douglas; Spironello, Wilson R; Winarni, Nurul; Andelman, Sandy J

2011-09-27

Terrestrial mammals are a key component of tropical forest communities as indicators of ecosystem health and providers of important ecosystem services. However, there is little quantitative information about how they change with local, regional and global threats. In this paper, the first standardized pantropical forest terrestrial mammal community study, we examine several aspects of terrestrial mammal species and community diversity (species richness, species diversity, evenness, dominance, functional diversity and community structure) at seven sites around the globe using a single standardized camera trapping methodology approach. The sites-located in Uganda, Tanzania, Indonesia, Lao PDR, Suriname, Brazil and Costa Rica-are surrounded by different landscape configurations, from continuous forests to highly fragmented forests. We obtained more than 51 000 images and detected 105 species of mammals with a total sampling effort of 12 687 camera trap days. We find that mammal communities from highly fragmented sites have lower species richness, species diversity, functional diversity and higher dominance when compared with sites in partially fragmented and continuous forest. We emphasize the importance of standardized camera trapping approaches for obtaining baselines for monitoring forest mammal communities so as to adequately understand the effect of global, regional and local threats and appropriately inform conservation actions.

Community violence exposure in early adolescence: Longitudinal associations with hippocampal and amygdala volume and resting state connectivity.

PubMed

Saxbe, Darby; Khoddam, Hannah; Piero, Larissa Del; Stoycos, Sarah A; Gimbel, Sarah I; Margolin, Gayla; Kaplan, Jonas T

2018-06-11

Community violence exposure is a common stressor, known to compromise youth cognitive and emotional development. In a diverse, urban sample of 22 adolescents, participants reported on community violence exposure (witnessing a beating or illegal drug use, hearing gun shots, or other forms of community violence) in early adolescence (average age 12.99), and underwent a neuroimaging scan 3-5 years later (average age 16.92). Community violence exposure in early adolescence predicted smaller manually traced left and right hippocampal and amygdala volumes in a model controlling for age, gender, and concurrent community violence exposure, measured in late adolescence. Community violence continued to predict hippocampus (but not amygdala) volumes after we also controlled for family aggression exposure in early adolescence. Community violence exposure was also associated with stronger resting state connectivity between the right hippocampus (using the manually traced structure as a seed region) and bilateral frontotemporal regions including the superior temporal gyrus and insula. These resting state connectivity results held after controlling for concurrent community violence exposure, SES, and family aggression. Although this is the first study focusing on community violence in conjunction with brain structure and function, these results dovetail with other research linking childhood adversity with smaller subcortical volumes in adolescence and adulthood, and with altered frontolimbic resting state connectivity. Our findings suggest that even community-level exposure to neighborhood violence can have detectable neural correlates in adolescents. © 2018 John Wiley & Sons Ltd.

Monitoring of Yeast Communities and Volatile Flavor Changes During Traditional Korean Soy Sauce Fermentation.

PubMed

Song, Young-Ran; Jeong, Do-Youn; Baik, Sang-Ho

2015-09-01

Flavor development in soy sauce is significantly related to the diversity of yeast species. Due to its unique fermentation with meju, the process of making Korean soy sauce gives rise to a specific yeast community and, therefore, flavor profile; however, no detailed analysis of the identifying these structure has been performed. Changes in yeast community structure during Korean soy sauce fermentation were examined using both culture-dependent and culture-independent methods with simultaneous analysis of the changes in volatile compounds by GC-MS analysis. During fermentation, Candida, Pichia, and Rhodotorula sp. were the dominant species, whereas Debaryomyces, Torulaspora, and Zygosaccharomyces sp. were detected only at the early stage. In addition, Cryptococcus, Microbotryum, Tetrapisispora, and Wickerhamomyces were detected as minor strains. Among the 62 compounds identified in this study, alcohols, ketones, and pyrazines were present as the major groups during the initial stages, whereas the abundance of acids with aldehydes increased as the fermentation progressed. Finally, the impacts of 10 different yeast strains found to participate in fermentation on the formation of volatile compounds were evaluated under soy-based conditions. It was revealed that specific species produced different profiles of volatile compounds, some of which were significant flavor contributors, especially volatile alcohols, aldehydes, esters, and ketones. © 2015 Institute of Food Technologists®

Unexpected and novel putative viruses in the sediments of a deep-dark permanently anoxic freshwater habitat.

PubMed

Borrel, Guillaume; Colombet, Jonathan; Robin, Agnès; Lehours, Anne-Catherine; Prangishvili, David; Sime-Ngando, Télesphore

2012-11-01

Morphological diversity, abundance and community structure of viruses were examined in the deep and anoxic sediments of the volcanic Lake Pavin (France). The sediment core, encompassing 130 years of sedimentation, was subsampled every centimeter. High viral abundances were recorded and correlated to prokaryotic densities. Abundances of viruses and prokaryotes decreased with the depth, contrasting the pattern of virus-to-prokaryote ratio. According to fingerprint analyses, the community structure of viruses, bacteria and archaea gradually changed, and communities of the surface (0-10 cm) could be discriminated from those of the intermediate (11-27 cm) and deep (28-40 cm) sediment layers. Viral morphotypes similar to virions of ubiquitous dsDNA viruses of bacteria were observed. Exceptional morphotypes, previously never reported in freshwater systems, were also detected. Some of these resembled dsDNA viruses of hyperthermophilic and hyperhalophilic archaea. Moreover, unusual types of spherical and cubic virus-like particles (VLPs) were observed. Infected prokaryotic cells were detected in the whole sediment core, and their vertical distribution correlated with both viral and prokaryotic abundances. Pleomorphic ellipsoid VLPs were visible in filamentous cells tentatively identified as representatives of the archaeal genus Methanosaeta, a major group of methane producers on earth.

Insights into the pan-microbiome: skin microbial communities of Chinese individuals differ from other racial groups

PubMed Central

Leung, Marcus H. Y.; Wilkins, David; Lee, Patrick K. H.

2015-01-01

Many studies have characterized microbiomes of western individuals. However, studies involving non-westerners are scarce. This study characterizes the skin microbiomes of Chinese individuals. Skin-associated genera, including Propionibacterium, Corynebacterium, Staphylococcus, and Enhydrobacter were prevalent. Extensive inter-individual microbiome variations were detected, with core genera present in all individuals constituting a minority of genera detected. Species-level analyses presented dominance of potential opportunistic pathogens in respective genera. Host properties including age, gender, and household were associated with variations in community structure. For all sampled sites, skin microbiomes within an individual is more similar than that of different co-habiting individuals, which is in turn more similar than individuals living in different households. Network analyses highlighted general and skin-site specific relationships between genera. Comparison of microbiomes from different population groups revealed race-based clustering explained by community membership (Global R = 0.968) and structure (Global R = 0.589), contributing to enlargement of the skin pan-microbiome. This study provides the foundation for subsequent in-depth characterization and microbial interactive analyses on the skin and other parts of the human body in different racial groups, and an appreciation that the human skin pan-microbiome can be much larger than that of a single population. PMID:26177982

Unexpected and novel putative viruses in the sediments of a deep-dark permanently anoxic freshwater habitat

PubMed Central

Borrel, Guillaume; Colombet, Jonathan; Robin, Agnès; Lehours, Anne-Catherine; Prangishvili, David; Sime-Ngando, Télesphore

2012-01-01

Morphological diversity, abundance and community structure of viruses were examined in the deep and anoxic sediments of the volcanic Lake Pavin (France). The sediment core, encompassing 130 years of sedimentation, was subsampled every centimeter. High viral abundances were recorded and correlated to prokaryotic densities. Abundances of viruses and prokaryotes decreased with the depth, contrasting the pattern of virus-to-prokaryote ratio. According to fingerprint analyses, the community structure of viruses, bacteria and archaea gradually changed, and communities of the surface (0–10 cm) could be discriminated from those of the intermediate (11–27 cm) and deep (28–40 cm) sediment layers. Viral morphotypes similar to virions of ubiquitous dsDNA viruses of bacteria were observed. Exceptional morphotypes, previously never reported in freshwater systems, were also detected. Some of these resembled dsDNA viruses of hyperthermophilic and hyperhalophilic archaea. Moreover, unusual types of spherical and cubic virus-like particles (VLPs) were observed. Infected prokaryotic cells were detected in the whole sediment core, and their vertical distribution correlated with both viral and prokaryotic abundances. Pleomorphic ellipsoid VLPs were visible in filamentous cells tentatively identified as representatives of the archaeal genus Methanosaeta, a major group of methane producers on earth. PMID:22648129

Composition and abundance of microbiota in the pharynx in patients with laryngeal carcinoma and vocal cord polyps.

PubMed

Gong, Hongli; Wang, Boyan; Shi, Yi; Shi, Yong; Xiao, Xiyan; Cao, Pengyu; Tao, Lei; Wang, Yuezhu; Zhou, Liang

2017-08-01

The pharynx is an important site of microbiota colonization, but the bacterial populations at this site have been relatively unexplored by culture-independent approaches. The aim of this study was to characterize the microbiota structure of the pharynx. Pyrosequencing of 16S rRNA gene libraries was used to characterize the pharyngeal microbiota using swab samples from 68 subjects with laryngeal cancer and 28 subjects with vocal cord polyps. Overall, the major phylum was Firmicutes, with Streptococcus as the predominant genus in the pharyngeal communities. Nine core operational taxonomic units detected from Streptococcus, Fusobacterium, Prevotella, Granulicatella, and Veillonella accounted for 21.3% of the total sequences detected. However, there was no difference in bacterial communities in the pharynx from patients with laryngeal cancer and vocal cord polyps. The relative abundance of Firmicutes was inversely correlated with Fusobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes. The correlation was evident at the genus level, and the relative abundance of Streptococcus was inversely associated with Fusobacterium, Leptotrichia, Neisseria, Actinomyces, and Prevotella. This study presented a profile for the overall structure of the microbiota in pharyngeal swab samples. Inverse correlations were found between Streptococcus and other bacterial communities, suggesting that potential antagonism may exist among pharyngeal microbiota.

Predicting effects of climate change on the composition and function of soil microbial communities

NASA Astrophysics Data System (ADS)

Dubinsky, E.; Brodie, E.; Myint, C.; Ackerly, D.; van Nostrand, J.; Bird, J.; Zhou, J.; Andersen, G.; Firestone, M.

2008-12-01

Complex soil microbial communities regulate critical ecosystem processes that will be altered by climate change. A critical step towards predicting the impacts of climate change on terrestrial ecosystems is to determine the primary controllers of soil microbial community composition and function, and subsequently evaluate climate change scenarios that alter these controllers. We surveyed complex soil bacterial and archaeal communities across a range of climatic and edaphic conditions to identify critical controllers of soil microbial community composition in the field and then tested the resulting predictions using a 2-year manipulation of precipitation and temperature using mesocosms of California annual grasslands. Community DNA extracted from field soils sampled from six different ecosystems was assayed for bacterial and archaeal communities using high-density phylogenetic microarrays as well as functional gene arrays. Correlations among the relative abundances of thousands of microbial taxa and edaphic factors such as soil moisture and nutrient content provided a basis for predicting community responses to changing soil conditions. Communities of soil bacteria and archaea were strongly structured by single environmental predictors, particularly variables related to soil water. Bacteria in the Actinomycetales and Bacilli consistently demonstrated a strong negative response to increasing soil moisture, while taxa in a greater variety of lineages responded positively to increasing soil moisture. In the climate change experiment, overall bacterial community structure was impacted significantly by total precipitation but not by plant species. Changes in soil moisture due to decreased rainfall resulted in significant and predictable alterations in community structure. Over 70% of the bacterial taxa in common with the cross-ecosystem study responded as predicted to altered precipitation, with the most conserved response from Actinobacteria. The functional consequences of these predictable changes in community composition were measured with functional arrays that detect genes involved in the metabolism of carbon, nitrogen and other elements. The response of soil microbial communities to altered precipitation can be predicted from the distribution of microbial taxa across moisture gradients.

Unifying Inference of Meso-Scale Structures in Networks.

PubMed

Tunç, Birkan; Verma, Ragini

2015-01-01

Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

Unsupervised Spatial, Temporal and Relational Models for Social Processes

DTIC Science & Technology

2012-02-01

Andrej Mrvar . A partitioning approach to structural balance. Social Networks, 18(2):149 – 168, 1996 . [37] Thi V. Duong, Hung H. Bui, Dinh Q. Phung, and...partitioning provided by Doreian and Mrvar [36], who demonstrate that there was increasing evidence over time that 62 CHAPTER 4. COMMUNITY DETECTION this...foursome was a genuine group. Doreian and Mrvar used a block modeling approach optimiz- ing structural balance, a measure of cohesion incorporating

Bacterial community structure transformed after thermophilically composting human waste in Haiti

PubMed Central

Kramer, Sasha; Roy, Monika; Reid, Francine C.; Dubinsky, Eric A.

2017-01-01

Recycling human waste for beneficial use has been practiced for millennia. Aerobic (thermophilic) composting of sewage sludge has been shown to reduce populations of opportunistically pathogenic bacteria and to inactivate both Ascaris eggs and culturable Escherichia coli in raw waste, but there is still a question about the fate of most fecal bacteria when raw material is composted directly. This study undertook a comprehensive microbial community analysis of composting material at various stages collected over 6 months at two composting facilities in Haiti. The fecal microbiota signal was monitored using a high-density DNA microarray (PhyloChip). Thermophilic composting altered the bacterial community structure of the starting material. Typical fecal bacteria classified in the following groups were present in at least half the starting material samples, yet were reduced below detection in finished compost: Prevotella and Erysipelotrichaceae (100% reduction of initial presence), Ruminococcaceae (98–99%), Lachnospiraceae (83–94%, primarily unclassified taxa remained), Escherichia and Shigella (100%). Opportunistic pathogens were reduced below the level of detection in the final product with the exception of Clostridium tetani, which could have survived in a spore state or been reintroduced late in the outdoor maturation process. Conversely, thermotolerant or thermophilic Actinomycetes and Firmicutes (e.g., Thermobifida, Bacillus, Geobacillus) typically found in compost increased substantially during the thermophilic stage. This community DNA-based assessment of the fate of human fecal microbiota during thermophilic composting will help optimize this process as a sanitation solution in areas where infrastructure and resources are limited. PMID:28570610

Effects of storage temperature on bacterial growth rates and community structure in fresh retail sushi.

PubMed

Hoel, S; Jakobsen, A N; Vadstein, O

2017-09-01

This study was conducted to assess the effects of different storage temperatures (4-20°C), on bacterial concentrations, growth rates and community structure in fresh retail sushi, a popular retail product with a claimed shelf life of 2-3 days. The maximum specific growth rate based on aerobic plate count (APC) at 4°C was 0·06 h -1 and displayed a sixfold increase (0·37 h -1 ) at 20°C. Refrigeration resulted in no growth of hydrogen sulphide (H 2 S)-producing bacteria, but this group had the strongest temperature response. The bacterial community structure was determined by PCR/DGGE (denaturing gradient gel electrophoresis). Multivariate analysis based on Bray-Curtis similarities demonstrated that temperature alone was not the major determinant for the bacterial community structure. The total concentration of aerobic bacteria was the variable that most successfully explained the differences between the communities. The dominating organisms, detected by sequencing of DNA bands excised from the DGGE gel, were Brochothrix thermosphacta and genera of lactic acid bacteria (LAB). The relationship between growth rates and storage temperatures clearly demonstrates that these products are sensitive to deviations from optimal storage temperature, possibly resulting in loss of quality during shelf life. Regardless of the storage temperature, the bacterial communities converged towards a similar structure and density, but the storage temperature determined how fast the community reached its carrying capacity. Little information is available on the microbial composition of ready-to-eat food that are prepared with raw fish, subjected to contamination during handling, and susceptible to microbial growth during cold storage. Moreover, the data are a good first possibility to simulate growth of APC, H 2 S-producing bacteria and LAB under different temperature scenarios that might occur during production, distribution or storage. © 2017 The Society for Applied Microbiology.

Quantifying forest vertical structure to determine bird habitat quality in the Greenbelt Corridor, Denton, TX

NASA Astrophysics Data System (ADS)

Matsubayashi, Shiho

This study presents the integration of light detection and range (LiDAR) and hyperspectral remote sensing to create a three-dimensional bird habitat map in the Greenbelt Corridor of the Elm Fork of the Trinity River. This map permits to examine the relationship between forest stand structure, landscape heterogeneity, and bird community composition. A biannual bird census was conducted at this site during the breeding seasons of 2009 and 2010. Census data combined with the three-dimensional map suggest that local breeding bird abundance, community structure, and spatial distribution patterns are highly influenced by vertical heterogeneity of vegetation surface. For local breeding birds, vertical heterogeneity of canopy surface within stands, connectivity to adjacent forest patches, largest forest patch index, and habitat (vegetation) types proved to be the most influential factors to determine bird community assemblages. Results also highlight the critical role of secondary forests to increase functional connectivity of forest patches. Overall, three-dimensional habitat descriptions derived from integrated LiDAR and hyperspectral data serve as a powerful bird conservation tool that shows how the distribution of bird species relates to forest composition and structure at various scales.

Seasonal induced changes in spinach rhizosphere microbial community structure with varying salinity and drought.

PubMed

Mark Ibekwe, A; Ors, Selda; Ferreira, Jorge F S; Liu, Xuan; Suarez, Donald L

2017-02-01

Salinity is a common problem under irrigated agriculture, especially in low rainfall and high evaporative demand areas of southwestern United States and other semi-arid regions around the world. However, studies on salinity effects on soil microbial communities are relatively few while the effects of irrigation-induced salinity on soil chemical and physical properties and plant growth are well documented. In this study, we examined the effects of salinity, temperature, and temporal variability on soil and rhizosphere microbial communities in sand tanks irrigated with prepared solutions designed to simulate saline wastewater. Three sets of experiments with spinach (Spinacia oleracea L., cv. Racoon) were conducted under saline water during different time periods (early winter, late spring, and early summer). Bacterial 16S V4 rDNA region was amplified utilizing fusion primers designed against the surrounding conserved regions using MiSeq® Illumina sequencing platform. Across the two sample types, bacteria were relatively dominant among three phyla-the Proteobacteria, Cyanobacteria, and Bacteroidetes-accounted for 77.1% of taxa detected in the rhizosphere, while Proteobacteria, Bacteroidetes, and Actinobacteria accounted for 55.1% of taxa detected in soil. The results were analyzed using UniFrac coupled with principal coordinate analysis (PCoA) to compare diversity, abundance, community structure, and specific bacterial groups in soil and rhizosphere samples. Permutational analysis of variance (PERMANOVA) analysis showed that soil temperature (P=0.001), rhizosphere temperature (P=0.001), rhizosphere salinity (P=0.032), and evapotranspiration (P=0.002) significantly affected beta diversity of soil and rhizosphere microbial communities. Furthermore, salinity had marginal effects (P=0.078) on soil beta diversity. However, temporal variability differentially affected rhizosphere microbial communities irrigated with saline wastewater. Therefore, microbial communities in soils impacted by saline irrigation water respond differently to irrigation water quality and season of application due to temporal effects associated with temperature. Published by Elsevier B.V.

Fungal communities in ancient peatlands developed from different periods in the Sanjiang Plain, China

PubMed Central

Tian, Lei; Ma, Lina; Luo, Shasha; Zhang, Jianfeng; Li, Xiujun

2017-01-01

Peatlands in the Sanjiang Plain could be more vulnerable to global warming because they are located at the southernmost boundary of northern peatlands. Unlike bacteria, fungi are often overlooked, even though they play important roles in substance circulation in the peatland ecosystems. Accordingly, it is imperative that we deepen our understanding of fungal community structure and diversity in the peatlands. In this study, high-throughput Illumina sequencing was used to study the fungal communities in three fens in the Sanjiang Plain, located at the southern edge of northern peatlands. Peat soil was collected from the three fens which developed during different periods. A total of 463,198 fungal ITS sequences were obtained, and these sequences were classified into at least six phyla, 21 classes, more than 60 orders and over 200 genera. The fungal community structures were distinct in the three sites and were dominated by Ascomycota and Basidiomycota. However, there were no significant differences between these three fens in any α-diversity index (p > 0.05). Soil age and the carbon (C) accumulation rate, as well as total carbon (TC), total nitrogen (TN), C/N ratio, and bulk density were found to be closely related to the abundance of several dominant fungal taxa. We captured a rich fungal community and confirmed that the dominant taxa were those which were frequently detected in other northern peatlands. Soil age and the C accumulation rate were found to play important roles in shaping the fungal community structure. PMID:29236715

Conventional wastewater treatment and reuse site practices modify bacterial community structure but do not eliminate some opportunistic pathogens in reclaimed water.

PubMed

Kulkarni, Prachi; Olson, Nathan D; Paulson, Joseph N; Pop, Mihai; Maddox, Cynthia; Claye, Emma; Rosenberg Goldstein, Rachel E; Sharma, Manan; Gibbs, Shawn G; Mongodin, Emmanuel F; Sapkota, Amy R

2018-10-15

Water recycling continues to expand across the United States, from areas that have access to advanced, potable-level treated reclaimed water, to those having access only to reclaimed water treated at conventional municipal wastewater treatment plants. This expansion makes it important to further characterize the microbial quality of these conventionally-treated water sources. Therefore, we used 16S rRNA gene sequencing to characterize total bacterial communities present in differentially-treated wastewater and reclaimed water (n = 67 samples) from four U.S. wastewater treatment plants and one associated spray irrigation site conducting on-site ultraviolet treatment and open-air storage. The number of observed operational taxonomic units was significantly lower (p < 0.01) in effluent, compared to influent, after conventional treatment. Effluent community structure was influenced more by treatment method than by influent community structure. The abundance of Legionella spp. increased as treatment progressed in one treatment plant that performed chlorination and in another that seasonally chlorinated. Overall, the alpha-diversity of bacterial communities in reclaimed water decreased (p < 0.01) during wastewater treatment and spray irrigation site ultraviolet treatment (p < 0.01), but increased (p < 0.01) after open-air storage at the spray irrigation site. The abundance of Legionella spp. was higher at the sprinkler system pumphouse at the spray irrigation site than in the influent from the treatment plant supplying the site. Legionella pneumophila was detected in conventionally treated effluent samples and in samples collected after ultraviolet treatment at the spray irrigation site, while Legionella feeleii persisted throughout on-site treatment at the spray irrigation site, and, along with Mycobacterium gordonae, was also detected at the sprinkler system pumphouse at the spray irrigation site. These data could inform the development of future treatment technologies and reuse guidelines that address a broader assemblage of the bacterial community of reclaimed water, resulting in reuse practices that may be more protective of public health. Copyright © 2018 Elsevier B.V. All rights reserved.

Evaluation of antimicrobial susceptibilities and virulence factors of Staphylococcus aureus strains isolated from community-acquired and health-care associated pediatric infections.

PubMed

Karbuz, Adem; Karahan, Zeynep Ceren; Aldemir-Kocabaş, Bilge; Tekeli, Alper; Özdemir, Halil; Güriz, Haluk; Gökdemir, Refik; İnce, Erdal; Çiftçi, Ergin

2017-01-01

Karbuz A, Karahan ZC, Aldemir-Kocabaş B, Tekeli A, Özdemir H, Güriz H, Gökdemir R, İnce E, Çiftçi E. Evaluation of antimicrobial susceptibilities and virulence factors of Staphylococcus aureus strains isolated from community-acquired and health-care associated pediatric infections. Turk J Pediatr 2017; 59: 395-403. The aim of this study was to investigate the enterotoxins and Panton-Valentine leukocidin (PVL) gene as virulence factor, identification if antimicrobial sensitivity patterns, agr (accessory gene regulator) types and sequence types and in resistant cases to obtain SCCmec (staphylococcal cassette chromosome mec) gene types which will be helpful to decide empirical therapy and future health politics for S. aureus species. Total of 150 isolates of S. aureus were isolated from the cultures of the child patients in January 2011 and December 2012. In this study, the penicillin resistance was observed as 93.8%. PVL and mecA was detected positive in 8.7% and in 6% of all S. aureus strains, respectively. Two MRSA (methicillin resistant S.aureus) strains were detected as SCCmec type III and SCCmec type V and five MRSA strains were detected as SCCmec type IV. SET-I and SET-G were the most common detected enterotoxins. In both community-associated and healthcare-associated MRSA strains, agr type 1 was detected most commonly. The most common sequence types were ST737 in 13 patients than ST22 in eight patients and ST121 in six patients. This study highlights a necessity to review the cause of small changes in the structural genes in order to determine whether it is a cause or outcome; community-acquired and healthcare associated strains overlap.

Do priority effects outweigh environmental filtering in a guild of dominant freshwater macroinvertebrates?

PubMed

Little, Chelsea J; Altermatt, Florian

2018-04-11

Abiotic conditions have long been considered essential in structuring freshwater macroinvertebrate communities. Ecological drift, dispersal and biotic interactions also structure communities, and although these mechanisms are more difficult to detect, they may be of equal importance in natural communities. Here, we hypothesized that in 10 naturally replicated headwater streams in eastern Switzerland, locally dominant amphipod species would be associated with differences in environmental conditions. We conducted repeated surveys of amphipods and used a hierarchical joint species distribution model to assess the influence of different drivers on species co-occurrences. The species had unique environmental requirements, but a distinct spatial structure in their distributions was unrelated to habitat. Species co-occurred much less frequently than predicted by the model, which was surprising because laboratory and field evidence suggests they are capable of coexisting in equal densities. We suggest that niche preemption may limit their distribution and that a blocking effect related to the specific linear configuration of streams determines which species colonizes and dominates a given stream catchment, thus suggesting a new solution a long-standing conundrum in freshwater ecology. © 2018 The Author(s).

Temporal change in biological community structure in the Fountain Creek basin, Colorado, 2001-2008

USGS Publications Warehouse

Zuellig, Robert E.; Bruce, James F.; Stogner, Sr., Robert W.

2010-01-01

In 2001, the U.S. Geological Survey, in cooperation with Colorado Springs City Engineering, began a study to better understand the relations between environmental characteristics and biological communities in the Fountain Creek basin in order to aide water-resource management and guide future monitoring activities. To accomplish this task, environmental (streamflow, habitat, and water chemistry) and biological (fish and macroinvertebrate) data were collected annually at 24 sites over a 6- or 8-year period (fish, 2003 to 2008; macroinvertebrates, 2001 to 2008). For this report, these data were first analyzed to determine the presence of temporal change in macroinvertebrate and fish community structure among years using nonparametric multivariate statistics. Where temporal change in the biological communities was found, these data were further analyzed using additional nonparametric multivariate techniques to determine which subset of selected streamflow, habitat, or water-chemistry variables best described site-specific changes in community structure relative to a gradient of urbanization. This study identified significant directional patterns of temporal change in macroinvertebrate and fish community structure at 15 of 24 sites in the Fountain Creek basin. At four of these sites, changes in environmental variables were significantly correlated with the concurrent temporal change identified in macroinvertebrate and fish community structure (Monument Creek above Woodmen Road at Colorado Springs, Colo.; Monument Creek at Bijou Street at Colorado Springs, Colo.; Bear Creek near Colorado Springs, Colo.; Fountain Creek at Security, Colo.). Combinations of environmental variables describing directional temporal change in the biota appeared to be site specific as no single variable dominated the results; however, substrate composition variables (percent substrate composition composed of sand, gravel, or cobble) collectively were present in 80 percent of the environmental variable subsets that were significantly correlated with temporal change in the macroinvertebrate and fish community structure. Other important environmental variables related to temporal change in the biological community structure included those describing channel form (streambank height) and streamflow (normalized annual mean daily flow, high flood-pulse count). Site-specific results from this study were derived from a relatively small number of observations (6 or 8 years of data); therefore, additional years of data may reveal other sites with temporal change in biological community structure, or could define stronger and more consistent linkages between environmental variables and observed temporal change. Likewise current variable subsets could become weaker. Nonetheless, there were several sites where temporal change was detected in this study that could not be explained by the available environmental variables studied herein. Modification of current data-collection activities may be necessary to better understand site-specific temporal relations between biological communities and environmental variables.

Netgram: Visualizing Communities in Evolving Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2015-01-01

Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems. PMID:26356538

Community Structures of Fecal Bacteria in Cattle from Different Animal Feeding Operations▿†

PubMed Central

Shanks, Orin C.; Kelty, Catherine A.; Archibeque, Shawn; Jenkins, Michael; Newton, Ryan J.; McLellan, Sandra L.; Huse, Susan M.; Sogin, Mitchell L.

2011-01-01

The fecal microbiome of cattle plays a critical role not only in animal health and productivity but also in food safety, pathogen shedding, and the performance of fecal pollution detection methods. Unfortunately, most published molecular surveys fail to provide adequate detail about variability in the community structures of fecal bacteria within and across cattle populations. Using massively parallel pyrosequencing of a hypervariable region of the rRNA coding region, we profiled the fecal microbial communities of cattle from six different feeding operations where cattle were subjected to consistent management practices for a minimum of 90 days. We obtained a total of 633,877 high-quality sequences from the fecal samples of 30 adult beef cattle (5 individuals per operation). Sequence-based clustering and taxonomic analyses indicate less variability within a population than between populations. Overall, bacterial community composition correlated significantly with fecal starch concentrations, largely reflected in changes in the Bacteroidetes, Proteobacteria, and Firmicutes populations. In addition, network analysis demonstrated that annotated sequences clustered by management practice and fecal starch concentration, suggesting that the structures of bovine fecal bacterial communities can be dramatically different in different animal feeding operations, even at the phylum and family taxonomic levels, and that the feeding operation is a more important determinant of the cattle microbiome than is the geographic location of the feedlot. PMID:21378055

Influences of space, soil, nematodes and plants on microbial community composition of chalk grassland soils.

PubMed

Yergeau, Etienne; Bezemer, T Martijn; Hedlund, Katarina; Mortimer, Simon R; Kowalchuk, George A; Van Der Putten, Wim H

2010-08-01

Microbial communities respond to a variety of environmental factors related to resources (e.g. plant and soil organic matter), habitat (e.g. soil characteristics) and predation (e.g. nematodes, protozoa and viruses). However, the relative contribution of these factors on microbial community composition is poorly understood. Here, we sampled soils from 30 chalk grassland fields located in three different chalk hill ridges of Southern England, using a spatially explicit sampling scheme. We assessed microbial communities via phospholipid fatty acid (PLFA) analyses and PCR-denaturing gradient gel electrophoresis (DGGE) and measured soil characteristics, as well as nematode and plant community composition. The relative influences of space, soil, vegetation and nematodes on soil microorganisms were contrasted using variation partitioning and path analysis. Results indicate that soil characteristics and plant community composition, representing habitat and resources, shape soil microbial community composition, whereas the influence of nematodes, a potential predation factor, appears to be relatively small. Spatial variation in microbial community structure was detected at broad (between fields) and fine (within fields) scales, suggesting that microbial communities exhibit biogeographic patterns at different scales. Although our analysis included several relevant explanatory data sets, a large part of the variation in microbial communities remained unexplained (up to 92% in some analyses). However, in several analyses, significant parts of the variation in microbial community structure could be explained. The results of this study contribute to our understanding of the relative importance of different environmental and spatial factors in driving the composition of soil-borne microbial communities. © 2009 Society for Applied Microbiology and Blackwell Publishing Ltd.

Accuracy, reproducibility, and interpretation of fatty acid methyl ester profiles of model bacterial communities

USGS Publications Warehouse

Kidd, Haack S.; Garchow, H.; Odelson, D.A.; Forney, L.J.; Klug, M.J.

1994-01-01

We determined the accuracy and reproducibility of whole-community fatty acid methyl ester (FAME) analysis with two model bacterial communities differing in composition by using the Microbial ID, Inc. (MIDI), system. The biomass, taxonomic structure, and expected MIDI-FAME profiles under a variety of environmental conditions were known for these model communities a priori. Not all members of each community could be detected in the composite profile because of lack of fatty acid 'signatures' in some isolates or because of variations (approximately fivefold) in fatty acid yield across taxa. MIDI- FAME profiles of replicate subsamples of a given community were similar in terms of fatty acid yield per unit of community dry weight and relative proportions of specific fatty acids. Principal-components analysis (PCA) of MIDI-FAME profiles resulted in a clear separation of the two different communities and a clustering of replicates of each community from two separate experiments on the first PCA axis. The first PCA axis accounted for 57.1% of the variance in the data and was correlated with fatty acids that varied significantly between communities and reflected the underlying community taxonomic structure. On the basis of our data, community fatty acid profiles can be used to assess the relative similarities and differences of microbial communities that differ in taxonomic composition. However, detailed interpretation of community fatty acid profiles in terms of biomass or community taxonomic composition must be viewed with caution until our knowledge of the quantitative and qualitative distribution of fatty acids over a wide variety of taxa and the effects of growth conditions on fatty acid profiles is more extensive.

Liming in the sugarcane burnt system and the green harvest practice affect soil bacterial community in northeastern São Paulo, Brazil.

PubMed

Val-Moraes, Silvana Pompeia; de Macedo, Helena Suleiman; Kishi, Luciano Takeshi; Pereira, Rodrigo Matheus; Navarrete, Acacio Aparecido; Mendes, Lucas William; de Figueiredo, Eduardo Barretto; La Scala, Newton; Tsai, Siu Mui; de Macedo Lemos, Eliana Gertrudes; Alves, Lúcia Maria Carareto

2016-12-01

Here we show that both liming the burnt sugarcane and the green harvest practice alter bacterial community structure, diversity and composition in sugarcane fields in northeastern São Paulo state, Brazil. Terminal restriction fragment length polymorphism fingerprinting and 16S rRNA gene cloning and sequencing were used to analyze changes in soil bacterial communities. The field experiment consisted of sugarcane-cultivated soils under different regimes: green sugarcane (GS), burnt sugarcane (BS), BS in soil amended with lime applied to increase soil pH (BSL), and native forest (NF) as control soil. The bacterial community structures revealed disparate patterns in sugarcane-cultivated soils and forest soil (R = 0.786, P = 0.002), and overlapping patterns were shown for the bacterial community structure among the different management regimes applied to sugarcane (R = 0.194, P = 0.002). The numbers of operational taxonomic units (OTUs) found in the libraries were 117, 185, 173 and 166 for NF, BS, BSL and GS, respectively. Sugarcane-cultivated soils revealed higher bacterial diversity than NF soil, with BS soil accounting for a higher richness of unique OTUs (101 unique OTUs) than NF soil (23 unique OTUs). Cluster analysis based on OTUs revealed similar bacterial communities in NF and GS soils, while the bacterial community from BS soil was most distinct from the others. Acidobacteria and Alphaproteobacteria were the most abundant bacterial phyla across the different soils with Acidobacteria Gp1 accounting for a higher abundance in NF and GS soils than burnt sugarcane-cultivated soils (BS and BSL). In turn, Acidobacteria Gp4 abundance was higher in BS soils than in other soils. These differential responses in soil bacterial community structure, diversity and composition can be associated with the agricultural management, mainly liming practices, and harvest methods in the sugarcane-cultivated soils, and they can be detected shortly after harvest.

Insights into the structure of plant-insect communities: Specialism and generalism in a regional set of non-pollinating fig wasp communities

NASA Astrophysics Data System (ADS)

Farache, F. H. A.; Cruaud, A.; Rasplus, J.-Y.; Cerezini, M. T.; Rattis, L.; Kjellberg, F.; Pereira, R. A. S.

2018-07-01

Insects show a multitude of symbiotic interactions that may vary in degree of specialization and structure. Gall-inducing insects and their parasitoids are thought to be relatively specialized organisms, but despite their ecological importance, the organization and structure of the interactions they establish with their hosts has seldom been investigated in tropical communities. Non-pollinating fig wasps (NPFW) are particularly interesting organisms for the study of ecological networks because most species strictly develop their offspring within fig inflorescences, and show a multitude of life history strategies. They can be gall-makers, cleptoparasites or parasitoids of pollinating or of other non-pollinating fig wasps. Here we analysed a set of non-pollinating fig wasp communities associated with six species of Ficus section Americanae over a wide area. This allowed us to investigate patterns of specialization in a diverse community composed of monophagous and polyphagous species. We observed that most NPFW species were cleptoparasites and parasitoids, colonizing figs several days after oviposition by pollinators. Most species that occurred in more than one host were much more abundant in a single preferential host, suggesting specialization. The food web established between wasps and figs shows structural properties that are typical of specific antagonistic relationships, especially of endophagous insect networks. Two species that occurred in all available hosts were highly abundant in the network, suggesting that in some cases generalized species can be more competitive than strict specialists. The Neotropical and, to a lesser extent, Afrotropical NPFW communities seem to be more generalized than other NPFW communities. However, evidence of host sharing in the Old World is quite limited, since most studies have focused on particular taxonomic groups (genera) of wasps instead of sampling the whole NPFW community. Moreover, the lack of quantitative information in previous studies prevents us from detecting patterns of host preferences in polyphagous species.

Spatial correlation analysis of urban traffic state under a perspective of community detection

NASA Astrophysics Data System (ADS)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

Diversity of herbaceous plants and bacterial communities regulates soil resistome across forest biomes.

PubMed

Hu, Hang-Wei; Wang, Jun-Tao; Singh, Brajesh K; Liu, Yu-Rong; Chen, Yong-Liang; Zhang, Yu-Jing; He, Ji-Zheng

2018-04-24

Antibiotic resistance is ancient and prevalent in natural ecosystems and evolved long before the utilization of synthetic antibiotics started, but factors influencing the large-scale distribution patterns of natural antibiotic resistance genes (ARGs) remain largely unknown. Here, a large-scale investigation over 4000 km was performed to profile soil ARGs, plant communities and bacterial communities from 300 quadrats across five forest biomes with minimal human impact. We detected diverse and abundant ARGs in forests, including over 160 genes conferring resistance to eight major categories of antibiotics. The diversity of ARGs was strongly and positively correlated with the diversity of bacteria, herbaceous plants and mobile genetic elements (MGEs). The ARG composition was strongly correlated with the taxonomic structure of bacteria and herbs. Consistent with this strong correlation, structural equation modelling demonstrated that the positive effects of bacterial and herb communities on ARG patterns were maintained even when simultaneously accounting for multiple drivers (climate, spatial predictors and edaphic factors). These findings suggest a paradigm that the interactions between aboveground and belowground communities shape the large-scale distribution of soil resistomes, providing new knowledge for tackling the emerging environmental antibiotic resistance. © 2018 Society for Applied Microbiology and John Wiley & Sons Ltd.

Hexavalent chromium reduction under fermentative conditions with lactate stimulated native microbial communities.

PubMed

Somenahally, Anil C; Mosher, Jennifer J; Yuan, Tong; Podar, Mircea; Phelps, Tommy J; Brown, Steven D; Yang, Zamin K; Hazen, Terry C; Arkin, Adam P; Palumbo, Anthony V; Van Nostrand, Joy D; Zhou, Jizhong; Elias, Dwayne A

2013-01-01

Microbial reduction of toxic hexavalent chromium (Cr(VI)) in-situ is a plausible bioremediation strategy in electron-acceptor limited environments. However, higher [Cr(VI)] may impose stress on syntrophic communities and impact community structure and function. The study objectives were to understand the impacts of Cr(VI) concentrations on community structure and on the Cr(VI)-reduction potential of groundwater communities at Hanford, WA. Steady state continuous flow bioreactors were used to grow native communities enriched with lactate (30 mM) and continuously amended with Cr(VI) at 0.0 (No-Cr), 0.1 (Low-Cr) and 3.0 (High-Cr) mg/L. Microbial growth, metabolites, Cr(VI), 16S rRNA gene sequences and GeoChip based functional gene composition were monitored for 15 weeks. Temporal trends and differences in growth, metabolite profiles, and community composition were observed, largely between Low-Cr and High-Cr bioreactors. In both High-Cr and Low-Cr bioreactors, Cr(VI) levels were below detection from week 1 until week 15. With lactate enrichment, native bacterial diversity substantially decreased as Pelosinus spp., and Sporotalea spp., became the dominant groups, but did not significantly differ between Cr concentrations. The Archaea diversity also substantially decreased after lactate enrichment from Methanosaeta (35%), Methanosarcina (17%) and others, to mostly Methanosarcina spp. (95%). Methane production was lower in High-Cr reactors suggesting some inhibition of methanogens. Several key functional genes were distinct in Low-Cr bioreactors compared to High-Cr. Among the Cr resistant microbes, Burkholderia vietnamiensis, Comamonas testosterone and Ralstonia pickettii proliferated in Cr amended bioreactors. In-situ fermentative conditions facilitated Cr(VI) reduction, and as a result 3.0 mg/L Cr(VI) did not impact the overall bacterial community structure.

[Effect of flooding time on community structure and abundance of Geobacteraceae in paddy soil].

PubMed

You, Jiaohua; Xia, Shuhong; Wang, Baoli; Qu, Dong

2011-06-01

The dynamic characteristics of community structure and relative abundance of Geobacteraceae were investigated to understand their response to microbial iron (III) reducing in flooded paddy soil. The paddy soil was incubated anaerobically and the amount of Fe(II) was determined during the flooding incubation. We retrieved Geobacteraceae sequences from clone libraries constructed for different time points (1 h and day 1, 5, 10, 20 and 30) after flooding of the paddy soil. The diversity and community structure were analyzed by using RFLP method, and the relative abundance of Geobacteraceae was detected by real-time PCR. Microbial reduction of iron (III) changed greatly in early time and was stable after incubated for 20 d in paddy soil. The largest iron reduction potential was 10.16 mg/g with a Vmax of 1.064 mg/(g x d) at the time of 4.84 d whereas this process achieved plateau after 20 days flooding. Diversity of Geobacteraceae, given by alpha indices, fluctuated during the flooding incubation. Two peaks of diversity were observed in treatments of 5 d and 20 d respectively, while significant low diversity appeared in samples of 10 d and 30 d. Beta indices described the differences between community structures of Geobacteraceae and hence reflected the variation of the flooding situation over time. In all samples, 10 RFLP-based preponderant types were found, which fell into clade 1 and clade 2 of Geobacteraceae. The relative abundance of Geobacteraceae was the lowest in 1 d (1.20% ) and the highest in 20 d (4.54%). The dynamic variation of Geobacteraceae diversity, community structure and abundance are closely related to microbial iron (III) reducing in flooding paddy soil.

ECOLOGICAL DETERMINANTS OF POPULATION STRUCTURE AND GENE FLOW BETWEEN SYMPATRIC FUNGAL SPECIES IN THE GENUS COLLEOTRICHUM FROM DIVERSE GRASS COMMUNITIES

EPA Science Inventory

This comparative analysis will allow us to detect historical events of interest such as population fragmentations, range expansions, and colonization in the Colletotrichum species that inhabit pooid grasses. What is learned from C. cereale populations in agro...

Administration of two probiotic strains during early childhood does not affect the endogenous gut microbiota composition despite probiotic proliferation.

PubMed

Laursen, Martin Frederik; Laursen, Rikke Pilmann; Larnkjær, Anni; Michaelsen, Kim F; Bahl, Martin Iain; Licht, Tine Rask

2017-08-17

Probiotics are increasingly applied to prevent and treat a range of infectious, immune related and gastrointestinal diseases. Despite this, the mechanisms behind the putative effects of probiotics are poorly understood. One of the suggested modes of probiotic action is modulation of the endogenous gut microbiota, however probiotic intervention studies in adults have failed to show significant effects on gut microbiota composition. The gut microbiota of young children is known to be unstable and more responsive to external factors than that of adults. Therefore, potential effects of probiotic intervention on gut microbiota may be easier detectable in early life. We thus investigated the effects of a 6 month placebo-controlled probiotic intervention with Bifidobacterium animalis subsp. lactis (BB-12®) and Lactobacillus rhamnosus (LGG®) on gut microbiota composition and diversity in more than 200 Danish infants (N = 290 enrolled; N = 201 all samples analyzed), as assessed by 16S rRNA amplicon sequencing. Further, we evaluated probiotic presence and proliferation by use of specific quantitative polymerase chain reaction (qPCR). Probiotic administration did not significantly alter gut microbiota community structure or diversity as compared to placebo. The probiotic strains were detected in 91.3% of the fecal samples from children receiving probiotics and in 1% of the placebo treated children. Baseline gut microbiota was not found to predict the ability of probiotics to establish in the gut after the 6 month intervention. Within the probiotics group, proliferation of the strains LGG® and BB-12® in the gut was detected in 44.7% and 83.5% of the participants, respectively. A sub-analysis of the gut microbiota including only individuals with detected growth of the probiotics LGG® or BB-12® and comparing these to placebo revealed no differences in community structure or diversity. Six months of probiotic administration during early life did not change gut microbiota community structure or diversity, despite active proliferation of the administered probiotic strains. Therefore, alteration of the healthy infant gut microbiota is not likely to be a prominent mechanism by which these specific probiotics works to exert beneficial effects on host health. NCT02180581 . Registered 30 June 2014.

Phylogenetic and Functional Diversity of Microbial Communities Associated with Subsurface Sediments of the Sonora Margin, Guaymas Basin

PubMed Central

Vigneron, Adrien; Cruaud, Perrine; Roussel, Erwan G.; Pignet, Patricia; Caprais, Jean-Claude; Callac, Nolwenn; Ciobanu, Maria-Cristina; Godfroy, Anne; Cragg, Barry A.; Parkes, John R.; Van Nostrand, Joy D.; He, Zhili; Zhou, Jizhong; Toffin, Laurent

2014-01-01

Subsurface sediments of the Sonora Margin (Guaymas Basin), located in proximity of active cold seep sites were explored. The taxonomic and functional diversity of bacterial and archaeal communities were investigated from 1 to 10 meters below the seafloor. Microbial community structure and abundance and distribution of dominant populations were assessed using complementary molecular approaches (Ribosomal Intergenic Spacer Analysis, 16S rRNA libraries and quantitative PCR with an extensive primers set) and correlated to comprehensive geochemical data. Moreover the metabolic potentials and functional traits of the microbial community were also identified using the GeoChip functional gene microarray and metabolic rates. The active microbial community structure in the Sonora Margin sediments was related to deep subsurface ecosystems (Marine Benthic Groups B and D, Miscellaneous Crenarchaeotal Group, Chloroflexi and Candidate divisions) and remained relatively similar throughout the sediment section, despite defined biogeochemical gradients. However, relative abundances of bacterial and archaeal dominant lineages were significantly correlated with organic carbon quantity and origin. Consistently, metabolic pathways for the degradation and assimilation of this organic carbon as well as genetic potentials for the transformation of detrital organic matters, hydrocarbons and recalcitrant substrates were detected, suggesting that chemoorganotrophic microorganisms may dominate the microbial community of the Sonora Margin subsurface sediments. PMID:25099369

Bacterial communities in an ultrapure water containing storage tank of a power plant.

PubMed

Bohus, Veronika; Kéki, Zsuzsa; Márialigeti, Károly; Baranyi, Krisztián; Patek, Gábor; Schunk, János; Tóth, Erika M

2011-12-01

Ultrapure waters (UPWs) containing low levels of organic and inorganic compounds provide extreme environment. On contrary to that microbes occur in such waters and form biofilms on surfaces, thus may induce corrosion processes in many industrial applications. In our study, refined saltless water (UPW) produced for the boiler of a Hungarian power plant was examined before and after storage (sampling the inlet [TKE] and outlet [TKU] waters of a storage tank) with cultivation and culture independent methods. Our results showed increased CFU and direct cell counts after the storage. Cultivation results showed the dominance of aerobic, chemoorganotrophic α-Proteobacteria in both samples. In case of TKU sample, a more complex bacterial community structure could be detected. The applied molecular method (T-RFLP) indicated the presence of a complex microbial community structure with changes in the taxon composition: while in the inlet water sample (TKE) α-Proteobacteria (Sphingomonas sp., Novosphingobium hassiacum) dominated, in the outlet water sample (TKU) the bacterial community shifted towards the dominance of α-Proteobacteria (Rhodoferax sp., Polynucleobacter sp., Sterolibacter sp.), CFB (Bacteroidetes, formerly Cytophaga-Flavobacterium-Bacteroides group) and Firmicutes. This shift to the direction of fermentative communities suggests that storage could help the development of communities with an increased tendency toward corrosion.

Detection of abandoned mines/caves using airborne LWIR hyperspectral data

NASA Astrophysics Data System (ADS)

Shen, Sylvia S.; Roettiger, Kurt A.

2012-09-01

The detection of underground structures, both natural and man-made, continues to be an important requirement in both the military/intelligence and civil communities. There are estimates that as many as 70,000 abandoned mines/caves exist across the nation. These mines represent significant hazards to public health and safety, and they are of concern to Government agencies at the local, state, and federal levels. NASA is interested in the detection of caves on Mars and the Moon in anticipation of future manned space missions. And, the military/ intelligence community is interested in detecting caves, mines, and other underground structures that may be used to conceal the production of weapons of mass destruction or to harbor insurgents or other persons of interest by the terrorists. Locating these mines/caves scattered over millions of square miles is an enormous task, and limited resources necessitate the development of an efficient and effective broad area search strategy using remote sensing technologies. This paper describes an internally-funded research project of The Aerospace Corporation (Aerospace) to assess the feasibility of using airborne hyperspectral data to detect abandoned cave/mine entrances in a broad-area search application. In this research, we have demonstrated the potential utility of using thermal contrast between the cave/mine entrance and the ambient environment as a discriminatory signature. We have also demonstrated the use of a water vapor absorption line at12.55 μm and a quartz absorption feature at 9.25 μm as discriminatory signatures. Further work is required to assess the broader applicability of these signatures.

Effects of Cd and Pb on soil microbial community structure and activities.

PubMed

Khan, Sardar; Hesham, Abd El-Latif; Qiao, Min; Rehman, Shafiqur; He, Ji-Zheng

2010-02-01

Soil contamination with heavy metals occurs as a result of both anthropogenic and natural activities. Heavy metals could have long-term hazardous impacts on the health of soil ecosystems and adverse influences on soil biological processes. Soil enzymatic activities are recognized as sensors towards any natural and anthropogenic disturbance occurring in the soil ecosystem. Similarly, microbial biomass carbon (MBC) is also considered as one of the important soil biological activities frequently influenced by heavy metal contamination. The polymerase chain reaction-denaturing gradient gel electrophoresis (DGGE) has recently been used to investigate changes in soil microbial community composition in response to environmental stresses. Soil microbial community structure and activities are difficult to elucidate using single monitoring approach; therefore, for a better insight and complete depiction of the soil microbial situation, different approaches need to be used. This study was conducted in a greenhouse for a period of 12 weeks to evaluate the changes in indigenous microbial community structure and activities in the soil amended with different application rates of Cd, Pb, and Cd/Pb mix. In a field environment, soil is contaminated with single or mixed heavy metals; so that, in this research, we used the selected metals in both single and mixed forms at different application rates and investigated their toxic effects on microbial community structure and activities, using soil enzyme assays, plate counting, and advanced molecular DGGE technique. Soil microbial activities, including acid phosphatase (ACP), urease (URE), and MBC, and microbial community structure were studied. A soil sample (0-20 cm) with an unknown history of heavy metal contamination was collected and amended with Cd, Pb, and Cd/Pb mix using the CdSO(4) and Pb(NO(3))(2) solutions at different application rates. The amended soils were incubated in the greenhouse at 25 +/- 4 degrees C and 60% water-holding capacity for 12 weeks. During the incubation period, samples were collected from each pot at 0, 2, 9, and 12 weeks for enzyme assays, MBC, numeration of microbes, and DNA extraction. Fumigation-extraction method was used to measure the MBC, while plate counting techniques were used to numerate viable heterotrophic bacteria, fungi, and actinomycetes. Soil DNAs were extracted from the samples and used for DGGE analysis. ACP, URE, and MBC activities of microbial community were significantly lower (p < 0.05) in the metal-amended samples than those in the control. The enzyme inhibition extent was obvious between different incubation periods and varied as the incubation proceeded, and the highest rate was detected in the samples after 2 weeks. However, the lowest values of ACP and URE activities (35.6% and 36.6% of the control, respectively) were found in the Cd(3)/Pb(3)-treated sample after 2 weeks. Similarly, MBC was strongly decreased in both Cd/Pb-amended samples and highest reduction (52.4%) was detected for Cd(3)/Pb(3) treatment. The number of bacteria and actinomycetes were significantly decreased in the heavy metal-amended samples compared to the control, while fungal cells were not significantly different (from 2.3% to 23.87%). In this study, the DGGE profile indicated that the high dose of metal amendment caused a greater change in the number of bands. DGGE banding patterns confirmed that the addition of metals had a significant impact on microbial community structure. In soil ecosystem, heavy metals exhibit toxicological effects on soil microbes which may lead to the decrease of their numbers and activities. This study demonstrated that toxicological effects of heavy metals on soil microbial community structure and activities depend largely on the type and concentration of metal and incubation time. The inhibition extent varied widely among different incubation periods for these enzymes. Furthermore, the rapid inhibition in microbial activities such as ACP, URE, and MBC were observed in the 2 weeks, which should be related to the fact that the microbes were suddenly exposed to heavy metals. The increased inhibition of soil microbial activities is likely to be related to tolerance and adaptation of the microbial community, concentration of pollutants, and mechanisms of heavy metals. The DGGE profile has shown that the structure of the bacterial community changed in amended heavy metal samples. In this research, the microbial community structure was highly affected, consistent with the lower microbial activities in different levels of heavy metals. Furthermore, a great community change in this study, particularly at a high level of contamination, was probably a result of metal toxicity and also unavailability of nutrients because no nutrients were supplied during the whole incubation period. The added concentrations of heavy metals have changed the soil microbial community structure and activities. The highest inhibitory effects on soil microbial activities were observed at 2 weeks of incubation. The bacteria were more sensitive than actinomycetes and fungi. The DGGE profile indicated that bacterial community structure was changed in the Cd/Pb-amended samples, particularly at high concentrations. The investigation of soil microbial community structure and activities together could give more reliable and accurate information about the toxic effects of heavy metals on soil health.

Investigation of water quality and aquatic-community structure in Village and Valley Creeks, City of Birmingham, Jefferson County, Alabama, 2000-01

USGS Publications Warehouse

McPherson, Ann K.; Abrahamsen, Thomas A.; Journey, Celeste A.

2002-01-01

The U.S. Geological Survey conducted a 16-month investigation of water quality, aquatic-community structure, bed sediment, and fish tissue in Village and Valley Creeks, two urban streams that drain areas of highly intensive residential, commercial, and industrial land use in Birmingham, Alabama. Water-quality data were collected between February 2000 and March 2001 at four sites on Village Creek, three sites on Valley Creek, and at two reference sites near Birmingham?Fivemile Creek and Little Cahaba River, both of which drain less-urbanized areas. Stream samples were analyzed for major ions, nutrients, fecal bacteria, trace and major elements, pesticides, and selected organic constituents. Bed-sediment and fish-tissue samples were analyzed for trace and major elements, pesticides, polychlorinated biphenyls, and additional organic compounds. Aquatic-community structure was evaluated by conducting one survey of the fish community and in-stream habitat and two surveys of the benthic-invertebrate community. Bed-sediment and fish-tissue samples, benthic-invertebrates, and habitat data were collected between June 2000 and October 2000 at six of the nine water-quality sites; fish communities were evaluated in April and May 2001 at the six sites where habitat and benthic-invertebrate data were collected. The occurrence and distribution of chemical constituents in the water column and bed sediment provided an initial assessment of water quality in the streams. The structure of the aquatic communities, the physical condition of the fish, and the chemical analyses of fish tissue provided an indication of the cumulative effects of water quality on the aquatic biota. Water chemistry was similar at all sites, characterized by strong calcium-bicarbonate component and magnesium components. Median concentrations of total nitrogen and total phosphorus were highest at the headwaters of Valley Creek and lowest at the reference site on Fivemile Creek. In Village Creek, median concentrations of nitrite and ammonia increased in a downstream direction. In Valley Creek, median concentrations of nitrate, nitrite, ammonia, organic nitrogen, suspended phosphorus, and orthophosphate decreased in a downstream direction. Median concentrations of Escherichia coli and fecal coliform bacteria were highest at the most upstream site of Valley Creek and lowest at the reference site on Fivemile Creek. Concentrations of enterococci exceeded the U.S. Environmental Protection Agency criterion in 80 percent of the samples; concentrations of Escherichia coli exceeded the criterion in 56 percent of the samples. Concentrations of bacteria at the downstream sites on Village and Valley Creeks were elevated during high flow rather than low flow, indicating the presence of nonpoint sources. Surface-water samples were analyzed for chemical compounds that are commonly found in wastewater and urban runoff. The median number of wastewater indicators was highest at the most upstream site on Valley Creek and lowest at the reference site on Fivemile Creek. Concentrations of total recoverable cadmium, copper, lead, and zinc in surface water exceeded acute and chronic aquatic life criteria in up to 24 percent of the samples that were analyzed for trace and major elements. High concentrations of trace and major elements in the water column were detected most frequently during high flow, indicating the presence of nonpoint sources. Of the 24 pesticides detected in surface water, 17 were herbicides and 7 were insecticides. Atrazine, simazine, and prometon were the most commonly detected herbicides; diazinon, chlorpyrifos, and carbaryl were the most commonly detected insecticides. Concentrations of atrazine, carbaryl, chlorpyrifos, diazinon, and malathion periodically exceeded criteria for the protection of aquatic life. Trace-element priority pollutants, pesticides, and other organic compounds were detected in higher concentrations in bed sediment at the Village and Valley Creek sites t

[Characterization of microbial community in produced water from a petroleum reservoir subjected to alkali-surfactant-polymer ASP flooding].

PubMed

Hao, Qin Qin; Shi, Rong Jiu; Hao, Jin Sheng; Zhao, Jin Yi; Li, Guo Qiao; Zhao, Feng; Han, Si Qin; Zhang, Ying

2017-10-01

Injection of alkali, surfactant and polymer (ASP) into oil reservoir can substantially increase oil recovery compared with water-flooding strategy. However, the effects of these agents on the microbial diversity and community structure, which is important for water management and corrosion control in oil industry, are hitherto poorly understood. Here, we disclosed the microbial diversity and community structure in the produced water collected from four producing wells of an ASP-flooded oilfield at Daqing, China, using high-throughput sequencing technique. Results showed that the average pH in produced water was as high as 9.65. The microbial diversity varied from well to well, and the Shannon diversity index was between 2.00 to 3.56. The Proteobacteria (85.5%-98.3%), γ-proteobacteria (83.7%-97.8%), and alkaliphilic Nitrincola (51.8%-82.5%) were the most dominant phylogenetic taxa at the phylum, class, and genus levels, respectively. A total of 12 potentially sulfide-producing genera were detected, and the most abundant taxon was Sulfurospirillum (0.4%-7.4%). The microbial community of ASP-flooded petroleum reservoir was distinct, showing an alkaliphilic or alkalitolerant potential; a reduced diversity and more simple structure were observed compared with those of the water-flooded petroleum reservoirs that were previously reported.

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations

PubMed Central

Almog, Assaf; Besamusca, Ferry; MacMahon, Mel; Garlaschelli, Diego

2015-01-01

The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by “communities” of units whose dynamics, represented by time series of activity, is more strongly correlated internally than with the rest of the system. Recent studies have shown that the binary projections of various financial and neural time series exhibit nontrivial dynamical features that resemble those of the original data. This implies that a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. Here, we explore whether the binary signatures of multiple time series can replicate the same complex community organization of the financial market, as the original weighted time series. We adopt a method that has been specifically designed to detect communities from cross-correlation matrices of time series data. Our analysis shows that the simpler binary representation leads to a community structure that is almost identical with that obtained using the full weighted representation. These results confirm that binary projections of financial time series contain significant structural information. PMID:26226226

Effects of oxytetracycline on archaeal community, and tetracycline resistance genes in anaerobic co-digestion of pig manure and wheat straw.

PubMed

Wang, Xiaojuan; Pan, Hongjia; Gu, Jie; Qian, Xun; Gao, Hua; Qin, Qingjun

2016-12-01

In this study, the effects of different concentrations of oxytetracycline (OTC) on biogas production, archaeal community structure, and the levels of tetracycline resistance genes (TRGs) were investigated in the anaerobic co-digestion products of pig manure and wheat straw. PCR denaturing gradient gel electrophoresis analysis and real-time quantitative polymerase chain reaction (RT-qPCR) (PCR) were used to detect the archaeal community structure and the levels of four TRGs: tet(M), tet(Q), tet(W), and tet(C). The results showed that anaerobic co-digestion with OTC at concentrations of 60, 100, and 140 mg/kg (dry weight of pig manure) reduced the cumulative biogas production levels by 9.9%, 10.4%, and 14.1%, respectively, compared with that produced by the control, which lacked the antibiotic. The addition of OTC substantially modified the structure of the archaeal community. Two orders were identified by phylogenetic analysis, that is, Pseudomonadales and Methanomicrobiales, and the methanogen present during anaerobic co-digestion with OTC may have been resistant to OTC. The abundances of tet(Q) and tet(W) genes increased as the OTC concentration increased, whereas the abundances of tet(M) and tet(C) genes decreased as the OTC concentration increased.

How does conversion of natural tropical rainforest ecosystems affect soil bacterial and fungal communities in the Nile river watershed of Uganda?

PubMed

Alele, Peter O; Sheil, Douglas; Surget-Groba, Yann; Lingling, Shi; Cannon, Charles H

2014-01-01

Uganda's forests are globally important for their conservation values but are under pressure from increasing human population and consumption. In this study, we examine how conversion of natural forest affects soil bacterial and fungal communities. Comparisons in paired natural forest and human-converted sites among four locations indicated that natural forest soils consistently had higher pH, organic carbon, nitrogen, and calcium, although variation among sites was large. Despite these differences, no effect on the diversity of dominant taxa for either bacterial or fungal communities was detected, using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Composition of fungal communities did generally appear different in converted sites, but surprisingly, we did not observe a consistent pattern among sites. The spatial distribution of some taxa and community composition was associated with soil pH, organic carbon, phosphorus and sodium, suggesting that changes in soil communities were nuanced and require more robust metagenomic methods to understand the various components of the community. Given the close geographic proximity of the paired sampling sites, the similarity between natural and converted sites might be due to continued dispersal between treatments. Fungal communities showed greater environmental differentiation than bacterial communities, particularly according to soil pH. We detected biotic homogenization in converted ecosystems and substantial contribution of β-diversity to total diversity, indicating considerable geographic structure in soil biota in these forest communities. Overall, our results suggest that soil microbial communities are relatively resilient to forest conversion and despite a substantial and consistent change in the soil environment, the effects of conversion differed widely among sites. The substantial difference in soil chemistry, with generally lower nutrient quantity in converted sites, does bring into question, how long this resilience will last.

How Does Conversion of Natural Tropical Rainforest Ecosystems Affect Soil Bacterial and Fungal Communities in the Nile River Watershed of Uganda?

PubMed Central

Alele, Peter O.; Sheil, Douglas; Surget-Groba, Yann; Lingling, Shi; Cannon, Charles H.

2014-01-01

Uganda's forests are globally important for their conservation values but are under pressure from increasing human population and consumption. In this study, we examine how conversion of natural forest affects soil bacterial and fungal communities. Comparisons in paired natural forest and human-converted sites among four locations indicated that natural forest soils consistently had higher pH, organic carbon, nitrogen, and calcium, although variation among sites was large. Despite these differences, no effect on the diversity of dominant taxa for either bacterial or fungal communities was detected, using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Composition of fungal communities did generally appear different in converted sites, but surprisingly, we did not observe a consistent pattern among sites. The spatial distribution of some taxa and community composition was associated with soil pH, organic carbon, phosphorus and sodium, suggesting that changes in soil communities were nuanced and require more robust metagenomic methods to understand the various components of the community. Given the close geographic proximity of the paired sampling sites, the similarity between natural and converted sites might be due to continued dispersal between treatments. Fungal communities showed greater environmental differentiation than bacterial communities, particularly according to soil pH. We detected biotic homogenization in converted ecosystems and substantial contribution of β-diversity to total diversity, indicating considerable geographic structure in soil biota in these forest communities. Overall, our results suggest that soil microbial communities are relatively resilient to forest conversion and despite a substantial and consistent change in the soil environment, the effects of conversion differed widely among sites. The substantial difference in soil chemistry, with generally lower nutrient quantity in converted sites, does bring into question, how long this resilience will last. PMID:25118069

Microbial community composition and dynamics of moving bed biofilm reactor systems treating municipal sewage.

PubMed

Biswas, Kristi; Turner, Susan J

2012-02-01

Moving bed biofilm reactor (MBBR) systems are increasingly used for municipal and industrial wastewater treatment, yet in contrast to activated sludge (AS) systems, little is known about their constituent microbial communities. This study investigated the community composition of two municipal MBBR wastewater treatment plants (WWTPs) in Wellington, New Zealand. Monthly samples comprising biofilm and suspended biomass were collected over a 12-month period. Bacterial and archaeal community composition was determined using a full-cycle community approach, including analysis of 16S rRNA gene libraries, fluorescence in situ hybridization (FISH) and automated ribosomal intergenic spacer analysis (ARISA). Differences in microbial community structure and abundance were observed between the two WWTPs and between biofilm and suspended biomass. Biofilms from both plants were dominated by Clostridia and sulfate-reducing members of the Deltaproteobacteria (SRBs). FISH analyses indicated morphological differences in the Deltaproteobacteria detected at the two plants and also revealed distinctive clustering between SRBs and members of the Methanosarcinales, which were the only Archaea detected and were present in low abundance (<5%). Biovolume estimates of the SRBs were higher in biofilm samples from one of the WWTPs which receives both domestic and industrial waste and is influenced by seawater infiltration. The suspended communities from both plants were diverse and dominated by aerobic members of the Gammaproteobacteria and Betaproteobacteria. This study represents the first detailed analysis of microbial communities in full-scale MBBR systems and indicates that this process selects for distinctive biofilm and planktonic communities, both of which differ from those found in conventional AS systems.

Geometric identification and damage detection of structural elements by terrestrial laser scanner

NASA Astrophysics Data System (ADS)

Hou, Tsung-Chin; Liu, Yu-Wei; Su, Yu-Min

2016-04-01

In recent years, three-dimensional (3D) terrestrial laser scanning technologies with higher precision and higher capability are developing rapidly. The growing maturity of laser scanning has gradually approached the required precision as those have been provided by traditional structural monitoring technologies. Together with widely available fast computation for massive point cloud data processing, 3D laser scanning can serve as an efficient structural monitoring alternative for civil engineering communities. Currently most research efforts have focused on integrating/calculating the measured multi-station point cloud data, as well as modeling/establishing the 3D meshes of the scanned objects. Very little attention has been spent on extracting the information related to health conditions and mechanical states of structures. In this study, an automated numerical approach that integrates various existing algorithms for geometric identification and damage detection of structural elements were established. Specifically, adaptive meshes were employed for classifying the point cloud data of the structural elements, and detecting the associated damages from the calculated eigenvalues in each area of the structural element. Furthermore, kd-tree was used to enhance the searching efficiency of plane fitting which were later used for identifying the boundaries of structural elements. The results of geometric identification were compared with M3C2 algorithm provided by CloudCompare, as well as validated by LVDT measurements of full-scale reinforced concrete beams tested in laboratory. It shows that 3D laser scanning, through the established processing approaches of the point cloud data, can offer a rapid, nondestructive, remote, and accurate solution for geometric identification and damage detection of structural elements.

Effects of haze pollution on microbial community changes and correlation with chemical components in atmospheric particulate matter.

PubMed

Sun, Yujiao; Xu, Shangwei; Zheng, Danyang; Li, Jie; Tian, Hezhong; Wang, Yong

2018-05-10

In this study, particulate matter (PM) with aerodynamic diameters of ≤2.5 and ≤10 μm (PM 2.5 and PM 10 , respectively), which was found at different concentrations in spring, was collected in Beijing. The chemical composition and bacterial community diversity of PM were determined, and the relationship between them was studied by 16S rRNA sequencing and mathematical statistics. Chemical composition analysis revealed greater relative percentages of total organic compounds (TOC) and secondary ions (NO 3 - , SO 4 2- , and NH 4 + ). The concentrations of Ca 2+ , Na + , Mg 2+ , K + and SO 4 2- increased in high-concentration PM, which was associated with the contribution of soil, dust and soot. Microbiological analysis revealed 1191 operational taxonomic units. Microbial community structure was stable at the phylum level. The most abundant phyla were Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes and Cyanobacteria. Community clustering analysis at the genus level showed that the difference in bacterial community structure between different PM concentrations (clean air vs. smog) was greater than that between different particle sizes. The dominant genera varied in different concentrations of PM. An unclassified genus of Cyanobacteria and Comamonadaceae were most abundant in low- and high-concentration PM, respectively. The microbial community structure was dynamic at the genus level due to different environmental factors. The dominant bacteria in high-concentration PM were widely distributed in soils, indicating that the soil contributed more to the increase in the PM. The individual microbes that were detected did not increase significantly as the PM concentration increased. The bacterial community structure was strongly correlated with K + , Ca 2+ , Na + , Mg 2+ , SO 4 2- and TOC in high-concentration PM and had a good correlation with NO 3 - , Cl - , NH 4 + and TIC in low-concentration PM. Soil and dust contributed to the increase in the concentration of the particles, and the relevant chemical components also produced differences in the bacterial community structure in different concentrations of PM. Copyright © 2018. Published by Elsevier B.V.

Associations between soil bacterial community structure and nutrient cycling functions in long-term organic farm soils following cover crop and organic fertilizer amendment.

PubMed

Fernandez, Adria L; Sheaffer, Craig C; Wyse, Donald L; Staley, Christopher; Gould, Trevor J; Sadowsky, Michael J

2016-10-01

Agricultural management practices can produce changes in soil microbial populations whose functions are crucial to crop production and may be detectable using high-throughput sequencing of bacterial 16S rRNA. To apply sequencing-derived bacterial community structure data to on-farm decision-making will require a better understanding of the complex associations between soil microbial community structure and soil function. Here 16S rRNA sequencing was used to profile soil bacterial communities following application of cover crops and organic fertilizer treatments in certified organic field cropping systems. Amendment treatments were hairy vetch (Vicia villosa), winter rye (Secale cereale), oilseed radish (Raphanus sativus), buckwheat (Fagopyrum esculentum), beef manure, pelleted poultry manure, Sustane(®) 8-2-4, and a no-amendment control. Enzyme activities, net N mineralization, soil respiration, and soil physicochemical properties including nutrient levels, organic matter (OM) and pH were measured. Relationships between these functional and physicochemical parameters and soil bacterial community structure were assessed using multivariate methods including redundancy analysis, discriminant analysis, and Bayesian inference. Several cover crops and fertilizers affected soil functions including N-acetyl-β-d-glucosaminidase and β-glucosidase activity. Effects, however, were not consistent across locations and sampling timepoints. Correlations were observed among functional parameters and relative abundances of individual bacterial families and phyla. Bayesian analysis inferred no directional relationships between functional activities, bacterial families, and physicochemical parameters. Soil functional profiles were more strongly predicted by location than by treatment, and differences were largely explained by soil physicochemical parameters. Composition of soil bacterial communities was predictive of soil functional profiles. Differences in soil function were better explained using both soil physicochemical test values and bacterial community structure data than using soil tests alone. Pursuing a better understanding of bacterial community composition and how it is affected by farming practices is a promising avenue for increasing our ability to predict the impact of management practices on important soil functions. Copyright © 2016. Published by Elsevier B.V.

Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks

PubMed Central

Taylor, Dane; Caceres, Rajmonda S.; Mucha, Peter J.

2017-01-01

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K*. When layers are aggregated via a summation, we obtain K∗∝O(NL/T), where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than 𝒪(L−1/2). Moreover, we find that thresholding the summation can, in some cases, cause K* to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold. PMID:29445565

Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

PubMed

Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J

2017-01-01

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K * . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L , then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than ( L -1/2 ). Moreover, we find that thresholding the summation can, in some cases, cause K * to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

A framework for detecting communities of unbalanced sizes in networks

NASA Astrophysics Data System (ADS)

Žalik, Krista Rizman; Žalik, Borut

2018-01-01

Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

Core-periphery structure requires something else in the network

NASA Astrophysics Data System (ADS)

Kojaku, Sadamori; Masuda, Naoki

2018-04-01

A network with core-periphery structure consists of core nodes that are densely interconnected. In contrast to a community structure, which is a different meso-scale structure of networks, core nodes can be connected to peripheral nodes and peripheral nodes are not densely interconnected. Although core-periphery structure sounds reasonable, we argue that it is merely accounted for by heterogeneous degree distributions, if one partitions a network into a single core block and a single periphery block, which the famous Borgatti–Everett algorithm and many succeeding algorithms assume. In other words, there is a strong tendency that high-degree and low-degree nodes are judged to be core and peripheral nodes, respectively. To discuss core-periphery structure beyond the expectation of the node’s degree (as described by the configuration model), we propose that one needs to assume at least one block of nodes apart from the focal core-periphery structure, such as a different core-periphery pair, community or nodes not belonging to any meso-scale structure. We propose a scalable algorithm to detect pairs of core and periphery in networks, controlling for the effect of the node’s degree. We illustrate our algorithm using various empirical networks.

Composition and Dynamics of Bacterial Communities of a Drinking Water Supply System as Assessed by RNA- and DNA-Based 16S rRNA Gene Fingerprinting

PubMed Central

Eichler, Stefan; Christen, Richard; Höltje, Claudia; Westphal, Petra; Bötel, Julia; Brettar, Ingrid; Mehling, Arndt; Höfle, Manfred G.

2006-01-01

Bacterial community dynamics of a whole drinking water supply system (DWSS) were studied from source to tap. Raw water for this DWSS is provided by two reservoirs with different water characteristics in the Harz mountains of Northern Germany. Samples were taken after different steps of treatment of raw water (i.e., flocculation, sand filtration, and chlorination) and at different points along the supply system to the tap. RNA and DNA were extracted from the sampled water. The 16S rRNA or its genes were partially amplified by reverse transcription-PCR or PCR and analyzed by single-strand conformation polymorphism community fingerprints. The bacterial community structures of the raw water samples from the two reservoirs were very different, but no major changes of these structures occurred after flocculation and sand filtration. Chlorination of the processed raw water strongly affected bacterial community structure, as reflected by the RNA-based fingerprints. This effect was less pronounced for the DNA-based fingerprints. After chlorination, the bacterial community remained rather constant from the storage containers to the tap. Furthermore, the community structure of the tap water did not change substantially for several months. Community composition was assessed by sequencing of abundant bands and phylogenetic analysis of the sequences obtained. The taxonomic compositions of the bacterial communities from both reservoirs were very different at the species level due to their different limnologies. On the other hand, major taxonomic groups, well known to occur in freshwater, such as Alphaproteobacteria, Betaproteobacteria, and Bacteroidetes, were found in both reservoirs. Significant differences in the detection of the major groups were observed between DNA-based and RNA-based fingerprints irrespective of the reservoir. Chlorination of the drinking water seemed to promote growth of nitrifying bacteria. Detailed analysis of the community dynamics of the whole DWSS revealed a significant influence of both source waters on the overall composition of the drinking water microflora and demonstrated the relevance of the raw water microflora for the drinking water microflora provided to the end user. PMID:16517632

How anthropogenic changes may affect soil-borne parasite diversity? Plant-parasitic nematode communities associated with olive trees in Morocco as a case study.

PubMed

Ali, Nadine; Tavoillot, Johannes; Besnard, Guillaume; Khadari, Bouchaib; Dmowska, Ewa; Winiszewska, Grażyna; Fossati-Gaschignard, Odile; Ater, Mohammed; Aït Hamza, Mohamed; El Mousadik, Abdelhamid; El Oualkadi, Aïcha; Moukhli, Abdelmajid; Essalouh, Laila; El Bakkali, Ahmed; Chapuis, Elodie; Mateille, Thierry

2017-02-06

Plant-parasitic nematodes (PPN) are major crop pests. On olive (Olea europaea), they significantly contribute to economic losses in the top-ten olive producing countries in the world especially in nurseries and under cropping intensification. The diversity and the structure of PPN communities respond to environmental and anthropogenic forces. The olive tree is a good host plant model to understand the impact of such forces on PPN diversity since it grows according to different modalities (wild, feral and cultivated olives). A wide soil survey was conducted in several olive-growing regions in Morocco. The taxonomical and the functional diversity as well as the structures of PPN communities were described and then compared between non-cultivated (wild and feral forms) and cultivated (traditional and high-density olive cultivation) olives. A high diversity of PPN with the detection of 117 species and 47 genera was revealed. Some taxa were recorded for the first time on olive trees worldwide and new species were also identified. Anthropogenic factors (wild vs cultivated conditions) strongly impacted the PPN diversity and the functional composition of communities because the species richness, the local diversity and the evenness of communities significantly decreased and the abundance of nematodes significantly increased in high-density conditions. Furthermore, these conditions exhibited many more obligate and colonizer PPN and less persister PPN compared to non-cultivated conditions. Taxonomical structures of communities were also impacted: genera such as Xiphinema spp. and Heterodera spp. were dominant in wild olive, whereas harmful taxa such as Meloidogyne spp. were especially enhanced in high-density orchards. Olive anthropogenic practices reduce the PPN diversity in communities and lead to changes of the community structures with the development of some damaging nematodes. The study underlined the PPN diversity as a relevant indicator to assess community pathogenicity. That could be taken into account in order to design control strategies based on community rearrangements and interactions between species instead of reducing the most pathogenic species.

Caribbean-Wide, Long-Term Study of Seagrass Beds Reveals Local Variations, Shifts in Community Structure and Occasional Collapse

PubMed Central

van Tussenbroek, Brigitta I.; Cortés, Jorge; Collin, Rachel; Fonseca, Ana C.; Gayle, Peter M. H.; Guzmán, Hector M.; Jácome, Gabriel E.; Juman, Rahanna; Koltes, Karen H.; Oxenford, Hazel A.; Rodríguez-Ramirez, Alberto; Samper-Villarreal, Jimena; Smith, Struan R.; Tschirky, John J.; Weil, Ernesto

2014-01-01

The CARICOMP monitoring network gathered standardized data from 52 seagrass sampling stations at 22 sites (mostly Thalassia testudinum-dominated beds in reef systems) across the Wider Caribbean twice a year over the period 1993 to 2007 (and in some cases up to 2012). Wide variations in community total biomass (285 to >2000 g dry m−2) and annual foliar productivity of the dominant seagrass T. testudinum (<200 and >2000 g dry m−2) were found among sites. Solar-cycle related intra-annual variations in T. testudinum leaf productivity were detected at latitudes > 16°N. Hurricanes had little to no long-term effects on these well-developed seagrass communities, except for 1 station, where the vegetation was lost by burial below ∼1 m sand. At two sites (5 stations), the seagrass beds collapsed due to excessive grazing by turtles or sea-urchins (the latter in combination with human impact and storms). The low-cost methods of this regional-scale monitoring program were sufficient to detect long-term shifts in the communities, and fifteen (43%) out of 35 long-term monitoring stations (at 17 sites) showed trends in seagrass communities consistent with expected changes under environmental deterioration. PMID:24594732

Dynamics of Bacterial Communities in Two Unpolluted Soils after Spiking with Phenanthrene: Soil Type Specific and Common Responders

PubMed Central

Ding, Guo-Chun; Heuer, Holger; Smalla, Kornelia

2012-01-01

Considering their key role for ecosystem processes, it is important to understand the response of microbial communities in unpolluted soils to pollution with polycyclic aromatic hydrocarbons (PAH). Phenanthrene, a model compound for PAH, was spiked to a Cambisol and a Luvisol soil. Total community DNA from phenanthrene-spiked and control soils collected on days 0, 21, and 63 were analyzed based on PCR-amplified 16S rRNA gene fragments. Denaturing gradient gel electrophoresis (DGGE) fingerprints of bacterial communities increasingly deviated with time between spiked and control soils. In taxon specific DGGE, significant responses of Alphaproteobacteria and Actinobacteria became only detectable after 63 days, while significant effects on Betaproteobacteria were detectable in both soils after 21 days. Comparison of the taxonomic distribution of bacteria in spiked and control soils on day 63 as revealed by pyrosequencing indicated soil type specific negative effects of phenanthrene on several taxa, many of them belonging to the Gamma-, Beta-, or Deltaproteobacteria. Bacterial richness and evenness decreased in spiked soils. Despite the significant differences in the bacterial community structure between both soils on day 0, similar genera increased in relative abundance after PAH spiking, especially Sphingomonas and Polaromonas. However, this did not result in an increased overall similarity of the bacterial communities in both soils. PMID:22934091

Can the soil fauna of boreal forests recover from lead-derived stress in a shooting range area?

PubMed

Selonen, Salla; Liiri, Mira; Setälä, Heikki

2014-04-01

The responses of soil faunal communities to lead (Pb) contamination in a shooting range area and the recovery of these fauna after range abandonment were studied by comparing the communities at an active shotgun shooting range, an abandoned shooting range, and a control site, locating in the same forest. Despite the similar overall Pb pellet load at the shooting ranges, reaching up to 4 kg m(-2), Pb concentrations in the top soil of the abandoned range has decreased due to the accumulation of detritus on the soil surface. As a consequence, soil animal communities were shown to recover from Pb-related disturbances by utilizing the less contaminated soil layer. Microarthropods showed the clearest signs of recovery, their numbers and community composition being close to those detected at the control site. However, in the deepest organic soil layer, the negative effects of Pb were more pronounced at the abandoned than at the active shooting range, which was detected as altered microarthropod and nematode community structures, reduced abundances of several microarthropod taxa, and the total absence of enchytraeid worms. Thus, although the accumulation of fresh litter on soil surface can promote the recovery of decomposer communities in the top soil, the gradual release of Pb from corroding pellets may pose a long-lasting risk for decomposer taxa deeper in the soil.

Detecting Below-Ground Processes, Diversity, and Ecosystem Function in a Savanna Ecosystem Using Spectroscopy Across Different Vegetation Layers

NASA Astrophysics Data System (ADS)

Cavender-Bares, J.; Schweiger, A. K.; Madritch, M. D.; Gamon, J. A.; Hobbie, S. E.; Montgomery, R.; Townsend, P. A.

2017-12-01

Above-and below-ground plant traits are important for substrate input to the rhizosphere. The substrate composition of the rhizosphere, in turn, affects the diversity of soil organisms, influences soil biochemistry, and water content, and resource availability for plant growth. This has substantial consequences for ecosystem functions, such as above-ground productivity and stability. Above-ground plant chemical and structural traits can be linked to the characteristics of other plant organs, including roots. Airborne imaging spectroscopy has been successfully used to model and predict chemical and structural traits of the above-ground vegetation. However, remotely sensed images capture, almost exclusively, signals from the top of the canopy, providing limited direct information about understory vegetation. Here, we use a data set collected in a savanna ecosystem consisting of spectral measurements gathered at the leaf, the whole plant, and vegetation canopy level to test for hypothesized linkages between above- and below-ground processes that influence root biomass, soil biochemistry, and the diversity of the soil community. In this environment, consisting of herbaceous vegetation intermixed with shrubs and trees growing at variable densities, we investigate the contribution of different vegetation strata to soil characteristics and test the ability of imaging spectroscopy to detect these in plant communities with contrasting vertical structure.

A Dissolved Oxygen Threshold for Shifts in Bacterial Community Structure in a Seasonally Hypoxic Estuary.

PubMed

Spietz, Rachel L; Williams, Cheryl M; Rocap, Gabrielle; Horner-Devine, M Claire

2015-01-01

Pelagic ecosystems can become depleted of dissolved oxygen as a result of both natural processes and anthropogenic effects. As dissolved oxygen concentration decreases, energy shifts from macrofauna to microorganisms, which persist in these hypoxic zones. Oxygen-limited regions are rapidly expanding globally; however, patterns of microbial communities associated with dissolved oxygen gradients are not yet well understood. To assess the effects of decreasing dissolved oxygen on bacteria, we examined shifts in bacterial community structure over space and time in Hood Canal, Washington, USA-a glacial fjord-like water body that experiences seasonal low dissolved oxygen levels known to be detrimental to fish and other marine organisms. We found a strong negative association between bacterial richness and dissolved oxygen. Bacterial community composition across all samples was also strongly associated with the dissolved oxygen gradient, and significant changes in bacterial community composition occurred at a dissolved oxygen concentration between 5.18 and 7.12 mg O2 L(-1). This threshold value of dissolved oxygen is higher than classic definitions of hypoxia (<2.0 mg O2 L(-1)), suggesting that changes in bacterial communities may precede the detrimental effects on ecologically and economically important macrofauna. Furthermore, bacterial taxa responsible for driving whole community changes across the oxygen gradient are commonly detected in other oxygen-stressed ecosystems, suggesting that the patterns we uncovered in Hood Canal may be relevant in other low oxygen ecosystems.

Body size distributions signal a regime shift in a lake ...

EPA Pesticide Factsheets

Communities of organisms, from mammals to microorganisms, have discontinuous distributions of body size. This pattern of size structuring is a conservative trait of community organization and is a product of processes that occur at multiple spatial and temporal scales. In this study, we assessed whether body size patterns serve as an indicator of a threshold between alternative regimes. Over the past 7000 years, the biological communities of Foy Lake (Montana,USA) have undergone a major regime shift owing to climate change. We used a palaeoecological record of diatom communities to estimate diatom sizes, and then analysed the discontinuous distribution of organism sizes over time. We used Bayesian classification and regression tree models to determine that all time intervals exhibited aggregations of sizes separated by gaps in the distribution and found a significant change in diatom body size distributions approximately 150 years before the identified ecosystem regime shift. We suggest that discontinuity analysis is a useful addition to the suite of tools for the detection of early warning signals of regime shifts. Communities of organisms from mammals to microorganisms have discontinuous distributions of body size. This pattern of size structuring is a conservative trait of community organization and is a product of processes that occur at discrete spatial and temporal scales within ecosystems. Here, a paleoecological record of diatom community change is use

Avian community responses to post-fire forest structure: Implications for fire management in mixed conifer forests

USGS Publications Warehouse

White, Angela M.; Manley, Patricia N.; Tarbill, Gina; Richardson, T.L.; Russell, Robin E.; Safford, Hugh D.; Dobrowski, Solomon Z.

2015-01-01

Fire is a natural process and the dominant disturbance shaping plant and animal communities in many coniferous forests of the western US. Given that fire size and severity are predicted to increase in the future, it has become increasingly important to understand how wildlife responds to fire and post-fire management. The Angora Fire burned 1243 hectares of mixed conifer forest in South Lake Tahoe, California. We conducted avian point counts for the first 3 years following the fire in burned and unburned areas to investigate which habitat characteristics are most important for re-establishing or maintaining the native avian community in post-fire landscapes. We used a multi-species occurrence model to estimate how avian species are influenced by the density of live and dead trees and shrub cover. While accounting for variations in the detectability of species, our approach estimated the occurrence probabilities of all species detected including those that were rare or observed infrequently. Although all species encountered in this study were detected in burned areas, species-specific modeling results predicted that some species were strongly associated with specific post-fire conditions, such as a high density of dead trees, open-canopy conditions or high levels of shrub cover that occur at particular burn severities or at a particular time following fire. These results indicate that prescribed fire or managed wildfire which burns at low to moderate severity without at least some high-severity effects is both unlikely to result in the species assemblages that are unique to post-fire areas or to provide habitat for burn specialists. Additionally, the probability of occurrence for many species was associated with high levels of standing dead trees indicating that intensive post-fire harvest of these structures could negatively impact habitat of a considerable proportion of the avian community.

Accelerating the Mining of Influential Nodes in Complex Networks through Community Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halappanavar, Mahantesh; Sathanur, Arun V.; Nandi, Apurba

Computing the set of influential nodes with a given size to ensure maximal spread of influence on a complex network is a challenging problem impacting multiple applications. A rigorous approach to influence maximization involves utilization of optimization routines that comes with a high computational cost. In this work, we propose to exploit the existence of communities in complex networks to accelerate the mining of influential seeds. We provide intuitive reasoning to explain why our approach should be able to provide speedups without significantly degrading the extent of the spread of influence when compared to the case of influence maximization withoutmore » using the community information. Additionally, we have parallelized the complete workflow by leveraging an existing parallel implementation of the Louvain community detection algorithm. We then conduct a series of experiments on a dataset with three representative graphs to first verify our implementation and then demonstrate the speedups. Our method achieves speedups ranging from 3x - 28x for graphs with small number of communities while nearly matching or even exceeding the activation performance on the entire graph. Complexity analysis reveals that dramatic speedups are possible for larger graphs that contain a correspondingly larger number of communities. In addition to the speedups obtained from the utilization of the community structure, scalability results show up to 6.3x speedup on 20 cores relative to the baseline run on 2 cores. Finally, current limitations of the approach are outlined along with the planned next steps.« less

Ordination of breeding birds in relation to environmental gradients in three southeastern United States floodplain forests

USGS Publications Warehouse

Wakeley, J.S.; Guilfoyle, M.P.; Antrobus, T.J.; Fischer, R.A.; Barrow, W.C.; Hamel, P.B.

2007-01-01

We used an ordination approach to identify factors important to the organization of breeding bird communities in three floodplains: Cache River, Arkansas (AR), Iatt Creek, Louisiana (LA), and the Coosawhatchie River, South Carolina (SC), USA. We used 5-min point counts to sample birds in each study area each spring from 1995 to 1998, and measured ground-surface elevations and a suite of other habitat variables to investigate bird distributions and community characteristics in relation to important environmental gradients. In both AR and SC, the average number of Neotropical migrant species detected was lowest in semipermanently flooded Nyssa aquatica Linnaeus habitats and greatest in the highest elevation floodplain zone. Melanerpes carolinus Linnaeus, Protonotaria citrea Boddaert, Quiscalus quiscula Linnaeus, and other species were more abundant in N. aquatica habitats, whereas Wilsonia citrina Boddaert, Oporornis formosus Wilson, Vireo griseus Boddaert, and others were more abundant in drier floodplain zones. In LA, there were no significant differences in community metrics or bird species abundances among forest types. Canonical correspondence analyses revealed that structural development of understory vegetation was the most important factor affecting bird distributions in all three study areas; however, potential causes of these structural gradients differed. In AR and SC, differences in habitat structure were related to the hydrologic gradient, as indexed by ground-surface elevation. In LA, structural variations were related mainly to the frequency of canopy gaps. Thus, bird communities in all three areas appeared to be organized primarily in response to repeated localized disturbance. Our results suggest that regular disturbance due to flooding plays an important role in structuring breeding bird communities in floodplains subject to prolonged inundation, whereas other agents of disturbance (e.g., canopy gaps) may be more important in headwater systems subject to only short-duration flooding. Management for avian community integrity in these systems should strive to maintain forest zonation and natural disturbance regimes. ?? 2007 Springer Science+Business Media B.V.

Functional Potential of Soil Microbial Communities in the Maize Rhizosphere

PubMed Central

Xiong, Jingbo; Li, Jiabao; He, Zhili; Zhou, Jizhong; Yannarell, Anthony C.; Mackie, Roderick I.

2014-01-01

Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Here, we identified important functional genes that characterize the rhizosphere microbial community to understand metabolic capabilities in the maize rhizosphere using the GeoChip-based functional gene array method. Significant differences in functional gene structure were apparent between rhizosphere and bulk soil microbial communities. Approximately half of the detected gene families were significantly (p<0.05) increased in the rhizosphere. Based on the detected gyrB genes, Gammaproteobacteria, Betaproteobacteria, Firmicutes, Bacteroidetes and Cyanobacteria were most enriched in the rhizosphere compared to those in the bulk soil. The rhizosphere niche also supported greater functional diversity in catabolic pathways. The maize rhizosphere had significantly enriched genes involved in carbon fixation and degradation (especially for hemicelluloses, aromatics and lignin), nitrogen fixation, ammonification, denitrification, polyphosphate biosynthesis and degradation, sulfur reduction and oxidation. This research demonstrates that the maize rhizosphere is a hotspot of genes, mostly originating from dominant soil microbial groups such as Proteobacteria, providing functional capacity for the transformation of labile and recalcitrant organic C, N, P and S compounds. PMID:25383887

Contrasting taxonomic stratification of microbial communities in two hypersaline meromictic lakes

PubMed Central

Andrei, Adrian-Ştefan; Robeson, Michael S; Baricz, Andreea; Coman, Cristian; Muntean, Vasile; Ionescu, Artur; Etiope, Giuseppe; Alexe, Mircea; Sicora, Cosmin Ionel; Podar, Mircea; Banciu, Horia Leonard

2015-01-01

Hypersaline meromictic lakes are extreme environments in which water stratification is associated with powerful physicochemical gradients and high salt concentrations. Furthermore, their physical stability coupled with vertical water column partitioning makes them important research model systems in microbial niche differentiation and biogeochemical cycling. Here, we compare the prokaryotic assemblages from Ursu and Fara Fund hypersaline meromictic lakes (Transylvanian Basin, Romania) in relation to their limnological factors and infer their role in elemental cycling by matching taxa to known taxon-specific biogeochemical functions. To assess the composition and structure of prokaryotic communities and the environmental factors that structure them, deep-coverage small subunit (SSU) ribosomal RNA (rDNA) amplicon sequencing, community domain-specific quantitative PCR and physicochemical analyses were performed on samples collected along depth profiles. The analyses showed that the lakes harbored multiple and diverse prokaryotic communities whose distribution mirrored the water stratification patterns. Ursu Lake was found to be dominated by Bacteria and to have a greater prokaryotic diversity than Fara Fund Lake that harbored an increased cell density and was populated mostly by Archaea within oxic strata. In spite of their contrasting diversity, the microbial populations indigenous to each lake pointed to similar physiological functions within carbon degradation and sulfate reduction. Furthermore, the taxonomy results coupled with methane detection and its stable C isotope composition indicated the presence of a yet-undescribed methanogenic group in the lakes' hypersaline monimolimnion. In addition, ultrasmall uncultivated archaeal lineages were detected in the chemocline of Fara Fund Lake, where the recently proposed Nanohaloarchaeota phylum was found to thrive. PMID:25932617

Fast detection of the fuzzy communities based on leader-driven algorithm

NASA Astrophysics Data System (ADS)

Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

2018-03-01

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

Analysing taxonomic structures and local ecological processes in temperate forests in North Eastern China.

PubMed

Fan, Chunyu; Tan, Lingzhao; Zhang, Chunyu; Zhao, Xiuhai; von Gadow, Klaus

2017-10-30

One of the core issues of forest community ecology is the exploration of how ecological processes affect community structure. The relative importance of different processes is still under debate. This study addresses four questions: (1) how is the taxonomic structure of a forest community affected by spatial scale? (2) does the taxonomic structure reveal effects of local processes such as environmental filtering, dispersal limitation or interspecific competition at a local scale? (3) does the effect of local processes on the taxonomic structure vary with the spatial scale? (4) does the analysis based on taxonomic structures provide similar insights when compared with the use of phylogenetic information? Based on the data collected in two large forest observational field studies, the taxonomic structures of the plant communities were analyzed at different sampling scales using taxonomic ratios (number of genera/number of species, number of families/number of species), and the relationship between the number of higher taxa and the number of species. Two random null models were used and the "standardized effect size" (SES) of taxonomic ratios was calculated, to assess possible differences between the observed and simulated taxonomic structures, which may be caused by specific ecological processes. We further applied a phylogeny-based method to compare results with those of the taxonomic approach. As expected, the taxonomic ratios decline with increasing grain size. The quantitative relationship between genera/families and species, described by a linearized power function, showed a good fit. With the exception of the family-species relationship in the Jiaohe study area, the exponents of the genus/family-species relationships did not show any scale dependent effects. The taxonomic ratios of the observed communities had significantly lower values than those of the simulated random community under the test of two null models at almost all scales. Null Model 2 which considered the spatial dispersion of species generated a taxonomic structure which proved to be more consistent with that in the observed community. As sampling sizes increased from 20 m × 20 m to 50 m × 50 m, the magnitudes of SESs of taxonomic ratios increased. Based on the phylogenetic analysis, we found that the Jiaohe plot was phylogenetically clustered at almost all scales. We detected significant phylogenetically overdispersion at the 20 m × 20 m and 30 m × 30 m scales in the Liangshui plot. The results suggest that the effect of abiotic filtering is greater than the effects of interspecific competition in shaping the local community at almost all scales. Local processes influence the taxonomic structures, but their combined effects vary with the spatial scale. The taxonomic approach provides similar insights as the phylogenetic approach, especially when we applied a more conservative null model. Analysing taxonomic structure may be a useful tool for communities where well-resolved phylogenetic data are not available.

Metamorphosis of a Scleractinian Coral in Response to Microbial Biofilms

PubMed Central

Webster, Nicole S.; Smith, Luke D.; Heyward, Andrew J.; Watts, Joy E. M.; Webb, Richard I.; Blackall, Linda L.; Negri, Andrew P.

2004-01-01

Microorganisms have been reported to induce settlement and metamorphosis in a wide range of marine invertebrate species. However, the primary cue reported for metamorphosis of coral larvae is calcareous coralline algae (CCA). Herein we report the community structure of developing coral reef biofilms and the potential role they play in triggering the metamorphosis of a scleractinian coral. Two-week-old biofilms induced metamorphosis in less than 10% of larvae, whereas metamorphosis increased significantly on older biofilms, with a maximum of 41% occurring on 8-week-old microbial films. There was a significant influence of depth in 4- and 8-week biofilms, with greater levels of metamorphosis occurring in response to shallow-water communities. Importantly, larvae were found to settle and metamorphose in response to microbial biofilms lacking CCA from both shallow and deep treatments, indicating that microorganisms not associated with CCA may play a significant role in coral metamorphosis. A polyphasic approach consisting of scanning electron microscopy, fluorescence in situ hybridization (FISH), and denaturing gradient gel electrophoresis (DGGE) revealed that coral reef biofilms were comprised of complex bacterial and microalgal communities which were distinct at each depth and time. Principal-component analysis of FISH data showed that the Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Cytophaga-Flavobacterium of Bacteroidetes had the largest influence on overall community composition. A low abundance of Archaea was detected in almost all biofilms, providing the first report of Archaea associated with coral reef biofilms. No differences in the relative densities of each subdivision of Proteobacteria were observed between slides that induced larval metamorphosis and those that did not. Comparative cluster analysis of bacterial DGGE patterns also revealed that there were clear age and depth distinctions in biofilm community structure; however, no difference was detected in banding profiles between biofilms which induced larval metamorphosis and those where no metamorphosis occurred. This investigation demonstrates that complex microbial communities can induce coral metamorphosis in the absence of CCA. PMID:14766608

Microbial Community Structure of an Alluvial Aquifer Treated to Encourage Microbial Induced Calcite Precipitation

NASA Astrophysics Data System (ADS)

Ohan, J.; Saneiyan, S.; Lee, J.; Ntarlagiannis, D.; Burns, S.; Colwell, F. S.

2017-12-01

An oligotrophic aquifer in the Colorado River floodplain (Rifle, CO) was treated with molasses and urea to encourage microbial induced calcite precipitation (MICP). This would stabilize the soil mass by reducing porosity and strengthening the mineral fabric. Over the course of a 15-day treatment period, microbial biomass was collected from monitoring well groundwater for DNA extraction and sequencing. Bromide, a conservative tracer, was co-injected and subsequently detected in downgradient wells, confirming effective nutrient delivery. Conductivity increased during the injection regime and an overall decrease in pH was observed. Groundwater chemistry showed a marked increase in ammonia, suggesting urea hydrolysis - a process catalyzed by the enzyme urease - the primary enzyme implicated in MICP. Additionally, soluble iron was detected, suggesting a general increase in microbial activity; possibly as iron-reducing bacteria changed insoluble ferric oxide to soluble ferrous hydroxide in the anoxic aquifer. DNA sequencing of the 16S rRNA gene confirmed the presence of iron reducing bacteria, including Shewanella and Desulfuromonadales. Generally, a decrease in microbial community diversity was observed when pre-injection community taxa were compared with post-injection community taxa. Phyla indicative of anoxic aquifers were represented in accordance with previous literature at the Rifle site. Linear discriminant analysis showed significant differences in representative phyla over the course of the injection series. Geophysical monitoring of the site further suggested changes that could be due to MICP. Induced polarization increased the phase shift in the primary treated area, in agreement with laboratory experiments. Cross-hole seismic testing confirmed that the shear wave velocities increased in the treated soil mass, implying the soil matrix became more stable. Future investigations will help elucidate the viability and efficacy of MICP treatment in changing microbial community structure, and the functional attributes associated with community change, so that physical strengthening of soils by microbial action can be accomplished.

A comparison of selected diversity, similarity, and biotic indices for detecting changes in benthic-invertebrate community structure and stream quality

USGS Publications Warehouse

Lydy, M.J.; Crawford, Charles G.; Frey, J.W.

2000-01-01

Implementation of advanced wastewater treatment at the two municipal wastewater-treatment plants for Indianapolis, Indiana, resulted in substantial improvement in the quality of the receiving stream and significant changes in the benthic-invertebrate community. Diversity, similarity, and biotic indices were compared to determine which indices best reflected changes in the composition of the biota in the river. None of the indices perfectly reflected the changes in river quality or community structure. Similarity indices, especially percentage similarity, exhibit the most promise of the three classes of indices. Diversity indices were least useful, wrongly indicating that water quality deteriorated after the upgrade of the wastewater-treatment plants. The most descriptive tool in analyzing the data was the percentage of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa present. Using a mixture of indices and other analytical tools, such as EPT, in the analysis of biological data will ensure the most effective investigations of water quality.

Effects of the 2003 European heatwave on the benthic community of a severe transitional ecosystem (Comacchio Saltworks, Italy).

PubMed

Munari, Cristina

2011-12-01

The summer of 2003 was the warmest summer in Europe since the 16th century. Its consequences on the fauna of a transitional ecosystem were studied through biodiversity, functional and ecological indicators, from summer 2002 to winter 2005. The heatwave caused considerable changes in the benthic community structure and relative composition, persisting in 2005. Animal assemblages switched from mollusc- to annelida-dominated. Biodiversity and functional indicators captured changes in community structure and composition, proving to be powerful tools to detect responses related to global warming. Ecological indicators rendered a monotonic response oscillating between bad and poor ecological status across the study period. The resilience of mollusc biocoenosis resulted limited with respect to other taxa, posing concerns about their conservation if, as predicted, the frequency of summers as hot as that of 2003 will progressively increase to become the norm at the end of this century. Copyright © 2011 Elsevier Ltd. All rights reserved.

Tracking composition of microbial communities for simultaneous nitrification and denitrification in polyurethane foam.

PubMed

Chen, Yuan; Wang, Li; Ma, Fang; Yang, Ji-xian; Qiu, Shan

2014-01-01

The process of simultaneous nitrification and denitrification (SND) of immobilized microorganisms in polyurethane form is discussed. The effect of different positions within the polyurethane carrier on microbial community response for the SND process is investigated by a combination of denaturing gradient gel electrophoresis profiles of the 16S rRNA gene V3 region and scanning electron microscopy. Results show that polyurethane, which consists of a unique porous structure, is an ideal platform for biofilm stratification of aerobe, anaerobe and facultative microorganisms in regard to the SND process. The community structure diversity response to different positions was distinct. The distributions of various functional microbes, detected from the surface aerobic stratification to the interior anaerobic stratification of polyurethane, were mainly nitrifying and denitrifying bacteria. Meanwhile aerobic denitrifying bacteria such as Paracoccus sp., Agrobacterium rubi and Ochrobactrum sp. were also adhered to the interior and surface of polyurethane. The SND process occurring on polyurethane foam was carried out by two independent processes: nitrogen removal and aerobic denitrification.

Distinct bacterial communities across a gradient of vegetation from a preserved Brazilian Cerrado.

PubMed

de Araujo, Ademir Sergio Ferreira; Bezerra, Walderly Melgaço; Dos Santos, Vilma Maria; Rocha, Sandra Mara Barbosa; Carvalho, Nilza da Silva; de Lyra, Maria do Carmo Catanho Pereira; Figueiredo, Marcia do Vale Barreto; de Almeida Lopes, Ângela Celis; Melo, Vania Maria Maciel

2017-04-01

The Cerrado biome in the Sete Cidades National Park, an Ecological Reserve in Northeastern Brazil, has conserved its native biodiversity and presents a variety of plants found in other savannas in Brazil. Despite this finding the soil microbial diversity and community structure are poorly understood. Therefore, we described soil bacterial diversity and distribution along a savanna vegetation gradient taking into account the prevailing environmental factors. The bacterial composition was retrieved by sequencing a fragment of the 16S ribosomal RNA gene. The bacterial operational taxonomic units (OTUs) were assigned to 37 different phyla, 96 classes, and 83 genera. At the phylum level, a core comprised by Proteobacteria, Acidobacteria, Actinobacteria, Firmicutes, Verrucomicrobia and Planctomycetes, was detected in all areas of Cerrado. 'Cerrado stricto sensu' and 'Cerradao' share more similarities between edaphic properties and vegetation and also present more similar bacterial communities, while 'Floresta decidual' and 'Campo graminoide' show the largest environmental differences and also more distinct bacterial communities. Proteobacteria (26%), Acidobacteria (21%) and Actinobacteria (21%) were the most abundant phyla within the four areas. All the samples present similar bacteria richness (alpha diversity) and the observed differences among them (beta diversity) were more related to the abundance of specific taxon OTUs compared to their presence or absence. Total organic C, N and P are the main abiotic factors structuring the bacterial communities. In summary, our findings show the bacterial community structure was clearly different across the Cerrado gradient, but that these environments share a bacterial phylum-core comprising Proteobacteria, Acidobacteria, Actinobacteria, Verrucomicrobia and Planctomycetes with other Brazilian savannas.

Community structure of ammonia-oxidizing bacteria under long-term application of mineral fertilizer and organic manure in a sandy loam soil.

PubMed

Chu, Haiyan; Fujii, Takeshi; Morimoto, Sho; Lin, Xiangui; Yagi, Kazuyuki; Hu, Junli; Zhang, Jiabao

2007-01-01

The effects of mineral fertilizer (NPK) and organic manure on the community structure of soil ammonia-oxidizing bacteria (AOB) was investigated in a long-term (16-year) fertilizer experiment. The experiment included seven treatments: organic manure, half organic manure N plus half fertilizer N, fertilizer NPK, fertilizer NP, fertilizer NK, fertilizer PK, and the control (without fertilization). N fertilization greatly increased soil nitrification potential, and mineral N fertilizer had a greater impact than organic manure, while N deficiency treatment (PK) had no significant effect. AOB community structure was analyzed by PCR-denaturing gradient gel electrophoresis (PCR-DGGE) of the amoA gene, which encodes the alpha subunit of ammonia monooxygenase. DGGE profiles showed that the AOB community was more diverse in N-fertilized treatments than in the PK-fertilized treatment or the control, while one dominant band observed in the control could not be detected in any of the fertilized treatments. Phylogenetic analysis showed that the DGGE bands derived from N-fertilized treatments belonged to Nitrosospira cluster 3, indicating that N fertilization resulted in the dominance of Nitrosospira cluster 3 in soil. These results demonstrate that long-term application of N fertilizers could result in increased soil nitrification potential and the AOB community shifts in soil. Our results also showed the different effects of mineral fertilizer N versus organic manure N; the effects of P and K on the soil AOB community; and the importance of balanced fertilization with N, P, and K in promoting nitrification functions in arable soils.

Community Structure of Ammonia-Oxidizing Bacteria under Long-Term Application of Mineral Fertilizer and Organic Manure in a Sandy Loam Soil▿

PubMed Central

Chu, Haiyan; Fujii, Takeshi; Morimoto, Sho; Lin, Xiangui; Yagi, Kazuyuki; Hu, Junli; Zhang, Jiabao

2007-01-01

The effects of mineral fertilizer (NPK) and organic manure on the community structure of soil ammonia-oxidizing bacteria (AOB) was investigated in a long-term (16-year) fertilizer experiment. The experiment included seven treatments: organic manure, half organic manure N plus half fertilizer N, fertilizer NPK, fertilizer NP, fertilizer NK, fertilizer PK, and the control (without fertilization). N fertilization greatly increased soil nitrification potential, and mineral N fertilizer had a greater impact than organic manure, while N deficiency treatment (PK) had no significant effect. AOB community structure was analyzed by PCR-denaturing gradient gel electrophoresis (PCR-DGGE) of the amoA gene, which encodes the α subunit of ammonia monooxygenase. DGGE profiles showed that the AOB community was more diverse in N-fertilized treatments than in the PK-fertilized treatment or the control, while one dominant band observed in the control could not be detected in any of the fertilized treatments. Phylogenetic analysis showed that the DGGE bands derived from N-fertilized treatments belonged to Nitrosospira cluster 3, indicating that N fertilization resulted in the dominance of Nitrosospira cluster 3 in soil. These results demonstrate that long-term application of N fertilizers could result in increased soil nitrification potential and the AOB community shifts in soil. Our results also showed the different effects of mineral fertilizer N versus organic manure N; the effects of P and K on the soil AOB community; and the importance of balanced fertilization with N, P, and K in promoting nitrification functions in arable soils. PMID:17098920

Residuals-Based Subgraph Detection with Cue Vertices

DTIC Science & Technology

2015-11-30

Workshop, 2012, pp. 129–132. [5] M. E. J. Newman , “Finding community structure in networks using the eigenvectors of matrices,” Phys. Rev. E, vol. 74, no...from Data, vol. 1, no. 1, 2007. [7] M. W. Mahoney , L. Orecchia, and N. K. Vishnoi, “A spectral algorithm for improving graph partitions,” CoRR, vol. abs

F-15B Quiet Spike(TradeMark) Aeroservoelastic Flight-Test Data Analysis

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2007-01-01

System identification is utilized in the aerospace community for development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlin ear systems. In this report, we show the application of one such nonlinear system identification technique, structure detection, for the an alysis of Quiet Spike(TradeMark)(Gulfstream Aerospace Corporation, Savannah, Georgia) aeroservoelastic flight-test data. Structure detectio n is concerned with the selection of a subset of candidate terms that best describe the observed output. Structure computation as a tool fo r black-box modeling may be of critical importance for the development of robust, parsimonious models for the flight-test community. The ob jectives of this study are to demonstrate via analysis of Quiet Spike(TradeMark) aeroservoelastic flight-test data for several flight conditions that: linear models are inefficient for modelling aeroservoelast ic data, nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data an d the model structure and parameters vary as the flight condition is altered.

Disentangling the role of management, vegetation structure, and plant quality for Orthoptera in lowland meadows.

PubMed

Schirmel, Jens; Gerlach, Rebekka; Buhk, Constanze

2017-08-17

Seminatural grasslands provide habitats for various species and are important for biodiversity conservation. The understanding of the diverse responses of species and traits to different grassland management methods is therefore urgently needed. We disentangled the role of grassland management (fertilization and irrigation), vegetation structure (biomass, sward height) and plant quality (protein and fiber content) for Orthoptera communities in lowland hay meadows in Germany. We found vegetation structure to be the most important environmental category in explaining community structure of Orthoptera (species richness, total individuals, functional diversity and species composition). Intensively used meadows (fertilized, irrigated, high plant biomass) were characterized by assemblages with few species, low functional diversity, and low conservation value. Thereby, the relatively moderate fertilizer inputs in our study system of up to ∼75 kg N/ha/year reduced functional diversity of Orthoptera, while this negative effect of fertilization was not detectable when solely considering taxonomic aspects. We found strong support for a prominent role of plant quality in shaping Orthoptera communities and especially the trait composition. Our findings demonstrate the usefulness of considering both taxonomic and functional components (functional diversity) in biodiversity research and we suggest a stronger involvement of plant quality measures in Orthoptera studies. © 2017 Institute of Zoology, Chinese Academy of Sciences.

Influence of matrix type on tree community assemblages along tropical dry forest edges.

PubMed

Benítez-Malvido, Julieta; Gallardo-Vásquez, Julio César; Alvarez-Añorve, Mariana Y; Avila-Cabadilla, Luis Daniel

2014-05-01

• Anthropogenic habitat edges have strong negative consequences for the functioning of tropical ecosystems. However, edge effects on tropical dry forest tree communities have been barely documented.• In Chamela, Mexico, we investigated the phylogenetic composition and structure of tree assemblages (≥5 cm dbh) along edges abutting different matrices: (1) disturbed vegetation with cattle, (2) pastures with cattle and, (3) pastures without cattle. Additionally, we sampled preserved forest interiors.• All edge types exhibited similar tree density, basal area and diversity to interior forests, but differed in species composition. A nonmetric multidimensional scaling ordination showed that the presence of cattle influenced species composition more strongly than the vegetation structure of the matrix; tree assemblages abutting matrices with cattle had lower scores in the ordination. The phylogenetic composition of tree assemblages followed the same pattern. The principal plant families and genera were associated according to disturbance regimes as follows: pastures and disturbed vegetation (1) with cattle and (2) without cattle, and (3) pastures without cattle and interior forests. All habitats showed random phylogenetic structures, suggesting that tree communities are assembled mainly by stochastic processes. Long-lived species persisting after edge creation could have important implications in the phylogenetic structure of tree assemblages.• Edge creation exerts a stronger influence on TDF vegetation pathways than previously documented, leading to new ecological communities. Phylogenetic analysis may, however, be needed to detect such changes. © 2014 Botanical Society of America, Inc.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

DOE PAGES

Geng, Haifeng; Tran-Gyamfi, Mary B.; Lane, Todd W.; ...

2016-07-26

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. Here we subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-termmore » treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Lastly, taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.« less

Plant Biodiversity Drivers in Brazilian Campos Rupestres: Insights from Phylogenetic Structure

PubMed Central

Zappi, Daniela C.; Moro, Marcelo F.; Meagher, Thomas R.; Nic Lughadha, Eimear

2017-01-01

Old, climate-buffered infertile landscapes (Ocbils) have attracted increasing levels of interest in recent years because of their exceptionally diverse plant communities. Brazil’s campos rupestres (rupestrian grasslands) are home to almost 15% of Brazil’s native flora in less than 0.8% of Brazil’s territory: an ideal study system for exploring variation in floristic diversity and phylogenetic structure in sites differing in geology and phytophysiognomy. We found significant differences in floristic diversity and phylogenetic structure across a range of study sites encompassing open vegetation and forest on quartzite (FQ) and on ironstone substrates, commonly termed canga. Substrate and physiognomy were key in structuring floristic diversity in the Espinhaço and physiognomy was more important than substrate in structuring phylogenetic diversity, with neither substrate nor its interaction with physiognomy accounting for significant variation in phylogenetic structure. Phylogenetic clustering was significant in open vegetation on both canga and quartzite, reflecting the potential role of environmental filtering in these exposed montane communities adapted to multiple environmental stressors. In forest communities, phylogenetic clustering was significant only at relatively deep nodes of the phylogeny in FQ while no significant phylogenetic clustering was detected across forest on canga (FC), which may be attributable to proximity to the megadiverse Atlantic forest biome and/or comparatively benign environmental conditions in FC with relatively deep, nutrient-rich soils and access to edaphic water reliable in comparison to those for open vegetation on canga and open or forest communities on quartzite. Clades representing relatively old lineages are significantly over-represented in campos rupestres on quartzite, consistent with the Gondwanan Heritage Hypothesis of Ocbil theory. In contrast, forested sites on canga are recognized as Yodfels. To be effective, conservation measures must take account of the distinct communities which are encompassed within the broad term campos rupestres, and the differing vulnerabilities of Ocbils and Yodfels. PMID:29312396

Plant Biodiversity Drivers in Brazilian Campos Rupestres: Insights from Phylogenetic Structure.

PubMed

Zappi, Daniela C; Moro, Marcelo F; Meagher, Thomas R; Nic Lughadha, Eimear

2017-01-01

Old, climate-buffered infertile landscapes (Ocbils) have attracted increasing levels of interest in recent years because of their exceptionally diverse plant communities. Brazil's campos rupestres (rupestrian grasslands) are home to almost 15% of Brazil's native flora in less than 0.8% of Brazil's territory: an ideal study system for exploring variation in floristic diversity and phylogenetic structure in sites differing in geology and phytophysiognomy. We found significant differences in floristic diversity and phylogenetic structure across a range of study sites encompassing open vegetation and forest on quartzite (FQ) and on ironstone substrates, commonly termed canga . Substrate and physiognomy were key in structuring floristic diversity in the Espinhaço and physiognomy was more important than substrate in structuring phylogenetic diversity, with neither substrate nor its interaction with physiognomy accounting for significant variation in phylogenetic structure. Phylogenetic clustering was significant in open vegetation on both canga and quartzite, reflecting the potential role of environmental filtering in these exposed montane communities adapted to multiple environmental stressors. In forest communities, phylogenetic clustering was significant only at relatively deep nodes of the phylogeny in FQ while no significant phylogenetic clustering was detected across forest on canga (FC), which may be attributable to proximity to the megadiverse Atlantic forest biome and/or comparatively benign environmental conditions in FC with relatively deep, nutrient-rich soils and access to edaphic water reliable in comparison to those for open vegetation on canga and open or forest communities on quartzite. Clades representing relatively old lineages are significantly over-represented in campos rupestres on quartzite, consistent with the Gondwanan Heritage Hypothesis of Ocbil theory. In contrast, forested sites on canga are recognized as Yodfels. To be effective, conservation measures must take account of the distinct communities which are encompassed within the broad term campos rupestres , and the differing vulnerabilities of Ocbils and Yodfels.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geng, Haifeng; Tran-Gyamfi, Mary B.; Lane, Todd W.

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. Here we subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-termmore » treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Lastly, taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.« less

Atrazine, chlorpyrifos, and iprodione effect on the biodiversity of bacteria, actinomycetes, and fungi in a pilot biopurification system with a green cover.

PubMed

Elgueta, Sebastian; Correa, Arturo; Campo, Marco; Gallardo, Felipe; Karpouzas, Dimitrios; Diez, Maria Cristina

2017-09-02

The use of biopurification systems can mitigate the effects of pesticide contamination on farms. The primary aim of this study was to evaluate the effect of pesticide dissipation on microbial communities in a pilot biopurification system. The pesticide dissipation of atrazine, chlorpyrifos and iprodione (35 mg kg -1 active ingredient [a.i.]) and biological activity were determined for 40 days. The microbial communities (bacteria, actinomycetes and fungi) were analyzed using denaturing gradient gel electrophoresis (DGGE). In general, pesticide dissipation was the highest by day 5 and reached 95%. The pesticides did not affect biological activity during the experiment. The structure of the actinomycete and bacterial communities in the rhizosphere was more stable during the evaluation than that in the communities in the control without pesticides. The rhizosphere fungal communities, detected using DGGE, showed small and transitory shifts with time. To conclude, rhizosphere microbial communities were not affected during pesticide dissipation in a pilot biopurification system.

Phase transitions in community detection: A solvable toy model

NASA Astrophysics Data System (ADS)

Ver Steeg, Greg; Moore, Cristopher; Galstyan, Aram; Allahverdyan, Armen

2014-05-01

Recently, it was shown that there is a phase transition in the community detection problem. This transition was first computed using the cavity method, and has been proved rigorously in the case of q = 2 groups. However, analytic calculations using the cavity method are challenging since they require us to understand probability distributions of messages. We study analogous transitions in the so-called “zero-temperature inference” model, where this distribution is supported only on the most likely messages. Furthermore, whenever several messages are equally likely, we break the tie by choosing among them with equal probability, corresponding to an infinitesimal random external field. While the resulting analysis overestimates the thresholds, it reproduces some of the qualitative features of the system. It predicts a first-order detectability transition whenever q > 2 (as opposed to q > 4 according to the finite-temperature cavity method). It also has a regime analogous to the “hard but detectable” phase, where the community structure can be recovered, but only when the initial messages are sufficiently accurate. Finally, we study a semisupervised setting where we are given the correct labels for a fraction ρ of the nodes. For q > 2, we find a regime where the accuracy jumps discontinuously at a critical value of ρ.

Effect of semi-permeable cover system on the bacterial diversity during sewage sludge composting.

PubMed

Robledo-Mahón, Tatiana; Aranda, Elisabet; Pesciaroli, Chiara; Rodríguez-Calvo, Alfonso; Silva-Castro, Gloria Andrea; González-López, Jesús; Calvo, Concepción

2018-06-01

Sewage sludge composting is a profitable process economically viable and environmentally friendly. In despite of there are several kind of composting types, the use of combined system of semipermeable cover film and aeration air-floor is widely developed at industrial scale. However, the knowledge of the linkages between microbial communities structure, enzyme activities and physico-chemical factors under these conditions it has been poorly explored. Thus, the aim of this study was to investigate the bacterial dynamic and community structure using next generation sequencing coupled to analyses of microbial enzymatic activity and culturable dependent techniques in a full-scale real composting plant. Sewage sludge composting process was conducted using a semi-permeable Gore-tex cover, in combination with an air-insufflation system. The highest values of enzymatic activities such as dehydrogenase, protease and arylsulphatase were detected in the first 5 days of composting; suggesting that during this period of time a greater degrading activity of organic matter took place. Culturable bacteria identified were in agreement with the bacteria found by massive sequencing technologies. The greatest bacterial diversity was detected between days 15 and 30, with Actinomycetales and Bacillales being the predominant orders at the beginning and end of the process. Bacillus was the most representative genus during all the process. A strong correlation between abiotic factors as total organic content and organic matter and enzymatic activities such as dehydrogenase, alkaline phosphatase, and ß-glucosidase activity was found. Bacterial diversity was strongly influenced by the stage of the process, community-structure change was concomitant with a temperature rise, rendering favorable conditions to stimulate microbial activity and facilitate the change in the microbial community linked to the degradation process. Moreover, results obtained confirmed that the use of semipermeable cover in the composting of sewage sludge allow a noticeable reduction in the process-time comparing to conventional open windrows. Copyright © 2018 Elsevier Ltd. All rights reserved.

Nested PCR Biases in Interpreting Microbial Community Structure in 16S rRNA Gene Sequence Datasets

PubMed Central

Yu, Guoqin; Fadrosh, Doug; Goedert, James J.; Ravel, Jacques; Goldstein, Alisa M.

2015-01-01

Background Sequencing of the PCR-amplified 16S rRNA gene has become a common approach to microbial community investigations in the fields of human health and environmental sciences. This approach, however, is difficult when the amount of DNA is too low to be amplified by standard PCR. Nested PCR can be employed as it can amplify samples with DNA concentration several-fold lower than standard PCR. However, potential biases with nested PCRs that could affect measurement of community structure have received little attention. Results In this study, we used 17 DNAs extracted from vaginal swabs and 12 DNAs extracted from stool samples to study the influence of nested PCR amplification of the 16S rRNA gene on the estimation of microbial community structure using Illumina MiSeq sequencing. Nested and standard PCR methods were compared on alpha- and beta-diversity metrics and relative abundances of bacterial genera. The effects of number of cycles in the first round of PCR (10 vs. 20) and microbial diversity (relatively low in vagina vs. high in stool) were also investigated. Vaginal swab samples showed no significant difference in alpha diversity or community structure between nested PCR and standard PCR (one round of 40 cycles). Stool samples showed significant differences in alpha diversity (except Shannon’s index) and relative abundance of 13 genera between nested PCR with 20 cycles in the first round and standard PCR (P<0.01), but not between nested PCR with 10 cycles in the first round and standard PCR. Operational taxonomic units (OTUs) that had low relative abundance (sum of relative abundance <0.167) accounted for most of the distortion (>27% of total OTUs in stool). Conclusions Nested PCR introduced bias in estimated diversity and community structure. The bias was more significant for communities with relatively higher diversity and when more cycles were applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies. PMID:26196512

Nested PCR Biases in Interpreting Microbial Community Structure in 16S rRNA Gene Sequence Datasets.

PubMed

Yu, Guoqin; Fadrosh, Doug; Goedert, James J; Ravel, Jacques; Goldstein, Alisa M

2015-01-01

Sequencing of the PCR-amplified 16S rRNA gene has become a common approach to microbial community investigations in the fields of human health and environmental sciences. This approach, however, is difficult when the amount of DNA is too low to be amplified by standard PCR. Nested PCR can be employed as it can amplify samples with DNA concentration several-fold lower than standard PCR. However, potential biases with nested PCRs that could affect measurement of community structure have received little attention. In this study, we used 17 DNAs extracted from vaginal swabs and 12 DNAs extracted from stool samples to study the influence of nested PCR amplification of the 16S rRNA gene on the estimation of microbial community structure using Illumina MiSeq sequencing. Nested and standard PCR methods were compared on alpha- and beta-diversity metrics and relative abundances of bacterial genera. The effects of number of cycles in the first round of PCR (10 vs. 20) and microbial diversity (relatively low in vagina vs. high in stool) were also investigated. Vaginal swab samples showed no significant difference in alpha diversity or community structure between nested PCR and standard PCR (one round of 40 cycles). Stool samples showed significant differences in alpha diversity (except Shannon's index) and relative abundance of 13 genera between nested PCR with 20 cycles in the first round and standard PCR (P<0.01), but not between nested PCR with 10 cycles in the first round and standard PCR. Operational taxonomic units (OTUs) that had low relative abundance (sum of relative abundance <0.167) accounted for most of the distortion (>27% of total OTUs in stool). Nested PCR introduced bias in estimated diversity and community structure. The bias was more significant for communities with relatively higher diversity and when more cycles were applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies.

Trophic look at soft-bottom communities - Short-term effects of trawling cessation on benthos

NASA Astrophysics Data System (ADS)

Dannheim, Jennifer; Brey, Thomas; Schröder, Alexander; Mintenbeck, Katja; Knust, Rainer; Arntz, Wolf E.

2014-01-01

The trophic structure of the German Bight soft-bottom benthic community was evaluated for potential changes after cessation of bottom trawling. Species were collected with van-Veen grabs and beam trawls. Trophic position (i.e. nitrogen stable isotope ratios, δ15N) and energy flow (i.e. species metabolism approximated by body mass scaled abundance) of dominant species were compared in trawled areas and an area protected from fisheries for 14 months in order to detect trawling cessation effects by trophic characteristics. At the community level, energy flow was lower in the protected area, but we were unable to detect significant changes in trophic position. At the species level energy flow in the protected area was lower for predating/scavenging species but higher for interface feeders. Species trophic positions of small predators/scavengers were lower and of deposit feeders higher in the protected area. Major reasons for trophic changes after trawling cessation may be the absence of artificial and additional food sources from trawling likely to attract predators and scavengers, and the absence of physical sediment disturbance impacting settlement/survival of less mobile species and causing a gradual shift in food availability and quality. Our results provide evidence that species or community energy flow is a good indicator to detect trawling induced energy-flow alterations in the benthic system, and that in particular species trophic properties are suitable to capture subtle and short-term changes in the benthos following trawling cessation.

Spectral partitioning in equitable graphs.

PubMed

Barucca, Paolo

2017-06-01

Graph partitioning problems emerge in a wide variety of complex systems, ranging from biology to finance, but can be rigorously analyzed and solved only for a few graph ensembles. Here, an ensemble of equitable graphs, i.e., random graphs with a block-regular structure, is studied, for which analytical results can be obtained. In particular, the spectral density of this ensemble is computed exactly for a modular and bipartite structure. Kesten-McKay's law for random regular graphs is found analytically to apply also for modular and bipartite structures when blocks are homogeneous. An exact solution to graph partitioning for two equal-sized communities is proposed and verified numerically, and a conjecture on the absence of an efficient recovery detectability transition in equitable graphs is suggested. A final discussion summarizes results and outlines their relevance for the solution of graph partitioning problems in other graph ensembles, in particular for the study of detectability thresholds and resolution limits in stochastic block models.

Music in the moment? Revisiting the effect of large scale structures.

PubMed

Lalitte, P; Bigand, E

2006-12-01

The psychological relevance of large-scale musical structures has been a matter of debate in the music community. This issue was investigated with a method that allows assessing listeners' detection of musical incoherencies in normal and scrambled versions of popular and contemporary music pieces. Musical excerpts were segmented into 28 or 29 chunks. In the scrambled version, the temporal order of these chunks was altered with the constraint that the transitions between two chunks never created local acoustical and musical disruptions. Participants were required (1) to detect on-line incoherent linking of chunks, (2) to rate aesthetic quality of pieces, and (3) to evaluate their overall coherence. The findings indicate a moderate sensitivity to large-scale musical structures for popular and contemporary music in both musically trained and untrained listeners. These data are discussed in light of current models of music cognition.

Spectral partitioning in equitable graphs

NASA Astrophysics Data System (ADS)

Barucca, Paolo

2017-06-01

Graph partitioning problems emerge in a wide variety of complex systems, ranging from biology to finance, but can be rigorously analyzed and solved only for a few graph ensembles. Here, an ensemble of equitable graphs, i.e., random graphs with a block-regular structure, is studied, for which analytical results can be obtained. In particular, the spectral density of this ensemble is computed exactly for a modular and bipartite structure. Kesten-McKay's law for random regular graphs is found analytically to apply also for modular and bipartite structures when blocks are homogeneous. An exact solution to graph partitioning for two equal-sized communities is proposed and verified numerically, and a conjecture on the absence of an efficient recovery detectability transition in equitable graphs is suggested. A final discussion summarizes results and outlines their relevance for the solution of graph partitioning problems in other graph ensembles, in particular for the study of detectability thresholds and resolution limits in stochastic block models.

Temporal and spatial constraints on community assembly during microbial colonization of wood in seawater.

PubMed

Kalenitchenko, Dimitri; Fagervold, Sonja K; Pruski, Audrey M; Vétion, Gilles; Yücel, Mustafa; Le Bris, Nadine; Galand, Pierre E

2015-12-01

Wood falls on the ocean floor form chemosynthetic ecosystems that remain poorly studied compared with features such as hydrothermal vents or whale falls. In particular, the microbes forming the base of this unique ecosystem are not well characterized and the ecology of communities is not known. Here we use wood as a model to study microorganisms that establish and maintain a chemosynthetic ecosystem. We conducted both aquaria and in situ deep-sea experiments to test how different environmental constraints structure the assembly of bacterial, archaeal and fungal communities. We also measured changes in wood lipid concentrations and monitored sulfide production as a way to detect potential microbial activity. We show that wood falls are dynamic ecosystems with high spatial and temporal community turnover, and that the patterns of microbial colonization change depending on the scale of observation. The most illustrative example was the difference observed between pine and oak wood community dynamics. In pine, communities changed spatially, with strong differences in community composition between wood microhabitats, whereas in oak, communities changed more significantly with time of incubation. Changes in community assembly were reflected by changes in phylogenetic diversity that could be interpreted as shifts between assemblies ruled by species sorting to assemblies structured by competitive exclusion. These ecological interactions followed the dynamics of the potential microbial metabolisms accompanying wood degradation in the sea. Our work showed that wood is a good model for creating and manipulating chemosynthetic ecosystems in the laboratory, and attracting not only typical chemosynthetic microbes but also emblematic macrofaunal species.

Detection of forest stand-level spatial structure in ectomycorrhizal fungal communities.

PubMed

Lilleskov, Erik A; Bruns, Thomas D; Horton, Thomas R; Taylor, D; Grogan, Paul

2004-08-01

Ectomycorrhizal fungal (EMF) communities are highly diverse at the stand level. To begin to understand what might lead to such diversity, and to improve sampling designs, we investigated the spatial structure of these communities. We used EMF community data from a number of studies carried out in seven mature and one recently fire-initiated forest stand. We applied various measures of spatial pattern to characterize distributions at EMF community and species levels: Mantel tests, Mantel correlograms, variance/mean and standardized variograms. Mantel tests indicated that in four of eight sites community similarity decreased with distance, whereas Mantel correlograms also found spatial autocorrelation in those four plus two additional sites. In all but one of these sites elevated similarity was evident only at relatively small spatial scales (< 2.6 m), whereas one exhibited a larger scale pattern ( approximately 25 m). Evenness of biomass distribution among cores varied widely among taxa. Standardized variograms indicated that most of the dominant taxa showed patchiness at a scale of less than 3 m, with a range from 0 to < or =17 m. These results have implications for both sampling scale and intensity to achieve maximum efficiency of community sampling. In the systems we examined, cores should be at least 3 m apart to achieve the greatest sampling efficiency for stand-level community analysis. In some cases even this spacing may result in reduced sampling efficiency arising from patterns of spatial autocorrelation. Interpretation of the causes and significance of these patterns requires information on the genetic identity of individuals in the communities.

Abundance and diversity of Archaea in heavy-metal-contaminated soils.

PubMed

Sandaa, R A; Enger, O; Torsvik, V

1999-08-01

The impact of heavy-metal contamination on archaean communities was studied in soils amended with sewage sludge contaminated with heavy metals to varying extents. Fluorescent in situ hybridization showed a decrease in the percentage of Archaea from 1.3% +/- 0.3% of 4', 6-diamidino-2-phenylindole-stained cells in untreated soil to below the detection limit in soils amended with heavy metals. A comparison of the archaean communities of the different plots by denaturing gradient gel electrophoresis revealed differences in the structure of the archaean communities in soils with increasing heavy-metal contamination. Analysis of cloned 16S ribosomal DNA showed close similarities to a unique and globally distributed lineage of the kingdom Crenarchaeota that is phylogenetically distinct from currently characterized crenarchaeotal species.

Four-year bacterial monitoring in the International Space Station-Japanese Experiment Module "Kibo" with culture-independent approach.

PubMed

Ichijo, Tomoaki; Yamaguchi, Nobuyasu; Tanigaki, Fumiaki; Shirakawa, Masaki; Nasu, Masao

2016-01-01

Studies on the relationships between humans and microbes in space habitation environments are critical for success in long-duration space missions, to reduce potential hazards to the crew and the spacecraft infrastructure. We performed microbial monitoring in the Japanese Experiment Module "Kibo", a part of the International Space Station, for 4 years after its completion, and analyzed samples with modern molecular microbiological techniques. Sampling was performed in September 2009, February 2011, and October 2012. The surface of the incubator, inside the door of the incubator, an air intake, air diffuser, and handrail were selected as sampling sites. Sampling was performed using the optimized swabbing method. Abundance and phylogenetic affiliation of bacteria on the interior surfaces of Kibo were determined by quantitative PCR and pyrosequencing, respectively. Bacteria in the phyla Proteobacteria (γ-subclass) and Firmicutes were frequently detected on the interior surfaces in Kibo. Families Staphylococcaceae and Enterobacteriaceae were dominant. Most bacteria detected belonged to the human microbiota; thus, we suggest that bacterial cells are transferred to the surfaces in Kibo from the astronauts. Environmental bacteria such as Legionella spp. were also detected. From the data on bacterial abundance and phylogenetic affiliation, Kibo has been microbiologically well maintained; however, the microbial community structure in Kibo may change with prolonged stay of astronauts. Continuous monitoring is required to obtain information on changes in the microbial community structure in Kibo.

Structural Characterization of New Peptide Variants Produced by Cyanobacteria from the Brazilian Atlantic Coastal Forest Using Liquid Chromatography Coupled to Quadrupole Time-of-Flight Tandem Mass Spectrometry

PubMed Central

Sanz, Miriam; Andreote, Ana Paula Dini; Fiore, Marli Fatima; Dörr, Felipe Augusto; Pinto, Ernani

2015-01-01

Cyanobacteria from underexplored and extreme habitats are attracting increasing attention in the search for new bioactive substances. However, cyanobacterial communities from tropical and subtropical regions are still largely unknown, especially with respect to metabolite production. Among the structurally diverse secondary metabolites produced by these organisms, peptides are by far the most frequently described structures. In this work, liquid chromatography/electrospray ionization coupled to high resolution quadrupole time-of-flight tandem mass spectrometry with positive ion detection was applied to study the peptide profile of a group of cyanobacteria isolated from the Southeastern Brazilian coastal forest. A total of 38 peptides belonging to three different families (anabaenopeptins, aeruginosins, and cyanopeptolins) were detected in the extracts. Of the 38 peptides, 37 were detected here for the first time. New structural features were proposed based on mass accuracy data and isotopic patterns derived from full scan and MS/MS spectra. Interestingly, of the 40 surveyed strains only nine were confirmed to be peptide producers; all of these strains belonged to the order Nostocales (three Nostoc sp., two Desmonostoc sp. and four Brasilonema sp.). PMID:26096276

Evaluation of the effectiveness of ophthalmic assistants as screeners for glaucoma in North India

PubMed Central

Sinha, S K; Astbury, N

2011-01-01

Aim To assess whether ophthalmic assistants are effective in screening people for glaucoma in India. Methodology The study subjects were examined by both trained ophthalmic assistants and an ophthalmologist in both hospital and community settings. Specific tests for the diagnosis of glaucoma suspects included visual field examination using frequency doubling technology perimetry, intraocular pressure measurement (Tonopen), A-scan central anterior chamber depth measurement and dilated optic disc examination. The findings recorded by the ophthalmic assistants were masked to the ophthalmologist to avoid measurement bias. Results In the hospital setting, there was a substantial level of agreement between the ophthalmic assistants and the ophthalmologist in the diagnosis of glaucoma suspects (89.29%, k=0.7, 95% confidence interval (CI)=0.54–0.86). The diagnostic accuracy of the ophthalmic assistants in detecting glaucoma suspects was high for sensitivity (95.2%, 95% CI=91.4–97.7%) but lower for specificity at 71.4% (95% CI=60.0–78.7%). In the community setting, there was a moderate level of agreement between the ophthalmic assistants and the ophthalmologist in the diagnosis of glaucoma suspects (78.23%, k=0.50, 95% CI=0.37–0.64). The diagnostic accuracy of the ophthalmic assistants in detecting glaucoma suspects was moderate for sensitivity (82.9, 95% CI=69.7–91.5%) but lower for specificity at 76.8% (95% CI=72.7–79.5%). Conclusion Ophthalmic assistants can be used for opportunistic case detection of glaucoma suspects in the community. Structured training of the ophthalmic assistants together with enhanced clinical experience would improve their performance in detecting glaucoma suspects in the community. PMID:21720416

Soil bacterial community responses to revegetation of moving sand dune in semi-arid grassland.

PubMed

Cao, Chengyou; Zhang, Ying; Cui, Zhenbo; Feng, Shuwei; Wang, Tingting; Ren, Qing

2017-08-01

Grasslands in semi-arid Northern China are widely desertified, thus inducing the formation of a large area of moving sand lands. Revegetation of the sandy land is commonly adopted to restore degraded grasslands. The structure of the soil microbial community might dramatically change during degradation and recovery because microorganisms are one of the major drivers of ecological process through their interactions with plants and soil. Assuming that soil properties are the key determinants of the structure of soil bacterial community within the same soil type, whether the vegetation type causes the significant difference in the structure of soil bacterial community during revegetation and restoration of the degraded grasslands remains poorly understood. Our study aimed to (1) investigate the response of soil bacterial communities to the changes during vegetation degradation and recovery and (2) evaluate whether the soil bacterial communities under plantations return to their native state. We detected the shifts in diversities and compositions of the soil bacterial communities and the relative abundance of dominant bacterial taxa by using the high-throughput Illumina MiSeq sequencing technique in an area covered by 32-year-old Caragana microphylla, Artemisia halodendron, Hedysarum fruticosum, Pinus sylvestris var. mongolica, Populus simonii, and Salix gordejevii sand-fixing plantations and in the native community (NC) dominated by elm, and moving sandy dune (MS). We found that the obtained operational taxonomic units by 16S rRNA gene sequencing and diversity index in MS were all significantly lower than those in NC, and the number and composition of dominant genera were significantly different between NC and MS. Interestingly, the compositions of bacterial communities and the dominant genera in different sand-fixation plantations (C. microphylla, A. halodendron, H. fruticosum, P. sylvestris var. mongolica, P. simonii, and S. gordejevii) were all similar to those of the native soil of NC, suggesting that the plantation type and soil properties exhibit a minimal effect on the compositions of soil microbial communities within a continuous landscape. These results revealed that the structure of the soil bacterial community of degraded sandy grassland (even degenerated into a mobile sand dunes) in semi-arid region can be reversibly restored by planting indigenous shrub or semi-shrub plantation on human time scales.

Structure and temporal dynamics of the bacterial communities associated to microhabitats of the coral Oculina patagonica.

PubMed

Rubio-Portillo, Esther; Santos, Fernando; Martínez-García, Manuel; de Los Ríos, Asunción; Ascaso, Carmen; Souza-Egipsy, Virginia; Ramos-Esplá, Alfonso A; Anton, Josefa

2016-12-01

Corals are known to contain a diverse microbiota that plays a paramount role in the physiology and health of holobiont. However, few studies have addressed the variability of bacterial communities within the coral host. In this study, bacterial community composition from the mucus, tissue and skeleton of the scleractinian coral Oculina patagonica were investigated seasonally at two locations in the Western Mediterranean Sea, to further understand how environmental conditions and the coral microbiome structure are related. We used denaturing gradient gel electrophoresis in combination with next-generation sequencing and electron microscopy to characterize the bacterial community. The bacterial communities were significantly different among coral compartments, and coral tissue displayed the greatest changes related to environmental conditions and coral health status. Species belonging to the Rhodobacteraceae and Vibrionaceae families form part of O. patagonica tissues core microbiome and may play significant roles in the nitrogen cycle. Furthermore, sequences related to the coral pathogens, Vibrio mediterranei and Vibrio coralliilyticus, were detected not only in bleached corals but also in healthy ones, even during cold months. This fact opens a new view onto unveiling the role of pathogens in the development of coral diseases in the future. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Disease and community structure: white-nose syndrome alters spatial and temporal niche partitioning in sympatric bat species

USGS Publications Warehouse

Jachowski, David S.; Dobony, Christopher A.; Coleman, Laci S.; Ford, W. Mark; Britzke, Eric R.; Rodrigue, Jane L.

2014-01-01

AimEmerging infectious diseases present a major perturbation with apparent direct effects such as reduced population density, extirpation and/or extinction. Comparatively less is known about the potential indirect effects of disease that likely alter community structure and larger ecosystem function. Since 2006, white-nose syndrome (WNS) has resulted in the loss of over 6 million hibernating bats in eastern North America. Considerable evidence exists concerning niche partitioning in sympatric bat species in this region, and the unprecedented, rapid decline in multiple species following WNS may provide an opportunity to observe a dramatic restructuring of the bat community.LocationWe conducted our study at Fort Drum Army Installation in Jefferson and Lewis counties, New York, USA, where WNS first impacted extant bat species in winter 2007–2008.MethodsAcoustical monitoring during 2003–2011 allowed us to test the hypothesis that spatial and temporal niche partitioning by bats was relaxed post-WNS.ResultsWe detected nine bat species pre- and post-WNS. Activity for most bat species declined post-WNS. Dramatic post-WNS declines in activity of little brown bat (Myotis lucifugus, MYLU), formerly the most abundant bat species in the region, were associated with complex, often species-specific responses by other species that generally favoured increased spatial and temporal overlap with MYLU.Main conclusionsIn addition to the obvious direct effects of disease on bat populations and activity levels, our results provide evidence that disease can have cascading indirect effects on community structure. Recent occurrence of WNS in North America, combined with multiple existing stressors, is resulting in dramatic shifts in temporal and spatial niche partitioning within bat communities. These changes might influence long-term population viability of some bat species as well as broader scale ecosystem structure and function.

Soil microcosm for testing the effects of chemical pollutants on soil fauna communities and trophic structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parmelee, R.W.; Wentsel, R.S.; Phillips, C.T.

1993-08-01

A microcosm technique is presented that uses community and trophic-level analysis of soil nematodes and microarthropods to determine the effects of chemicals on soil systems. Forest soil was treated with either copper, p-nitrophenol, or trinitrotoluene. Nematodes were sorted into bacterivore, fungivore, herbivore, and omnivore-predator trophic groups, and a hatchling category. Microarthropods were sorted to the acarine suborders Prostigmata, Mesostigmata, and Oribatida; the insectan order Collembola; and a miscellaneous group. Omnivore-predator nematodes and meso-stigmatid and oribatid mites were the groups most sensitive to copper and were significantly reduced at levels as low as 100 [mu]g g[sup [minus]1] copper. Total nematode andmore » microarthropod numbers declined above 200 [mu]g g[sup [minus]1] copper. Trophic structure analysis suggested that high sensitivity of nematode predators to intermediate levels of copper reduced predation on herbivore nematodes and resulted in greater numbers of nematodes compared to controls. p-Nitrophenol was very toxic to the nematode community, and all trophic groups were significantly reduced above 20 [mu]g g[sup [minus]1]. However, there was no effect of p-nitrophenol on microarthropods. Trinitrotoluene had no significant negative effect on total abundance of either groups of soil fauna, but oribatids were significantly reduced at 200 [mu]g g[sup [minus]1]. The results demonstrated that soil nematodes and microarthropods were sensitive indicators of environmental contaminants and that trophic-structure and community analysis has the potential to detect more subtle indirect effects of chemicals on soil food-web structure. The authors conclude that microcosms with field communities of soil microfauna offer high resolution of the ecotoxicological effects of chemicals in complex soil systems.« less

Application of Locked Nucleic Acid (LNA) Primer and PCR Clamping by LNA Oligonucleotide to Enhance the Amplification of Internal Transcribed Spacer (ITS) Regions in Investigating the Community Structures of Plant-Associated Fungi.

PubMed

Ikenaga, Makoto; Tabuchi, Masakazu; Kawauchi, Tomohiro; Sakai, Masao

2016-09-29

The simultaneous extraction of host plant DNA severely limits investigations of the community structures of plant-associated fungi due to the similar homologies of sequences in primer-annealing positions between fungi and host plants. Although fungal-specific primers have been designed, plant DNA continues to be excessively amplified by PCR, resulting in the underestimation of community structures. In order to overcome this limitation, locked nucleic acid (LNA) primers and PCR clamping by LNA oligonucleotides have been applied to enhance the amplification of fungal internal transcribed spacer (ITS) regions. LNA primers were designed by converting DNA into LNA, which is specific to fungi, at the forward primer side. LNA oligonucleotides, the sequences of which are complementary to the host plants, were designed by overlapping a few bases with the annealing position of the reverse primer. Plant-specific DNA was then converted into LNA at the shifted position from the 3' end of the primer-binding position. PCR using the LNA technique enhanced the amplification of fungal ITS regions, whereas those of the host plants were more likely to be amplified without the LNA technique. A denaturing gradient gel electrophoresis (DGGE) analysis displayed patterns that reached an acceptable level for investigating the community structures of plant-associated fungi using the LNA technique. The sequences of the bands detected using the LNA technique were mostly affiliated with known isolates. However, some sequences showed low similarities, indicating the potential to identify novel fungi. Thus, the application of the LNA technique is considered effective for widening the scope of community analyses of plant-associated fungi.

Application of Locked Nucleic Acid (LNA) Primer and PCR Clamping by LNA Oligonucleotide to Enhance the Amplification of Internal Transcribed Spacer (ITS) Regions in Investigating the Community Structures of Plant–Associated Fungi

PubMed Central

Ikenaga, Makoto; Tabuchi, Masakazu; Kawauchi, Tomohiro; Sakai, Masao

2016-01-01

The simultaneous extraction of host plant DNA severely limits investigations of the community structures of plant–associated fungi due to the similar homologies of sequences in primer–annealing positions between fungi and host plants. Although fungal-specific primers have been designed, plant DNA continues to be excessively amplified by PCR, resulting in the underestimation of community structures. In order to overcome this limitation, locked nucleic acid (LNA) primers and PCR clamping by LNA oligonucleotides have been applied to enhance the amplification of fungal internal transcribed spacer (ITS) regions. LNA primers were designed by converting DNA into LNA, which is specific to fungi, at the forward primer side. LNA oligonucleotides, the sequences of which are complementary to the host plants, were designed by overlapping a few bases with the annealing position of the reverse primer. Plant-specific DNA was then converted into LNA at the shifted position from the 3′ end of the primer–binding position. PCR using the LNA technique enhanced the amplification of fungal ITS regions, whereas those of the host plants were more likely to be amplified without the LNA technique. A denaturing gradient gel electrophoresis (DGGE) analysis displayed patterns that reached an acceptable level for investigating the community structures of plant–associated fungi using the LNA technique. The sequences of the bands detected using the LNA technique were mostly affiliated with known isolates. However, some sequences showed low similarities, indicating the potential to identify novel fungi. Thus, the application of the LNA technique is considered effective for widening the scope of community analyses of plant–associated fungi. PMID:27600711

Latitudinal concordance between biogeographic regionalization, community structure, and richness patterns: a study on the reptiles of China

NASA Astrophysics Data System (ADS)

Chen, Youhua; Srivastava, Diane S.

2015-02-01

Latitudinal patterns in species richness may be affected by both continuous variations in macroecological factors as well as discrete change between biogeographic regions. We examined whether latitudinal reptilian richness and community structure in China were best explained by three macroecological patterns (mid-domain effects, Rapoport's rule effects, or environmental correlates) within or across the ranges of biogeographic realms. The results showed that (1) there was a weak mid-domain effect within the Oriental Realm. However, the mid-domain effect was detected neither at the overall regional scale nor in the Palaearctic Realm. (2) Rapoport's rule was only weakly supported for reptilian fauna in China at lower latitudinal areas. (3) Environmental variables were more strongly correlated with species' latitudinal community structure and richness patterns at the scale of biogeographic realms. Based on the faunal similarity of reptilian community across latitudinal bands, we proposed a latitudinal delineation scheme at 34° N for dividing East Asia into Oriental and Palaearctic biogeographic realms. At last, at the functional group level, we also evaluated the relevant ecological patterns for lizard and snake species across different latitudinal bins, showing that the distributions of lizards presented strong mid-domain effects at the latitudinal ranges within the Oriental Realm and over the whole range but did not support Rapoport's rule. In comparison, snake species supported Rapoport's rule at low latitudinal zones but did not present any remarkable mid-domain effects at any spatial extents. In conclusion, biogeographic realms are an appropriate scale for studying macroecological patterns. Reptilian latitudinal richness patterns of China were explained by a combination of environmental factors and geometric constraints, while the latitudinal community structure patterns were greatly affected by environmental gradients. Functional guilds present differentiated macroecological patterns along the latitudinal gradients.

Effect of reclaimed water effluent on bacterial community structure in the Typha angustifolia L. rhizosphere soil of urbanized riverside wetland, China.

PubMed

Huang, Xingru; Xiong, Wei; Liu, Wei; Guo, Xiaoyu

2017-05-01

In order to evaluate the impact of reclaimed water on the ecology of bacterial communities in the Typha angustifolia L. rhizosphere soil, bacterial community structure was investigated using a combination of terminal restriction fragment length polymorphism and 16S rRNA gene clone library. The results revealed significant spatial variation of bacterial communities along the river from upstream and downstream. For example, a higher relative abundance of γ-Proteobacteria, Firmicutes, Chloroflexi and a lower proportion of β-Proteobacteria and ε-Proteobacteria was detected at the downstream site compared to the upstream site. Additionally, with an increase of the reclaimed water interference intensity, the rhizosphere bacterial community showed a decrease in taxon richness, evenness and diversity. The relative abundance of bacteria closely related to the resistant of heavy-metal was markedly increased, while the bacteria related for carbon/nitrogen/phosphorus/sulfur cycling wasn't strikingly changed. Besides that, the pathogenic bacteria markedly increased in the downstream rhizosphere soil since reclaimed water supplement, while the possible plant growth-promoting rhizobacteria obviously reduced in the downstream sediment. Together these data suggest cause and effect between reclaimed water input into the wetland, shift in bacterial communities through habitat change, and alteration of capacity for biogeochemical cycling of contaminants. Copyright © 2016. Published by Elsevier B.V.

Marine bioinvasions: Differences in tropical copepod communities between inside and outside a port

NASA Astrophysics Data System (ADS)

Soares, Marcelo de Oliveira; Campos, Carolina Coelho; Santos, Nívia Maria Oliveira; Barroso, Hortência de Sousa; Mota, Erika Maria Targino; Menezes, Maria Ozilea Bezerra de; Rossi, Sergio; Garcia, Tatiane Martins

2018-04-01

The difficulty of detecting non-indigenous species (NIS) in marine environments is an "invisible problem" in areas where plankton monitoring does not occur. In this study, we investigated the dominance of the NIS Temora turbinata and copepod community structure in two tropical marine habitats: inside an offshore port, which had turbid and calm waters, and outside the port, which was more hydrodynamic. Our study area was on the northeast coast of Brazil. We found 17 taxa of Copepoda, which were dominated by T. turbinata and the congener, T. stylifera. The high average density of the NIS (21.03 ind./m3) was in stark contrast with that of the native copepods (0.01-3.27 ind./m3). The NIS density was negatively correlated with the species richness and evenness of the native community, was significantly higher inside the port than outside, and was positively correlated with phytoplankton density. A multivariate analysis revealed that there was a significant difference in copepod community structure between inside and outside the port; outside the port, the community was more diverse, and the native T. stylifera was more abundant. We found that tropical copepod communities inside an offshore port have low diversity, and probably have little biotic resistance against NIS invasions. Our results, combined with those previously obtained, highlight the need to study the spatial distributions of NIS and native species in pelagic environments.

The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks.

PubMed

Blanken, Tessa F; Deserno, Marie K; Dalege, Jonas; Borsboom, Denny; Blanken, Peter; Kerkhof, Gerard A; Cramer, Angélique O J

2018-04-11

Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.

International non-governmental organizations' provision of community-based tuberculosis care for hard-to-reach populations in Myanmar, 2013-2014.

PubMed

Soe, Kyaw Thu; Saw, Saw; van Griensven, Johan; Zhou, Shuisen; Win, Le; Chinnakali, Palanivel; Shah, Safieh; Mon, Myo Myo; Aung, Si Thu

2017-03-24

National tuberculosis (TB) programs increasingly engage with international non-governmental organizations (INGOs), especially to provide TB care in complex settings where community involvement might be required. In Myanmar, however, there is limited data on how such INGO community-based programs are organized and how effective they are. In this study, we describe four INGO strategies for providing community-based TB care to hard-to-reach populations in Myanmar, and assess their contribution to TB case detection. We conducted a descriptive study using program data from four INGOs and the National TB Program (NTP) in 2013-2014. For each INGO, we extracted information on its approach and key activities, the number of presumptive TB cases referred and undergoing TB testing, and the number of patients diagnosed with TB and their treatment outcomes. The contribution of INGOs to TB diagnosis in their selected townships was calculated as the proportion of INGO-diagnosed new TB cases out of the total NTP-diagnosed new TB cases in the same townships. All four INGOs implemented community-based TB care in challenging contexts, targeting migrants, post-conflict areas, the urban poor, and other vulnerable populations. Two recruited community volunteers via existing community health volunteers or health structures, one via existing community leaderships, and one directly involved TB infected/affected individuals. Two INGOs compensated volunteers via performance-based financing, and two provided financial and in-kind initiatives. All relied on NTP laboratories for diagnosis and TB drugs, but provided direct observation treatment support and treatment follow-up. A total of 21 995 presumptive TB cases were referred for TB diagnosis, with 7 383 (34%) new TB cases diagnosed and almost all (98%) successfully treated. The four INGOs contributed to the detection of, on average, 36% (7 383/20 663) of the total new TB cases in their respective townships (range: 15-52%). Community-based TB care supported by INGOs successfully achieved TB case detection in hard-to-reach and vulnerable populations. This is vital to achieving the World Health Organization End TB Strategy targets. Strategies to ensure sustainability of the programs should be explored, including the need for longer-term commitment of INGOs.

Microbial community analysis of switchgrass planted and unplanted soil microcosms displaying PCB dechlorination

PubMed Central

Liang, Yi; Meggo, Richard; Hu, Dingfei; Schnoor, Jerald L.; Mattes, Timothy E.

2015-01-01

Polychlorinated biphenyls (PCBs) pose potential risks to human and environmental health because they are carcinogenic, persistent and bioaccumulative. In this study we investigated bacterial communities in soil microcosms spiked with PCB 52, 77 and 153. Switchgrass (Panicum virgatum) was employed to improve overall PCB removal and redox cycling (i.e. sequential periods of flooding followed by periods of no flooding) was performed in an effort to promote PCB dechlorination. Lesser chlorinated PCB transformation products were detected in all microcosms, indicating the occurrence of PCB dechlorination. Terminal restriction fragment length polymorphism (T-RFLP) and clone library analysis showed that PCB spiking, switchgrass planting and redox cycling affected the microbial community structure. Putative organohalide-respiring Chloroflexi populations, which were not found in unflooded microcosms, were enriched after two weeks of flooding in the redox-cycled microcosms. Sequences classified as Geobacter sp. were detected in all microcosms, and were most abundant in the switchgrass-planted microcosm spiked with PCB congeners. The presence of possible organohalide-respiring bacteria in these soil microcosms suggests they play a role in PCB dechlorination therein. PMID:25820643

Microbial community analysis of switchgrass planted and unplanted soil microcosms displaying PCB dechlorination.

PubMed

Liang, Yi; Meggo, Richard; Hu, Dingfei; Schnoor, Jerald L; Mattes, Timothy E

2015-08-01

Polychlorinated biphenyls (PCBs) pose potential risks to human and environmental health because they are carcinogenic, persistent, and bioaccumulative. In this study, we investigated bacterial communities in soil microcosms spiked with PCB 52, 77, and 153. Switchgrass (Panicum virgatum) was employed to improve overall PCB removal, and redox cycling (i.e., sequential periods of flooding followed by periods of no flooding) was performed in an effort to promote PCB dechlorination. Lesser chlorinated PCB transformation products were detected in all microcosms, indicating the occurrence of PCB dechlorination. Terminal restriction fragment length polymorphism (T-RFLP) and clone library analysis showed that PCB spiking, switchgrass planting, and redox cycling affected the microbial community structure. Putative organohalide-respiring Chloroflexi populations, which were not found in unflooded microcosms, were enriched after 2 weeks of flooding in the redox-cycled microcosms. Sequences classified as Geobacter sp. were detected in all microcosms and were most abundant in the switchgrass-planted microcosm spiked with PCB congeners. The presence of possible organohalide-respiring bacteria in these soil microcosms suggests that they play a role in PCB dechlorination therein.

Bacterial community dynamics during bioremediation of diesel oil-contaminated Antarctic soil.

PubMed

Vázquez, S; Nogales, B; Ruberto, L; Hernández, E; Christie-Oleza, J; Lo Balbo, A; Bosch, R; Lalucat, J; Mac Cormack, W

2009-05-01

The effect of nutrient and inocula amendment in a bioremediation field trial using a nutrient-poor Antarctic soil chronically contaminated with hydrocarbons was tested. The analysis of the effects that the treatments caused in bacterial numbers and hydrocarbon removal was combined with the elucidation of the changes occurring on the bacterial community, by 16S rDNA-based terminal restriction fragment length polymorphism (T-RFLP) typing, and the detection of some of the genes involved in the catabolism of hydrocarbons. All treatments caused a significant increase in the number of bacteria able to grow on hydrocarbons and a significant decrease in the soil hydrocarbon content, as compared to the control. However, there were no significant differences between treatments. Comparison of the soil T-RFLP profiles indicated that there were changes in the structure and composition of bacterial communities during the bioremediation trial, although the communities in treated plots were highly similar irrespective of the treatment applied, and they had a similar temporal dynamics. These results showed that nutrient addition was the main factor contributing to the outcome of the bioremediation experiment. This was supported by the lack of evidence of the establishment of inoculated consortia in soils, since their characteristic electrophoretic peaks were only detectable in soil profiles at the beginning of the experiment. Genetic potential for naphthalene degradation, evidenced by detection of nahAc gene, was observed in all soil plots including the control. In treated plots, an increase in the detection of catechol degradation genes (nahH and catA) and in a key gene of denitrification (nosZ) was observed as well. These results indicate that treatments favored the degradation of aromatic hydrocarbons and probably stimulated denitrification, at least transiently. This mesocosm study shows that recovery of chronically contaminated Antarctic soils can be successfully accelerated using biostimulation with nutrients, and that this causes a change in the indigenous bacterial communities and in the genetic potential for hydrocarbon degradation.

Universal phase transition in community detectability under a stochastic block model.

PubMed

Chen, Pin-Yu; Hero, Alfred O

2015-03-01

We prove the existence of an asymptotic phase-transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) and Proc. Natl. Acad. Sci. (USA) 103, 8577 (2006)] under a stochastic block model. The phase transition on community detectability occurs as the intercommunity edge connection probability p grows. This phase transition separates a subcritical regime of small p, where modularity-based community detection successfully identifies the communities, from a supercritical regime of large p where successful community detection is impossible. We show that, as the community sizes become large, the asymptotic phase-transition threshold p* is equal to √[p1p2], where pi(i=1,2) is the within-community edge connection probability. Thus the phase-transition threshold is universal in the sense that it does not depend on the ratio of community sizes. The universal phase-transition phenomenon is validated by simulations for moderately sized communities. Using the derived expression for the phase-transition threshold, we propose an empirical method for estimating this threshold from real-world data.

Assessment of benthic macroinvertebrates at Nile tilapia production using artificial substrate samplers.

PubMed

Moura E Silva, M S G; Graciano, T S; Losekann, M E; Luiz, A J B

2016-05-17

Biomonitoring is a cheap and effective tool for evaluation of water quality, and infer on the balance of aquatic ecosystems. The benthic macroinvertebrates are bioindicators sensitive to environmental changes, and can assist in detecting and preventing impacts such as organic enrichment and imbalance in the food chain. We compared the structure of benthic communities on artificial substrate samplers located in places near and far from net cages for production of Nile tilapia (Oreochromis niloticus). Samplers were manufactured with nylon net, using substrates such as crushed stone, gravel, loofah and cattail leaves. Samples were collected after 30 days of colonization, rinsed and then the specimens were identified and quantified. The following metrics were calculated: richness of Operational Taxonomic Units, Margalef richness, abundance of individuals, Shannon index and evenness index. The macrobenthic community structure was strongly modified according to the proximity of the net cages. Metrics showed significant differences (p < 0.05) between near and distant sites, for both periods (dry and rainy seasons). The position of the samplers significantly affected the structure of macroinvertebrate community, as near sites showed higher values for the community metrics, such as richness and diversity. Near sites presented a larger number of individuals, observed both in the dry and rainy seasons, with a predominance of Chironomidae (Diptera) in the dry season and Tubificidae (Oligochaeta) in the rainy season.

Biogeography of planktonic and coral-associated microorganisms across the Hawaiian Archipelago.

PubMed

Salerno, Jennifer L; Bowen, Brian W; Rappé, Michael S

2016-08-01

Factors driving the distribution of marine microorganisms are widely debated and poorly understood. Recent studies show that free-living marine microbes exhibit geographical patterns indicative of limited dispersal. In contrast, host-associated microbes face a different set of dispersal challenges, and hosts may function as habitat 'islands' for resident microbial populations. Here, we examine the biogeographical distributions of planktonic and adjacent coral-associated bacterial communities across the Hawaiian Archipelago, Johnston Atoll (∼1400 km southwest of Hawaii) and American Samoa in the Pacific Ocean and investigate the potential underlying processes driving observed patterns. Statistical analyses of bacterial community structure, determined using a small-subunit ribosomal RNA gene-based approach, showed that bacterioplankton and coral-associated bacterial communities were distinct, and correlated with geographical distance between sites. In addition, biogeographical patterns of bacterial associates paralleled those of their host coral Porites lobata, highlighting the specificity of these associations and the impact that host dispersal may have on bacterial biogeography. Planktonic and coral-associated bacterial communities from distant Johnston Atoll were shown to be connected with communities from the center of the Hawaiian Archipelago, a pattern previously observed in fish and invertebrates. No significant correlations were detected with habitat type, temperature or depth. However, non-distance-based geographical groupings were detected, indicating that, in addition to dispersal, unidentified environmental factors also affected the distributions of bacterial communities investigated here. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Unexpected Diversity during Community Succession in the Apple Flower Microbiome

PubMed Central

Shade, Ashley; McManus, Patricia S.; Handelsman, Jo

2013-01-01

ABSTRACT Despite its importance to the host, the flower microbiome is poorly understood. We report a culture-independent, community-level assessment of apple flower microbial diversity and dynamics. We collected flowers from six apple trees at five time points, starting before flowers opened and ending at petal fall. We applied streptomycin to half of the trees when flowers opened. Assessment of microbial diversity using tag pyrosequencing of 16S rRNA genes revealed that the apple flower communities were rich and diverse and dominated by members of TM7 and Deinococcus-Thermus, phyla about which relatively little is known. From thousands of taxa, we identified six successional groups with coherent dynamics whose abundances peaked at different times before and after bud opening. We designated the groups Pioneer, Early, Mid, Late, Climax, and Generalist communities. The successional pattern was attributed to a set of prevalent taxa that were persistent and gradually changing in abundance. These taxa had significant associations with other community members, as demonstrated with a cooccurrence network based on local similarity analysis. We also detected a set of less-abundant, transient taxa that contributed to general tree-to-tree variability but not to the successional pattern. Communities on trees sprayed with streptomycin had slightly lower phylogenetic diversity than those on unsprayed trees but did not differ in structure or succession. Our results suggest that changes in apple flower microbial community structure are predictable over the life of the flower, providing a basis for ecological understanding and disease management. PMID:23443006

A Dissolved Oxygen Threshold for Shifts in Bacterial Community Structure in a Seasonally Hypoxic Estuary

PubMed Central

Spietz, Rachel L.; Williams, Cheryl M.; Rocap, Gabrielle; Horner-Devine, M. Claire

2015-01-01

Pelagic ecosystems can become depleted of dissolved oxygen as a result of both natural processes and anthropogenic effects. As dissolved oxygen concentration decreases, energy shifts from macrofauna to microorganisms, which persist in these hypoxic zones. Oxygen-limited regions are rapidly expanding globally; however, patterns of microbial communities associated with dissolved oxygen gradients are not yet well understood. To assess the effects of decreasing dissolved oxygen on bacteria, we examined shifts in bacterial community structure over space and time in Hood Canal, Washington, USA−a glacial fjord-like water body that experiences seasonal low dissolved oxygen levels known to be detrimental to fish and other marine organisms. We found a strong negative association between bacterial richness and dissolved oxygen. Bacterial community composition across all samples was also strongly associated with the dissolved oxygen gradient, and significant changes in bacterial community composition occurred at a dissolved oxygen concentration between 5.18 and 7.12 mg O2 L-1. This threshold value of dissolved oxygen is higher than classic definitions of hypoxia (<2.0 mg O2 L-1), suggesting that changes in bacterial communities may precede the detrimental effects on ecologically and economically important macrofauna. Furthermore, bacterial taxa responsible for driving whole community changes across the oxygen gradient are commonly detected in other oxygen-stressed ecosystems, suggesting that the patterns we uncovered in Hood Canal may be relevant in other low oxygen ecosystems. PMID:26270047

Oceanographic structure drives the assembly processes of microbial eukaryotic communities.

PubMed

Monier, Adam; Comte, Jérôme; Babin, Marcel; Forest, Alexandre; Matsuoka, Atsushi; Lovejoy, Connie

2015-03-17

Arctic Ocean microbial eukaryote phytoplankton form subsurface chlorophyll maximum (SCM), where much of the annual summer production occurs. This SCM is particularly persistent in the Western Arctic Ocean, which is strongly salinity stratified. The recent loss of multiyear sea ice and increased particulate-rich river discharge in the Arctic Ocean results in a greater volume of fresher water that may displace nutrient-rich saltier waters to deeper depths and decrease light penetration in areas affected by river discharge. Here, we surveyed microbial eukaryotic assemblages in the surface waters, and within and below the SCM. In most samples, we detected the pronounced SCM that usually occurs at the interface of the upper mixed layer and Pacific Summer Water (PSW). Poorly developed SCM was seen under two conditions, one above PSW and associated with a downwelling eddy, and the second in a region influenced by the Mackenzie River plume. Four phylogenetically distinct communities were identified: surface, pronounced SCM, weak SCM and a deeper community just below the SCM. Distance-decay relationships and phylogenetic structure suggested distinct ecological processes operating within these communities. In the pronounced SCM, picophytoplanktons were prevalent and community assembly was attributed to water mass history. In contrast, environmental filtering impacted the composition of the weak SCM communities, where heterotrophic Picozoa were more numerous. These results imply that displacement of Pacific waters to greater depth and increased terrigenous input may act as a control on SCM development and result in lower net summer primary production with a more heterotroph dominated eukaryotic microbial community.

Continous application of bioorganic fertilizer induced resilient culturable bacteria community associated with banana Fusarium wilt suppression

PubMed Central

Fu, Lin; Ruan, Yunze; Tao, Chengyuan; Li, Rong; Shen, Qirong

2016-01-01

Fusarium wilt of banana always drives farmers to find new land for banana cultivation due to the comeback of the disease after a few cropping years. A novel idea for solving this problem is the continuous application of bioorganic fertilizer (BIO), which should be practiced from the beginning of banana planting. In this study, BIO was applied in newly reclaimed fields to pre-control banana Fusarium wilt and the culturable rhizobacteria community were evaluated using Biolog Ecoplates and culture-dependent denaturing gradient gel electrophoresis (CD-DGGE). The results showed that BIO application significantly reduced disease incidences and increased crop yields, respectivly. And the stabilized general bacterial metabolic potential, especially for the utilization of carbohydrates, carboxylic acids and phenolic compounds, was induced by BIO application. DGGE profiles demonstrated that resilient community structure of culturable rhizobacteria with higher richness and diversity were observed in BIO treated soils. Morever, enriched culturable bacteria affiliated with Firmicutes, Gammaproteobacteria and Actinobacteria were also detected. In total, continuous application of BIO effectively suppressed Fusarium wilt disease by stabilizing culturable bacterial metabolic potential and community structure. This study revealed a new method to control Fusarium wilt of banana for long term banana cultivation. PMID:27306096

Impact of an indigenous microbial enhanced oil recovery field trial on microbial community structure in a high pour-point oil reservoir.

PubMed

Zhang, Fan; She, Yue-Hui; Li, Hua-Min; Zhang, Xiao-Tao; Shu, Fu-Chang; Wang, Zheng-Liang; Yu, Long-Jiang; Hou, Du-Jie

2012-08-01

Based on preliminary investigation of microbial populations in a high pour-point oil reservoir, an indigenous microbial enhanced oil recovery (MEOR) field trial was carried out. The purpose of the study is to reveal the impact of the indigenous MEOR process on microbial community structure in the oil reservoir using 16Sr DNA clone library technique. The detailed monitoring results showed significant response of microbial communities during the field trial and large discrepancies of stimulated microorganisms in the laboratory and in the natural oil reservoir. More specifically, after nutrients injection, the original dominant populations of Petrobacter and Alishewanella in the production wells almost disappeared. The expected desirable population of Pseudomonas aeruginosa, determined by enrichment experiments in laboratory, was stimulated successfully in two wells of the five monitored wells. Unexpectedly, another potential population of Pseudomonas pseudoalcaligenes which were not detected in the enrichment culture in laboratory was stimulated in the other three monitored production wells. In this study, monitoring of microbial community displayed a comprehensive alteration of microbial populations during the field trial to remedy the deficiency of culture-dependent monitoring methods. The results would help to develop and apply more MEOR processes.

Continous application of bioorganic fertilizer induced resilient culturable bacteria community associated with banana Fusarium wilt suppression

NASA Astrophysics Data System (ADS)

Fu, Lin; Ruan, Yunze; Tao, Chengyuan; Li, Rong; Shen, Qirong

2016-06-01

Fusarium wilt of banana always drives farmers to find new land for banana cultivation due to the comeback of the disease after a few cropping years. A novel idea for solving this problem is the continuous application of bioorganic fertilizer (BIO), which should be practiced from the beginning of banana planting. In this study, BIO was applied in newly reclaimed fields to pre-control banana Fusarium wilt and the culturable rhizobacteria community were evaluated using Biolog Ecoplates and culture-dependent denaturing gradient gel electrophoresis (CD-DGGE). The results showed that BIO application significantly reduced disease incidences and increased crop yields, respectivly. And the stabilized general bacterial metabolic potential, especially for the utilization of carbohydrates, carboxylic acids and phenolic compounds, was induced by BIO application. DGGE profiles demonstrated that resilient community structure of culturable rhizobacteria with higher richness and diversity were observed in BIO treated soils. Morever, enriched culturable bacteria affiliated with Firmicutes, Gammaproteobacteria and Actinobacteria were also detected. In total, continuous application of BIO effectively suppressed Fusarium wilt disease by stabilizing culturable bacterial metabolic potential and community structure. This study revealed a new method to control Fusarium wilt of banana for long term banana cultivation.

Environmental Drivers of Inter-annual Variability in Beaufort Sea Marine Fish Community Structure

NASA Astrophysics Data System (ADS)

Majewski, A.; Atchison, S.; Eert, J.; Dempsey, M.; MacPhee, S.; Michel, C.; Reist, J.

2016-02-01

The Beaufort Sea is a complex and dynamic system influenced by a wide suite of oceanic and riverine inputs that affect the ecosystem. Interactions within the resulting water masses are largely driven by factors such as precipitation, wind, and ice cover. Thus, the Beaufort Sea environment is highly variable in both space and time, and this variability is reflected in the habitats of biota. Inherent system variability must be factored into baselines designed to detect changes resulting from anthropogenic stressors and natural drivers. Between 2012 and 2014, Fisheries and Oceans Canada conducted the first baseline survey of offshore marine fishes, their habitats, and ecological relationships in the Canadian Beaufort Sea. In 2012, benthic trawling was conducted at 28 stations spanning 20-1000 m depths across shelf and slope habitats, and selected stations were re-sampled in 2013 and 2014. Concurrent sampling of oceanographic parameters and sediment composition was conducted at each station. We examine the stability of marine fish assemblages over a three-year period, and compare results for shelf stations to previous research to develop longer-term perspectives. Oceanographic (e.g., salinity), physical (e.g., depth and sediment grain size) and geographic (e.g., distance from shore) parameters, and proxies for local productivity (i.e., water-column and benthic chlorophyll) are explored as explanatory variables affecting fish community structure among years. Establishing knowledge baselines and understanding variability in the community structure and habitat associations of Beaufort Sea marine fishes will support mitigation and conservation efforts by enhancing our ability to predict, detect and monitor the effects of hydrocarbon development and climate change on this pivotal ecosystem component.

Archaeal Communities in a Heterogeneous Hypersaline-Alkaline Soil

PubMed Central

Navarro-Noya, Yendi E.; Valenzuela-Encinas, César; Sandoval-Yuriar, Alonso; Jiménez-Bueno, Norma G.; Marsch, Rodolfo

2015-01-01

In this study the archaeal communities in extreme saline-alkaline soils of the former lake Texcoco, Mexico, with electrolytic conductivities (EC) ranging from 0.7 to 157.2 dS/m and pH from 8.5 to 10.5 were explored. Archaeal communities in the 0.7 dS/m pH 8.5 soil had the lowest alpha diversity values and were dominated by a limited number of phylotypes belonging to the mesophilic Candidatus Nitrososphaera. Diversity and species richness were higher in the soils with EC between 9.0 and 157.2 dS/m. The majority of OTUs detected in the hypersaline soil were members of the Halobacteriaceae family. Novel phylogenetic branches in the Halobacteriales class were detected in the soil, and more abundantly in soil with the higher pH (10.5), indicating that unknown and uncharacterized Archaea can be found in this soil. Thirteen different genera of the Halobacteriaceae family were identified and were distributed differently between the soils. Halobiforma, Halostagnicola, Haloterrigena, and Natronomonas were found in all soil samples. Methanogenic archaea were found only in soil with pH between 10.0 and 10.3. Retrieved methanogenic archaea belonged to the Methanosarcinales and Methanomicrobiales orders. The comparison of the archaeal community structures considering phylogenetic information (UniFrac distances) clearly clustered the communities by pH. PMID:26074731

Changing perspectives on community identity and function: A remote sensing and artifactual re-analysis of Barton Ramie, Belize

NASA Astrophysics Data System (ADS)

Weller, Errin Teresa

This dissertation presents the results of the remote sensing and artifact re-analysis of the archaeological site of Barton Ramie, Belize. The site was the focus of Dr. Gordon R. Willey's innovative archaeological program in the Belize River Valley to study ancient Maya settlement, environment, and population in 1954-1956. Through the use of artifact analysis combined with the examination of high-resolution Worldview-1 imagery and a Geographic Information System (GIS)-based spatial analysis, I consider how the inhabitants of Barton Ramie forged community functioning and identity. I focus on the range of intra-site diversity including differential access to labor, goods, land, and the activities evidenced in households and non-domestic structures. Using a community theory framework, emphasizing the many practices that tied the community together, I underscore the variability expressed in architectural elaboration, sumptuary goods, ritual, and specialization. That variability has profound implications for understanding community diversity and economic, social, and ritual functioning. High-resolution panchromatic Worldview-1 satellite imagery successfully detected the remains of Barton Ramie settlement. Surface archaeology has been largely destroyed due to extensive agricultural activities in recent decades. GIS analysis and ground-truthing determined that mound size is the primary factor enabling detection of ancient features. The confirmation of features in an intensively plowed environment has implications including settlement, survey, and population for other disturbed environments. I argue that the Barton Ramie community developed from a complex interaction of networks and practices. These include activities at the household level, articulation between households to form sub-communities (or neighborhoods), and a larger imagined community of the Barton Ramie polity. Individual households articulated to form seven discrete sub-communities, bounded by landscape features and indicated by interaction spheres in my GIS analysis. This analysis confirmed Dr. Willey's original observations on neighborhoods and settlement. Each subcommunity had a local ritual structure to integrate the households and mitigate the clear status differences. These differences are seen in high status households on prized land, using architectural elaboration, sumptuary goods, and ritual to maintain their status. Once Barton Ramie is understood as a heterogeneous polity connected to a wider economic network, it can be placed into the wider political interaction of the Belize Valley.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

PubMed

Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

2016-01-01

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Marine fish community structure and habitat associations on the Canadian Beaufort shelf and slope

NASA Astrophysics Data System (ADS)

Majewski, Andrew R.; Atchison, Sheila; MacPhee, Shannon; Eert, Jane; Niemi, Andrea; Michel, Christine; Reist, James D.

2017-03-01

Marine fishes in the Canadian Beaufort Sea have complex interactions with habitats and prey, and occupy a pivotal position in the food web by transferring energy between lower- and upper-trophic levels, and also within and among habitats (e.g., benthic-pelagic coupling). The distributions, habitat associations, and community structure of most Beaufort Sea marine fishes, however, are unknown thus precluding effective regulatory management of emerging offshore industries in the region (e.g., hydrocarbon development, shipping, and fisheries). Between 2012 and 2014, Fisheries and Oceans Canada conducted the first baseline survey of offshore marine fishes, their habitats, and ecological relationships in the Canadian Beaufort Sea. Benthic trawling was conducted at 45 stations spanning 18-1001 m depths across shelf and slope habitats. Physical oceanographic variables (depth, salinity, temperature, oxygen), biological variables (benthic chlorophyll and integrated water-column chlorophyll) and sediment composition (grain size) were assessed as potential explanatory variables for fish community structure using a non-parametric statistical approach. Selected stations were re-sampled in 2013 and 2014 for a preliminary assessment of inter-annual variability in the fish community. Four distinct fish assemblages were delineated on the Canadian Beaufort Shelf and slope: 1) Nearshore-shelf: <50 m depth, 2) Offshore-shelf: >50 and ≤200 m depths, 3) Upper-slope: ≥200 and ≤500 m depths, and 4) Lower-slope: ≥500 m depths. Depth was the environmental variable that best explained fish community structure, and each species assemblage was spatially associated with distinct aspects of the vertical water mass profile. Significant differences in the fish community from east to west were not detected, and the species composition of the assemblages on the Canadian Beaufort Shelf have not changed substantially over the past decade. This community analysis provides a framework for testing hypotheses regarding the trophic dynamics and ecosystem roles of Beaufort Sea marine fishes, including biological linkages (i.e., fish movements and trophic interactions) among offshore habitats. Understanding regional-scale habitat associations will also provide context to identify potentially unique and/or sensitive habitats and fish community characteristics, thus aiding identification of ecologically and biologically significant areas, and to inform conservation efforts.

Image Re-Ranking Based on Topic Diversity.

PubMed

Qian, Xueming; Lu, Dan; Wang, Yaxiong; Zhu, Li; Tang, Yuan Yan; Wang, Meng

2017-08-01

Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

Microbial community analysis of an Alabama coastal salt marsh impacted by the Deepwater Horizon Oil Spill

NASA Astrophysics Data System (ADS)

Beazley, M. J.; Martinez, R.; Rajan, S.; Powell, J.; Piceno, Y.; Tom, L.; Andersen, G. L.; Hazen, T. C.; Van Nostrand, J. D.; Zhou, J.; Mortazavi, B.; Sobecky, P. A.

2011-12-01

Microbial community responses of an Alabama coastal salt marsh environment to the Deepwater Horizon oil spill were studied by 16S rRNA (PhyloChip) and functional gene (GeoChip) microarray-based analysis. Oil and tar balls associated with the oil spill arrived along the Alabama coast in June 2010. Marsh and inlet sediment samples collected in June, July, and September 2010 from a salt marsh ecosystem at Point Aux Pines Alabama were analyzed to determine if bacterial community structure changed as a result of oil perturbation. Sediment total petroleum hydrocarbon (TPH) concentrations ranged from below detection to 189 mg kg-1 and were randomly dispersed throughout the salt marsh sediments. Total DNA extracted from sediment and particulates were used for PhyloChip and GeoChip hybridization. A total of 4000 to 8000 operational taxonomic units (OTUs) were detected in marsh and inlet samples. Distinctive changes in the number of detectable OTUs were observed between June, July, and September 2010. Surficial inlet sediments demonstrated a significant increase in the total number of OTUs between June and September that correlated with TPH concentrations. The most significant increases in bacterial abundance were observed in the phyla Actinobacteria, Firmicutes, Gemmatimonadetes, Proteobacteria, and Verrucomicrobia. Bacterial richness in marsh sediments also correlated with TPH concentrations with significant changes primarily in Acidobacteria, Actinobacteria, Firmicutes, Fusobacteria, Nitrospirae, and Proteobacteria. GeoChip microarray analysis detected 5000 to 8300 functional genes in marsh and inlet samples. Surficial inlet sediments demonstrated distinctive increases in the number of detectable genes and gene signal intensities in July samples compared to June. Signal intensities increased (> 1.5-fold) in genes associated with petroleum degradation. Genes related to metal resistance, stress, and carbon cycling also demonstrated increases in oiled sediments. This study demonstrates the value of applying phylogenetic and functional gene microarray technology to characterize the extensive microbial diversity of marsh environments. Moreover, this technology provides significant insight into bacterial community responses to anthropogenic oil events.